Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing food-based strategies to address anaemia in pregnancy in rural plains Nepal: a mixed methods study

Abstract Anaemia in pregnancy is a persistent health problem in Nepal and could be reduced through nutrition counselling and strengthened iron folic acid supplementation programmes. We analysed 24-hour diet recall data from 846 pregnant women in rural plains Nepal, using linear programming to identify the potential for optimised food-based strategies to increase iron adequacy. We then conducted qualitative research to analyse how anaemia was defined and recognised, how families used food-based strategies to address anaemia, and the acceptability of optimised food-based strategies. We did 16 interviews of recently pregnant mothers, three focus group discussions with fathers, three focus group discussions with mothers-in-law and four interviews with key informants. Dietary analyses showed optimised diets did not achieve 100 % of recommended iron intakes, but iron intakes could be doubled by increasing intakes of green leaves, egg and meat. Families sought to address anaemia through food-based strategies but were often unable to because of the perceived expense of providing an ‘energy-giving’ diet. Some foods were avoided because of religious or cultural taboos, or because they were low status and could evoke social consequences if eaten. There is a need for counselling to offer affordable ways for families to optimise iron adequacy. The participation of communities in tailoring advice to ensure cultural relevance and alignment with local norms is necessary to enable its effectiveness.
Field theory aspects of cosmology and black holes

In this thesis we study some aspects of cosmology and black holes using field theoretic techniques. In second chapter, we present Lagrangian formulation for the non-relativistic as well as relativistic generalized Chaplygin gas (GCG). In rest of the thesis we discuss alternative approaches to compute the fluxes of Hawking radiation. These methods are based on covariant gauge/gravitational anomalies and chiral effective action. We also discuss a criterion to differentiate various black hole vacua within the framework of covariant anomaly approach.
Weak gravitational lensing of intrinsically aligned galaxies

Subject of this paper is the weak lensing effect on galaxies that show intrinsically correlated ellipticities. In our model, we investigate the distortion of the ellipticity field if the galaxies experience an apparent shift in their position by weak lensing deflection and compare this effect to the shearing effect induced by tidal fields. Starting with a derivation of intrinsic ellipticity spectra by employing a tidal torquing model generating galactic angular momenta, we model the galaxy ellipticity by assuming that the galactic disk forms perpendicularly to the host halo angular momentum direction and derive intrinsic ellipticity E-mode and B-mode spectra from the angular momentum statistics. The lensing effect on the ellipticity field is modeled by employing the methodology developed in the framework of lensing of the cosmic microwave background polarisation. For EUCLID, ellipticity correlations are altered by lensing deflection on multipoles l>1000 by ~5% for the ellipticity E-modes and by ~30% for the B-modes, while a shallower survey would exhibit larger changes on larger angular scales. In addition to the convolving effect of lensing on the ellipticity spectra we investigate the E/B-mode conversion, and discuss the possibility of measuring correlations between different multipoles which is evoked by the homogeneity breaking effect of the lensing displacement. Our conclusion is that although shape correlations generated by weak gravitational shear is dominant, the shifting effect due to lensing is shaping the ellipticity spectra on small angular scales and causes a number of interesting phenomena, which might be observable by future surveys.
Evaluation of a convolutional neural network to identify scaphoid fractures on radiographs

This study aimed to develop and evaluate a convolutional neural network for identifying scaphoid fractures on radiographs. A dataset of 1918 wrist radiographs (600 patients) was taken from an orthopaedic referral centre between 2010 to 2020. A YOLOv3 and a MobileNetV3 convolutional neural network were trained for scaphoid detection and fracture classification, respectively. The diagnostic performance of the convolutional neural network was compared with the majority decision of four hand surgeons. The convolutional neural network achieved a sensitivity of 82% and specificity of 94%, with an area under the receiver operating characteristic of 92%, whereas the surgeons achieved a sensitivity of 76% and specificity of 96%. The comparison indicated that the convolutional neural network’s performance was similar to the majority vote of surgeons. It further revealed that convolutional neural network could be used in identifying scaphoid fractures on radiographs reliably, and has potential to achieve the expert-level performance. Level of evidence: III
Double white dwarf mergers and elemental surface abundances in extreme helium and R Coronae Borealis stars

The surface abundances of extreme helium (EHe) and R Coronae Borealis (RCB) stars are discussed in terms of a model for their origin in the merger of a carbon–oxygen white dwarf with a helium white dwarf. The model is expressed as a linear mixture of the individual layers of both constituent white dwarfs, taking account of the specific evolution of each star. In developing this recipe from previous versions, particular attention has been given to the intershell abundances of the asymptotic giant branch (AGB) star which evolved to become the carbon–oxygen white dwarf. Thus the surface composition of the merged star is estimated as a function of the initial mass and metallicity of its progenitor. The question of whether additional nucleosynthesis occurs during the white dwarf merger has been examined by including the results of recent hydrodynamical merger calculations which incorporate the major nuclear networks. The high observed abundances of carbon and oxygen must either originate by dredge-up from the core of the carbon–oxygen white dwarf during a cold merger or be generated directly by α burning during a hot merger. The presence of large quantities of 18O may be consistent with both scenarios, since a significant 18O pocket develops at the carbon/helium boundary in a number of our post-AGB models. The production of fluorine, neon and phosphorus in the AGB intershell propagates through to an overabundance at the surface of the merged stars, but generally not in sufficient quantity to match the observed abundances. However, the evidence for an AGB origin for these elements, together with near-normal abundances of magnesium, points to progenitor stars with initial masses in the range 1.9–3 M⊙. There is not yet sufficient understanding of the chemical structure of CO white dwarfs, or of nucleosynthesis during a double white dwarf merger, to discriminate the origin (fossil or prompt) of all the abundance anomalies observed in EHe and RCB stars. Further work is required to quantify the expected yields of argon and s-process elements in the AGB intershell, and to improve the predicted yields of all elements from a hot merger.
When dunes move together, structure of deserts emerges

Crescent shaped barchan dunes are highly mobile dunes that are usually presented as a prototypical model of sand dunes. Although they have been theoretically shown to be unstable when considered separately, it is well known that they form large assemblies in desert. Collisions of dunes have been proposed as a mechanism to redistribute sand between dunes and prevent the formation of heavily large dunes, resulting in a stabilizing effect in the context of a dense barchan field. Yet, no models are able to explain the spatial structures of dunes observed in deserts. Here, we use an agent-based model with elementary rules of sand redistribution during collisions to access the full dynamics of very large barchan dune fields. Consequently, stationnary, out of equilibrium states emerge. Trigging the dune field density by a sand load/lost ratio, we show that large dune fields exhibit two assymtotic regimes: a dilute regime, where sand dune nucleation is needed to maintain a dune field, and a dense regime, where dune collisions allow to stabilize the whole dune field. In this dense regime, spatial structures form: the dune field is structured in narrow corridors of dunes extending in the wind direction, as observed in dense barchan deserts.
Retrieving API Knowledge from Tutorials and Stack Overflow Based on Natural Language Queries

When encountering unfamiliar APIs, developers tend to seek help from API tutorials and Stack Overflow (SO). API tutorials help developers understand the API knowledge in a general context, while SO often explains the API knowledge in a specific programming task. Thus, tutorials and SO posts together can provide more API knowledge. However, it is non-trivial to retrieve API knowledge from both API tutorials and SO posts based on natural language queries. Two major problems are irrelevant API knowledge in two different resources and the lexical gap between the queries and documents. In this article, we regard a fragment in tutorials and a Question and Answering (Q&A) pair in SO as a knowledge item (KI). We generate ⟨ API, FRA⟩ pairs (FRA stands for fragment) from tutorial fragments and APIs and build ⟨ API, QA⟩ pairs based on heuristic rules of SO posts. We fuse ⟨ API, FRA⟩ pairs and ⟨ API, QA⟩ pairs to generate API knowledge (AK for short) datasets, where each data item is an ⟨ API, KI⟩ pair. We propose a novel approach, called PLAN, to automatically retrieve API knowledge from both API tutorials and SO posts based on natural language queries. PLAN contains three main stages: (1) API knowledge modeling, (2) query mapping, and (3) API knowledge retrieving. It first utilizes a deep-transfer-metric-learning-based relevance identification (DTML) model to effectively find relevant ⟨ API, KI⟩ pairs containing two different knowledge items (⟨ API, QA⟩ pairs and ⟨ API, FRA⟩ pairs) simultaneously. Then, PLAN generates several potential APIs as a way to reduce the lexical gap between the query and ⟨ API, KI⟩ pairs. According to potential APIs, we can select relevant ⟨ API, KI⟩ pairs to generate potential results. Finally, PLAN returns a list of ranked ⟨ API, KI⟩ pairs that are related to the query. We evaluate the effectiveness of PLAN with 270 queries on Java and Android AK datasets containing 10,072 ⟨ API, KI⟩ pairs. Our experimental results show that PLAN is effective and outperforms the state-of-the-art approaches. Our user study further confirms the effectiveness of PLAN in locating useful API knowledge.
Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision

Abstract The size of the texture extraction window impacts image tree species classification, and the determination of the optimal texture extraction window requires the supervision of a specific classifier for accuracy. Therefore, it is necessary to analyse which kind of classifier is more suitable and should be to choose. In this study, we extracted eight types of textures, namely mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation, changed the window size by gradient increase and used maximum likelihood classification (MLC) and random forest (RF) to supervise and determine their optimal extraction windows, respectively. Finally, the optimised time consumption and classification accuracy for tree species classification was identified. The time consumption of MLC was significantly less than that of RF; however, neither was very long; for most textures, the optimal texture extraction window determined by MLC supervision was larger than that determined by RF supervision; in the classification of most feature sets, the overall accuracy obtained by MLC was less than that of RF. Because the time consumption of the texture extraction was much greater than that of the image classification, the comprehensive trade-off indicates that using RF supervision to determine the optimal window for texture extraction was more conducive to tree species recognition.
‘. . . staff here are just dropped in the deep end’: The Impact of Roles on Communication and Supervisor Support in Youth Custody

Staff experience in youth custody are often categorised by strains, which are affected by communication and support. This research explores the association between staff roles, within a Secure Training Centre in the England, and the levels of communication and support. It enhances our understanding of the challenges faced by staff members working with young people in custodial settings and how levels of communication and support are dictated by staff roles. Through questionnaires (N = 74) and interviews (N = 15) with staff, statistically significant relationship between staff role and levels of communication and support was identified. Through triangulation, this article illustrates the effectiveness of the job demands–resources model in understanding staff experiences with communication and supervisory support in youth custody. It has wide-ranging implications by providing sociologists with an effective model for understanding job satisfaction and stress and by providing policy-makers and organisations delivering custodial services an understanding of the communication and support required to reduce stress and turnover.
MARKOV-RENEWAL PROGRAMMING

Abstract : A special structure in dynamic programming is the problem of programming over a Markov chain. This paper extends the solution algorithms to programming over a Markov - renewal process - in which times between transitions of the system from state i to state j are independent samples from an inter-transition distribution which may depend on both i and j. For these processes, a general reward structure and a decision mechanism are postulated; the problem is to make decisions at each transition to maximize the total expected reward at the end of the planning horizon. For finite-horizon problems, or infinite-horizon problems with discounting, there is no difficulty; the results are similar to previous work, expect for a new dependency upon the transition time distributions being generally present. In the cases where the horizon extends towards infinity, or when discounting vanishes, however, a fundamental dichotomy in the optimal solutions may occur. It then becomes important to specify whether the limiting experiment is: (i) undiscounted, with the number of transitions n approaches infinity , (ii) undiscounted, with a time horizon t approaches infinity , or (iii) infinite n or t , with discount factor a approaches 0 . In each case, a limiting form for the total expected reward is shown, and an algorithm developed to maximize the rate of return. The problem of finding the optimal or near-optimal policies In the case of ties in rate of return is still computationally unresolved. Extensions to non-ergodic processes are indicated, and special results for the two-state process are presented. Finally, an example of machine maintenance and repair is used to illustrate the generality of the approach and the special problems which may arise.
Improving Hand Hygiene Compliance through Collaborative Computational Design

Hand sanitization by healthcare staff remains one of the most effective ways for controlling infection in healthcare settings. However, predicting faithful adherence to Hand Hygiene Compliance (HHC) is difficult in complex environments such as inpatient hospital settings. The main challenge is understanding how different components of human and built systems interact to achieve specific goals such as HHC at the critical moments of care delivery. The aim of this explorative study was to evaluate how Human Factors derived visual salience cues and proximity-compatibility principles might be used in the design of healthcare spaces to support nurse moments of HHC through increased perceived behavioral control and intention. The investigative team used a Collaborative Computational Scenario Planning (CCSP) Model approach to determine the integrative effects of reinforcing and detracting operational and environmental factors on discrete moments of HHC behavior. Supervised Machine Learning analysis was conducted on data collected by a large academic medical center that included HHC observance in clinical staff spanning from 2017 to 2021 in two inpatient hospital units. The probabilistic outcomes of unit based HHC observance likelihood were used to compute Fuzzy Cognitive Model Edge Probabilities between Hand Hygiene (HH) cues and detected HHC at key moments. Hospital infection control experts were then engaged to identify the weight of various reinforcing and detracting operational and environmental factors contributing to HHC observance. Combining the quantitative and qualitative methods, allowed the team to then develop integrative CCSP models which facilitated predictive insight into the development of targeted environmental improvements that might contribute to HHC control and intention to support safer patient care.
Eye Movement Desensitization and Reprocessing to Reduce Post-Traumatic Stress Disorder and Related Symptoms among Forcibly Displaced People: A Systematic Review and Meta-Analysis

Purpose: This study is a meta-analysis on the efficacy of eye movement desensitization and reprocessing (EMDR) in reducing Post-Traumatic Stress Disorder (PTSD) and co-occurring depression, and anxiety symptoms among forcibly displaced people. Methods: A search was followed by data extraction and assessment of risk of bias. Within- and between-conditions effect sizes of posttest and follow-up outcomes using a random effects model were examined, with heterogeneity and subgroup analyses. Results: Twenty-two studies (N = 1964) were included and seventeen (N = 1652) had complete data for the meta-analysis. There were medium to large effect size reductions on PTSD, depression, and anxiety within-conditions. EMDR was more effective than other conditions at posttest for PTSD symptoms but outcomes for the posttests and follow-ups for depression and anxiety were inconclusive. Discussion: EMDR effectively reduced PTSD symptoms, depression, and anxiety among forcibly displaced people. More studies with larger samples and better designs are needed.
Factors Influencing the Willingness to Download COVID-19 Contact Tracing Apps: The Moderating Effect of Persuasive Design and Smartphone Usage Experience

The COVID-19 pandemic culminated in the global roll-out of contact tracing apps, also known as exposure notification apps (ENAs), to contain the spread of the virus. ENAs notify users about a possible infection and advises them to take certain health measures such as self-isolation and COVID-19 test. While some work has been done on the factors that influence ENA adoption, little attention has been paid to the moderating effect of persuasive design and smartphone usage experience. This paper sets out to bridge this gap using the Government of Canada’s official app “COVID Alert” and Canadian non-adopter population (n = 133) as a case study. Of the seven factors we investigated, perceived trust (β = 0.29, p < 0.01) and perceived enjoyment (β = 0.21, p < 0.05) turned out to be the only significant predictors of non-adopters’ willingness to download the app in the overall model. Both persuasive design and smartphone usage experience moderated some of the relationships in the model. For example, while perceived enjoyment is significant in the persuasive design submodel (β = 0.34, p < 0.01) and low-experience submodel (β = 0.36, p < 0.001), it is not in the control design and high-experience submodels. Moreover, perceived enjoyment (β = 0.36, p < 0.001) and privacy concern (β = -0.46, p < 0.05) turned out to be the strongest predictors in the low-experience and high-experience submodels, respectively. These results indicate that, to improve the download of ENAs, sponsors should implement and highlight app features that foster trust in marketing campaigns and app-store descriptions. Particularly, they should highlight their privacy-protection feature and hedonic attribute among the high-experience and low-experience groups, respectively.
SPATIALLY EXTENDED BRACKETT GAMMA EMISSION IN THE ENVIRONMENTS OF YOUNG STARS

The majority of atomic hydrogen Brγ emission detected in the spectra of young stellar objects is believed to arise from the recombination regions associated with the magnetospheric accretion of circumstellar disk material onto the forming star. In this paper, we present the results of a K-band integral field unit spectroscopic study of Brγ emission in eight young protostars: CW Tau, DG Tau, Haro 6–10, HL Tau, HV Tau C, RW Aur, T Tau, and XZ Tau. We spatially resolve Brγ emission structures in half of these young stars and find that most of the extended emission is consistent with the location and velocities of the known Herbig–Haro flows associated with these systems. At some velocities through the Brγ line profile, the spatially extended emission comprises 20% or more of the integrated flux in that spectral channel. However, the total spatially extended Brγ is typically less than ∼10% of the flux integrated over the full emission profile. For DG Tau and Haro 6–10 S, we estimate the mass outflow rate using simple assumptions about the hydrogen emission region and compare this to the derived mass accretion rate. We detect extended Brγ in the vicinity of the more obscured targets in our sample and conclude that spatially extended Brγ emission may exist toward other stars, but unattenuated photospheric flux probably limits its detectability.
Impact of scale-dependent bias and nonlinear evolution on the ISW

I summarize recent results from Smith, Hernandez-Monteagudo & Seljak (2009), a study of the impact of nonlinear evolution of gravitational potentials in the LCDM model on the Integrated Sachs-Wolfe (ISW) contribution to the cross-power spectrum of the CMB and a set of biased tracers of the mass. We use a large ensemble of N-body simulations to directly follow the potentials and compare the results to analytic perturbation theory (PT) methods. The PT predictions match our results to high precision for k<0.2 Mpc/h. We analyze the CMB-density tracer cross-spectrum using simulations and renormalized bias PT, and find good agreement. The usual assumption is that nonlinear evolution enhances the growth of structure and counteracts the linear ISW on small scales, leading to a change in sign of the CMB-LSS cross-spectrum at small scales. However, PT analysis suggests that this trend reverses at late times when the logarithmic growth rate f=d ln D/d ln a<1/2 or Omega_m (z)<0.3. Numerical results confirm these expectations and we find nonlinear enhancement of the ISW signal on small scales at late-times. On computing the total contribution to the angular spectrum, we find that nonlinearity and scale dependence of the bias are unable to influence the signal-to-noise of the current and future measurements.