Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature Measurement in Plasma-Assisted Combustor by TDLAS

The paper is focused on measurements of gas temperature distribution in plasma-assisted supersonic combustor. A tunable diode laser absorption spectroscopy (TDLAS) technique was applied for that. Spectra of H2O absorption were recorded using fast frequency scanning of a single distributed feedback (DFB) laser. Under conditions of high level of flow disturbances an optimal technique for fitting the experimental spectra was developed based on visualization of the transient absorption spectra as 2D images in the first step of data processing. In the most cases the high signal-to-noise ratio enabled the reconstruction of the temporal behavior of temperature with a resolution of ∼1ms. Direct wall-injected ethylene was used as the fuel in two geometrical configurations: fuel orifices upstream or downstream of the electrode system of near-surface electric discharge. Conformity of the data of DLAS measurement with the two-stage mechanism of plasma-assisted combustion is discussed. I. Introduction variety of optical diagnostic techniques are used for gaseous medium characterization at presence of electrical discharge plasma– optical emission spectrometry (OES), Thomson scattering (TS), coherent anti-stocks Raman scattering (CARS), Doppler line broadening, etc. These techniques, providing valuable data for specific types of plasma and specific range of plasma parameters, have some limitations. Tunable diode laser (DL) absorption spectroscopy (TDLAS) is a widely used spectroscopic technique for the detection of gas parameters in reacting flows [1–8]. This technique provides remote, non-intrusive measurements of the parameters of a hot zone with time resolution in the µs-ms range depending on the specific experimental conditions. The technique is usually based on the measurements of the ratio of the absorption line intensities of a test molecule. If a Boltzmann distribution of the energy levels is established, the ratio of the line intensities depends only on the kinetic temperature of the object. DLAS possesses many attractive features as the diagnostic technique. The spatial coherence of DLs enables to deliver a narrow laser beam to a probing zone without noticeable divergence, which makes it possible remote sensing. In contrast to any version of optical emission-fluorescence technique, DLAS does not need a large solid angle of light collection – all information, which can be deduced from absorption signal, is “frozen” in a narrow laser beam of low divergence. Therefore the thermal emission of an investigated hot zone and laser stray light can be easily eliminated by a set of diaphragms and lenses. As a consequence, the hottest and most dense zones of an object can be probed by DLAS.
Nonlinear Image Recovery with

One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function that enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate but are often far less accessible. This is particularly true when the objective function is nonconvex, and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function so that the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov chain that alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with space telescope data, and computed tomography.
A CNN-based four-layer DOI encoding detector using LYSO and BGO scintillators for small animal PET imaging

Objective. We propose a novel four-layer depth-of-interaction (DOI) encoding phoswich detector using lutetium–yttrium oxyothosilicate (LYSO) and bismuth germanate (BGO) scintillator crystal arrays for high sensitivity and high spatial resolution small animal PET imaging. Approach. The detector was comprised of a stack of four alternating LYSO and BGO scintillator crystal arrays coupled to an 8 × 8 multi-pixel photon counter (MPPC) array and read out by a PETsys TOFPET2 application specific integrated circuit. The four layers from the top (gamma ray entrance) to the bottom (facing the MPPC) consisted of a 24 × 24 array of 0.99 × 0.99 × 6 mm3 LYSO crystals, a 24 × 24 array of 0.99 × 0.99 × 6 mm3 BGO crystals, a 16 × 16 array of 1.53 × 1.53 × 6 mm3 LYSO crystals and a 16 × 16 array of 1.53 × 1.53 × 6 mm3 BGO crystals. Main results. Events that occurred in the LYSO and BGO layers were first separated by measuring the pulse energy (integrated charge) and duration (time over threshold (ToT)) from the scintillation pulses. Convolutional neural networks (CNNs) were then used to distinguish between the top and lower LYSO layers and between the upper and bottom BGO layers. Measurements with the prototype detector showed that our proposed method successfully identified events from all four layers. The CNN models achieved a classification accuracy of 91% for distinguishing the two LYSO layers and 81% for distinguishing the two BGO layers. The measured average energy resolution was 13.1% ± 1.7% for the top LYSO layer, 34.0% ± 6.3% for the upper BGO layer, 12.3% ± 1.3% for the lower LYSO layer, and 33.9% ± 6.9% for the bottom BGO layer. The timing resolution between each individual layer (from the top to the bottom) and a single crystal reference detector was 350 ps, 2.8 ns, 328 ps, and 2.1 ns respectively. Significance. In conclusion, the proposed four-layer DOI encoding detector achieved high performance and is an attractive choice for next-generation high sensitivity and high spatial resolution small animal positron emission tomography systems.
Supporting conservationists’ mental health through better working conditions

Biodiversity conservation work can be challenging but rewarding, and both aspects have potential consequences for conservationists’ mental health. Yet, little is known about patterns of mental health among conservationists and its associated workplace protective and risk factors. A better understanding might help improve working conditions, supporting conservationists’ job satisfaction, productivity, and engagement, while reducing costs from staff turnover, absenteeism, and presenteeism. We surveyed 2311 conservation professionals working in 122 countries through an internet survey shared via mailing lists, social media, and other channels. We asked them about experiences of psychological distress, working conditions, and personal characteristics. Over half were from and worked in Europe and North America, and most had a university‐level education, were in desk‐based academic and practitioner roles, and responded in English. Heavy workload, job demands, and organizational instability were linked to higher distress, but job stability and satisfaction with one's contributions to conservation were associated with lower distress. Respondents with low dispositional and conservation‐specific optimism, poor physical health, and limited social support, women, and early‐career professionals were most at risk of distress in our sample. Our results flag important risk factors that employers could consider, although further research is needed among groups underrepresented in our sample. Drawing on evidence‐based occupational health interventions, we suggest measures that could promote better working conditions and thus may improve conservationists’ mental health and abilities to protect nature.
On the distribution of points in a poisson dirichlet process

A probability density function important in the Poisson Dirichlet process of population genetics is studied. An accurate computational algorithm is given for this density and for the marginal distributions of the points in the Poisson Dirichlet process. The distribution of the maximal point of the process is tabulated. Rational polynomial approximations in θ, the mutation parameter, are found for the expected values of the first three maximal points.
‘All together now’: Facilitators and barriers to engagement in mutual aid during the first UK COVID-19 lockdown

Despite undeniable hardship, the onset of the COVID-19 pandemic also saw an outpour of community solidarity and mutual aid towards those in need. This study explored why people participated in mutual aid during the pandemic as well as the factors that contributed to continued involvement and/or its decline. We conducted remote interviews with 17 people in South-east England who had been involved in volunteering and local community mutual aid support groups during the first UK lockdown from March to May 2020. Using thematic analysis, we identified two themes: 1) The emergence of social groups and their psychosocial effects, and 2) Enduring connections and barriers to continued participation. Participants often reported an emergent shared identity, preferring the localised nature of these groups and the associated mutual nature of support. They also reported intentions to continue providing such support, should the need arise again, and any barriers to continued involvement in mutual aid were better explained by structural and systemic issues, rather than individual motivational factors.
Identification of a GABAergic neural circuit governing leptin signaling deficiency-induced obesity

The hormone leptin is known to robustly suppress food intake by acting upon the leptin receptor (LepR) signaling system residing within the agouti-related protein (AgRP) neurons of the hypothalamus. However, clinical studies indicate that leptin is undesirable as a therapeutic regiment for obesity, which is at least partly attributed to the poorly understood complex secondary structure and key signaling mechanism of the leptin-responsive neural circuit. Here, we show that the LepR-expressing portal neurons send GABAergic projections to a cohort of α3-GABAA receptor expressing neurons within the dorsomedial hypothalamic nucleus (DMH) for the control of leptin-mediated obesity phenotype. We identified the DMH as a key brain region that contributes to the regulation of leptin-mediated feeding. Acute activation of the GABAergic AgRP-DMH circuit promoted food intake and glucose intolerance, while activation of post-synaptic MC4R neurons in the DMH elicited exactly opposite phenotypes. Rapid deletion of LepR from AgRP neurons caused an obesity phenotype which can be rescued by blockage of GABAA receptor in the DMH. Consistent with behavioral results, these DMH neurons displayed suppressed neural activities in response to hunger or hyperglycemia. Furthermore, we identified that α3-GABAA receptor signaling within the DMH exerts potent bi-directional regulation of the central effects of leptin on feeding and body weight. Together, our results demonstrate a novel GABAergic neural circuit governing leptin-mediated feeding and energy balance via a unique α3-GABAA signaling within the secondary leptin-responsive neural circuit, constituting a new avenue for therapeutic interventions in the treatment of obesity and associated comorbidities.
Comparison of brief group behavioural parent training with individual parent training for preschool children with attention deficit hyperactivity disorder: A randomized feasibility study

Behaviour parent training (BPT) is first‐line treatment for preschool attention deficit hyperactivity disorder (ADHD). BPT in a group format can be a cost‐ and time‐effective alternative in low and middle‐income countries (LMIC) settings with limited resources. We conducted a randomized controlled trial to compare the feasibility and efficacy of group BPT with individual BPT in improving ADHD severity in the preschool age group over 12 weeks.
Signatures of Adaptation, Constraints, and Potential Redundancy in the Canonical Immune Genes of a Key Pollinator

Abstract All organisms require an immune system to recognize, differentiate, and defend against pathogens. From an evolutionary perspective, immune systems evolve under strong selective pressures exerted by fast-evolving pathogens. However, the functional diversity of the immune system means that different immune components and their associated genes may evolve under varying forms of selection. Insect pollinators, which provide essential ecosystem services, are an important system in which to understand how selection has shaped immune gene evolution as their populations are experiencing declines with pathogens highlighted as a potential contributing factor. To improve our understanding of the genetic variation found in the immune genes of an essential pollinator, we performed whole-genome resequencing of wild-caught Bombus terrestris males. We first assessed nucleotide diversity and extended haplotype homozygosity for canonical immune genes finding the strongest signatures of positive selection acting on genes involved in pathogen recognition and antiviral defense, possibly driven by growing pathogen spread in wild populations. We also identified immune genes evolving under strong purifying selection, highlighting potential constraints on the bumblebee immune system. Lastly, we highlight the potential loss of function alleles present in the immune genes of wild-caught haploid males, suggesting that such genes are potentially less essential for development and survival and represent redundancy in the gene repertoire of the bumblebee immune system. Collectively, our analysis provides novel insights into the recent evolutionary history of the immune system of a key pollinator, highlighting targets of selection, constraints to adaptation, and potential redundancy.
Modeling the impact of the habituation effect on information spreading processes with repeated contacts under an SI model

People are exposed to information from different sources whether or not such exposure is desired. Due to a limited ability to process information, only parts of the messages may be absorbed, and other parts may be ignored. Repeated stimuli lead to lower responses due to the learning process and the habituation effect. While this effect has been intensively studied, mainly in relation to visual stimulus, it is also incorporated in information spreading processes. Information spreading models often assume the possibility of repeated contact, but no habituation effect, which lowers the level of responsiveness of nodes in the network, has been implemented. Here, we study the impact of the habituation effect on information spreading with a susceptible–infected (SI) model, which is often the basis for other models. We assume that a decrease in habituation has an impact on propagation processes. Analysis of the results shows that the course of these propagation processes behave differently, significantly worsening their results. These processes are very sensitive, even to small changes in the level of habituation.
A New Approach to Implement Sigma Coordinate in a Numerical Model

This study shows a new way to implement terrain-following σ-coordinate in a numerical model, which does not lead to the well-known ”pressure gradient force (PGF)” problem. First, the causes of the PGF problem are analyzed with existing methods that are categorized into two different types based on the causes. Then, the new method that bypasses the PGF problem all together is proposed. By comparing these threemethods and analyzing the expression of the scalar gradient in a curvilinear coordinate system, this study finds out that only when using the covariant scalar equations of σ-coordinate will the PGF computational form have one term in each momentum component equation, thereby avoiding the PGF problem completely. A convenient way of implementing the covariant scalar equations of σ-coordinate in a numerical atmospheric model is illustrated, which is to set corresponding parameters in the scalar equations of the Cartesian coordinate. Finally, two idealized experiments manifest that the PGF calculated with the new method is more accurate than using the classic one. This method can be used for oceanic models as well, and needs to be tested in both the atmospheric and oceanic models. AMS subject classifications: 86-08, 86A05, 86A10
Association of alcohol consumption with all‐cause mortality, new‐onset stroke, and coronary heart disease in patients with abnormal glucose metabolism—Findings from a 10‐year follow‐up of the REACTION study

Type 2 diabetes mellitus (T2DM) and diabetic complications threaten human health seriously. Healthy lifestyles can lower the risk of cardiovascular disease (CVD) and long‐term complications. However, the relationship between alcohol consumption and CVD mortality is still controversial, and there is a lack of evidence from large‐scale longitudinal studies in the Chinese population. Based on the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study), this paper explores the association between alcohol consumption and all‐cause mortality, stroke, and coronary heart disease (CHD) in patients with abnormal glucose metabolism during a 10‐year follow‐up period to provide evidence for lifestyle counselling for these patients.
Host associations of biting midges (Diptera: Ceratopogonidae: Culicoides) at deer farms in Florida, USA

Documenting the host use of vector species is important for understanding the transmission dynamics of vector-borne pathogens. Biting midges (Diptera: Ceratopogonidae: Culicoides) are vectors of epizootic hemorrhagic disease virus (EHDV) and bluetongue virus (BTV) worldwide. However, relative to mosquitoes and many other vector groups, host associations of this group are poorly documented. In this study, we used PCR-based bloodmeal analysis to determine species-level host associations of 3,603 blood-engorged specimens belonging to 18 Culicoides species at 8 deer farms in Florida, USA. We used a binomial mixed model with a Bayesian framework to compare the effect of host composition on the feeding patterns of Culicoides spp. and employed the Morisita-Horn Index to investigate the similarity of host use between farms for Culicoides stellifer and Culicoides insignis. Results show that the estimated probability of Culicoides spp. feeding upon white-tailed deer depends on the availability of cattle or exotic game and demonstrates differences in host-feeding selection among species. Culicoides insignis had high host similarity across farms suggesting that its host-use patterns are somewhat conserved. Culicoides stellifer had lower host similarity across farms suggesting that it is a more opportunistic feeder. White-tailed deer are fed upon by many Culicoides species on deer farms in Florida, and while most Culicoides species feed on white-tailed deer, the ratio of white-tailed deer bloodmeals to other bloodmeals is likely influenced by host availability. Culicoides spp. taking a majority of their bloodmeals from farmed white-tailed deer should be assessed for their vector competence for EHDV and BTV.
Predicting long-term risk of reoperations following abdominal and pelvic surgery: a nationwide retrospective cohort study

Background: The risk of reoperations after abdominal and pelvic surgery is multifactorial and difficult to predict. The risk of reoperation is frequently underestimated by surgeons as most reoperations are not related to the initial procedure and diagnosis. During reoperation, adhesiolysis is often required, and patients have an increased risk of complications. Therefore, the aim of this study was to provide an evidence-based prediction model based on the risk of reoperation. Materials and methods: A nationwide cohort study was conducted including all patients undergoing an initial abdominal or pelvic operation between 1 June 2009 and 30 June 2011 in Scotland. Nomograms based on multivariable prediction models were constructed for the 2-year and 5-year overall risk of reoperation and risk of reoperation in the same surgical area. Internal cross-validation was applied to evaluate reliability. Results: Of the 72 270 patients with an initial abdominal or pelvic surgery, 10 467 (14.5%) underwent reoperation within 5 years postoperatively. Mesh placement, colorectal surgery, diagnosis of inflammatory bowel disease, previous radiotherapy, younger age, open surgical approach, malignancy, and female sex increased the risk of reoperation in all the prediction models. Intra-abdominal infection was also a risk factor for the risk of reoperation overall. The accuracy of the prediction model of risk of reoperation overall and risk for the same area was good for both parameters (c-statistic=0.72 and 0.72). Conclusions: Risk factors for abdominal reoperation were identified and prediction models displayed as nomograms were constructed to predict the risk of reoperation in the individual patient. The prediction models were robust in internal cross-validation.
Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning

Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by the consumption of imported food products or contracted during foreign travel, therefore, making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2313 S. Enteritidis genomes, collected by the UKHSA between 2014–2019, were used to train a ‘local classifier per node’ hierarchical classifier to attribute isolates to four continents, 11 sub-regions, and 38 countries (53 classes). The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661, respectively). A number of countries commonly visited by UK travelers were predicted with high accuracy (hF1: >0.9). Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in <4 min per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted.