bluuebunny/crossref_metadata_2025 huggingface dataset:
The Association Between Individualised Religiosity and Health Behaviour in Denmark: Are Social Networks a Mediating Factor?
Authors: Svensson Nanna Herning , Larrabee Sonderlund Anders , Wehberg Sonja , Hvidt Niels Christian , Søndergaard Jens , Thilsing Trine
Published: September 2022

The present study investigates whether social networks mediate the well-established positive association between religiosity and health behaviour. Most research has focused on traditional public religiosity (e.g. regular church attendance). This study, however, focuses on the Danish population in which non-traditional and private religiosity is common. We utilise data from the Danish population-based project, Early Detection and Prevention. Our results suggest that religiosity is linked to health behaviour; however, this association is not mediated by social network.
Interplay between local and landscape-scale effects on the taxonomic, functional and phylogenetic diversity of aerial insectivorous neotropical bats
Authors: López-Baucells Adrià , Rowley Sarah , Rocha Ricardo , Bobrowiec Paulo E. D. , Palmeirim Jorge M. , Farneda Fábio Z. , Meyer Christoph F. J.
Published: September 2022

Context

Human-modified landscapes are globally ubiquitous. It is critical to understand how habitat loss and fragmentation impact biodiversity from both a local habitat context and landscape-scale perspective to inform land management and conservation strategies.

Objectives

We used an experimentally fragmented landscape in the Brazilian Amazon to investigate variation in aerial insectivorous bat diversity in response to local habitat and wider landscape characteristics, applying a multiscale approach.

Methods

We conducted bat acoustic surveys at 33 sites, comprising old secondary forests and fragments of primary forest. Taxonomic, functional and phylogenetic diversity facets were calculated within a Hill numbers framework. We analysed responses to fragment size, interior-edge-matrix gradients, as well as local vegetation structure, continuous forest cover, edge density and patch density across five spatial scales (0.5−3 km) surrounding detector locations.

Results

Compared with continuous forest, secondary forest matrix around the smallest fragments harboured lower diversity. The overall negative effect of the matrix became less pronounced with increasing fragment size. In contrast, forest edges generally contained higher taxonomic, functional and phylogenetic diversity. We found subtle scale-sensitive associations for functional diversity, responding positively to forest cover (at the 1 km scale) and negatively to edge (1 km scale) and patch density (2.5 km scale).

Conclusions

Despite a low-contrast matrix of tall secondary forest surrounding fragments after ~ 30 years of forest recovery, aerial insectivorous bat diversity is not comparable to continuous primary forest. Assemblage functional diversity responds to compositional and configurational landscape characteristics at scales deserving further evaluation at guild and species level.
LADDER: Latent boundary-guided adversarial training
Authors: Zhou Xiaowei , Tsang Ivor W. , Yin Jie
Published: September 2022

Deep Neural Networks (DNNs) have recently achieved great success in many classification tasks. Unfortunately, they are vulnerable to adversarial attacks that generate adversarial examples with a small perturbation to fool DNN models, especially in model sharing scenarios. Adversarial training is proved to be the most effective strategy that injects adversarial examples into model training to improve the robustness of DNN models against adversarial attacks. However, adversarial training based on the existing adversarial examples fails to generalize well to standard, unperturbed test data. To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples. As opposed to most of the existing methods that generate adversarial examples in the input space, LADDER generates a myriad of high-quality adversarial examples through adding perturbations to latent features. The perturbations are made along the normal of the decision boundary constructed by an SVM with an attention mechanism. We analyze the merits of our generated boundary-guided adversarial examples from a boundary field perspective and visualization view. Extensive experiments and detailed analysis on MNIST, SVHN, CelebA, and CIFAR-10 validate the effectiveness of LADDER in achieving a better trade-off between standard accuracy and adversarial robustness as compared with vanilla DNNs and competitive baselines.
Astrocyte Responses to Complement Peptide C3a are Highly Context-Dependent
Authors: Pekna Marcela , Siqin Sumen , de Pablo Yolanda , Stokowska Anna , Torinsson Naluai Åsa , Pekny Milos
Published: September 2022

Astrocytes perform a range of homeostatic and regulatory tasks that are critical for normal functioning of the central nervous system. In response to an injury or disease, astrocytes undergo a pronounced transformation into a reactive state that involves changes in the expression of many genes and dramatically changes astrocyte morphology and functions. This astrocyte reactivity is highly dependent on the initiating insult and pathological context. C3a is a peptide generated by the proteolytic cleavage of the third complement component. C3a has been shown to exert neuroprotective effects, stimulate neural plasticity and promote astrocyte survival but can also contribute to synapse loss, Alzheimer’s disease type neurodegeneration and blood–brain barrier dysfunction. To test the hypothesis that C3a elicits differential effects on astrocytes depending on their reactivity state, we measured the expression of Gfap, Nes, C3ar1, C3, Ngf, Tnf and Il1b in primary mouse cortical astrocytes after chemical ischemia, after exposure to lipopolysaccharide (LPS) as well as in control naïve astrocytes. We found that C3a down-regulated the expression of Gfap, C3 and Nes in astrocytes after ischemia. Further, C3a increased the expression of Tnf and Il1b in naive astrocytes and the expression of Nes in astrocytes exposed to LPS but did not affect the expression of C3ar1 or Ngf. Jointly, these results provide the first evidence that the complement peptide C3a modulates the responses of astrocytes in a highly context-dependent manner.
Modelling and analysis of Parkinsonian gait
Authors: Parakkal Unni Midhun , Menon Prathyush P.
Published: September 2022

Freezing of gait is a late-stage debilitating symptom of Parkinson’s disease (PD) characterised by a sudden involuntary stoppage of forward progression of gait. The present understanding of PD gait is limited, and there is a need to develop mathematical models explaining PD gait’s underlying mechanisms. A novel hybrid system model is proposed in this paper, in which a mechanical model is coupled with a neuronal model. The proposed hybrid system model has event-dependent feedback and demonstrates PD-relevant behaviours such as freezing, high variability and stable gait. The model’s robustness is studied by analysing relevant parameters such as gain in the event-dependent feedback and level of activation of the central pattern generator neurons. The effect of augmented feedback on the model is also studied to understand different FoG management methods, such as sensory and auditory cues. The model indicates the frequency-dependent behaviours in PD, which are in line with the STN stimulation and external cueing-related studies. The model allows one to estimate the parameters from the data and thereby personalise the cueing regimes for patients. The model can be of help in understanding the mechanism of FoG and developing measures to counter its severity.
Historical and future trends in emergency pituitary referrals: a machine learning analysis
Authors: Pandit A. S. , Khan D. Z. , Hanrahan J. G. , Dorward N. L. , Baldeweg S. E. , Nachev P. , Marcus H. J.
Published: September 2022

Purpose

Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use.

Methods

A data-driven analysis was performed using all available electronic neurosurgical referrals (2014–2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance.

Results

462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic.

Conclusion

This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.
Seasonal and Spatial Variations in the Presence of Giardia and Cryptosporidium in Rural Drinking Water Supply Systems in Different Municipalities of Antioquia, Colombia
Authors: Escobar Catalina , Becerra-Agudelo Evelyn , Hernández Torres , Stiven Maicol , Londoño Yudy Andrea , Rodríguez Diana Catalina , Peñuela Gustavo A.
Published: September 2022

This study evaluated the seasonal and spatial variations in the presence of Giardia and Cryptosporidium in rural drinking water supply systems of different municipalities of Antioquia. The municipalities evaluated were Envigado, Caldas, Sabaneta, La Estrella, Itagüi, Bello, Barbosa, Copacabana, and Girardota, located in the department of Antioquia, Colombia. The experimentation was carried out over 9 sampling campaigns in the period between July 2019 and November 2020. This period encompassed the two seasons presented in Colombia: dry and wet. Each municipality included in this study has a conventional basic drinking water treatment system (DWTS) which includes sand trap, rapid filtration, and chlorination. The results showed good removal efficiencies of Cryptosporidium and Giardia in the DWTS evaluated. However, evaluation of other characteristics of water quality and of the water quality risk index for human consumption (IRCA) showed concerning water quality conditions in the rural drinking water systems supply. The prevailing risk levels of the drinking water are medium, high, or non-viable sanitary, which means the population is supplied with water that does not meet the minimum quality criteria established by Colombian regulations.
Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference
Authors: Oka Motonori , Okada Kensuke
Published: September 2022

Diagnostic classification models offer statistical tools to inspect the fined-grained attribute of respondents’ strengths and weaknesses. However, the diagnosis accuracy deteriorates when misspecification occurs in the predefined item–attribute relationship, which is encoded into a Q-matrix. To prevent such misspecification, methodologists have recently developed several Bayesian Q-matrix estimation methods for greater estimation flexibility. However, these methods become infeasible in the case of large-scale assessments with a large number of attributes and items. In this study, we focused on the deterministic inputs, noisy “and” gate (DINA) model and proposed a new framework for the Q-matrix estimation to find the Q-matrix with the maximum marginal likelihood. Based on this framework, we developed a scalable estimation algorithm for the DINA Q-matrix by constructing an iteration algorithm that utilizes stochastic optimization and variational inference. The simulation and empirical studies reveal that the proposed method achieves high-speed computation, good accuracy, and robustness to potential misspecifications, such as initial value choices and hyperparameter settings. Thus, the proposed method can be a useful tool for estimating a Q-matrix in large-scale settings.
Lifestyle and environmental factors may induce airway and systemic inflammation in firefighters
Authors: Orysiak Joanna , Młynarczyk Magdalena , Piec Robert , Jakubiak Agnieszka
Published: September 2022

Health status depends on multiple genetic and non-genetic factors. Nonheritable factors (such as lifestyle and environmental factors) have stronger impact on immune responses than genetic factors. Firefighters work is associated with exposure to air pollution and heat stress, as well as: extreme physical effort, mental stress, or a changed circadian rhythm, among others. All these factors can contribute to both, short-term and long-term impairment of the physical and mental health of firefighters. Increased levels of some inflammatory markers, such as pro-inflammatory cytokines or C-reactive protein (CRP) have been observed in firefighters, which can lead to local, acute inflammation that promotes a systemic inflammatory response. It is worth emphasizing that inflammation is one of the main hallmarks of cancer and also plays a key role in the development of cardiovascular and respiratory diseases. This article presents possible causes of the development of an inflammatory reaction in firefighters, with particular emphasis on airway inflammation caused by smoke exposure.
The challenges in the identification of Escherichia coli from environmental samples and their genetic characterization
Authors: Osińska Adriana , Korzeniewska Ewa , Korzeniowska-Kowal Agnieszka , Wzorek Anna , Harnisz Monika , Jachimowicz Piotr , Buta-Hubeny Martyna , Zieliński Wiktor
Published: September 2022

Escherichia coli bacteria are an essential indicator in evaluations of environmental pollution, which is why they must be correctly identified. This study aimed to determine the applicability of various methods for identifying E. coli strains in environmental samples. Bacterial strains preliminary selected on mFc and Chromocult media as E. coli were identified using MALDI Biotyper techniques, based on the presence of genes characteristic of E. coli (uidA, uspA, yaiO), as well as by 16S rRNA gene sequencing. The virulence and antibiotic resistance genes pattern of bacterial strains were also analyzed to investigate the prevalence of factors that may indicate adaptation to unsupportive environmental conditions and could have any significance in further identification of E. coli. Of the strains that had been initially identified as E. coli with culture-based methods, 36–81% were classified as E. coli with the use of selected techniques. The value of Cohen’s kappa revealed the highest degree of agreement between the results of 16S rRNA gene sequencing, the results obtained in the MALDI Biotyper system, and the results of the analysis based on the presence of the yaiO gene. The results of this study could help in the selection of more accurate and reliable methods which can be used in a preliminary screening and more precise identification of E. coli isolated from environmental samples.
Internalised Weight Stigma Mediates Relationships Between Perceived Weight Stigma and Psychosocial Correlates in Individuals Seeking Bariatric Surgery: a Cross-sectional Study
Authors: Bidstrup Hugh , Brennan Leah , Hindle Annemarie , Kaufmann Leah , de la Piedad Garcia Xochitl
Published: September 2022

Purpose

Research suggests that internalised weight stigma may explain the relationship between perceived weight stigma and adverse psychological correlates (e.g. depression, disordered eating, body image disturbances). However, few studies have assessed this mechanism in individuals seeking bariatric surgery, even though depression and disordered eating are more common in this group than the general population.

Materials and Methods

We used data from a cross-sectional study with individuals seeking bariatric surgery (n = 217; 73.6% female) from Melbourne, Australia. Participants (Mage = 44.1 years, SD = 11.9; MBMI = 43.1, SD = 7.9) completed a battery of self-report measures on weight stigma and biopsychosocial variables, prior to their procedures. Bias-corrected bootstrapped mediations were used to test the mediating role of internalised weight stigma. Significance thresholds were statistically corrected to reduce the risk of Type I error due to the large number of mediation tests conducted.

Results

Controlling for BMI, internalised weight stigma mediated the relationship between perceived weight stigma and psychological quality of life, symptoms of depression and anxiety, stress, adverse coping behaviours, self-esteem, exercise avoidance, some disordered eating measures and body image subscales, but not physical quality of life or pain.

Conclusion

Although the findings are cross-sectional, they are mostly consistent with previous research in other cohorts and provide partial support for theoretical models of weight stigma. Interventions addressing internalised weight stigma may be a useful tool for clinicians to reduce the negative correlates associated with weight stigma.

Graphical abstract
Strategies implemented by South African teachers to ensure continuing mathematics education during COVID-19
Authors: Vale Pamela , Graven Mellony
Published: September 2022

The COVID-19 pandemic continues to present severe challenges to the education sector more than 2 years after the first case was detected. We explore the strategies South African teachers used to support continued mathematics learning at home during the COVID-19 pandemic across different stages of the response to it and across different contexts. We explore these strategies, first, in relation to the initial shift to emergency remote teaching when learners needed to be reached in their homes under severe lockdown regulations, then through the gradual reopening of schools later as regulations were relaxed. Activity theory informs our perspective on the way in which teachers worked to mediate learning across school and home activity systems. Twenty-five Grade 4–7 mathematics teachers from the Eastern Cape responded to two questionnaires given at the start of the COVID-19 lockdown period and during the phased return to school. Questions focused on the strategies used to support mathematics learning. The results show that strategies focused on engaging caregivers through various technologies and getting resources into the home. WhatsApp, a free internet-based messaging service, was the most frequently used communication app across all types of schools for both messaging parents and sending instructional material and support in the form of videos, pictures and text messages. Department issued workbooks and teacher designed work packs were key resources sent to homes. Differences are evident in the range of use of different technologies across wealthier and poorer schools. Primary teachers’ voices inform possible ways forward for the purpose of managing young student mathematical learning gaps and meeting ongoing learning needs.
Platformization and Publishing: Changes in Literary Publishing
Authors: Spjeldnæs Kari
Published: September 2022

This article explores the influence of platformization on literary publishing, focusing on the impact of audiobooks and streaming services on editorial processes and business objectives. Increased importance of some genres, sliding concepts of quality, increasing dependency on social media, diminishing economic margins, and stricter editorial decisions are transitioning traits highlighted in this study on Norwegian trade publishers. Further, the analysis brings forward three central challenges to today's publishers: dependence on digital platforms to obtain readership, favoring simple narrative structures countering broader catalogues, and increased press on prioritizing in a market characterized by customization.
A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset
Authors: Liotto Carmine , Petrillo Alberto , Santini Stefania , Toscano Gianluca , Tufano Vincenza
Published: September 2022

The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the subject-exclusive and balances among the classes criteria, it is possible predicting the health status of unseen patients. Numerical results, carried out considering Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database, disclose a classification accuracy of $$96.2\%$$

96.2
%

. Finally, a cross-database performance evaluation and a comparison analysis w.r.t. the current state-of-art further disclose the effectiveness and the efficiency of the proposed solution, as well as its benefits in terms of patient health status prediction.
Numerical approximation of nonlinear SPDE’s
Authors: Ondreját Martin , Prohl Andreas , Walkington Noel J.
Published: September 2022

The numerical analysis of stochastic parabolic partial differential equations of the form $$\begin{aligned} du + A(u)\, dt = f \,dt + g \, dW, \end{aligned}$$

d
u
+
A
(
u
)

d
t
=
f

d
t
+
g

d
W
,

is surveyed, where A is a nonlinear partial operator and W a Brownian motion. This manuscript unifies much of the theory developed over the last decade into a cohesive framework which integrates techniques for the approximation of deterministic partial differential equations with methods for the approximation of stochastic ordinary differential equations. The manuscript is intended to be accessible to audiences versed in either of these disciplines, and examples are presented to illustrate the applicability of the theory.
Estimation of Behavioral Addiction Prevalence During COVID-19 Pandemic: A Systematic Review and Meta-analysis
Authors: Alimoradi Zainab , Lotfi Aida , Lin Chung-Ying , Griffiths Mark D. , Pakpour Amir H.
Published: September 2022

Purpose of Review

The COVID-19 pandemic changed people’s lifestyles and such changed lifestyles included the potential of increasing addictive behaviors. The present systematic review and meta-analysis aimed to estimate the prevalence of different behavioral addictions (i.e., internet addiction, smartphone addiction, gaming addiction, social media addiction, food addiction, exercise addiction, gambling addiction, and shopping addiction) both overall and separately.

Recent Findings

Four databases (PubMed,Scopus, ISI Web of Knowledge, andProQuest) were searched. Peer-reviewed papers published in English between December 2019 and July 2022 were reviewed and analyzed. Search terms were selected using PECO-S criteria: population (no limitation in participants’ characteristics), exposure (COVID-19 pandemic), comparison (healthy populations), outcome (frequency or prevalence of behavioral addiction), and study design (observational study). A total of 94 studies with 237,657 participants from 40 different countries (mean age 25.02 years; 57.41% females). The overall prevalence of behavioral addiction irrespective of addiction type (after correcting for publication bias) was 11.1% (95%CI: 5.4 to 16.8%). The prevalence rates for each separate behavioral addiction (after correcting for publication bias) were 10.6% for internet addiction, 30.7% for smartphone addiction, 5.3% for gaming addiction, 15.1% for social media addiction, 21% for food addiction, 9.4% for sex addiction, 7% for exercise addiction, 7.2% for gambling addiction, and 7.2% for shopping addiction. In the lockdown periods, prevalence of food addiction, gaming addiction, and social media addiction was higher compared to non-lockdown periods. Smartphone and social media addiction was associated with methodological quality of studies (i.e., the higher the risk of boas, the higher the prevalence rate). Other associated factors of social media addiction were the percentage of female participants, mean age of participants, percentage of individuals using the internet in country, and developing status of country. The percentage of individuals in the population using the internet was associated with all the prevalence of behavioral addiction overall and the prevalence of sex addiction and gambling addiction. Gaming addiction prevalence was associated with data collection method (online vs. other methods) that is gaming addiction prevalence was much lower using online methods to collect the data.

Behavioral addictions appeared to be potential health issues during the COVID-19 pandemic. Healthcare providers and government authorities should foster some campaigns that assist people in coping with stress during COVID-19 pandemics to prevent them from developing behavioral addictions during COVID-19 and subsequent pandemics.
Vibration of an axially loaded heterogeneous fixed–fixed beam with an intermediate roller support
Authors: Kiss L. P. , Szeidl G. , Messaoudi A.
Published: September 2022

The present paper investigates how an axial load can change the natural frequencies of heterogeneous fixed–fixed beams with an intermediate roller support. The problem is treated as a three-point boundary value problem (eigenvalue problem) that is paired with homogeneous boundary conditions. The Green functions are determined for the unloaded and pre-loaded beams as well—in the later case, both for compression and tension. With these, the eigenvalue problems can be transformed into eigenvalue problems governed by a homogeneous Fredholm integral equations. It is then replaced by an algebraic eigenvalue problem, that is solved numerically with an effective solution algorithm which is based on the boundary element method.
Membrane creation and symport/antiport rules solving QSAT
Authors: Orellana-Martín David , Valencia-Cabrera Luis , Pérez-Jiménez Mario J.
Published: September 2022

In Membrane Computing, different variants of devices can be found by changing both syntactical and semantic ingredients. These devices are usually called membrane systems or P systems, and they recall the structure and behavior of living cells in the nature. In this sense, rules are introduced as a way for objects to interact with membranes, giving P systems the ability to solve computational problems. Some of these rules, as division, separation and creation rules are inspired by the membrane division through the mitosis process or new membranes are created through gemmation. These rules seem to be crucial in the path to solve computationally hard problems. In this work, creation rules are used in classical P systems with symport/antiport rules, where objects travel through membranes without changing to achieve enough computational power to efficiently solve PSPACE-complete problems. More precisely, a solution to the problem is given by means of a uniform family of these systems. This paper was originally submitted to the International Conference on Membrane Computing 2021.
Mycorrhiza, Charcoal, and Rocket Salad Powder as Eco-friendly Methods for Controlling Broomrape Weed in Inter-planted Faba Bean with Flax
Authors: Saudy Hani Saber , El-Metwally Ibrahim Mohamed , Telb Sara Telb Sobieh , Abd-Alwahed Saaeid Helmy Abd-Alhalim
Published: September 2022

Till now, there is no complete program that could be implemented to eradicate the parasitic weeds such as broomrape because of their complex life cycle. Therefore, the current research aimed to find new and safe agricultural practices to solve, partially at least, the issues of broomrape in faba bean fields. The experiment was conducted for two winter seasons of 2018/2019 and 2019/2020 in naturally infested field with broomrape. Treatments involved application of mycorrhiza, charcoal and rocket salad powder, glyphosate–isopropylammonium herbicide, and weedy check applied whether with sole planting of faba bean and interplanting with flax. The experiment was designed in a strip plot based on completely randomized block arrangement with six replicates. Broomrape number and weight, infested faba bean plants, faba bean agronomic traits, and seed nutrient uptake were estimated. In both seasons, glyphosate whether with sole or interplanting patterns recorded the lowest values of broomrape number plot‒1and broomrape weight plot‒1without significant variation with charcoal and rocket salad applied in interplanted plots. The maximum increases in faba bean seed yield were observed with application of charcoal × sole or interplanting pattern and rocket salad × interplanting pattern in the 2018/2019 season as well as charcoal, mycorrhiza, and rocket salad with sole pattern in the 2019/2020 season. Charcoal, mycorrhiza, and rocket salad were effective practices whether under sole or interplanting patterns for improving nutrient uptake, especially in the first season. In the second season, rocket salad with sole or interplanting pattern was the stable interaction for enhancing all nutrient uptake. Reducing broomrape hazards expressed in low number and weight with less infested faba bean plants was confined by application of mycorrhiza, charcoal, and rocket salad powder whether with sole planting of faba bean and interplanting with flax. Hence, faba bean farmers are advised to apply such promising safe practices for sustaining faba bean cultivation in lands infested by broomrape.
Physical Characteristics Explain Ball-Carrying Capability in Sub-Elite Rugby Union Players
Authors: Hart Alexander S. , Erskine Robert M. , Mclaughlin Tom J. , Clark David R.
Published: September 2022

Purpose

The aims of the present study were two-fold: (i) to investigate the relationship between physical characteristics and the game statistics associated with ball-carrying capability amongst sub-elite rugby union players, and (ii) to predict the level of change in these physical characteristics required to improve the associated game statistic via regression analysis.

Methods

Thirty-eight senior professional players (forwards, n = 22; backs, n = 16) were assessed for body mass (BM), back squat (BS) single-repetition maximum (1RM) normalised to BM (1RM/BM), 10 m sprint velocity (S10), 10 m sprint momentum (SM10), and the game statistics from 22 games within the 2019/20 RFU Championship season. The relationship between these measures and the predicted level of change in a physical measure required to improve the total number of the associated game statistic by one were assessed by Pearson’s correlation coefficient and simple regression analyses.

Results

In forwards, an ~ 11.5% reduction in BM, an ~ 11.8% improvement in BS 1RM/BM, or an ~ 11.5% increase in S10 was required to improve the game statistics associated with ball-carrying capability. In backs, a ~ 19.3% increase in BM or a ~ 15.6% improvement in SM10 was required.

Conclusions

These findings demonstrate that improvements in lower-body relative strength, acceleration performance, and position-specific alterations in body mass are required to maximise the ball-carrying capability and therefore match outcome of sub-elite rugby union players.
Light history modulates growth and photosynthetic responses of a diatom to ocean acidification and UV radiation
Authors: Li Wei , Wang Tifeng , Campbell Douglas A. , Gao Kunshan
Published: September 2022

To examine the synergetic effects of ocean acidification (OA) and light intensity on the photosynthetic performance of marine diatoms, the marine centric diatom Thalassiosira weissflogii was cultured under ambient low CO2 (LC, 390 μatm) and elevated high CO2 (HC, 1000 μatm) levels under low-light (LL, 60 μmol m−2 s−1) or high-light (HL, 220 μmol m−2 s−1) conditions for over 20 generations. HL stimulated the growth rate by 128 and 99% but decreased cell size by 9 and 7% under LC and HC conditions, respectively. However, HC did not change the growth rate under LL but decreased it by 9% under HL. LL combined with HC decreased both maximum quantum yield (FV/FM) and effective quantum yield (ΦPSII), measured under either low or high actinic light. When exposed to UV radiation (UVR), LL-grown cells were more prone to UVA exposure, with higher UVA and UVR inducing inhibition of ΦPSII compared with HL-grown cells. Light use efficiency (α) and maximum relative electron transport rate (rETRmax) were inhibited more in the HC-grown cells when UVR (UVA and UVB) was present, particularly under LL. Our results indicate that the growth light history influences the cell growth and photosynthetic responses to OA and UVR.
Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction
Authors: Cheredath Aswathi , Uppangala Shubhashree , C. S Asha , Jijo Ameya , R Vani Lakshmi , Kumar Pratap , Joseph David , G.A Nagana Gowda , Kalthur Guruprasad , Adiga Satish Kumar
Published: September 2022

This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100% accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.
On connections between Amplitude Flow and Error Reduction for phase retrieval and ptychography
Authors: Melnyk Oleh
Published: September 2022

In this paper, we consider two iterative algorithms for the phase retrieval problem: the well-known Error Reduction method and the Amplitude Flow algorithm, which performs minimization of the amplitude-based squared loss via the gradient descent. We show that Error Reduction can be interpreted as a scaled gradient method applied to minimize the same amplitude-based squared loss, which allows to establish its convergence properties. Moreover, we show that for a class of measurement scenarios, such as ptychography, both methods have the same computational complexity and sometimes even coincide.
The status and development proposal of carbon sources and sinks monitoring satellite system
Authors: Meng Guang , Wen Yuan , Zhang Miaomiao , Gu Yilei , Xiong Wei , Wang Zijun , Niu Shengda
Published: September 2022

In order to mitigate global warming, the international communities actively explore low-carbon and green development methods. According to the Paris Agreement that came into effect in 2016, there will be a global stocktaking plan to carry out every 5 years from 2023 onwards. In September 2020, China proposed a "double carbon" target of carbon peaking before 2030 and carbon neutrality before 2060. Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions. China's existing carbon monitoring methods have insufficient detection accuracy, low spatial resolution, and narrow swath, which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring. In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks, it is recommended to make full use of the foundation of the existing satellites, improve the detection technical specifications of carbon sources and sinks monitoring measures, and build a multi-means and comprehensive, LEO-GEO orbit carbon monitoring satellite system to achieve higher precision, higher resolution and multi-dimensional carbon monitoring. On this basis, it is recommended to strengthen international cooperation, improve data sharing policy, actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards, establish an independent and controllable global carbon monitoring and evaluation system, and contribute China's strength to the global realization of carbon peaking and carbon neutrality.
Biomedical Materials and Devices with Focus on Orthopaedic and Cardio-vascular Problems
Authors: Stanzl-Tschegg Stefanie E.
Published: September 2022

Materials and developments of medical devices are discussed based on trying to understand nature’s construction principles. A roadmap explaining the papertopics is shown in Fig. 1 of the introduction. Guidelines for producing biomedical materials and devices are discussed. Finally, future development of new technological procedures are mentioned helping human beings to become older and remain healthier during their higher age. Serious problems are pointed out, which will lead to serious social conflicts.
Sequence of U-Shaped Convolutional Networks for Assessment of Degree of Delamination Around Scribe
Authors: Rozsivalova Veronika , Dolezel Petr , Stursa Dominik , Rozsival Pavel
Published: September 2022

The application of protective layers is the primary method of keeping metallic structures resistant to degradation. The measurement of the layer resistance to delamination is one of the important indicators of the protection quality. Therefore, ISO 4628 standard has been issued to handle and quantify the main coating defects. Here, an innovative assessment of degree of delamination around a scribe according to ISO 4628 standard has been practically realized. It utilizes an computer-driven deep learning-based method. The assessment method is composed of two shallow U-shaped convolutional networks in a row; the first for preliminary and the second for refined detection of delamination area around a scribe. The experiments performed on 586 samples showed that the proposed sequence of U-shaped convolutional networks meets the edge computing standards, provides good generalization capability, and provides precise delamination area detection for a large variability of surfaces.
Global Medievalism
Authors: Young Helen , Finn Kavita Mudan
Published: September 2022

The typical vision of the Middle Ages western popular culture represents to its global audience is deeply Eurocentric. The Lord of the Rings and Game of Thrones imagined entire medievalist worlds, but we see only a fraction of them through the stories and travels of the characters. Organised around the theme of mobility, this Element seeks to deconstruct the Eurocentric orientations of western popular medievalisms which typically position Europe as either the whole world or the centre of it, by making them visible and offering alternative perspectives. How does popular culture represent medievalist worlds as global-connected by the movement of people and objects? How do imagined mobilities allow us to create counterstories that resist Eurocentric norms? This study represents the start of what will hopefully be a fruitful and inclusive conversation of what the Middle Ages did, and should, look like.
European Social History
Authors: Drayton Richard
Published: September 2022

Why did European history come so late to the global turn? Europe’s past had of course always been constructed relative to its Islamic or Mongol peripheries, and later its colonial offshore. But only recently has it been understood that European and extra-European history are in a dynamic relationship of reciprocal influence. Intellectual and economic history recognized this before social history, which in its post-1960 flowering took it for granted that European social forms were both more advanced and categorically different from others. During the 1970s and 1980s, however, a generation after political decolonization, new work began to explore the impact of peripheries on the European core, and to measure Europe from the outside. After 2000, a globalized European social history became visible. Its evasion of the constraints of the national paradigm has opened up striking new pan- and trans-European historical projects and methods. These are provoking new questions of how we might reconfigure European history in ways which understand eastern and central Europe in their own terms, rather than simply as the retarded extensions of “advanced” western European phenomena.
EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH
Authors: Miyata Akihiro , Matsuyama Naoki
Published: September 2022

In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.
Arbitrarily slow decay in the Möbius disjointness conjecture
Authors: ALGOM AMIR , WANG ZHIREN
Published: September 2022

Sarnak’s Möbius disjointness conjecture asserts that for any zero entropy dynamical system
$(X,T)$
,
$({1}/{N})\! \sum _{n=1}^{N}\! f(T^{n} x) \mu (n)= o(1)$
for every
$f\in \mathcal {C}(X)$
and every
$x\in X$
. We construct examples showing that this
$o(1)$
can go to zero arbitrarily slowly. In fact, our methods yield a more general result, where in lieu of
$\mu (n)$
, one can put any bounded sequence
$a_{n}$
such that the Cesàro mean of the corresponding sequence of absolute values does not tend to zero. Moreover, in our construction, the choice of x depends on the sequence
$a_{n}$
but
$(X,T)$
does not.
The nonlinear evolution of internal tides. Part 2. Lagrangian transport by periodic and modulated waves
Authors: Sutherland Bruce R. , Yassin Houssam
Published: September 2022

We examine Lagrangian transport by a nonlinearly evolving vertical mode-1 internal tide in non-uniform stratification. In a companion paper (Sutherland & Dhaliwal, J. Fluid Mech., 2022, in press) it was shown that a parent internal tide can excite successive superharmonics that superimpose to form a solitary wave train. Despite this transformation, here we show that the collective forcing by the parent wave and superharmonics is effectively steady in time. Thus we derive relatively simple formulae for the Stokes drift and induced Eulerian flow associated with the waves under the assumption that the parent waves and superharmonics are long compared with the fluid depth. In all cases, the Stokes drift exhibits a mixed mode-1 and mode-2 vertical structure with the flow being in the waveward direction at the surface. If the background rotation is non-negligible, the vertical structure of the induced Eulerian flow is equal and opposite to that of the Stokes drift. This flow periodically increases and decreases at the inertial frequency with maximum magnitude twice that of the Stokes drift. When superimposed with the Stokes drift, the Lagrangian flow at the surface periodically changes from positive to negative over one inertial period. If the background rotation is zero, the induced Eulerian flow evolves non-negligibly in time and space for horizontally modulated waves: the depth below the surface of the positive Lagrangian flow becomes shallower ahead of the peak of the amplitude envelope and becomes deeper in the lee of the peak. These predictions are well-captured by fully nonlinear numerical simulations.
Three-dimensional large-scale and very-large-scale coherent structures in a turbulent axisymmetric jet
Authors: Samie Milad , Aparece-Scutariu Vlad , Lavoie Philippe , Shin Dong-hyuk , Pollard Andrew
Published: September 2022

The direct numerical simulation database from Shin et al. (J. Fluid Mech., vol. 823, 2017, pp. 1–25) is used to study three-dimensional vortical and very-large-scale coherent structures in a turbulent round jet at a Reynolds number of

$7300$

. In particular, horseshoe vortices and their role in the formation of very-large-scale coherent structures in the jet near and intermediate fields are assessed. The swirling strength criterion together with conditional averaging are used to visualize volumetric vortical structures. It is shown that, similar to wall-bounded turbulent flows, the turbulent jet is populated with symmetric and asymmetric horseshoe-like vortices, which induce high-momentum and low-momentum regions in the flow. However, unlike what is found for wall-bounded flows, inverse horseshoe-like vortices are common in the turbulent jet. They prevail in the shear region around the potential core in the jet near field and contribute to the mixing of the potential core in the jet. In the jet near field, groups of axially aligned horseshoe structures induce long streaky structures, which are periodic in the azimuthal and streamwise directions. In the jet intermediate field, very-large-scale motions (VLSMs) consisting of high-momentum regions, flanked on either side by low-momentum regions, are found to be associated with groups of horseshoe vortices. Instantaneous three-dimensional flow fields suggest that horseshoe vortices tend to concatenate and form organized spiral as well as axially aligned coherent VLSMs. A detection scheme is introduced to identify and average over these VLSMs. This conditional averaging reveals that spiral VLSMs and axially aligned VLSMs constitute

$72\,\%$

and

$28\,\%$

of the total VLSMs, respectively.