The current paper investigates the effects of geometric design parameters on the fatigue failure of the drive axle housing using the Finite Element Method (FEM). The study examines the effects of various factors on the fatigue life of the drive axle housing, such as axle housing wall thickness, housing cross-sectional rounding radius, and rounding radius of the central part of the housing. Based on the known material properties and dynamic loads, a CAD/FEM model of the drive axle housing was developed, and a structural analysis was carried out. Based on the results of the structural analysis, critical places on the housing were determined, and fatigue analysis and lifetime prediction were performed. Through a series of simulations, the study reveals that increasing housing wall thickness can significantly improve fatigue performance. Similarly, increasing the rounding radius at the housing cross-section, as well as the rounding radius at the central part of the housing can also lead to improved fatigue performance. However, the effect of increasing the value of these two radii is not as significant as the effect of the wall thickness. These findings give useful information regarding the design and manufacture of drive axle housings for vehicles, intending to reduce the likelihood of fatigue failure.

Purpose of review To review the current state of knowledge on the relationship between allergic sensitization and asthma; to lay out a roadmap for the development of IgE biomarkers that differentiate, in individual sensitized patients, whether their sensitization is important for current or future asthma symptoms, or has little or no relevance to the disease. Recent findings The evidence on the relationship between sensitization and asthma suggests that some subtypes of allergic sensitization are not associated with asthma symptoms, whilst others are pathologic. Interaction patterns between IgE antibodies to individual allergenic molecules on component-resolved diagnostics (CRD) multiplex arrays might be hallmarks by which different sensitization subtypes relevant to asthma can be distinguished. These different subtypes of sensitization are associated amongst sensitized individuals at all ages, with different clinical presentations (no disease, asthma as a single disease, and allergic multimorbidity); amongst sensitized preschool children with and without lower airway symptoms, with different risk of subsequent asthma development; and amongst sensitized patients with asthma, with differing levels of asthma severity. Summary The use of machine learning-based methodologies on complex CRD data can help us to design better diagnostic tools to help practising physicians differentiate between benign and clinically important sensitization.

Introduction: The research aimed to determine individual variations in different core temperature measurements before the experiment, after submersion, after 20 min of exposure for heat stroke. Methods: Rats were divided into three groups depending on the temperature and length of exposure to water: CG, G41-20 and G41-UD. The protocol was made according to the earlier described methodology of heat shock induction. Results: A significant difference was observed in the G41-UD group; p < 0.0005. The lowest body temperature of the rats was observed, from normothermia, and the highest temperature after death, 37.87 ± 0.62 °C vs 41.20 ± 0.76 °C, the difference between all three groups is p < 0.0005. Conclusion: Exposure of Wistar rats to water temperatures in the CG and G41 groups led to a significant change in core temperature. In the control group, the thermoregulatory mechanism firmly established normothermia, while hyperthermia was revealed in the G41 group during the 20-minute exposure.

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made.

ABSTRACT Background Primary glomerular disease (PGD) is a major cause of end-stage kidney disease (ESKD) leading to kidney replacement therapy (KRT). We aimed to describe incidence (trends) in individuals starting KRT for ESKD due to PGD and to examine their survival and causes of death. Methods We used data from the European Renal Association (ERA) Registry on 69 854 patients who started KRT for ESKD due to PGD between 2000 and 2019. ERA primary renal disease codes were used to define six PGD subgroups. We examined age and sex standardized incidence, trend of the incidence and survival. Results The standardized incidence of KRT for ESKD due to PGD was 16.6 per million population (pmp), ranging from 8.6 pmp in Serbia to 20.0 pmp in France. Immunoglobulin A nephropathy (IgAN) and focal segmental glomerulosclerosis (FSGS) had the highest incidences, of 4.6 pmp and 2.6 pmp, respectively. Histologically non-examined PGDs represented over 50% of cases in Serbia, Bosnia and Herzegovina, and Romania and were also common in Greece, Estonia, Belgium and Sweden. The incidence declined from 18.6 pmp in 2000 to 14.5 pmp in 2013, after which it stabilized. All PGD subgroups had 5-year survival probabilities above 50%, with crescentic glomerulonephritis having the highest risk of death [adjusted hazard ratio 1.8 (95% confidence interval 1.6–1.9)] compared with IgAN. Cardiovascular disease was the most common cause of death (33.9%). Conclusion The incidence of KRT for ESKD due to PGD showed large differences between countries and was highest and increasing for IgAN and FSGS. Lack of kidney biopsy facilities in some countries may have affected accurate assignment of the cause of ESKD. The recognition of the incidence and outcomes of KRT among different PGD subgroups may contribute to a more individualized patient care approach.

Developmental disorders (DDs), such as autism spectrum disorder (ASD), incorporate various conditions; once identified, further diagnostics are necessary to specify their type and severity. The aim of this exploratory study was to identify genetic variants that can help differentiate ASD early from other DDs. We selected 36 children (mean age 60.1 months) with DDs using Developmental Behavioral Scales (DBS) through “EDUS-Education for All”, an organization providing services for children with DDs in Bosnia and Herzegovina. We further rated children’s autistic traits with the preschool version of the Childhood Autism Rating Scale, second edition (CARS-II). We defined ASD if scores were >25.5 and other DDs if scores were <25.5. Diagnosis of ASD and DD were independently confirmed by child psychiatrists. Whole exome sequencing (WES) was performed by Veritas Genetics, USA, using Illumina NovaSeq 6000 (Illumina Inc., San Diego, CA, USA) NGS sequencing apparatus. We tested genetic association by applying SKAT-O, which optimally combines the standard Sequence Kernel Association Test (SKAT) and burden tests to identify rare variants associated with complex traits in samples of limited power. The analysis yielded seven genes (DSE, COL10A1, DLK2, CSMD1, FAM47E, PPIA, and PYDC2) to potentially differentiate observed phenotypic characteristics between our cohort participants with ASD and other DDs. Our exploratory study in a small sample of participants with ASD and other DDs contributed to gene discovery in differentiating ASD from DDs. A replication study is needed in a larger sample to confirm our results.

The paper explores the recently published Dictionary of Obscure Sorrows (Koenig 2021) as a corpus of neologisms coined to express different emotions that are usually (and universally) experienced but not easily expressed by words. Created from different languages in contact, the newly-coined words will be used to further explore theoretical frameworks on linguistic creativity and the concept of the dictionary as the definitions of the words are given in English. The aim of the paper is to focus on the words proposed to express different emotions related to specific kinds of fear, isolation and anxiety. In relation to the words' manifestation in letter or sound, the paper will also address mentalese (Pinker 1994) as a framework and a concept proposing that lexicons need to co-operate in this unique kind of a dictionary that does not call for an active usage of the words coined but rather presents a dictionary that is a container of new emotions. / Keywords: mentalese, new emotions, languages in contact, multilingual dictionary, contemporary English

This scientific paper investigates the application of the Voltaire-Gurset-Riemann method in solving partial differential equations, using a flickering wire as an example. The method proves to be a powerful tool in the analysis of dynamic systems, providing a deeper understanding of flicker behavior in a wire. The developed numerical solutions enable precise modeling and prediction of the behavior of the flickering structure. This study highlights the key steps in applying the method to a concrete example, providing a useful basis for further research in the field of partial differential equations

Let $M$ be a finite volume hyperbolic Riemann surface with arbitrary signature, and let $\chi$ be an arbitrary $m$-dimensional multiplier system of weight $k$. Let $R(s,\chi)$ be the associated Ruelle zeta function, and $\varphi(s,\chi)$ the determinant of the scattering matrix. We prove the functional equation that $R(s,\chi)\varphi(s,\chi) = R(-s,\chi)\varphi(s,\chi)H(s,\chi)$ where $H(s,\chi)$ is a meromorphic function of order one explicitly determined using the topological data of $M$ and of $\chi$, and the trigonometric function $\sin(s)$. From this, we determine the order of the divisor of $R(s,\chi)$ at $s=0$ and compute the lead coefficient in its Laurent expansion at $s=0$. When combined with results by Kitano and by Yamaguchi, we prove further instances of the Fried conjecture, which states that the R-torsion of the above data is simply expressed in terms of $R(0,\chi)$.

Background : Mammographic (or breast) density is an established risk factor for breast cancer. There are a variety of approaches to measurement including quantitative, semi-automated and automated approaches. We present a new automated measure, AutoCumulus, learnt from applying deep learning to semi-automated measures. Methods: We used mammograms of 9,057 population-screened women in the BRAIx study for which semi-automated measurements of mammographic density had been made by experienced readers using the CUMULUS software. The dataset was split into training, testing, and validation sets (80%, 10%, 10%, respectively). We applied a deep learning regression model (fine-tuned ConvNeXtSmall) to estimate percentage density and assessed performance by the correlation between estimated and measured percent density and a Bland-Altman plot. The automated measure was tested on an independent CSAW-CC dataset in which density had been measured using the LIBRA software, comparing measures for left and right breasts, sensitivity for high sensitivity, and areas under the receiver operating characteristic curve (AUCs). Results: Based on the testing dataset, the correlation in percent density between the automated and human measures was 0.95, and the differences were only slightly larger for women with higher density. Based on the CSAW-CC dataset, AltoCumulus outperformed LIBRA in correlation between left and right breast (0.95 versus 0.79; P<0.001), specificity for 95% sensitivity (13% versus 10% (P<0.001)), and AUC (0.638 cf. 0.597; P<0.001). Conclusion: We have created an automated measure of mammographic density that is accurate and gives superior performance on repeatability within a woman, and for prediction of interval cancers, than another well-established automated measure.