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The neutron Time-of-Flight (TOF) research facility at CERN, n_TOF, has been a pioneering platform for neutron cross-section measurements since its inception in 2001. It boasts three distinct experimental areas, each tailored to address a specific range of neutron energies. This paper delves into the intricacies of the n_TOF facility, including its recent upgrade during the Long Shutdown 2 (LS2) at CERN. Additionally, it highlights the key characteristics of the detectors employed for capture and fission cross-section measurements, paving the way for future research endeavors.

Chromium is a very relevant element regarding criticality safety in nuclear reactors because of its presence in stainless steel, an important structural material. Currently, there are serious discrepancies between the different evaluations regarding the neutron capture cross sections of 50Cr and 53Cr, most probably related to the difficulty of reducing and then estimating the very large neutron scattering effects on the shape of the resonances. In this context, there is a recent entry in the Nuclear Energy Agency (NEA) High Priority Request List (HPRL) to measure these reactions between 1 and 100 keV with an accuracy of 8-10%. In response to this request, we have performed a time-of-flight experiment at CERN n_TOF (Switzerland) and a complementary activation experimenton 50Crat30and90keVatCNAHiSPANoS(Spain).Theexperiments are presented herein, together with a discussion on the quality of the preliminary data and the results to be expected.

The experimental setup of the new measurement of 239Pu fission and capture cross-section in the n_TOF time-of-flight facility at CERN is presented. The measurement aims to address the needs and demands of nuclear data users. The experiment incorporates an innovative fast Fission Fragment Detector and the n_TOF Total Absorption Calorimeter, enabling the implementation of the fission tagging technique. Preliminary results exhibit the robust performance of the detector systems, along with the high quality of the new 239Pu samples. These samples were exclusively produced for this measurement by the European Commission’s Joint Research Centre in Geel.

Abstract Objective To describe multilevel phenotypes of airway obstruction identified on drug‐induced sleep endoscopy (DISE) in adults. Study Design Retrospective chart review. Setting Tertiary care center. Methods Video recordings of DISE on adult patients were retrospectively scored. A cross‐correlation matrix was created to detect significant correlations between DISE findings at anatomical subsites. Three multilevel phenotypes resulted from the matrix: complete collapse at the tongue base with complete collapse at the epiglottis (T2‐E2), complete circumferential obstruction at the velum with complete lateral pharyngeal wall collapse at the oropharynx (V2C‐O2LPW), and incomplete collapse at the velum with complete collapse due to tonsillar hypertrophy (V0/1‐O2T). The mean difference (MD) and 95% confidence interval (CI) were calculated for demographic and polysomnogram metrics of each phenotype compared to all other subjects. Results Phenotype 1 (T2‐E2) (n = 88) had older ages (MD 5.784 years, CI [1.992, 9.576]), lower body mass index (BMI) (MD –1.666 kg/m2, CI [02.570, −0.762]), and smaller neck circumferences (MD –0.448 in., CI [−9.14, −0.009]) than the other phenotypes. Phenotype 2 (V2C‐O2LPW) (n = 25) had higher BMIs (MD 2.813 kg/m2, CI [1.362, 4.263]), higher neck circumference (MD 0.714 in., CI [0.004, 1.424]), and higher apnea‐hypopnea index (MD 8.252, CI [0.463, 16.041]). Phenotype 3 (V0/1‐O2T) (n = 20) had younger ages (MD –17.697, CI [−25.215, −11.179]). Conclusion Three distinct multilevel phenotypes of obstruction were identified on DISE, suggesting different anatomic subsites collapse in a nonrandom pattern. The phenotypes appear to represent distinct patient groups and their identification may have implications in terms of pathophysiology and treatment modalities.

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Digital Safety refers to the knowledge and skills needed for the intentional protection of users in the digital environment. With children having access to digital devices at a young age, it has become essential for them to be educated on how to be safe in the digital world. Through a week-long summer camp on digital safety, elementary-age learners were introduced to four topics (digital identity and digital footprint, cyberbullying, netiquette, and digital security and privacy). This study found that the digital safety immersion camp was beneficial to elementary school learners based on the achievement, attitude, and behavior data that were collected. Posttest scores were statistically significant from the pre-test. Cyberbullying topic had the highest pre- and post-knowledge, whereas netiquette and online behavior, and digital security and privacy had comparatively lesser scores. Students demonstrated positive attitudes in the post-camp survey and they also included several lessons learned from the camp in the Pixton comic strip, which they created as the final project from the camp. The findings from this study contribute to the current literature on preparing elementary school students' knowledge and skills related to digital safety and have implications for students, teachers, administrators, and parents. [ABSTRACT FROM AUTHOR]

We present the first incorporation of the Common Representative Intermediates version 2.2 tropospheric chemistry mechanism, CRI v2.2, combined with stratospheric chemistry, into the global chemistry–climate United Kingdom Chemistry and Aerosols (UKCA) model to give the CRI-Strat 2 mechanism. A rigorous comparison of CRI-Strat 2 with the earlier version, CRI-Strat, is performed in UKCA in addition to an evaluation of three mechanisms, CRI-Strat 2, CRI-Strat and the standard UKCA chemical mechanism, StratTrop v1.0, against a wide array of surface and airborne chemical data. CRI-Strat 2 comprises a state-of-the-art isoprene scheme, optimized against the Master Chemical Mechanism v3.3.1, which includes isoprene peroxy radical isomerization, HOx recycling through the addition of photolabile hydroperoxy aldehydes (HPALDs), and isoprene epoxy diol (IEPOX) formation. CRI-Strat 2 also features updates to several rate constants for the inorganic chemistry, including the reactions of inorganic nitrogen and O(1D). The update to the isoprene chemistry in CRI-Strat 2 increases OH over the lowest 500 m in tropical forested regions by 30 %–50 % relative to CRI-Strat, leading to an improvement in model–observation comparisons for surface OH and isoprene relative to CRI-Strat and StratTrop. Enhanced oxidants also cause a 25 % reduction in isoprene burden and an increase in oxidation fluxes of isoprene and other biogenic volatile organic compounds (BVOCs) at low altitudes with likely impacts on subsequent aerosol formation, atmospheric lifetime, and climate. By contrast, updates to the rate constants of O(1D) with its main reactants relative to CRI-Strat reduces OH in much of the free troposphere, producing a 2 % increase in the methane lifetime, and increases the tropospheric ozone burden by 8 %, primarily from reduced loss via O(1D)+H2O. The changes to inorganic nitrogen reaction rate constants increase the NOx burden by 4 % and shift the distribution of nitrated species closer to that simulated by StratTrop. CRI-Strat 2 is suitable for multi-decadal model integrations and the improved representation of isoprene chemistry provides an opportunity to explore the consequences of HOx recycling in the United Kingdom Earth System Model (UKESM1). This new mechanism will enable a re-evaluation of the impact of BVOCs on the chemical composition of the atmosphere and further probe the feedback between the biosphere and the climate.

Aromatic hydrocarbons are a class of volatile organic compounds associated with anthropogenic activity and make up a significant fraction of urban volatile organic compound (VOC) emissions that contribute to the formation of secondary organic aerosol (SOA). Benzene is one of the most abundant species emitted from vehicles, biomass burning and industry. An iodide time-of-flight chemical ionisation mass spectrometer (ToF-CIMS) and nitrate ToF-CIMS were deployed at the Jülich Plant Atmosphere Chamber as part of a series of experiments examining benzene oxidation by OH under high- and low-NOx conditions, where a range of organic oxidation products were detected. The nitrate scheme detects many oxidation products with high masses, ranging from intermediate volatile organic compounds (IVOCs) to extremely low volatile organic compounds (ELVOCs), including C12 dimers. In comparison, very few species with C≥6 and O≥8 were detected with the iodide scheme, which detected many more IVOCs and semi-volatile organic compounds (SVOCs) but very few ELVOCs and low volatile organic compounds (LVOCs). A total of 132 and 195 CHO and CHON oxidation products are detected by the iodide ToF-CIMS in the low- and high-NOx experiments respectively. Ring-breaking products make up the dominant fraction of detected signal and 21 and 26 of the products listed in the Master Chemical Mechanism (MCM) were detected. The time series of highly oxidised (O≥6) and ring-retaining oxidation products (C6 and double-bond equivalent = 4) equilibrate quickly, characterised by a square form profile, compared to MCM and ring-breaking products which increase throughout oxidation, exhibiting sawtooth profiles. Under low-NOx conditions, all CHO formulae attributed to radical termination reactions of first-generation benzene products, and first-generation auto-oxidation products are observed. Several N-containing species that are either first-generation benzene products or first-generation auto-oxidation products are also observed under high-NOx conditions. Hierarchical cluster analysis finds four clusters, of which two describe photo-oxidation. Cluster 2 shows a negative dependency on the NO2/NOx ratio, indicating it is sensitive to NO concentration and thus likely to contain NO addition products and alkoxy-derived termination products. This cluster has the highest average carbon oxidation state (OSC‾) and the lowest average carbon number. Where nitrogen is present in a cluster member of cluster 2, the oxygen number is even, as expected for alkoxy-derived products. In contrast, cluster 1 shows no dependency on the NO2/NOx ratio and so is likely to contain more NO2 addition and peroxy-derived termination products. This cluster contains fewer fragmented species, as the average carbon number is higher and OSC‾ lower than cluster 2, and more species with an odd number of oxygen atoms. This suggests that clustering of time series which have features pertaining to distinct chemical regimes, for example, NO2/NOx perturbations, coupled with a priori knowledge, can provide insight into identification of potential functionality.

A rapidly growing number of algorithms are available to researchers who apply statistical or machine learning methods to answer social science research questions. The unique advantages and limitations of each algorithm are relatively well known, but it is not possible to know in advance which algorithm is best suited for the particular research question and the data set at hand. Typically, researchers end up choosing, in a largely arbitrary fashion, one or a handful of algorithms. In this article, we present the Super Learner--a powerful new approach to statistical learning that leverages a variety of data-adaptive methods, such as random forests and spline regression, and systematically chooses the one, or a weighted combination of many, that produces the best forecasts. We illustrate the use of the Super Learner by predicting violence among inmates from the 2005 Census of State and Federal Adult Correctional Facilities. Over the past 40 years, mass incarceration has drastically weakened prisons' capacities to ensure inmate safety, yet we know little about the characteristics of prisons related to inmate victimization. We discuss the value of the Super Learner in social science research and the implications of our findings for understanding prison violence.

Objective: This study aimed to determine perinatal outcomes based on fasting bile acid concentrations in pregnant women diagnosed with intrahepatic cholestasis of pregnancy (ICP) and to assess pregnancy management according to fasting bile acid levels. Methods: This study is carried out with 160 patients who were diagnosed with Intrahepatic Cholestasis of Pregnancy and gave birth in the same clinic. The patients were divided into two groups based on their fasting bile acid concentrations: Group 1 (54 patients), with levels between 10 and 40 micromol/liter, defining mild ICP according to the literature, and Group 2 (106 patients), with levels above 40 micromol/liter, defining severe ICP. The birth weeks, delivery methods, indications for cesarean section if applicable, birth weights, 1st and 5th minute APGAR scores, neonatal intensive care unit admission, presence of meconium-stained amniotic fluid at birth, and fetal death data of both groups were recorded and compared. Results: Patients with severe ICP were found to have statistically higher ALT and total bilirubin values than patients with mild ICP. (p<0.001, p<0.001 respectively). The presence of meconium was significantly higher in group 2 patients compared to patients group 1 patients (26.4% and 9.3%, respectively; p=0.011). It was found that the weeks of diagnosis of cholestasis and weeks of labor were significantly greater in the patients with meconium in the amniotic fluid compared to the group without meconium (34,9±3,4; 33,0±3,5; p=0,002 ve 38,1±0,9; 36,9±1,5 respectively; p<0,001). Group 2 patients were found to have significantly higher incidence rates of ALT values above 75 and AST values above 130 compared to group 1 patients (p<0,001). Conclusion: Intrahepatic cholestasis of pregnancy causes unpredictable fetal complications. The patients with values of bile acid above 40 and ALT values above 75 and/or having meconium stained amniotic fluid during labor should evaluated carefully due to the association with poor perinatal outcomes. [ABSTRACT FROM AUTHOR]

Graph theory is widely used today in many fields, including mathematical chemistry. In this area, numerous topological indices have been studied. Topological indices are used to define the topology of a molecular graph and relate it to various relevant properties. The most commonly used topological indices are degree-based and distance-based indices. This article examines the Gutman index, which holds great significance in chemistry. The Gutman index is a well-known topological index that simultaneously uses both degree and distance. These graphs are important because they enable the study of the properties of a graph under different transformations. It has been extensively studied in the literature and finds numerous applications in chemistry, physics, and other fields. Transformation graphs are graphs that are used to model molecular structures, physical networks, and algorithms in fields such as chemistry, computer science and physics. Original graph's symmetry, connectivity and other structural features can be revealed through these transformations. This article focuses on studying the Gutman index of some transformation graphs to provide a new topological index that can be used to examine their properties, and general formulas have been obtained. [ABSTRACT FROM AUTHOR]

Due to their durability and efficiency, hermetic reciprocating compressors (HRCs) are used in refrigeration and air conditioning. Compressor performance and reliability concerns reduce system efficiency and raise maintenance costs. Machine learning (ML) is being used to improve hermetic reciprocating compressor performance, reliability, and energy economy. ML is used in hermetic reciprocating compressors for issue identification, performance improvement, predictive maintenance, and energy management. This research compared HRC performance factors such as mass flow rate, cooling capacity, compression power, coefficient of performance, exhaust line losses, and volumetric efficiency. Simple regression, probabilistic neural network, gradient boosted, polynomial regression, and random forest (RF) were used to examine and evaluate these parameters as outputs. Over three cycles, the Fluid-Structure Interaction (FSI) approach assessed compressor performance parameters. For compressor speeds of 1300, 2100, and 3000 rpm, mass flow rate, compression power, cooling efficiency coefficient, and exhaust line energy losses varied by 10%, 4%, 5%, and 6%. To gather ML algorithm inputs, the research used experimental, fluid-structure interaction, and ML methodologies. Experimental and FSI approaches produced 108 data points. These data points were randomly assigned, with 70% for learning and 30% for prediction. The mean convergence criterion for mass flow rate, cooling capacity, compression power, cooling efficiency coefficient, exhaust line energy losses, and volumetric efficiency parameters was 0.9966, 0.9969, 0.9572, 0.0561, 0.9925, and 0.4640 for all ML methods. Simple regression, probabilistic neural networks, gradient boosted, polynomial regression, and RF convergence criteria were 0.8978, 0.9999, 0.6016, 0.4439, and 0.7761. [ABSTRACT FROM AUTHOR]

The most popular approach to determine compressor losses is to generate compressor pressure-volume indication diagrams. The pressure-volume indication diagram of a novel hermetic reciprocating compressor for household refrigerators operating with R600a was experimentally obtained under American Society of Heating, Refrigerating and Air-Conditioning Engineers (54.4°C/−23.3°C) and additional specific working conditions of (40°C/−25°C), (25°C/−20°C) and (35°C/−15°C). Apart from most studies, experiments have been performed using a calorimeter. The alteration of suction and discharge pressures with crank angle of the investigated compressor was presented as both graphical and numerical information during condensation and evaporation, separately. As a result of the analyses, total compressor losses increased with decreasing the condensation pressure. These losses were 10%, 8.7%, 5.7% and 4.6% of the total work and they were observed at the same speed of 1300 rotation per minute (rpm) as the lowest one under all operating conditions. Besides, the suction line losses having the largest losses in the compressor were calculated as 50.1%, 50.9%, 64.9% and 67.9% of the total one, and it has been determined that they increased with increasing the condensation pressure. Due to the more complex design of the equipment in the suction line, the losses in this region were found to be higher than those in the discharge line, as expected. [ABSTRACT FROM AUTHOR]