Search

Abstract In addition to having some of the worst health outcomes in the region, Haiti faces a political and economic crisis. The most recent humanitarian crisis includes an increase in homicides and kidnappings in the capital Port-au-Prince. This study is a cross-sectional, mixed methods online survey of health workers and medical students in Port-au-Prince from May 20 – September 15, 2023. It provides evidence of the kidnapping risk healthcare workers face and shares the perspective of a medical community operating in a challenging context to provide a continuity of care under the threat of violence. The survey of Haitian health workers and students show a significant risk of kidnapping with 44% of respondents reporting that they had a colleague kidnapped in the previous 2 years. 5 of the 249 respondents had been kidnapped and all were young, female health workers. 74% of health workers and students surveyed reported they plan to continue their profession abroad. Although teletraining was viewed as a positive opportunity to continue training cadres of medical professionals, health workers shared numerous limitations present for the expansion of telemedicine in the Haitian context. In addition to describing the experience of the Haitian healthcare professional during this crisis and documenting barriers to teletraining and telemedicine, this survey documents design considerations for mobile phone surveys with healthcare providers working in areas affected by conflict.

Application of Kaplan turbines is widespread in low-water-head and large-capacity hydropower plants. An understanding of the failure mechanism of Kaplan Turbines is a key factor to provide useful solutions for their prevention or early treatment and to guarantee their workability. The long-term performance of Kaplan turbines depends on many factors such as cavitation, erosion, fatigue, and material defects. Cavitation in Kaplan turbines leads to flow instability, vibrations, surface damage, and reduce the machine performance. Therefore, this paper investigates the factors leading to cavitation in Kaplan turbine and presents practical solutions for it. Thermal-sprayed coatings are frequently applied due to their high wear resistance, cost effectiveness, weight reduction, and less negative impacts on base metal. Moreover, HVOF is used to create coatings with a high density and bonding strength. At high temperatures, cermet coatings, including nanoparticles, exhibit exceptional wear resistance. WC-based nanostructured and multifaceted coatings are utilized due to their high wear resistance. In addition, chromium carbide in WC-based coatings increases their oxidation and wear resistance.

BackgroundThis study estimated to what extent the number of measurements of cardiometabolic risk factors (e.g., blood pressure, cholesterol, glycated haemoglobin) were impacted by the COVID-19 pandemic and whether these have recovered to expected levels.Methods and findingsA cohort of individuals aged ≥18 years in England with records in the primary care-COVID-19 General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) were identified. Their records of 12 risk factor measurements were extracted between November 2018 and March 2024. Number of measurements per 1,000 individuals were calculated by age group, sex, ethnicity, and area deprivation quintile. The observed number of measurements were compared to a composite expectation band, derived as the union of the 95% confidence intervals of 2 estimates: (1) a projected trend based on data prior to the COVID-19 pandemic; and (2) an assumed stable trend from before pandemic. Point estimates were calculated as the mid-point of the expectation band. A cohort of 49,303,410 individuals aged ≥18 years were included. There was sharp drop in all measurements in March 2020 to February 2022, but overall recovered to the expected levels during March 2022 to February 2023 except for blood pressure, which had prolonged recovery. In March 2023 to March 2024, blood pressure measurements were below expectation by 16% (-19 per 1,000) overall, in people aged 18 to 39 (-23%; -18 per 1,000), 60 to 79 (-17%; -27 per 1,000), and ≥80 (-31%; -57 per 1,000). There was suggestion that recovery in blood pressure measurements was socioeconomically patterned. The second most deprived quintile had the highest deviation (-20%; -23 per 1,000) from expectation compared to least deprived quintile (-13%; -15 per 1,000).ConclusionsThere was a substantial reduction in routine measurements of cardiometabolic risk factors following the COVID-19 pandemic, with variable recovery. The implications for missed diagnoses, worse prognosis, and health inequality are a concern.

Kaplan turbines are widely used in low-water-head and large-capacity hydropower plants. The design of such systems is based on long-term performance. Nevertheless, many failures are reported in the industry, which lead to economic losses for the purpose of major repairs or replacement of a part of the system. Therefore, it is very important to know the failures, their causes, and finally provide useful solutions for their prevention or early treatment. In the present article, the authors focused on the failure detection of a Kaplan turbine blade in an Iraqi hydropower plant, and then, determined the direction of their future research. Quantometric test, tensile test, compression test, microhardness test, metallographic examination and study of the damaged surface were conducted from damaged runner blade. It was revealed that the raw material for manufacturing runner blade of Kaplan turbine was 304 stainless steel by employing cold drawing or rolling process. In addition, annealing treatment has also been done. In addition, it was found that the predominant damage in the studied part was severe cavitation.

Abstract We study the inflationary scenario in the Tsallis entropy-based cosmology. The Friedmann equations in this setup can be derived by using the first law of thermodynamics. To derive the relations of the power spectra of the scalar and tensor perturbations in this setup, we reconstruct an f(R) gravity model which is thermodynamically equivalent to our model in the slow-roll approximation. In this way, we find the inflationary observables, including the scalar spectral index and the tensor-to-scalar ratio in our scenario. Then, we investigate two different potentials in our scenario, including the quadratic potential and the potential associated with the natural inflation in which the inflaton is an axion or a pseudo-Nambu–Goldstone boson. We examine their observational viability in light of the Planck 2018 CMB data. We show that although the results of these potentials are in tension with the observations in the standard inflationary setting, their consistency with the observations can be significantly improved within the setup of the Tsallis entropy-based inflation. Moreover, we place constraints on the parameters of the considered inflationary models by using the current observational data.

Background Aspirin, an effective, low‐cost pharmaceutical, can significantly reduce mortality if used promptly after acute myocardial infarction (AMI). However, many AMI survivors do not receive aspirin within a few hours of symptom onset. Our aim was to quantify the mortality benefit of self‐administering aspirin at chest pain onset, considering the increased risk of bleeding and costs associated with widespread use. Methods and Results We developed a population simulation model to determine the impact of self‐administering 325 mg aspirin within 4 hours of severe chest pain onset. We created a synthetic cohort of adults ≥ 40 years old experiencing severe chest pain using 2019 US population estimates, AMI incidence, and sensitivity/specificity of chest pain for AMI. The number of annual deaths delayed was estimated using evidence from a large, randomized trial. We also estimated the years of life saved (YOLS), costs, and cost per YOLS. Initiating aspirin within 4 hours of severe chest pain onset delayed 13 016 (95% CI, 11 643–14 574) deaths annually, after accounting for deaths due to bleeding (963; 926–1003). This translated to an estimated 166 309 YOLS (149391–185 505) at the cost of $643 235 (633 944–653 010) per year, leading to a cost‐effectiveness ratio of $3.70 (3.32–4.12) per YOLS. Conclusions For

The requirements for the quality and accuracy of manufactured products are constantly increasing. Reliability, durability, and accuracy of machine operation largely depend on the quality of surface treatment. Study on the cutting process, in particular milling, makes it possible to find out the operating conditions of the cutting tool, determine the acting cutting forces, torques, vibrations and temperatures on it. Along with studying the influence of various parameters on the milling process, it is necessary to pay special attention to establishing the nature and degree of influence of the cutting condition (cutting speed, depth of cut and feed rate). In this paper milling operation of Al6061-T6 was performed in a dry condition. The effects of cutting parameters on cutting forces and moments in different directions and around various axes were studied. By applying Taguchi technique and performing experiments, it was shown that cutting force was significantly affected by feed rate followed by rotational speed and cutting depth. Cutting moment is mainly influenced by feed rate, cutting depth, and rotational speed. Moreover, feed rate is the most effective factor on minimizing cutting forces and moments. Finally, the optimum cutting parameters was revealed to get a minimum cutting forces and moments.

Studying the spectral behavior of objects using remote sensing and spectroscopy can be used to identify the source of the raw materials. Therefore, this research first studied the spectral behavior and composition of the ancient artifacts of the Ziggurat temple located in the Khuzestan province, southwest Iran, using FieldSpec3 spectro-radiometer, The Spectral Geologist (TSG), the Prism software, and X-Ray Diffraction (XRD) analysis. A Comparison of the spectral curves and absorption features of the samples shows that the spectral behavior of brick and pottery samples differs mostly from that of soil samples. The next step was to examine the potential of Sentinel-2 images to classify the area using the Spectral Angle Mapper (SAM) classifier for detecting the origin of the raw materials utilized in the construction of the Ziggurat's brick and pottery samples. The SAM results showed that the raw materials originated mostly from outside the area, which are confirmed by the results of TSG and Prism software. Comparison of the XRD results with the SAM and TSG validated that XRD results confirmed the SAM results. However, only some of the TSG similarity results were consistent with the XRD. These results suggest that the Sentinel-2 image was successful in finding the source of raw materials. The results can help decision makers to make informed decisions about the conservation and preservation of the temple and its artifacts. Also, they can use these techniques to investigate the origin of other ancient structures and artifacts in different locations.

Abstract There are currently no approved pharmacological treatment options for aortic stenosis (AS), and there are limited identified drug targets for this chronic condition. It remains unclear whether inflammation plays a role in AS pathogenesis and whether immunomodulation could become a therapeutic target. We evaluated the potentially causal association between inflammation and AS by investigating the genetically proxied effects of tocilizumab (IL6 receptor, IL6R, inhibitor), canakinumab (IL1β inhibitor) and colchicine (β-tubulin inhibitor) through a Mendelian randomisation (MR) approach. Genetic proxies for these drugs were identified as single nucleotide polymorphisms (SNPs) in the gene, enhancer or promoter regions of IL6R, IL1β or β-tubulin gene isoforms, respectively, that were significantly associated with serum C-reactive protein (CRP) in a large European genome-wide association study (GWAS; 575,531 participants). These were paired with summary statistics from a large GWAS of AS in European patients (653,867 participants) to then perform primary inverse-variance weighted random effect and sensitivity MR analyses for each exposure. This analysis showed that genetically proxied tocilizumab was associated with reduced risk of AS (OR 0.56, 95% CI 0.45–0.70 per unit decrease in genetically predicted log-transformed CRP). Genetically proxied canakinumab was not associated with risk of AS (OR 0.80, 95% CI 0.51–1.26), and only one suitable SNP was identified to proxy the effect of colchicine (OR 34.37, 95% CI 1.99–592.89). The finding that genetically proxied tocilizumab was associated with reduced risk of AS is concordant with an inflammatory hypothesis of AS pathogenesis. Inhibition of IL6R may be a promising therapeutic target for AS management.

Abstract Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity and all-cause mortality. Kaplan Meier curves were plotted for each derived phenotype. Finally, we tested the performance of an established risk prediction model (QRISK) by adding TDA-derived features. We identified four subgroups of patients with diabetes and divergent comorbidity patterns differing in their risk of future cardiovascular, renal, and other microvascular outcomes. Phenotype 1 (young with chronic inflammatory conditions) and phenotype 2 (young with CAD) included relatively younger patients with diabetes compared to phenotypes 3 (older with hypertension and renal disease) and 4 (older with previous CVA), and those subgroups had a higher frequency of pre-existing cardio-renal diseases. Within ten years of follow-up, 2592 patients (26%) experienced MACE, 2515 patients (25%) died, and 2020 patients (20%) suffered RF. QRISK3 model’s AUC was augmented from 67.26% (CI 67.25–67.28%) to 67.67% (CI 67.66–67.69%) by adding specific TDA-derived phenotype and the distances to both extremities of the TDA graph improving its performance in the prediction of CV outcomes. We confirmed the importance of accounting for multimorbidity when risk stratifying heterogenous cohort of patients with new diagnosis of diabetes. Our unsupervised machine learning method improved the prediction of clinical outcomes.

AimSleep duration has been suggested to be associated with hypertension (HTN). However, evidence of the nature of the relationship and its direction has been inconsistent. Therefore, we performed a meta-analysis to assess the relationship between sleep duration and risk of HTN incidence, and to distinguish more susceptible populations.MethodsPubMed, Embase, Scopus, Web of Science, and ProQuest were searched from January 2000 to May 2023 for cohort studies comparing short and long sleep durations with 7-8 hours of sleep for the risk of HTN incidence. Random-effect model (the DerSimonian-Laird method) was applied to pool risk ratios (RR) and 95% confidence interval (CI).ResultsWe included sixteen studies ranging from 2.4 to 18 years of follow-up duration evaluating HTN incidence in 1,044,035 people. Short sleep duration was significantly associated with a higher risk of developing HTN (HR: 1.07, 95% CI: 1.06-1.09). The association was stronger when the sleep duration was less than 5 hours (HR: 1.11, 95% CI: 1.08-1.14). In contrast to males, females (HR: 1.07, 95% CI: 1.04-1.09) were more vulnerable to developing HTN due to short sleep duration. No significant difference between different follow-up durations and age subgroups was observed. Long sleep duration was not associated with an increased incidence of HTN.ConclusionShort sleep duration was associated with higher risk of HTN incidence, however, there was no association between long sleep duration and incidence of HTN. These findings highlight the importance of implementing target-specific preventive and interventional strategies for vulnerable populations with short sleep duration to reduce the risk of HTN.

Abstract Approved therapies for the treatment of patients with pulmonary arterial hypertension (PAH) mediate pulmonary vascular vasodilatation by targeting distinct biological pathways. International guidelines recommend that patients with an inadequate response to dual therapy with a phosphodiesterase type‐5 inhibitor (PDE5i) and endothelin receptor antagonist (ERA), are recommended to either intensify oral therapy by adding a selective prostacyclin receptor (IP) agonist (selexipag), or switching from PDE5i to a soluble guanylate‐cyclase stimulator (sGCS; riociguat). The clinical equipoise between these therapeutic choices provides the opportunity for evaluation of individualized therapeutic effects. Traditionally, invasive/hospital‐based investigations are required to comprehensively assess disease severity and demonstrate treatment benefits. Regulatory‐approved, minimally invasive monitors enable equivalent measurements to be obtained while patients are at home. In this 2 × 2 randomized crossover trial, patients with PAH established on guideline‐recommended dual therapy and implanted with CardioMEMS™ (a wireless pulmonary artery sensor) and ConfirmRx™ (an insertable cardiac rhythm monitor), will receive ERA + sGCS, or PDEi + ERA + IP agonist. The study will evaluate clinical efficacy via established clinical investigations and remote monitoring technologies, with remote data relayed through regulatory‐approved online clinical portals. The primary aim will be the change in right ventricular systolic volume measured by magnetic resonance imaging (MRI) from baseline to maximal tolerated dose with each therapy. Using data from MRI and other outcomes, including hemodynamics, physical activity, physiological measurements, quality of life, and side effect reporting, we will determine whether remote technology facilitates early evaluation of clinical efficacy, and investigate intra‐patient efficacy of the two treatment approaches.

Abstract Background The significant role of red blood cell distribution width (RDW) and D‐Dimer as prognostic factors in patients with some blood malignancies has been reported recently. Aim We designed and performed a meta‐analysis to investigate the prognostic roles of RDW and D‐Dimer in subjects with diffuse large B‐cell lymphoma (DLBCL). Materials and Methods We systematically reviewed PubMed‐Medline, SCOPUS, EMBASE, Web of Science Core Collection, and Google Scholar up to the present to look for publications on prognostic effects of RDW and D‐Dimer in DLBCL patients. For investigation of the associations between RDW and D‐Dimer with the overall survival (OS) and progression‐free survival (PFS) of the DLBCL cases, hazard ratio (HR) with 95% confidence intervals (CIs) was used. Results We included 13 eligible studies in the present meta‐analysis. The results of pooled analysis showed that increased levels of RDW was related to poor OS (HR = 2.01, 95% CI: 1.62–2.48, p value

To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants.

Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to address them. However, this task is always time-consuming and costly. On the other hand, there is an essential need for an expert in this field to carry out this process manually. Therefore, in the present study, the authors propose a new methodology to automatically manage this task through a deep-learning technique. Moreover, due to the different nature of the risk data, it is not possible to consider a single neural network architecture for all of them. To overcome this problem, a Genetic Algorithm (GA) was employed to find the best architecture and hyperparameters. Finally, the risks were processed and predicted using the new proposed methodology without sending data to other servers, i.e., external servers. The results of the analysis for the first risk, i.e., latency and real-time processing, showed that using the proposed methodology can improve the detection accuracy of the failure mode by 71.52%, 54.72%, 72.47%, and 75.73% compared to the unique algorithm with the activation function of Relu and number of neurons 32, respectively, related to the one, two, three, and four hidden layers.

Introduction and Objective Iran is one of the countries that soil erodibility is becoming one of the acute environmental problems and every year millions of tons of fertile soil are left unusable due to lack of proper management. In order to effectively protect and prevent the adverse effects of soil erosion, it is necessary to identify the factors affecting soil erosion and provide an appropriate estimate of their amount in the country. In this regard, the present study was conducted to estimate soil erodibility (K-factor) for a soil depth of 0-30 cm in Iran.Materials and Methods For this purpose, two database were used, including the Harmonized World Soil Database (HWSD) and global gridded soil information (SoilGrids), as well as RStudio and ArcGIS software. First, SoilGrids data was prepared at four depths of 0, 5, 15 and 30 cm and averaging was done. Also, the HWSD database was received in vector format for a depth of 0 to 30 cm. Finally, these data have been used to estimate the erodibility factor based on the soil content of organic carbon, clay, sand and silt using the EPIC equation. Finally, Relative Error (RE), Median Absolute Deviation (MAD) and Root Mean Square Error (RMSE) were calculated to compare two databases.Results and Discussion Assessments indicate that the average percentage of clay particles in the sub-basins of Iran varies between 15 and 32% and the average for the whole country is 23%. On the other hand, the average percentage of silt particles in the sub-basins of Iran varies between 19 and 45%, and the average for the whole country is 32%, among which the maximum and minimum percentage of silt particles are in the sub-basins of Qarasu-GorganRoud and Kavir Lut, respectively. Also, the average percentage of sand particles in the sub-basins of Iran varies between 28 and 65, and on the other hand, the average for the whole country is 44%, the minimum of which is related to the sub-basin of Karkheh and the maximum is related to the sub-basin of Kavir Lut. In Gavkhouni and Kavir Lut sub-basins, the reason the low soil erodibility factor is the high percentage of sand in these sub-basins, so that the percentage of sand in the Lut and Gavkhouni basins is 65 and 47% of the total soil particles, respectively. Considering that the average percentage of organic carbon in the sub-basins of Iran varies between 0.3 and 3.9, respectively, these values are related to the sub-basins of Hamun-e-Hirmand and Sefidroud-Haraz, so it can be said that the majority of the country's sub-basins are in poor conditions in terms of the percentage of organic matter. The results show that the southwestern, western and northeastern parts of the country have the maximum amount of soil erodibility factor, and the central Iran and desert parts of Iran have lower erodibility due to having a higher percentage of sand particles. Also, the results show that the lowest average amount of soil erodibility at the sub-basin scale using SoilGrids data with a value of 0.033 (ton*h/Mj*mm) is related to Lut Desert and also its maximum value is 0.045 (ton*h/ Mj*mm) related to it is the sub-basin of Haleh. In addition, the maximum and minimum values of the erodibility index with the HWSD data as an average of the basins of Iran are corresponding to the Mand and Gavkhouni sub-basins, respectively, with values of 0.042 and 0.033 (ton*h/ Mj*mm). So, the results showed that the average soil erodibility factor in Iran using two the HWSD and SoilGrids databases was 0.036 and 0.038 (ton*h/ Mj*mm) respectively.Conclusion and SuggestionsThe study of soil erodibility with the data of SoilGrids and HWSD at the sub-basins scale showed that the maximum and minimum RE in Atrak and South Balochestan sub-basins are 21 and 1 percent, respectively, and the amount of RE is about 5% for the country average; Therefore, it can be concluded that although the RE between the two databases is not high, SoilGrids data is a more suitable source for soil and water resource modeling due to its continuity and better spatial resolution. Finally, it should be said that although this database is modeled using a larger number of profiles (about 150,000 soil profiles in the world), so they have appropriate accuracy. However, it is necessary to state that investigating the uncertainty of these data in order to improve the results of this database in order to improve the results of that in different parts of the country is recommended to researchers and researchers. Also, it is noteworthy that the EPIC model was used to estimate soil erodibility in this study, while its evaluation and comparison with other soil erodibility estimation models in the country is suggested to other researchers and experts.

سابقه و هدف: دین و باورهای دینی در بیشتر جوامع نقش مهمی در زندگی انسان­ها ایفا می­کند؛ در جامعۀ ایرانی با ‌توجه به شرایط فرهنگی این نقش بارزتر است. ازاین‌رو پژوهش حاضر با هدف تحلیل کیفی نظر متخصصان ایرانی دربارۀ نقش باورهای معنوی و اعتقادی مؤثر در تنظیم هیجان انجام شد. روش کار: مطالعۀ حاضر از نوع کیفی و به‌روش تحلیل محتوای نهفته است. جامعۀ آماری متخصصان و صاحب‌نظران تنظیم هیجان در ایران بودند که با روش نمونه‌گیری هدفمند تا حد اشباع با 11 نفر از آنان مصاحبۀ نیمه­ساختاریافته صورت گرفت. در این پژوهش همۀ موارد اخلاقی رعایت شده است و مؤلفان تضاد منافعی گزارش نکرده‌اند. یافته‌ها: با اتمام مصاحبه‌­ها، پس از حذف و همسان‌سازی کدهای مشابه و تکراری، نتایج تحلیل داده­‌ها به سه طبقۀ اصلی و شش زیر‌طبقه منجر شد. طبقات شامل نگرش­‌های مذهبی و معنوی، آموزه­های معنوی/ مذهبی تنظیم­کنندۀ هیجان و تمایز باور­ها از خرافات بود. نتیجه‌گیری: یافته‌ها نشان داد که باورهای معنوی و اعتقادی نقش پررنگی در تنظیم هیجان افراد دارند؛ بنابراین به مشاوران و درمانگران سلامت روان پیشنهاد می‌­شود که در ارتباط با مراجعان و در جلسات درمانی به این مهم توجه ویژه‌ای داشته باشند.

This article presents the static performance of composite steering knuckle due to drive on an equivalent road, including different types of roughness and maneuvers. To achieve this purpose, the driving of a full-vehicle model was simulated using the multi-body dynamics (MBD) method, and the imposed loads on connection points of the steering knuckle to different components of the suspension system were extracted considering various maneuvers. Next, CATIA software was used to prepare a smooth model of the steering knuckle by employing coordinate measuring machine (CMM) data. Stress analysis was performed under the maximum value of the loading history in finite element (FE) software. Eventually, the safety factor was calculated based on some well-known criteria for static failure of the composite materials. Moreover, the optimum value of tungsten carbide as a reinforcing substance in aluminum composite was estimated to increase failure strength. The results show that an increase in tungsten carbide leads to an increase in the strength of the steering knuckle under purely axial loads (normal stress criterion) and also that an increase in this substance leads to a decrease in the strength of the part under shear loads (shear stress criterion). Therefore, based on the nature of the loads (i.e., multi-axial non-proportional random amplitude loading conditions) applied to the automotive steering knuckle due to actual conditions, this metal matrix composite (aluminum matrix and tungsten carbide as reinforcement) is not practical.

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Summary: Background: Whether the accuracy of the phenotype ascribed to patients in electronic health records (EHRs) is associated with variation in prognosis and care provision is unknown. We investigated this for heart failure (HF, characterised as HF with preserved ejection fraction [HFpEF], HF with reduced ejection fraction [HFrEF] and unspecified HF). Methods: We included individuals aged 16 years and older with a new diagnosis of HF between January 2, 1998 and February 28, 2022 from linked primary and secondary care records in the Clinical Practice Research Datalink in England. We investigated the provision of guideline-recommended diagnostic investigations and pharmacological treatments. The primary outcome was a composite of HF hospitalisation or all-cause death, and secondary outcomes were time to HF hospitalisation, all-cause death and death from cardiovascular causes. We used Kaplan–Meier curves and log rank tests to compare survival across HF phenotypes and adjusted for potential confounders in Cox proportional hazards regression analyses. Findings: Of a cohort of 95,262 individuals, 1271 (1.3%) were recorded as having HFpEF, 10,793 (11.3%) as HFrEF and 83,198 (87.3%) as unspecified HF. Individuals recorded as unspecified HF were older with a higher prevalence of dementia. Unspecified HF, compared to patients with a recorded HF phenotype, were less likely to receive specialist assessment, echocardiography or natriuretic peptide testing in the peri-diagnostic period, or receive angiotensin-converting enzyme inhibitors, beta blockers or mineralocorticoid receptor antagonists up to 12 months after diagnosis (risk ratios compared to HFrEF, 0.64, 95% CI 0.63–0.64; 0.59, 0.58–0.60; 0.57, 0.55–0.59; respectively) and had significantly worse outcomes (adjusted hazard ratios compared to HFrEF, HF hospitalisation and death 1.66, 95% CI 1.59–1.74; all-cause mortality 2.00, 1.90–2.10; cardiovascular death 1.77, 1.65–1.90). Interpretation: Our findings suggested that absence of specification of HF phenotype in routine EHRs is inversely associated with clinical investigations, treatments and survival, representing an actionable target to mitigate prognostic and health resource burden. Funding: Japan Research Foundation for Healthy Aging and British Heart Foundation.

The standard cosmological model is in the midst of a stress test, thanks to the tension between supernova-based measurements of the Hubble constant H _0 and inferences of its values from cosmic microwave background (CMB) anisotropies. Numerous explanations for the present-day cosmic acceleration require the presence of a new fundamental scalar field, as do early dark energy solutions to the Hubble tension. This raises the possibility that multiple fields cooperatively contribute to the dark energy component in bursts throughout cosmic time due to distinct initial conditions and couplings. Here, this cascading dark energy scenario is illustrated through a realization that effectively reduces to a two-field model, with two epochs in which dark energy is cosmologically significant. The model is compared to measurements of the CMB and baryon acoustic oscillations, as well as both PANTHEON and SH0ES observations of Type Ia supernovae. Neglecting the linear perturbations, it is found that this scenario ameliorates the Hubble tension, improving over purely late-time models of dark energy and the agreement between the galaxy survey measurements of baryon acoustic oscillations.

Abstract We study the non-Gaussianity and secondary Gravitational Waves (GWs) in the process of the Primordial Black Holes (PBHs) production from inflation. In our work, we focus on the $$\alpha $$ α -attractor inflation model in which a tiny bump in the inflaton potential enhances the amplitude of the curvature perturbations at some scales and consequently leads to the PBHs production with different mass scales. We implement the computational code BINGO which calculates the non-Gaussianity parameter in different triangle configurations. Our examination implies that in this setup, the non-Gaussianity gets amplified significantly in the equilateral shape around the scales in which the power spectrum of the scalar perturbations undergoes a sharp declination. The imprints of these non-Gaussianities can be probed in the scales corresponding to the BBN and $$\mu $$ μ -distortion events, or in smaller scales, and detection of such signatures in the future observations may confirm the idea of our model for the generation of PBHs or rule it out. Moreover, we investigate the secondary GWs in this framework and show that in our model, the peak of the present fractional energy density is obtained as $$\Omega _{\mathrm{GW0}} \sim 10^{-8}$$ Ω GW 0 ∼ 10 - 8 at different frequencies which depends on the model parameters. These results lie well within the sensitivity region of some GWs detectors at some frequencies, and therefore the observational compatibility of our model can be evaluated by the forthcoming data from these detectors. We further provide some estimations for the tilts of the induced GWs spectrum in the different intervals of frequency, and demonstrate that the spectrum obeys the power-law relation $$\Omega _{\mathrm{GW0}}\sim f^{n}$$ Ω GW 0 ∼ f n in those frequency bands.

In the present study, a 63-year-old patient with no history of immunodeficiency was described. Accordingly, he referred to our center with fever, sweating, shaking chills, cough, and retrosternal chest pain that started three weeks ago. Pre-operative CT scan revealed a cystic mass with peripheral enhancement in the anterior mediastinum. The patient underwent open thoracic surgery and as a result, an infected thymic cyst was seen which was confirmed by performing pathologic examinations after resection.

Abstract Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

The main aim of the present paper is to assess the fatigue lifetime of ductile cast iron knuckles as one of the critical components of an automotive steering system. To this end, a real driving path, including various maneuvers, such as acceleration, braking, cornering, and moving on various types of road roughness, was considered. Different load histories, which are applied on various joints of the component (i.e., lower control arm, steering linkage, and Macpherson strut), were extracted through Multi-Body Dynamics (MBD) analysis of a full vehicle model. The achievements of previous studies have proved that the steering knuckle fails from the steering linkage and due to the rotational motion. Therefore, only this destructive load history was considered in future analyses of the present study. The CAD model was created using Coordinate Measuring Machine (CMM) data and some corrections in CATIA software. Furthermore, transient dynamic analysis was performed, and the time history of von Misses equivalent stress was obtained at the root of the steering linkage (which is exactly the location of failure based on the laboratory data as well as finite element simulations validated by the author in the previous studies). To predict the fatigue life of a component, two different methodologies were considered. Firstly, some well-known criteria were employed for equalization of load spectrum to a Constant Amplitude Loading (CAL). Then, fatigue analysis under sinusoidal loading was performed. Secondly, the fatigue life of the component considering Variable Amplitude Loading (VAL) was predicted using the Critical Plane Method (CPM), employing the Rain-flow cycle counting technique, and utilizing the Miner–Palmgren damage accumulation rule. Eventually, to evaluate the prediction accuracy of different methodologies, the obtained results were compared with the full-scale axial variable amplitude fatigue test which was performed by the corresponding author. The results indicated that the prediction of variable amplitude fatigue lifetime by Finite Element (FE) analysis in the time domain has about 21% error compared to reality. Additionally, the relative error between the results obtained from two different methodologies is about 20%, which is acceptable due to the scattering of the fatigue phenomenon results, the complex geometry of the part, and the complicated loading.

Abstract Background A small number of studies suggested that air pollution was associated with idiopathic pulmonary fibrosis (IPF) exacerbation, incidence and mortality. However, no studies to date were conducted in regions where air pollution is substantial. We aimed to investigate whether there are associations between acute increases in air pollution and hospitalization of patients with a confirmed primary diagnosis of IPF in Beijing. Methods Daily count of IPF hospitalizations (International Classification of Disease-10th Revision, J84.1) was obtained from an administrative database for 2013–2017 while daily city-wide average concentrations of PM10, PM2.5, NO2, Ozone, SO2 were obtained from 35 municipal monitoring stations for the same period. The association between daily IPF hospitalization and average concentration of each pollutant was analyzed with a generalized additive model estimating Poisson distribution. Results Daily 24-h mean PM2.5 concentration during 2013–2017 was 76.7 μg/m3. The relative risk (RR) of IPF hospitalization per interquartile range (IQR) higher (72 μg/m3) in PM2.5 was 1.049 (95% CI 1.024–1.074) and 1.031 (95% CI 1.007–1.056) for lag0 and moving averages 0–1 days respectively. No significant associations were observed for other lags. Statistically significant positive associations were also observed at lag0 with SO2, Ozone and NO2 (in men only). Positive associations were seen at moving averages 0–30 days for PM10 (RR per 86 μg/m3: 1.021, 95% CI 0.994–1.049), NO2 (RR per 30 μg/m3: 1.029, 95% CI 0.999–1.060), and SO2 (RR per 15 μg/m3: 1.060 (95% CI 1.025–1.097), but not with PM2.5 or Ozone. Conclusions Despite improvement in air quality since the implementation of clean air policy in 2013, acute exposure to higher levels of air pollution is significantly associated with IPF hospitalization in Beijing. Air quality policy should be continuously enforced to protect vulnerable IPF populations as well as the general public.

Summary: The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and “black-box” nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate.

The present study makes an attempt to describe spectral characteristics of the surface features in some parts of the Khuzestan region, using reflectance spectroscopy and ASTER data related to the hydrocarbon micro-seepage in Maroun and Mansouri oil fields. The study involves surface sediment sampling with appropriate dispersion of the samples, and laboratory analysis in terms of sediment and mineralogy through spectroscopy analysis and geological map. Soil samples were collected from 45 and 20 locations points for Maroun and Mansouri oilfields, respectively. ASD-Fieldspec3 spectroradiometer and The Spectral Geologist (TSG) software were used to determine the hydrocarbon absorptions and their mineral constituents. Four classes were identified as hydrocarbon absorption ranges, i.e.: 1220–1320 and 1350–1550 nm indicating presence of light hydrocarbons; 1700–1750 and 2290–2360 nm pertaining to heavy hydrocarbons. Results suggest that Maroun oilfield samples have stronger absorptions in heavy hydrocarbons absorption but weaker for the Mansouri oilfield samples. The Mansouri samples have an acceptable absorption in light hydrocarbons absorption (1175 nm). Furthermore, these spectra were used as ground truth for creating mineral zonation maps using Spectral Angle Mapper (SAM) classification applied to ASTER data. The results also suggest that surface indicators of hydrocarbon are associated with alteration minerals such as kaolinite, siderite and goethite for the Maroun oilfield whereas Mansouri oilfield exhibits only kaolinite group of minerals. In addition, a comparison by reflectance between the contaminated and uncontaminated soil samples was also carried out.

Abstract Background Substance using often cause a wide range of social, health, and psychological problems. This study aimed to develop and validate a questionnaire of barriers of treatment in substance users. Methods In this cross-sectional study, the initial questionnaire was designed based on the evaluation of previous studies. The preliminary tool including 35 Likert-scaled items. After assuring the face validity of the questionnaire, 13 experts’ opinions were obtained for assessing or improving the content validity. The reliability was investigated by internal consistency methods using Cronbach’s alpha. For measuring the structural validity, the exploratory factor analysis was performed to determine the dimensionality of the questionnaire using principal components extraction and Varimax rotation. Results The preliminary questionnaire consisted of 35 items. After completing the face validity and summarizing the experts’ suggestions, 8 items were removed. By calculating the content validity ratio and coefficient, 11 questions were deleted. The internal consistency was calculated to be 0.84 using Cronbach’s alpha. In the last stage and according to the results of the factor analysis, three factors fear of or unawareness of treatment, doubt or inefficiency, and social stigma were identified from the 10-items questionnaire, which explained 67.34% of the total variance. Conclusion Considering the necessity of using a validated tool for planning and evaluating effective interventions on people who use substance is inevitable. The Substance use Treatment Barriers Questionnaire is designed with 10 items and 3 dimensions, which has appropriate validity and reliability and can be used to determine the obstacles for treatment or factors that lead to discontinuing treatment.

Abstract One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the main two scalable uncertainty estimation methods. However, deep Bayesian neural networks suffer from lack of expressiveness, and more expressive models such as deep kernel learning, which is an extension of sparse Gaussian process, captures only the uncertainty from the higher-level latent space. Therefore, the deep learning model under it lacks interpretability and ignores uncertainty from the raw data. In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for a more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure, diabetes and depression applied to large-scale electronic medical records, we demonstrate that our method is better at capturing uncertainty than both Gaussian processes and deep Bayesian neural networks in terms of indicating data insufficiency and identifying misclassifications, with a comparable generalization performance. Furthermore, by assessing the accuracy and area under the receiver operating characteristic curve over the predictive probability, we show that our method is less susceptible to making overconfident predictions, especially for the minority class in imbalanced datasets. Finally, we demonstrate how uncertainty information derived by the model can inform risk factor analysis towards model interpretability.

Abstract Background Myocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities. Methods Using large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline). We used Cox regression to determine the risk of all-cause mortality with age as the underlying time variable and tested for excess risk due to interaction between cardiometabolic conditions. Results At baseline, the mean age was 51 years, and 7% (N = 145,910) have had a cardiometabolic condition. After a 7-year mean follow-up, 146,994 died. The sex-adjusted hazard ratios (HR) (95% confidence interval [CI]) of all-cause mortality by baseline disease status, compared to those without cardiometabolic disease, were MI = 1.51 (1.49–1.52), diabetes = 1.52 (1.51–1.53), stroke = 1.84 (1.82–1.86), MI and diabetes = 2.14 (2.11–2.17), MI and stroke = 2.35 (2.30–2.39), diabetes and stroke = 2.53 (2.50–2.57) and all three = 3.22 (3.15–3.30). Adjusting for other concurrent comorbidities attenuated these estimates, including the risk associated with having all three conditions (HR = 1.81 [95% CI 1.74–1.89]). Excess risks due to interaction between cardiometabolic conditions, particularly when all three conditions were present, were not significantly greater than expected from the individual disease effects. Conclusion Myocardial infarction, stroke and diabetes were associated with excess mortality, without evidence of any amplification of risk in people with all three diseases. The presence of other comorbidities substantially contributed to the excess mortality risks associated with cardiometabolic disease multimorbidity.

Releasing the 12.5-year pulsar timing array data, the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has recently reported the evidence for a stochastic common-spectrum which would herald the detection of a stochastic gravitational wave background (SGWB) for the first time. We investigate if the signal could be generated from the end of a ∼10 MeV but still phenomenologically viable double-field inflation when the field configuration settles to its true vacuum. During the double-field inflation at such scales, bubbles of true vacuum that can collapse to LIGO mass, and heavier primordial black holes form. We show that only when this process happens with a first-order phase transition, the produced gravitational wave spectrum can match with the NANOGrav acclaimed SGWB signal. We show that the produced gravitational wave spectrum matches the NANOGrav SGWB signal only when this process happens through a first-order phase transition. Using LATTICEEASY, we also examine the previous observation in the literature that by lowering the scale of preheating, despite the shift of the peak frequency of the gravitational wave profile to smaller values, the amplitude of the SGWB could be kept almost constant. We notice that this observation breaks down at the preheating scale, M≲10−14mPl.

The first goal of the current study is to estimate the fatigue life of the middle steel piles of an integrated bridge installed in water and subject to the impact of sea waves. In the following, the authors have tried to improve the service life of this critical part of the bridge, which is also the main purpose of the study. To this end, conventional shot peening, as one of the most well-known surface treatments, was used. Axial fatigue tests were performed on samples fabricated from IPE-220 steel piles in two states without and with shot peening surface treatment. Next, the modified S-N curve was entered into the finite element software to define the effect of shot peening treatment. Different analysis, including thermal, thermal-structural coupled, and transient dynamic, were performed and various outputs were extracted for the entire structure. In all these analyses, changes in air temperature have been neglected. The most important achievement of this research is the discovery that motionless water cannot cause serious damage to steel piles. Moreover, application of conventional shot peening can increase the fatigue life of steel piles, or in other words the service life of the bridge, subjected to the impact of sea waves by about 22%.

Welcome to the world of marine structures, as this fascinating compilation of cutting-edge research unfolds before your eyes [...]

In this study, the authors first attempted to evaluate the efficiency of available systems for natural gas quality analysis using various examples. For this purpose, a model for such gas analysis systems was designed and the structural reliability of these systems were calculated. In the following, the main shortcomings of the existing methods for evaluating the reliability of gas analysis systems were discussed. Finally, a new probabilistic approach for the reliability assessment of such systems was proposed. This approach included a subsystem of measuring instruments that depended on the number of measured parameters. Specifically, it was suitable for measuring a single parameter of a gas mixture, but in order to check its effectiveness, a number of criteria were considered to identify and record system failures. For each criterion, various mathematical equations were constructed for reliability indices, including an operating time distribution function, reliability function, and average time to failure function. Finally, the obtained values and the reliability evaluation of gas analysis systems were discussed. Additionally, the main advantages of using the new method compared to the existing methods were enumerated. Furthermore, instead of assessing the standard structural reliability, a probabilistic assessment of reliability based on the accuracy of measurements was proposed.

Background: Khuzestan province is one of the largest industrial provinces in Iran, with high air pollution. One of the main air pollutants is nitrogen dioxide (NO2) in the atmosphere, which is linked to several epidemiological and environmental effects. Thus, spatial and temporal monitoring of NO2 is crucial for land managers. So, the aim of this study was the spatiotemporal evaluation of NO2 in Khuzestan Province, Iran, using Sentinel 5 (TROPOMI). Methods: Initially, the amount of tropospheric NO2 concentration was estimated in each month. In the next step, using ArcMap software, the monthly average of tropospheric NO2 was extracted from 12 months. Moreover, the location of 100 important air pollutant industries in the Khuzestan Province was extracted using Google Earth image. Thus, using the monthly average NO2 concentrations and the location of the cities and industries, the monthly average pollution of this pollutant was extracted for the cities and industries. Finally, the cities and industries of air pollution in Khuzestan Province were prioritized based on of tropospheric NO2 concentration. Findings: The maximum concentrations of this gas was in Bavi, Ahvaz, Abadan, and Mahshahr cities; and respectively, this gas had the lowest values of NO2 in Izeh, Lali, and Baghmalek cities. Moreover, in the cold months of the year, Khorramshahr and Mahshahr had higher concentrations of NO2 in comparison to the warm months of the year. The results of the spatial analysis revealed that the highest concentration of NO2 was in the Farabi Petrochemical Company, Khuzestan Steel Company, Ghadir, and Razi Petrochemicals, respectively. Conclusion: According to the findings of this study, it can be deduced the influence of local emission sources of NO2 in Khuzestan Province is related to population density, high traffic of motor vehicles, and industrial activities.

Pulmonary aspergillosis frequently complicates existing in tuberculosis pulmonary cavity, but the coexistence of aspergillosis and echinococcal cyst is really rare. Here in, we report a case of a 37 years old non-diabetic lady presented to internal department that she treated with the diagnosis of Aspergiloma. She was admitted in our department with internal medicine consult complaints of cough with productive sputum, chest pain and dyspnea without fever. Clinical examination revealed fine crackles in upper segment of right lung with opacity in the upper zone of right lung in CXR. She has chest CT scan revealed an inflammative mass clinging to the chest wall with cavity in the anterior segment of the right upper lobe and the mass that it seams way out to bronche. When hydatid cysts show typical appearances like “water-lily” or “crescent sign” the diagnosis is straight forward. However, atypical appearances can pose problems.

Introduction There is a significant growth in the use of digital technology and methods in health-related research, further driven by the COVID-19 pandemic. This has offered a potential to apply digital health research in hidden, marginalised and excluded populations who are traditionally not easily reached due to economic, societal and legal barriers. To better inform future digital health studies of these vulnerable populations, we proposed a scoping review to comprehensively map published evidence and guidelines on the applications and challenges of digital health research methods to hard-to-reach communities.Methods and analysis This review will follow the Arksey and O’ Malley methodological framework for scoping reviews. The framework for the review will employ updated methods developed by the Joanna Briggs Institute including the Preferred Reporting Items for Systematic reviews and Meta-Analysis Scoping Review checklist. PubMed, the Cochrane Library, PsycINFO, Google Scholar and Greenfile are the identified databases for peer-reviewed quantitative and qualitative studies in-scope of the review. Grey literature focused on guidance and best practice in digital health research, and hard-to-reach populations will also be searched following published protocols. The review will focus on literature published between 1 February 2012 and 1 February 2022. Two reviewers are engaged in the review. After screening the title and abstract to determine the eligibility of each article, a thorough full-text review of eligible articles will be conducted using a data extraction framework. Key extracted information will be mapped in tabular and visualised summaries to categorise the breadth of literature and identify key digital methods, including their limitations and potential, for use in hard-to-reach populations.Ethics and dissemination This scoping review does not require ethical approval. The results of the scoping review will consist of peer-reviewed publications, presentations and knowledge mobilisation activities including a lay summary posted via social media channels and production of a policy brief.

BackgroundVaccination, one of the most important and effective ways of preventing infectious diseases, has recently been used to control the COVID-19 pandemic. The present meta-analysis study aimed to evaluate the effectiveness of COVID-19 vaccines in reducing the incidence, hospitalization, and mortality from COVID-19.MethodsA systematic search was performed independently in Scopus, PubMed via Medline, ProQuest, and Google Scholar electronic databases as well as preprint servers using the keywords under study. We used random-effect models and the heterogeneity of the studies was assessed using I2 and χ2 statistics. In addition, the Pooled Vaccine Effectiveness (PVE) obtained from the studies was calculated by converting based on the type of outcome.ResultsA total of 54 studies were included in this meta-analysis. The PVE against SARS-COV 2 infection were 71% [odds ratio (OR) = 0.29, 95% confidence intervals (CI): 0.23–0.36] in the first dose and 87% (OR = 0.13, 95% CI: 0.08–0.21) in the second dose. The PVE for preventing hospitalization due to COVID-19 infection was 73% (OR = 0.27, 95% CI: 0.18–0.41) in the first dose and 89% (OR = 0.11, 95% CI: 0.07–0.17) in the second dose. With regard to the type of vaccine, mRNA-1273 and combined studies in the first dose and ChAdOx1 and mRNA-1273 in the second dose had the highest effectiveness in preventing infection. Regarding the COVID-19-related mortality, PVE was 68% (HR = 0.32, 95% CI: 0.23–0.45) in the first dose and 92% (HR = 0.08, 95% CI: 0.02–0.29) in the second dose.ConclusionThe results of this meta-analysis indicated that vaccination against COVID-19 with BNT162b2 mRNA, mRNA-1273, and ChAdOx1, and also their combination, was associated with a favorable effectiveness against SARS-CoV2 incidence rate, hospitalization, and mortality rate in the first and second doses in different populations. We suggest that to prevent the severe form of the disease in the future, and, in particular, in the coming epidemic picks, vaccination could be the best strategy to prevent the severe form of the disease.Systematic review registrationPROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42021289937].

Abstract Background Whether elevated blood pressure (BP) is a modifiable risk factor for atrial fibrillation (AF) is not established. We tested (1) whether the association between BP and risk of AF is causal, (2) whether it varies according to individual’s genetic susceptibility for AF, and (3) the extent to which specific BP-lowering drugs are expected to reduce this risk. Methods First, causality of association was assessed through two-sample Mendelian randomization, using data from two independent genome-wide association studies that included a population of one million Europeans in total. Second, the UK Biobank data of 329,237 participants at baseline was used to study the effect of BP on AF according to genetic susceptibility of developing AF. Third, a possible treatment effect with major BP-lowering drug classes on AF risk was predicted through genetic variants in genes encode the therapeutic targets of each drug class. Estimated drug effects were compared with effects on incident coronary heart disease, for which direct trial evidence exists. Results The two-sample Mendelian randomization analysis indicated that, on average, exposure to a higher systolic BP increased the risk of AF by 19% (odds ratio per each 10-mmHg [OR] 1.19 [1.12 to 1.27]). This association was replicated in the UK biobank using individual participant data. However, in a further genetic risk-stratified analysis, there was evidence for a linear gradient in the relative effects of systolic BP on AF; while there was no conclusive evidence of an effect in those with low genetic risk, a strong effect was observed among those with high genetic susceptibility for AF. The comparison of predicted treatment effects using genetic proxies for three main drug classes (angiotensin-converting enzyme inhibitors, beta-blockers, and calcium channel blockers) suggested similar average effects for the prevention of atrial fibrillation and coronary heart disease. Conclusions The effect of elevated BP on the risk of AF is likely to be causal, suggesting that BP-lowering treatment may be effective in AF prevention. However, average effects masked clinically important variations, with a more pronounced effect in individuals with high genetic susceptibility risk for AF.

Abstract We show how in a matrix inflationary model in which there is a non-minimal coupling between the matrix inflatons and gravity — hence dubbed Non-M-flation — some of the disadvantages of the minimal model can be avoided. In particular, the number of D3 branes can be reduced substantially to < ∼ O 100 $$ \underset{\sim }{

In the present paper, the author has attempted to examine fatigue life of automotive safety components under axial variable amplitude loading conditions. To this end, the time components of stress tensor in the critical area of the knuckle and Liu-Zenner criterion were used to calculate the time history of equivalent stress. Then, the probability distribution function (PDF) of stress was extracted by applying the stress level counting technique. The Fourier curve fitting method was employed in order to formulate the PDF. Subsequently, the fatigue life of the component was estimated using the probabilistic approach. Finally, the results of the higher order of Fourier curve fitting and different counting ranges were compared with the experimental results. Thus, the best algorithm for precisely determining fatigue life of automotive components was obtained.

Abstract Background and Objectives: Liver is one of the vital organs of the human body. Antioxidants have been shown to play important roles in reducing liver injuries. The aim of this study was to examine the effects of gallic acid supplementation and resistance exercise on liver damage biomarkers in male rats intoxicated by steroid anabolic. Materials and Methods: Forty-two male Wistar rats were randomly divided into six equal groups: control, sham, steroid anabolic (5 mg/kg), steroid anabolic (5 mg/kg) + gallic acid supplementation (50 mg/kg), steroid anabolic (5 mg/kg) + resistance exercise, and steroid anabolic (5 mg/kg) + gallic acid supplementation (50 mg/kg) + resistance exercise. Except for control and sham groups, all groups received (injection) steroid anabolic at a dose of 5 mg / kg body weight once a week. The resistance exercise protocol was comprised of three weekly exercise sessions by 5 rep/3 set of climbing ladder for eight weeks. Data were analyzed via ANOVA and Tukeychr('39')s post hoc test at a significance level of P

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.

Magnesium is the eighth-most abundant element in the world and its alloys have a widespread application in various industries such as electronic and transport (i.e., air, land, and sea) engineering, due to their significant mechanical properties, excellent machinability, high strength to weight ratios, and low cost. Although monolithic Mg metal is known as the lightest industrial metal (magnesium density is 30% less than the density of the aluminum, and this unique property increases the attractiveness of its usage in the transportation industry), one of the significant limitations of magnesium, which affects on its applications in various industries, is very high reactivity of this metal (magnesium with an electronegativity of 31.1 can give electrons to almost all metals and corrodes quickly). To overcome this problem, scholars are trying to produce magnesium (Mg) alloys that are more resistant to a variety of loads and environmental conditions. In this regard, Mg alloys include well-known materials such as aluminum (Al), Zinc (Zn), Manganese (Mn), Silicon (Si), and Copper (Cu), etc., and their amount directly affects the properties of final products. In the present review paper, the authors attempted to present the latest achievements, methods, and influential factors (finish-rolling, pore defects, pH value, microstructure, and manufacturing processes, etc.) on the fatigue life and corrosion resistance of most significant Mg alloys, including AM50, AM60, AZ31, AZ61, AZ80, AZ91, ZK60, and WE43, under various conditions. The summarized results and practical hints presented in this paper can be very useful to enhance the reliability and quality of Mg-made structures.