Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced complexity hard- and soft-input BCH decoding with applications in concatenated codes

Error correction coding for optical communication and storage requires high rate codes that enable high data throughput and low residual errors. Recently, different concatenated coding schemes were proposed that are based on binary BCH codes with low error correcting capabilities. In this work, low ‐ complexity hard ‐ and soft ‐ input decoding methods for such codes are investigated. We propose three concepts to reduce the complexity of the decoder. For the algebraic decoding we demonstrate that Peterson's algorithm can be more efficient than the Berlekamp–Massey algorithm for single, double, and triple error correcting BCH codes. We propose an inversion ‐ less version of Peterson's algorithm and a corresponding decoding architecture. Furthermore, we propose a decoding approach that combines algebraic hard ‐ input decoding with soft ‐ input bit ‐ flipping decoding. An acceptance criterion is utilised to determine the reliability of the estimated codewords. For many received codewords the stopping criterion indicates that the hard ‐ decoding result is sufficiently reliable, and the costly soft ‐ input decoding can be omitted. To reduce the memory size for the soft ‐ values, we propose a bit ‐ flipping decoder that stores only the positions and soft values of a small number of code symbols. This method significantly reduces the memory requirements and has little adverse effect on the decoding performance
Guided wave propagation and scattering at composite delaminations

Composite structures, consisting of highly anisotropic layers of polymer matrix reinforced with high strength carbon fibers, are widely used for aerospace applications due to their low weight and high strength. However, impact during aircraft operation can lead to barely visible and difficult to detect damage. Depending on impact severity, delaminations can occur that reduce the structural integrity and load carrying capacity. Efficient structural health monitoring (SHM) of composite panels can be achieved using guided ultrasonic waves propagating along the structure. Guided ultrasonic wave propagation and scattering at circular delaminations was modelled using full three-dimensional (3D) Finite Element (FE) simulations in ABAQUS. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. The guided ultrasonic wave propagation and scattered field at an artificial delamination was measured using a noncontact laser interferometer and quantified. Good agreement between experiments and Finite Element predictions was found and the energy trapping on top of a shallow delamination was verified. The influence of delamination shape and depth was investigated from a FE parameter study. The sensitivity of guided waves for the detection of delaminations due to barely visible impact damage (BVID) in composite panels has been verified.
Physical Laws Shape Up HOX Gene Collinearity

Hox gene collinearity (HGC) is a multi-scalar property of many animal phyla particularly important in embryogenesis. It relates entities and events occurring in Hox clusters inside the chromosome DNA and in embryonic tissues. These two entities differ in linear size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC), where the Hox genes are located in the order (Hox1, Hox2, Hox3 …) along the 3′ to 5′ direction of DNA in the genome and a corresponding sequence of ontogenetic units (E1, E2, E3, …) located along the Anterior—Posterior axis of the embryo. Expression of Hox1 occurs in E1, Hox2 in E2, Hox3 in E3, etc. Besides SC, a temporal collinearity (TC) has been also observed in many vertebrates. According to TC, first Hox1 is expressed in E1; later, Hox2 is expressed in E2, followed by Hox3 in E3, etc. Lately, doubt has been raised about whether TC really exists. A biophysical model (BM) was formulated and tested during the last 20 years. According to BM, physical forces are created which pull the Hox genes one after the other, driving them to a transcription factory domain where they are transcribed. The existing experimental data support this BM description. Symmetry is a physical–mathematical property of matter that was explored in depth by Noether who formulated a ground-breaking theory (NT) that applies to all sizes of matter. NT may be applied to biology in order to explain the origin of HGC in animals developing not only along the A/P axis, but also to animals with circular symmetry.
Simulation of Fluvial Patterns With GANs Trained on a Data Set of Satellite Imagery

Models that can generate realistic Earth surface patterns are important both for geomorphological applications and as prior models for underdetermined inverse problems. Generative machine learning methods such as GANs and the increasing availability of large remote sensing data sets represents an exciting combination for this purpose. Several studies show promising results for GANs trained on artificial data sets in geostatistics, but it is necessary to further quantify how well such models reproduce and generalize real data. The conditioning ability of GANs is often evaluated based on output which originates from a trained generator. In reality, geophysical data necessarily arises from elsewhere. Here, we use more realistic training data than in previous studies and evaluate performance using an extensive set of metrics and real images outside the training data set. The data set consists of multispectral satellite imagery of 38 large river deltas, a type of Earth surface pattern which is limited in number. The channel network is used to create training images with four sedimentary facies, which are subsequently used to train a Wasserstein GAN of deltaic 2D patterns. GANs successfully reproduce all training data characteristics and produce manifold the number of combinations with respect to the training data. However, there does not seem to be an infinite number of discrete combinations of facies, and the posterior landscapes are not well‐shaped for efficient exploration in the presence of so‐called hard data. Thus, GANs should have many exciting applications in geosciences, but it will depend on the type of measurement data.
Model‐based biclustering for overdispersed count data with application in microbial ecology

Different studies have shown that microbial communities living in animals (humans included), in or around plants have a significant impact on health and disease of their host and on various services, such as adaptation under stressing environment. The basic input data to study microbiomes is a matrix representing abundance data of micro‐organisms across different sampling units. Such a matrix typically corresponds to taxonomic profiles derived from the high‐throughput sequencing of environmental samples. Biclustering is one way to study the interactions between the structure of micro‐organism communities and the environmental samples they come from. We propose a latent block model (LBM) and an associated inference procedure for the biclustering of rows and columns of abundance matrices. The LBM assumes that micro‐organisms (rows) and environmental samples (columns) can both be clustered into groups characterizing preferential interaction or avoidance. We use the Poisson–Gamma distribution to model the overdispersion observed in microbial abundance data and introduce row and column effects to account for the sequencing effort in each sample and the mean abundance of each micro‐organism. Because the latent variables are not independent conditionally on the observed ones, classical maximum likelihood inference is intractable. We then derive a variational‐based inference algorithm and propose a strategy to select the number of biclusters. We illustrate the flexibility and performance of our approach both on a simulation study and on three ecological datasets. The model‐based framework allows us to adapt to peculiarities of microbial ecological abundance data and allows us to explore relationships between entities of two different natures. We implemented our method in the cobiclust R package available on the CRAN and built a website with example of usage (https://julieaubert.github.io/cobiclust/cobiclust‐example1.html).
Understanding value creation in digital context: An empirical investigation of B2B

In recent years, the context of B2B relationships has changed rapidly with the advent of new technologies that are reshaping the space dimension of business interactions and the time frame in which they occur. Notwithstanding the magnitude of this evolution, the literature only partially captures the implications of value creation in the B2B digital context, leaving the contextual nature of value to remain vague and described mainly at an abstract level. This lack of a micro-foundation of value creation in context also makes its management exceedingly difficult. This study proposes a conceptual model that explains value creation in the digitally interconnected B2B business environment. Our analysis of 34 qualitative interviews with B2B managers showed the importance of a granular examination of value creation in context by identifying its dimensions and processes. At the same time, we showed the need to combine a micro theoretical focus with the transition to a more holistic and systemic view.
MPS‐Based Model to Solve One‐Dimensional Shallow Water Equations

Here, a moving particle simulation method is presented to spatially integrate the cross‐sectional average shallow water equations using a prediction‐correction procedure for the time discretization. A density‐ratio equation is derived for the water depth computation according to the particle number density concept. The newly derived equation does not miscalculate the water depth in case an incorrect searching radius parameter is adopted, unlike the typical volume‐summation formula in meshless shallow water flows. A new one‐dimensional Spiky kernel function is developed to satisfy the unity condition employed in the Newton‐Raphson iteration to calculate the water depth. Dynamic stabilization is adopted to capture shockwave problems, a case‐independent technique based on the inelastic collision with unequal masses. The convective flux term is eliminated under the Lagrangian framework, and so the momentum is adequately conserved without the need for any special treatment. The proposed scheme maintains the exact C‐property, meaning that the water depth gradient and bed slope are hydrostatically well balanced within a discretized solution domain. Compared to analytical solutions and experimental data, the results of this study reveal that the present model is a robust numerical solver without unphysical oscillations. It can capture various shock problems, including steep gradient shock‐front, discontinuous wet bed, transcritical flow regimes, and irregular bed topography. The present model can also simulate the dry‐wet flow transition influenced by friction without experiencing any divisions by zero, negative values, or unphysical perturbations. This advantage is basically due to the water particles' absence and presence for the dry and wet regions, respectively.
Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.
An exploratory study of clinical profile, stigma and pathways to care among primary cannabis use disorder patients in India

ABSTRACT Background Cannabis use is highly prevalent globally, yet little is known about primary cannabis use disorder patients, especially in India. The present study’s objective was to assess clinical profile, internalized stigma, and pathways to care among this population in India. Methods A total of thirty patients with cannabis use disorder were recruited over a nine-month period using an exploratory cross-sectional design. Assessments were undertaken using a semi-structured proforma, Internalized Stigma of Mental Illness (ISMI) – Hindi version, and WHO – Mental Health Encounter Form. Data was analyzed using SPSS V20.0. Results All participants were males, with a mean age of 24.57 (7.76) years. Marijuana (ganja) was the commonest form of cannabis consumed (n = 26; 86.7%). The mean duration of cannabis use and dependence was 5.03 (3.69) and 3.98 (3.22) years. The most common reason for seeking treatment was family pressure (n = 21; 70%). The mean score of ISMI-Hindi was 2.66 (0.55), suggesting moderate internalized stigma. Most participants’ first treatment contact was a psychiatrist at a tertiary care de-addiction center (n = 29; 96.7%). Conclusion This study highlights the need for interventions to reduce stigma in patients with cannabis use disorder patients.
Paddling with Maxine Sheets-Johnstone: Exploring the moving body in sport

This paper aims to contribute to the theoretical discussion and empirical application of phenomenology in the sociology of sport by drawing on Maxine Sheets-Johnstone’s theories. I propose that Sheets-Johnstone’s movement-focused phenomenology can be complementary to Merleau-Ponty’s phenomenology of the body in the analyses of sporting moves and the learning of new moves. I mainly applied two concepts from Sheets-Johnstone, tactile-kinesthetic/kinetic dynamics and emotion-motion dynamics, to explore the moving body in waka ama (outrigger canoe) paddling, based on my beginner’s and other competent paddlers’ experiences. Findings demonstrate that a moving/paddling body is spontaneously a tactile-kinesthetic/kinetic, emotion-motion and intercorporeal body. These bodily dimensions enrich our understandings of the ways of learning new movements, doing sport and doing sport together.
A Tree-Ring-Based Precipitation Reconstruction since 1760 CE from Northeastern Tibetan Plateau, China

Hydroclimatic conditions and related water resources change in the Tibetan Plateau is one of the main concerns for future sustainable development in China. This study presents a 254-year precipitation reconstruction from August of the previous year to June of the current year for the northeastern Tibetan Plateau based on tree-ring width data of tree-ring cores of Picea crassifolia from three sampling sites. The precipitation reconstruction explained 51.4% of the variance in instrumental precipitation during the calibration period 1958–2013. Dry periods with precipitation below the 254-year average value occurred during 1848–1865, 1873–1887, 1898–1923, and 1989–2003, and wet periods (precipitation above the mean) occurred during 1769–1785, 1798–1833, 1924–1938, 1957–1968, and 2004–2013. Spatial correlation analyses with the precipitation gridded dataset showed that our reconstruction contains some strong regional-scale precipitation signals for the upper Yellow River Basin. Our precipitation reconstruction also agreed in general with other dendroclimatic precipitation reconstructions from surrounding regions. In addition, reconstructed precipitation changes were consistent with the streamflow variation of the Yellow River.
Current Situation of Medication Errors in Saudi Arabia: A Nationwide Observational Study

Objectives Medication errors are common and highly preventable events that significantly affect patients’ health. This nationwide study primarily aims to quantify the rate and level of harm from the reported medication errors and to determine the medication process stages in which the reported errors occurred. Methods This retrospective observational study concerns medication errors reported to the General Department of Pharmaceutical Care database from March 2018 to June 2019. The database stores all aspects of medication error information, including patient, medication, and error information, along with the job position of the staff involved and contributing factors. The medication use process was categorized into these stages: ordering/prescribing, transcribing, dispensing, administering, and monitoring. We recorded each medication error based on categories from the U.S. National Coordinating Council for Medication Error Reporting and Prevention. Results A total of 71,332 medication error events were reported to the database. Physicians made 63,120 (88.5%) reported errors, and pharmacists most frequently detected the errors (75.9%). The majority of reported errors appeared at the prescribing phase (84.8%), followed by the transcribing (5.8%) and dispensing (5.7%) phases. A total of 4182 (5.8%) errors reached the patient. Health care professionals’ work overload and lack of experience were associated with 31.6% and 22.7% of the reported errors, respectively. Conclusions Our study highlights the concern regarding medication errors and their low reporting by indicating that pharmacists reported and detected the majority of errors. Promoting a no-blame culture and education for health professionals is vital for improving the error-reporting rate.
The answer depends on pragmatic norms and epistemic reflection. A linguistic and epistemological analysis of the Danish Short Form 36 Health Survey (SF-36)

Purpose The SF-36 is a commonly used tool for measuring health status in a general population. Despite the overall moderate to high validity scores, we believe that certain communicative dynamics of the questionnaire deserve more careful attention. Our aim was to examine how pragmatic dynamics and epistemic reflection may influence answers to the SF-36.Methods We applied a three-step Gricean analysis, which included identification of the items in which pragmatic dynamics are most likely to have a significant effect, examination of how Gricean maxims might affect the answers given to the items identified, and finally, assessment of whether the combined influence of linguistic context-sensitivity and pragmatic norms is benign.Results Items 6, 9a, 10 and 11a–d were included in the analysis. Regarding these items, our analysis showed that the pragmatic dynamics of scalar implicatures are crucial to the interpretation of answer options. In addition, we raised concerns specifically about the answer option ‘Ved ikke’; rather than representing a neutral midpoint, the answer is compatible with both a positive and a negative answer option. Nonetheless, we found that the communicative dynamics of the questionnaire are mostly benign.Conclusion Compared to the significance of scalar implicatures, the potential effects of epistemic reflection that we identified are minor because they concern only items with a ‘Don’t know’ answer option. However, we raised the concern that attention to epistemic error possibilities might prompt respondents to opt for a ‘Don’t know’ answer despite having evidence supporting a different answer. Therefore, although pragmatic norms of communication are far more significant than attention to epistemic error possibilities in shaping answers to the SF-36, we think that both factors belong in a description of how the questionnaire works.
Model estimation and selection for partial linear varying coefficient EV models with longitudinal data

In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously. With some regularization conditions, the resulting estimators of parameters are asymptotically normal and the estimators of nonparametric varying coefficient achieves the optimal convergence rate. Furthermore, we present simulation studies and a real example analysis to evaluate the finite sample performance of the proposed method.
Big in the tropics: Ecogeographical clines in peccary size reveal the converse of Bergmann’s rule

Bergmann's rule is an ecogeographical rule that describes a negative relationship between body size and temperature. Here, we used a multivariate measure of skull size (centroid size) as a proxy for body size to test the influence of temperature, precipitation, elevation, human influence and competition on size in Dicotyles tajacu and Tayassu pecari.
Founder Teams: Campaign Success and Venture Failure

<br>This paper examines whether solo founders are more likely to succeed in an initial equity<br>crowdfunding (ECF) campaign and are subsequently less likely to fail than founder teams<br>for a large sample of initial ECF campaigns conducted on the three largest UK platforms:<br>Crowdcube, Seedrs and SyndicateRoom. The results show that solo founders have a lower<br>probability of conducting successful initial ECF offerings than founder teams, and are<br>also more likely to fail thereafter. The implication that founder teams enjoy more success<br>is due to the fact that the quality of their human capital may likely attract professional<br>investors who can act as a certification effect. Likewise, the monitoring role of professional<br>investors helps to minimize moral hazard concerns and thus lowers the likelihood<br>of failure for ECF founder teams. The results also establish that founder team human<br>capital characteristics are significant determinants of initial ECF campaign outcomes<br>and venture failure.