Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy and fairness of rain rules for interrupted one-day cricket matches

In this paper, we investigate the relative merits of rain rules for one-day cricket matches. We suggest that interrupted one-day matches present a missing data problem: the outcome of the complete match cannot be observed, and instead the outcome of the interrupted match, as determined at least in part by the rain rule in question, is observed. Viewing the outcome of the interrupted match as an imputation of the missing outcome of the complete match, standard characteristics to assess the performance of classification tests can be used to assess the performance of a rain rule. In particular, we consider the overall and conditional accuracy and the predictive value of a rain rule. We propose two requirements for a ‘fair’ rain rule, and show that a fair rain rule must satisfy an identity involving its conditional accuracies. Estimating the performance characteristics of various rain rules from a sample of complete one-day matches our results suggest that the Duckworth–Lewis method, currently adopted by the International Cricket Council, is essentially as accurate as and somewhat more fair than its best competitors. A rain rule based on the iso-probability principle also performs well but might benefit from re-calibration using a more representative data base.
Topics in physics beyond the standard model

In this work, topics in physics beyond the standard model of high energy phyics relevant to the experimental studies at the Large Hadron Collider (LHC) at CERN are discussed. We introduce an effective theory to describe the dynamics of heavy Majorana particles, whose existence is predicted in many extensions of the standard model. Further, it is demonstrated how by combining the two independent concepts of a weakly interacting massive particle accounting for the dark matter observed in our universe and gauge coupling unification, strong phenomenological trends can be inferred. The scenario we present predicts new colored states within LHC reach that can be either collider stable or decay promptly to final states including a Higgs particle. In both cases, interesting LHC signatures are expected. The LHC discovery of the long predicted Higgs boson as well as new lower bounds on supersymmetric particle masses provide motivation to re-evaluate the issue of fine-tuning in supersymmetric theories. We provide an overview of the current state of this ongoing discussion. In the last part of this work we introduce an alternative approach termed stable near-criticality, in which the standard limits on superparticle masses stemming from finetuning considerations are relaxed. This approach requires specific and robust correlations among soft mass terms. We discuss how reduced fine-tuning can be achieved even if the gauge and Yukawa couplings are considered variable parameters. Finally, we illustrate our findings with a possible model implementation and demonstrate the validity of our approach by analysing a representative benchmark point.
Global prevalence of Cryptosporidium spp. in pigs: a systematic review and meta-analysis

Abstract Abstract Cryptosporidium spp. are significant opportunistic pathogens causing diarrhoea in humans and animals. Pigs are one of the most important potential hosts for Cryptosporidium. We evaluated the prevalence of Cryptosporidium in pigs globally using published information and a random-effects model. In total, 131 datasets from 36 countries were included in the final quantitative analysis. The global prevalence of Cryptosporidium in pigs was 16.3% (8560/64 809; 95% confidence interval [CI] 15.0–17.6%). The highest prevalence of Cryptosporidium in pigs was 40.8% (478/1271) in Africa. Post-weaned pigs had a significantly higher prevalence (25.8%; 2739/11 824) than pre-weaned, fattening and adult pigs. The prevalence of Cryptosporidium was higher in pigs with no diarrhoea (12.2%; 371/3501) than in pigs that had diarrhoea (8.0%; 348/4874). Seven Cryptosporidium species (Cryptosporidium scrofarum, Cryptosporidium suis, Cryptosporidium parvum, Cryptosporidium muris, Cryptosporidium tyzzeri, Cryptosporidium andersoni and Cryptosporidium struthioni) were detected in pigs globally. The proportion of C. scrofarum was 34.3% (1491/4351); the proportion of C. suis was 31.8% (1385/4351) and the proportion of C. parvum was 2.3% (98/4351). The influence of different geographic factors (latitude, longitude, mean yearly temperature, mean yearly relative humidity and mean yearly precipitation) on the infection rate of Cryptosporidium in pigs was also analysed. The results indicate that C. suis is the dominant species in pre-weaned pigs, while C. scrofarum is the dominant species in fattening and adult pigs. The findings highlight the role of pigs as possible potential hosts of zoonotic cryptosporidiosis and the need for additional studies on the prevalence, transmission and control of Cryptosporidium in pigs.
Fitting finite mixture models using iterative Monte Carlo classification

ABSTRACT Parameters of a finite mixture model are often estimated by the expectation–maximization (EM) algorithm where the observed data log-likelihood function is maximized. This paper proposes an alternative approach for fitting finite mixture models. Our method, called the iterative Monte Carlo classification (IMCC), is also an iterative fitting procedure. Within each iteration, it first estimates the membership probabilities for each data point, namely the conditional probability of a data point belonging to a particular mixing component given that the data point value is obtained, it then classifies each data point into a component distribution using the estimated conditional probabilities and the Monte Carlo method. It finally updates the parameters of each component distribution based on the classified data. Simulation studies were conducted to compare IMCC with some other algorithms for fitting mixture normal, and mixture t, densities.
Domestic Burdens Amid Covid-19 and Women’s Mental Health in Middle-Income Africa

This article analyzes two longitudinal datasets (October – December 2020; April 2021) of 1,000 and 900 women in Kenya and Nigeria, respectively, alongside in-depth qualitative interviews with women at risk of changes to time use, to study two pandemic issues: women’s substitution of paid for unpaid work and how these shifts compromise their mental health. Women devote more time to domestic care (30–38 percent), less time to employment (29–46 percent), and become unemployed (12–17 percent). A rise in domestic work is correlated with depressive (Nigeria) and anxiety symptoms (Kenya and Nigeria). Women with greater agency (Kenya) and fewer children (Nigeria) are less likely to report a domestic burden or loss in paid activities. Social protection programs may fill the void of assistance traditionally provided by informal networks in the short term, while campaigns shifting norms around household work may preserve women’s economic participation in the long term. HIGHLIGHTS Women in Kenya and Nigeria reported increases in domestic labor amid the pandemic. Women’s agency is negatively associated with the domestic burden and a reduction in paid activities in Kenya. Women in households with two or more children face greater domestic burdens and losses in paid activities in Nigeria. Increases in domestic work render women more likely to be anxious (Kenya and Nigeria) and depressed (Nigeria).
NGAL as Biomarker of Clinical and Subclinical Damage of Kidney Function after Coronary Angiography

Contrast-induced acute kidney injury (CI-AKI) is a serious complication after angiographic examinations in cardiology. Diagnosis may be delayed based on standard serum creatinine, and subclinical forms of kidney damage may not be detected at all. In our study, we investigate the clinical use in these directions of a “damage”-type biomarker—neutrophil gelatinase-associated lipocalin (NGAL). Among patients with a high-risk profile undergoing scheduled coronary angiography and/or angioplasty, plasma NGAL was determined at baseline and at 4th and 24th h after contrast administration. In the CI-AKI group, NGAL increased significantly at the 4th hour (Me 109.3 (IQR 92.1–148.7) ng/mL versus 97.6 (IQR 69.4–127.0) ng/mL, p = 0.006) and at the 24th hour (Me 131.0 (IQR 81.1–240.8) ng/mL, p = 0.008). In patients with subclinical CI-AKI, NGAL also increased significantly at the 4th hour (Me 94.0 (IQR 75.5–148.2) ng/mL, p = 0.002) and reached levels close to those in patients with CI-AKI. Unlike the new biomarker, however, serum creatinine did not change significantly in this group. The diagnostic power of NGAL is extremely good—AUC 0.847 (95% CI: 0.677–1.000; p = 0.001) in CI-AKI and AUC 0.731 (95% CI: 0.539–0.924; p = 0.024) in subclinical CI-AKI. NGAL may be a reliable biomarker for the early diagnosis of clinical and subclinical forms of renal injury after contrast angiographic studies.
Automated Identification of Performance Changes at Code Level

To develop software with optimal performance, even small performance changes need to be identified. Identifying performance changes is challenging since the performance of software is influenced by non-deterministic factors. Therefore, not every performance change is measurable with reasonable effort. In this work, we discuss which performance changes are measurable at code level with reasonable measurement effort and how to identify them. We present (1) an analysis of the boundaries of measuring performance changes, (2) an approach for determining a configuration for reproducible performance change identification, and (3) an evaluation comparing of how well our approach is able to identify performance changes in the application server Jetty compared with the usage of Jetty’s own performance regression benchmarks.Thereby, we find (1) that small performance differences are only measurable by fine-grained measurement workloads, (2) that performance changes caused by the change of one operation can be identified using a unit-test-sized workload definition and a suitable configuration, and (3) that using our approach identifies small performance regressions more efficiently than using Jetty’s performance regression benchmarks.
A New Sea Ice Concentration Retrieval Algorithm Based on Relationship Between AMSR2 89-GHz Polarization and Landsat 8 Observations

A newly developed linear sea ice concentration (SIC) retrieval algorithm based on passive microwave Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements is proposed. SIC is retrieved by a linear function of the polarization ratio (PR) at 89 GHz (PR<inline-formula> <tex-math notation="LaTeX">$_{\mathbf {89}}$ </tex-math></inline-formula>) corrected for atmospheric influence. We use Landsat 8 SIC data to derive the coefficients of the linear function. Results using this linear algorithm are compared to those of ASI2 developed by Lu et al. (2018), which is a nonlinear 89-GHz algorithm with polarization difference (PD) at 89 GHz (PD<inline-formula> <tex-math notation="LaTeX">$_{\mathbf {89}}$ </tex-math></inline-formula>) that also includes a correction for atmospheric influence. Both algorithms are compared with independent SIC data derived from Landsat 8, ship-based observation, and synthetic aperture radar (SAR) and both tend to underestimate the ship-based and Landsat 8 SICs, particularly over thin ice. However, the proposed algorithm tends to provide results with lower bias and root-mean-square error (RMSE) for different ice categories.
Evaluation of a Previously Developed Predictive Model for Infective Endocarditis in 320 Patients Presenting with Fever at 4 Centers in Japan Between January 2018 and December 2020

Background In our previous single-center study, we developed an infective endocarditis (IE) prediction model among patients with undiagnosed fever (UF) based on 5 factors that can be obtained on admission: ambulance transfer, presence of cardiac murmur or pleural effusion, blood neutrophil percentage, and platelet count. The present study aimed to retrospectively evaluate the prediction model for IE in 320 patients presenting with fever at 4 university hospitals in Japan from January 2018 to December 2020. Material/Methods Patients aged ≥20 years admitted to 4 hospitals with I-330 (IE) or R-50-9 (UF) according to the International Statistical Classification of Diseases and Related Health Problems-10 were enrolled. More than 2 physicians at each hospital reviewed the patient diagnoses using the modified Duke criteria, allocating “definite IE” to IE group (n=119) and “non-definite IE” to UF group (n=201). Five factors on admission were analyzed by multivariate logistic regression. The discriminative ability and calibration of the model were evaluated using the area under the curve (AUC) and the shrinkage coefficient, respectively. Results A total of 320 patients were enrolled. The odds ratios (95% confidence intervals) were as follows: ambulance transfer 1.81 (0.91–3.55); cardiac murmur 13.13 (6.69–27.36); pleural effusion 2.34 (0.62–2.42); blood neutrophil percentage 1.09 (1.06–1.14); and platelet count 0.96 (0.93–0.99). The AUC was 0.783 (0.732–0.834) with a shrinkage coefficient of 0.961. Conclusions The IE prediction model is useful to estimate the probability of IE immediately after admission for fever in patients aged ≥20 years.
Longitudinal Profiles of Anti-Platelet Factor 4 Antibodies in Thai People Who Received ChAdOx1 nCoV-19 Vaccination

Anti-platelet factor 4 (anti-PF4) antibodies were identified as pathogenic antibodies for vaccine-induced immune thrombocytopenia and thrombosis (VITT) in subjects receiving ChAdOx1 nCoV-19 vaccinations. We performed a prospective cohort study to determine the prevalence of anti-PF4 and the effect of the ChAdOx1 nCoV-19 vaccine on anti-PF4 in healthy Thai subjects. Anti-PF4 antibodies were measured before and four weeks after receiving the first vaccination. Participants with detectable antibodies were scheduled for repeat anti-PF4 analysis at 12 weeks after the second vaccination. Of 396 participants, ten participants (2.53%; 95% confidence interval [CI], 1.22–4.59) were positive for anti-PF4 before receiving vaccinations. Twelve people (3.03%; 95% CI, 1.58–5.23) had detectable anti-PF4 after the first vaccination. There was no difference in the optical density (OD) values of anti-PF4 antibodies when comparisons were made between pre-vaccination and four weeks after the first vaccination (p = 0.0779). There was also no significant difference in OD values in participants with detectable antibodies. No subjects experienced thrombotic complications. Pain at the injection site was associated with an increased risk of being anti-PF4 positive at an odds ratio of 3.44 (95% CI, 1.06–11.18). To conclude, the prevalence of anti-PF4 was low in Thais and did not significantly change over time.
Large-Scale Clinical Evaluation of Rapid Blood Culture Identification Panels for Bloodstream Infections at a Tertiary Hospital

The prompt implementation of optimal antibacterial therapy through the rapid identification of the causative organisms is essential for improving outcomes for critically ill patients with bloodstream infections. We evaluated the clinical performance of the FilmArray blood culture identification (BCID) panel for rapidly identifying causative pathogens in the bloodstream using large-scale clinical samples. We analyzed the results of identification using a BCID panel performed on 2005 positive blood culture bottles from September 2019 to June 2022. Pathogen detection efficiency and interval from Gram staining to identification using the BCID panel were compared to those of conventional identification systems—VITEK MS MALDI-TOF Mass Spectrometer and Vitek2—and antibiotic susceptibility testing—Vitek2. We detected 2167 isolates from 2005 positive blood culture bottles. In these isolates, the BCID panel showed 93% full agreement—both organisms and antimicrobial resistance genes were matched, and no off-target organisms were detected. Species-level discordance was found in 0.6% of tests. Sixty-five isolates (3.0%) were only detected by BCID, whereas 22 isolates (1.0%) from the on-target panel were not detected by BCID. This large-scale study demonstrated that the BCID panel was a reliable and rapid identification method for directly identifying bloodstream pathogens in a positive blood culture.
Computation Resource Optimization for Large-Scale Intelligent Urban Rail Transit: A Mean-Field Game Approach

Offloading tasks in smart devices (SDs) to an edge intelligence-empowered service centre (EISC) is a promising solution to support burgeoning intelligent applications in large-scale intelligent urban rail transits (URTs). However, dynamic computation resource allocation is still a crucial challenge, facing that various large-scale SDs share the EISC computation resources and the reality that the allocated computation resource for an SD is coupled with the offloading rate of all SDs. This paper proposes a joint dynamic offloading rate control and computation resource optimization method for large-scale intelligent URTs. Firstly, we model the large-scale multi-agent computation resource competition problem by a multi-player differential game (MPDG) and prove that the Nash equilibrium (NE) based optimal solution exists for each SDs. Then, we transform the MPDG model into a mean-field game (MFG). By introducing the mean-field into the game, we can solve the multi-agent optimization problem with a single-agent optimization method. We illustrate the rationality of the MFG model and propose an iterative solution method based on the finite difference method to derive the solution. Finally, we propose a z-transforming-based control method to dynamically reschedule computation resources among intelligent applications to achieve a satisfactory quality of service (QoS). Extensive simulation results show that our proposed scheme can significantly improve the performance of intelligent URTs.
Small RNA Profiling of Cucurbit Yellow Stunting Disorder Virus from Susceptible and Tolerant Squash (Cucurbita pepo) Lines

RNA silencing is a crucial mechanism of the antiviral immunity system in plants. Small RNAs guide Argonaut proteins to target viral RNA or DNA, preventing virus accumulation. Small RNA profiles in Cucurbita pepo line PI 420328 with tolerance to cucurbit yellow stunting disorder virus (CYSDV) were compared with those in Gold Star, a susceptible cultivar. The lower CYSDV symptom severity in PI 420328 correlated with lower virus titers and fewer sRNAs derived from CYSDV (vsRNA) compared to Gold Star. Elevated levels of 21- and 22-nucleotide (nt) size class vsRNAs were observed in PI 420328, indicating more robust and efficient RNA silencing in PI 420328. The distribution of vsRNA hotspots along the CYSDV genome was similar in both PI 420328 and Gold Star. However, the 3’ UTRs, CPm, and p26 were targeted at a higher frequency in PI 420328.
Estimating the mean and its effects on Neyman smooth tests of normality for ARMA models

Goodness‐of‐fit tests represent an important part of any model building strategy. In time series modelling, many test procedures concentrate on specifying the model structure. However it is also of interest to check distributional assumptions. In autoregressive moving‐average (ARMA) time series models, available results based on the smooth test paradigm assume that the mean of the process is known. However in practical applications, the mean is unknown and it needs to be estimated. Under general assumptions on the estimation procedures, the asymptotic distributions of the smooth test statistics based on estimation of all the parameters, including the mean, are derived. Surprisingly it is found that mean estimation has an impact on the asymptotic behaviours. This finding is in sharp contrast with portmanteau test procedures, where the degrees of freedom of the approximate distributions remain unchanged whether the mean is estimated or not. The test statistic relies on the order of the family, and a data‐driven choice of that parameter is discussed, giving a fully automatic testing procedure. Theoretical and empirical comparisons between the smooth test statistic assuming a known mean and the new test statistic are presented. Consistency is studied. In a simulation study, the effects of assuming incorrectly the mean being known are illustrated. An application using annual data on the productivity of potatoes (per acre) in Prince Edward Island for the time period 1957–2014 illustrates the procedure. The Canadian Journal of Statistics 44: 241–270; 2016 © 2016 Statistical Society of Canada
In Vitro Cell Proliferation and Migration Properties of Oral Mucosal Fibroblasts: A Comparative Study on the Effects of Cord Blood- and Peripheral Blood-Platelet Lysate

Cord blood-platelet lysate (CB-PL), containing growth factors such as a platelet-derived growth factor, has a similar efficacy to peripheral blood-platelet lysate (PB-PL) in initiating cell growth and differentiation, which makes it a unique alternative to be implemented into oral ulceration healing. This research study aimed to compare the effectiveness of CB-PL and PB-PL in promoting oral wound closure in vitro. Alamar blue assay was used to determine the optimal concentration of CB-PL and PB-PL in enhancing the proliferation of human oral mucosal fibroblasts (HOMF). The percentage of wound closure was measured using the wound-healing assay for CB-PL and PB-PL at the optimal concentration of 1.25% and 0.3125%, respectively. The gene expressions of cell phenotypic makers (Col. I, Col. III, elastin and fibronectin) were determined via qRT-PCR. The concentrations of PDGF-BB were quantified using ELISA. We found that CB-PL was as effective as PB-PL in promoting wound-healing and both PL were more effective compared to the control (CTRL) group in accelerating the cell migration in the wound-healing assay. The gene expressions of Col. III and fibronectin were significantly higher in PB-PL compared to CB-PL. The PDGF-BB concentration of PB-PL was the highest and it decreased after the wound closed on day 3. Therefore, we concluded that PL from both sources can be a beneficial treatment for wound-healing, but PB-PL showed the most promising wound-healing properties in this study.
Cointegration Analysis in the Presence of Structural Breaks in the Deterministic Trend

When analysing macroeconomic data it is often of relevance to allow for structural breaks in the statistical analysis. In particular, cointegration analysis in the presence of structural breaks could be of interest. We propose a cointegration model with piecewise linear trend and known break points. Within this model it is possible to test cointegration rank, restrictions on the cointegrating vector as well as restrictions on the slopes of the broken linear trend.