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Purpose. The aim is to identify and consider the mechanism of formation of national-state identity with the help of symbols and images of sports of the highest achievements in North Ossetia - Alania.Methodology and Approach. The authors have conducted an empirical study using focused interviews of people not connected with sports. The topic of the focused interview is “the Role of sport of higher achievements in the formation of national and state identity” in the Republic of North Ossetia-Alania (50 respondents).Results. On the basis of the study conducted on the territory of the Republic of North Ossetia – Alania, the influence of high-level sports on ethno-territorial and national-state identity and their interaction was revealed, through the holding of sports mega-events and sports victories of “ their “ athletes.Theoretical and Practical Implications. Theoretical and practical importance of the study is that its results can be used in the development of scientific issues related to national and state identity on the one hand, and sports of higher achievements on the other hand.

Abstract We introduced a novel method for visualizing large diophantine datasets and in particular found that mapping the known integer triplets $$\{a,b,c\}$$ { a , b , c } solving either equations of the type $$a^3+b^3+c^3=d$$ a 3 + b 3 + c 3 = d or $$a^3+b^3+c^3=d^3$$ a 3 + b 3 + c 3 = d 3 on certain proper subgroups of the circle group exposed a very clear breaking in their symmetry and a strongly non-ergodic distribution of the solutions of sums of three cubes that had never been described before. This method could be further applied to a larger diversity of diophantine problems, informing both number-theoretical conjectures and novel methods in computer sciences on the way, along with paving the road for specific uses of machine learning in exploring diophantine datasets with possible applications in cryptography among others.

Vehicle comfort, handling, and stability can be improved by a semi-active suspension with advanced control algorithms using the vertical velocities of the sprung mass (SM) and the unsprung masses (UMs) as inputs. Displacement and acceleration sensors are often used to estimate vertical velocities of UMs. However, these sensors are expensive and are susceptible to degradation. Virtual sensors (VS) have been proposed as a solution, and previous research using simulation data has shown that artificial neural networks (ANNs) can provide usable UM vertical velocity estimates. This study aims at finding ANNs structure and input sample window size to achieve best performance on real-world data. Novel dataset was created and used to test VSs based on eight structures of ANNs combining multilayer perceptron, convolutional neural network, long-term short-term memory (LSTM), and bidirectional LSTM layers. This article presents the results of 104 combinations of ANN structure and sample window size, which required 6240 training sessions. A Bayesian search was used to tune the hyperparameters of ANNs’ layers minimizing the root-mean-square error (RMSE) of estimations on the validation data, while a grid search was used to select the sample window size that minimizes RMSE of estimation on test data, ensuring that selected combinations are well generalized. The VS based on ANN with convolutional layers, achieved the lowest RMSE of 0.0210 m/s, and processing time of 0.421 ms for a window size of 23 samples while estimating vertical velocities of vehicle UMs from real-world data.

Abstract For the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner.

In a ‘very close replication’ study using the same attributes as the original, Chandrashekar et al. (2021) report a failure to replicate some choose–reject problems documented in Shafir (1993). We find that several of the original attributes have changed their valence three decades later, and we compose new versions with updated attributes that fully replicate Shafir’s (1993) original findings. Despite their apparent exactitude, ‘very close replications’ across contexts or time, when stimuli may have changed their meaning or valence, can be highly misleading, further exacerbating replication concerns.

The digitisation of the transportation sector and data availability have opened up new opportunities to implement data-driven methods for improving company performance. This article analyses demand and freight rate forecasting techniques in the context of the road freight transportation company. The European market was analysed in this research, and direction from the Netherlands to Italy was selected for the case study. Performed investigation showed that econometric models such as Auto-Regressive Integrated Moving Average (ARIMA) used for demand prognosis provide good results. Freight rate forecasting is different; econometric models, including multivariate models ARIMA with exogenous variables (ARIMAX) and Seasonal ARIMAX (SARIMAX), do not perform satisfactorily under specified time intervals, therefore MultiLayer Perceptron (MLP) was used as a solution. It can be seen that Artificial Intelligence (AI) based methods provide better results. Despite its success, the AI-based approach alone is not recommended for practical implementation since forecasted input parameters are necessary. Lastly, the study uncovers a valuable insight. A strong correlation (0.86) between spot and contract rates was found, and the article shows how current spot rates can be used for contract rate forecasting. First published online 7 February 2024

Abstract Background Hospitals worldwide have implemented Rapid Response Systems (RRS) to facilitate early recognition and prompt response by trained personnel to deteriorating patients. A key concept of this system is that it should prevent ‘events of omission’, including failure to monitor patients’ vital signs, delayed detection, and treatment of deterioration and delayed transfer to an intensive care unit. Time matters when a patient deteriorates, and several in-hospital challenges may prevent the RRS from functioning adequately. Therefore, we must understand and address barriers for timely and adequate responses in cases of patient deterioration. Thus, this study aimed to investigate whether implementing (2012) and developing (2016) an RRS was associated with an overall temporal improvement and to identify needs for further improvement by studying; patient monitoring, omission event occurrences, documentation of limitation of medical treatment, unexpected death, and in-hospital- and 30-day mortality rates. Methods We performed an interprofessional mortality review to study the trajectory of the last hospital stay of patients dying in the study wards in three time periods (P1, P2, P3) from 2010 to 2019. We used non-parametric tests to test for differences between the periods. We also studied overall temporal trends in in-hospital- and 30-day mortality rates. Results Fewer patients experienced omission events (P1: 40%, P2: 20%, P3: 11%, P = 0.01). The number of documented complete vital sign sets, median (Q1,Q3) P1: 0 (0,0), P2: 2 (1,2), P3: 4 (3,5), P = 0.01) and intensive care consultations in the wards ( P1: 12%, P2: 30%, P3: 33%, P = 0.007) increased. Limitations of medical treatment were documented earlier (median days from admission were P1: 8, P2: 8, P3: 3, P = 0.01). In-hospital and 30-day mortality rates decreased during this decade (rate ratios 0.95 (95% CI: 0.92–0.98) and 0.97 (95% CI: 0.95–0.99)). Conclusion The RRS implementation and development during the last decade was associated with reduced omission events, earlier documentation of limitation of medical treatments, and a temporal reduction in the in-hospital- and 30-day mortality rates in the study wards. The mortality review is a suitable method to evaluate an RRS and provide a foundation for further improvement. Trial registration Retrospectively registered.

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

Abstract Introduction Short term hypothermia has been suggested to have cardio protective properties in acute myocardial infarction (AMI) by reducing infarct size as assessed by troponins. There are limited data on the kinetics of these biomarkers in comatose out-of-hospital cardiac arrest (OHCA) patients, with and without AMI, undergoing targeted temperature management (TTM) in the ICU. Purpose The aim of this post hoc analyses was to evaluate and compare the kinetics of two high-sensitivity cardiac troponins in OHCA survivors, with and without acute myocardial infarction (AMI), during TTM of different durations [24 h (standard) vs. 48 h (prolonged)]. Methods In a sub-cohort (n = 114) of the international, multicentre, randomized controlled study “TTH48” we measured high-sensitive troponin T (hs-cTnT), high-sensitive troponin I (hs-cTnI) and CK-MB at the following time points: Arrival, 24 h, 48 h and 72 h from reaching the target temperature range of 33 ± 1 °C. All patients diagnosed with an AMI at the immediate coronary angiogram (CAG)—18 in the 24-h group and 25 in the 48-h group—underwent PCI with stent implantation. There were no stent thromboses. Results Both the hs-cTnT and hs-cTnI changes over time were highly influenced by the cause of OHCA (AMI vs. non-AMI). In contrast to non-AMI patients, both troponins remained elevated at 72 h in AMI patients. There was no difference between the two time-differentiated TTM groups in the kinetics for the two troponins. Conclusion In comatose OHCA survivors with an aetiology of AMI levels of both hs-cTnI and hs-cTnT remained elevated for 72 h, which is in contrast to the well-described kinetic profile of troponins in normotherm AMI patients. There was no difference in kinetic profile between the two high sensitive assays. Different duration of TTM did not influence the kinetics of the troponins. Trial registration: Clinicaltrials.gov Identifier: NCT01689077, 20/09/2012.

Introduction In surgery, serious adverse events have effects on the patient journey, the patient outcome and may constitute a burden to the surgeon involved. This study aims to investigate facilitators and barriers to transparency around, reporting of and learning from serious adverse events among surgeons.Methods Based on a qualitative study design, we recruited 15 surgeons (4 females and 11 males) with 4 different surgical subspecialties from four Norwegian university hospitals. The participants underwent individual semistructured interviews and data were analysed according to principles of inductive qualitative content analysis.Results and discussion We identified four overarching themes. All surgeons reported having experienced serious adverse events, describing these as part of ‘the nature of surgery’. Most surgeons reported that established strategies failed to combine facilitation of learning with taking care of the involved surgeons. Transparency about serious adverse events was by some felt as an extra burden, fearing that openness on technical-related errors could affect their future career negatively. Positive implications of transparency were linked with factors such as minimising the surgeon’s feeling of personal burden with positive impact on individual and collective learning. A lack of facilitation of individual and structural transparency factors could entail ‘collateral damage’. Our participants suggested that both the younger generation of surgeons in general, and the increasing number of women in surgical professions, might contribute to ‘maturing’ the culture of transparency.Conclusion and implications This study suggests that transparency associated with serious adverse events is hampered by concerns at both personal and professional levels among surgeons. These results emphasise the importance of improved systemic learning and the need for structural changes; it is crucial to increase the focus on education and training curriculums and offer advice on coping strategies and establish arenas for safe discussions after serious adverse events.

Abstract Hypotension after cardiac arrest could aggravate prolonged hypoxic ischemic encephalopathy. The association of circulatory shock at hospital admission with outcome after cardiac arrest has not been well studied. The objective of this study was to investigate the independent association of circulatory shock at hospital admission with neurologic outcome, and to evaluate whether cardiovascular comorbidities interact with circulatory shock. 4004 adult patients with out-of-hospital cardiac arrest enrolled in the International Cardiac Arrest Registry 2006–2017 were included in analysis. Circulatory shock was defined as a systolic blood pressure below 90 mmHg and/or medical or mechanical supportive measures to maintain adequate perfusion during hospital admission. Primary outcome was cerebral performance category (CPC) dichotomized as good, (CPC 1–2) versus poor (CPC 3–5) outcome at hospital discharge. 38% of included patients were in circulatory shock at hospital admission, 32% had good neurologic outcome at hospital discharge. The adjusted odds ratio for good neurologic outcome in patients without preexisting cardiovascular disease with circulatory shock at hospital admission was 0.60 [0.46–0.79]. No significant interaction was detected with preexisting comorbidities in the main analysis. We conclude that circulatory shock at hospital admission after out-of-hospital cardiac arrest is independently associated with poor neurologic outcome.

The article examines the situation of the Tatars and other Muslim minorities in Bulgaria during the communist period. The policy of the state in relation to Muslim minorities after the proclamation of the People`s Republic of Bulgaria and the establishment of socialism in the state according to the Soviet model, when the political, economic and social models of the USSR were imported and introduced without taking into account the national characteristics of Bulgaria, are analyzed. As in the Soviet Union (especially in the early stage of its formation, religion was banned and this applied to all confessions without exception. The Bulgarian Communist Party (BCP) made every effort eradicate religious identity and, in particular, Islamic identity. It was planned to replace the religious ideological fragment with a socialist one, and then, on its platform, form and stimulate the development of the national, modernist and Soviet identity of Muslims. Moreover, the emphasis was also placed on improving the way of life and the material situation of the Muslim population, which, according to the Marxist theory of culture, should have contributed to a more effective formation of socialist consciousness. The ruling party saw in the Muslim religious consciousness and rudiments of the Ottoman past, an obstacle on the way of socialist progress and formation of socialist consciousness. Emasculating elements of the religious worldview from the mind of people, the BCP set itself the task of creating a modern, secular, socialist personality. To this end, in 1946–1989 the government implemented a number of economic, educational and cultural establishments.

According to the International Monetary Fund, the People’s Republic of China is nowadays the largest economic power in the world. Therefore, it is unsurprising that China was represented in nearly every global market. Contrary to the belief that Chinese investments in underdeveloped countries should lead to their development and improvements in standards, Peking was accused of using the countries in development to strengthen its economy and to increase its political influence. The modus operandi was allegedly a striking example of projecting soft power in the expansion of hard power in many regions worldwide. The combination of economy, trade, investments and security questions was clear. This phenomenon was evident in Africa because China had a special and robust economic and geostrategic interest in this region. This research critically analyzed how this strategy can be considered a new security paradigm. In addition, there was a straightforward project from the Chinese government to extend this model to other regions to obtain global leadership.

Many previous studies have demonstrated that transcutaneous vagus nerve stimulation (VNS) has the potential to exhibit therapeutic effects similar to its invasive counterpart. An objective assessment of VNS requires a reliable biomarker of successful vagal activation. Although many potential biomarkers have been proposed, most studies have focused on heart rate variability (HRV). Despite the physiological rationale for HRV as a biomarker for assessing vagal stimulation, data on its effects on HRV are equivocal. To further advance this field, future studies investigating VNS should contain adequate methodological specifics that make it possible to compare the results between studies, to replicate studies, and to enhance the safety of study participants. This article describes the design and methodology of a randomized study evaluating the effect of short-term noninvasive stimulation of the auricular branch of the vagus nerve on parameters of HRV. Primary records of rhythmograms of all the subjects, as well as a dataset with clinical, instrumental, and laboratory data of all the current study subjects are in the public domain for possible secondary analysis to all interested researchers. The physiological interpretation of the obtained data is not considered in the article.

This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles’ Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle’s semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles.

Abstract Background Exposure to extreme arterial partial pressures of oxygen (PaO2) and carbon dioxide (PaCO2) following the return of spontaneous circulation (ROSC) after out-of-hospital cardiac arrest (OHCA) is common and may affect neurological outcome but results of previous studies are conflicting. Methods Exploratory study of the International Cardiac Arrest Registry (INTCAR) 2.0 database, including 2162 OHCA patients with ROSC in 22 intensive care units in North America and Europe. We tested the hypothesis that exposure to extreme PaO2 or PaCO2 values within 24 h after OHCA is associated with poor neurological outcome at discharge. Our primary analyses investigated the association between extreme PaO2 and PaCO2 values, defined as hyperoxemia (PaO2 > 40 kPa), hypoxemia (PaO2 6.7 kPa) and hypocapnemia (PaCO2 40 kPa with PaCO2 6.7 kPa and neurological outcome. To define a cut point for the onset of poor neurological outcome, we tested a model with increasing and decreasing PaO2 levels and decreasing PaCO2 levels. Cerebral Performance Category (CPC), dichotomized to good (CPC 1–2) and poor (CPC 3–5) was used as outcome measure. Results Of 2135 patients eligible for analysis, 700 were exposed to hyperoxemia or hypoxemia and 1128 to hypercapnemia or hypocapnemia. Our primary analyses did not reveal significant associations between exposure to extreme PaO2 or PaCO2 values and neurological outcome (P = 0.13–0.49). Our secondary analyses showed no significant associations between combinations of PaO2 and PaCO2 and neurological outcome (P = 0.11–0.86). There was no PaO2 or PaCO2 level significantly associated with poor neurological outcome. All analyses were adjusted for relevant co-variates. Conclusions Exposure to extreme PaO2 or PaCO2 values in the first 24 h after OHCA was common, but not independently associated with neurological outcome at discharge.

Abnormalities in gut microbiota diversity are considered important mechanisms in metabolic disorders in polycystic ovarian syndrome (PCOS). However, the data on the association of these disorders with the PCOS phenotype remain controversial. The objectives of this study were to estimate the alpha diversity of the gut microbiota of healthy women and PCOS patients depending on phenotype. The study participants (184 premenopausal women: 63 with PCOS, 121 without PCOS) were recruited during the annual employment assessment in the Irkutsk Region and the Buryat Republic (Russia) in 2016–2019. For PCOS diagnosis, we used the Rotterdam (2003) criteria and definitions of PCOS phenotypes. Five indexes of alpha diversity (ASV, Shannon, Simpson, Chao, and ACE) were estimated for the gut microbiota in all participants using amplicon metasequencing. As a result, two out of five alpha diversity indexes showed a statistical difference between the non-PCOS and PCOS groups. We did not find a significant difference in the alpha diversity of gut microbiota in the subgroups of women with hyperandrogenic PCOS phenotypes vs non-androgenic phenotype D and the group of women with the presence of only one of the PCOS criteria. Nevertheless, “classic” PCOS phenotypes demonstrated the most significant decrease in alpha diversity compared with healthy women without any signs of PCOS.

Androgen assessment is a key element for diagnosing polycystic ovary syndrome (PCOS), and defining a “normal” level of circulating androgens is critical for epidemiological studies. We determined the upper normal limits (UNLs) for androgens in a population-based group of premenopausal “healthy control” women, overall and by ethnicity (Caucasian and Asian), in the cross-sectional Eastern Siberia PCOS Epidemiology and Phenotype (ESPEP) Study (ClinicalTrials.gov ID: NCT05194384) conducted in 2016–2019. Overall, we identified a “healthy control” group consisting of 143 healthy premenopausal women without menstrual dysfunction, hirsutism, polycystic ovaries, or medical disorders. We analyzed serum total testosterone (TT) by using liquid chromatography with tandem mass spectrometry (LC-MS/MS), and DHEAS, sex-hormone-binding globulin (SHBG), TSH, prolactin, and 17-hydroxyprogesterone (17OHP) were assessed with an enzyme-linked immunosorbent assay (ELISA). The UNLs for the entire population for the TT, free androgen index (FAI), and DHEAS were determined as the 98th percentiles in healthy controls as follows: 67.3 (95% confidence interval (CI): 48.1, 76.5) ng/dl, 5.4 (3.5, 14.0), and 355 (289, 371) μg/dl, respectively. The study results demonstrated that the UNLs for TT and FAI varied by ethnicity, whereas the DHEAS UNLs were comparable in the ethnicities studied.

OBJECTIVES:. To investigate rebound hyperthermia following targeted temperature management after cardiac arrest and its impact on functional outcome. DESIGN:. Post hoc analysis. SETTING:. Ten European ICUs. PATIENTS:. Patients included in the time-differentiated therapeutic hypothermia in out-of-hospital cardiac arrest survivors trial treated with targeted temperature management at 33°C for 48 or 24 hours. Favorable functional outcome was defined as a Cerebral Performance Category of 1 or 2 at 6 months. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Of 338 included patients, 103 (30%) experienced rebound hyperthermia defined as a maximum temperature after targeted temperature management and rewarming exceeding 38.5°C. Using multivariate logistic regression analysis, increasing age (odds ratio, 0.97; 95% CI, 0.95–0.99; p = 0.02) and severe acute kidney injury within 72 hours of ICU admission (odds ratio, 0.35; 95% CI, 0.13–0.91; p = 0.03) were associated with less rebound hyperthermia, whereas male gender (odds ratio, 3.94; 95% CI, 1.34–11.57; p = 0.01), highest C-reactive protein value (odds ratio, 1.04; 95% CI, 1.01–1.07; p = 0.02), and use of mechanical chest compression during cardiopulmonary resuscitation (odds ratio, 2.00; 95% CI, 1.10–3.67; p = 0.02) were associated with more rebound hyperthermia. Patients with favorable functional outcome spent less time after rewarming over 38.5°C (2.5% vs 6.3%; p = 0.03), 39°C (0.14% vs 2.7%; p < 0.01), and 39.5°C (0.03% vs 0.71%; p < 0.01) when compared with others. Median time to rebound hyperthermia was longer in the unfavorable functional outcome group (33.2 hr; interquartile range, 14.3–53.0 hr vs 6.5 hr; interquartile range, 2.2–34.1; p < 0.01). In a predefined multivariate binary logistic regression model, rebound hyperthermia was associated with decreased odds of favorable functional outcome (odds ratio, 0.42; 95% CI, 0.22–0.79). CONCLUSIONS:. One-third of targeted temperature management patients experience rebound hyperthermia, and it is more common in younger male patients with an aggravated inflammatory response and those treated with a mechanical chest compression device. Later onset of rebound hyperthermia and temperatures exceeding 38.5°C associate with unfavorable outcome.

Extended Abstract Abstract The purpose of this study was to identify the dimensions and components of organizational resilience in Iranian banks using interpretive approach and qualitative method. The study population included senior managers and experts in the banking industry and tools used for data collection, interviewing and questionnaires. For this purpose, after reviewing the literature and research background, in-depth and semi-structured interviews were conducted with 12 banking industry experts and initial model was identified. The technique used for analyzing the interviews was theme analysis which involved three stages of coding including open, axial and selective. Confirmatory factor analysis technique and Smart PLS software were used to confirm the designed structure. Finally, the two main aspects of micro banking management include the components of liquidity management, capital management, risk management, sound management of arrears, rumor management, information and communication systems, crisis management, financial management, management features, observation and Problem prediction, corruption control, the need for change and dynamics, and macro-banking dimension including bank business structure, corporate governance and policy structure, and banking laws and 94 subscales were identified and confirmed. Considering these factors can lead to the improvement of resiliency in the banking industry. Introduction Today, the world is affected by decisions people make in extremely complicated situations with high uncertainty. The chaotic result of these decisions has caused uncertainty in phenomena to be very high (Bergland & Macinen, 2019). One type of these organizations, which both influences and is affected by these tensions and riots, is bank. Optimal banking activities and the effective use of marketing tools to realize their goals, including exploiting capital and equipping it, will highly influence various economic activities and the general state of the country’s economy (Ostadi et al., 2017). When organizational resilience in the banking industry of the country is not studied and investigated properly, stagnation and even bankruptcy crises in the banks follow and this crisis could have devastating effects on the country's economy, given the interdependency of the state economy and the banks. Identifying the dimensions and components of organizational resilience can give managers the opportunity to work on detecting key points and resilience dimensions and components so as to increase this resilience in their organization, thus they can establish a coherent and consistent strategy and plan to improve the situation of banking system so that this system can deal with crises and incidents. Since Iran is at risk of invasion (because of its geopolitical location, natural geography and manifesto of fighting against arrogance and counteracting to capitalist systems), resilience of organizations, especially banks, is of paramount importance and priority. Accordingly, the purpose of this study is to identifying resilience components in the banking industry of Iran. Considering the purpose of the study, some investigations were initiated on the resilience of the banking industry, and then resilience models in organizations and banks were examined. Then, interviews were conducted with experts of the banking industry, and the initial model was identified using expert opinions through thematic analysis approach. The model was then confirmed using the confirmatory factor analysis (CFA) technique. Materials and Methods In this research, the qualitative research strategy has been used to achieve the purpose of research. Semi-structured interviews were used to collect the required research data. The interviews were conducted with 12 experts in the field of Iranian banking at the senior level. 10 interviews were conducted to reach data saturation and 2 interviews were performed afterward. Thus, these individuals were selected to interview and identify the dimensions and components of organizational resilience model in the Iranian banks. To analyze the data collected from the interviews, the thematic analysis technique was used. Furthermore, in order to confirm the research model, the statistical confirmatory factor analysis was also used. For this purpose, a questionnaire containing 159 items was designed and then after its reliability and validity was approved, it was distributed among the statistical population consisting of bank managers and banking experts of the country. Discussion and Results In this research, an organizational resilience model in banks of Iran was designed. The results of interviews and surveys showed that a comprehensive model for organizational resilience in the banking industry of the country should take two main dimensions in consideration. The first dimension is the management of micro-banking, whose components typically can be managed at the level of each bank in the country. The components of this dimension are at the level of the enterprise and include financial management (with four variables of: liquidity management, capital management, proper management of deferred debts and risk management), information and communication management, enterprise strategy management (ESM), banking supply chain management, human resource management and its focus is on monitoring and controlling and change management. In this dimension, there is the ability to manage components and variables at the enterprise level of bank. The second level is related to macro banking management, which is in the field of banking policy. The components and variables of this dimension are beyond the control of a bank and require management and policy at a level beyond the enterprise level. Country's banking management lies at this level. Country’s banking management is responsible for macro banking management and policy making. The main components of this level are changes in the field of banking business in the country, political variables, regulatory and monitoring policies, governance variables, and components related to the policy and legislation. Conclusion Based on the results, resilience and it’s scope required consideration in different aspects.In related research in the field of financial and banking resilience, the indicators were more financial in nature or attempted to compare the resilience of two or more organizations according to a specific criterion. In this study, it was attempted to develop a comprehensive model consisting of resilience dimensions and components in different scopes of internal, external, human, managerial, financial and so on which banks and financial organizations can Promote their resilience. The results of this research can enhance the theoretical foundations of past research and expand the concept of organizational resilience in the field of financial services, in particular the banking industry. Also, the introduced model can help managers to have a better and more comprehensive understanding of their organization and potential disruptions with respect to identified components of resilience.

Abstract Background Although targeted temperature management (TTM) is recommended in comatose survivors after cardiac arrest (CA), the optimal method to deliver TTM remains unknown. We performed a meta-analysis to evaluate the effects of different TTM methods on survival and neurological outcome after adult CA. Methods We searched on the MEDLINE/PubMed database until 22 February 2019 for comparative studies that evaluated at least two different TTM methods in CA patients. Data were extracted independently by two authors. We used the Newcastle-Ottawa Scale and a modified Cochrane ROB tools for assessing the risk of bias of each study. The primary outcome was the occurrence of unfavorable neurological outcome (UO); secondary outcomes included overall mortality. Results Our search identified 6886 studies; 22 studies (n = 8027 patients) were included in the final analysis. When compared to surface cooling, core methods showed a lower probability of UO (OR 0.85 [95% CIs 0.75–0.96]; p = 0.008) but not mortality (OR 0.88 [95% CIs 0.62–1.25]; p = 0.21). No significant heterogeneity was observed among studies. However, these effects were observed in the analyses of non-RCTs. A significant lower probability of both UO and mortality were observed when invasive TTM methods were compared to non-invasive TTM methods and when temperature feedback devices (TFD) were compared to non-TFD methods. These results were significant particularly in non-RCTs. Conclusions Although existing literature is mostly based on retrospective or prospective studies, specific TTM methods (i.e., core, invasive, and with TFD) were associated with a lower probability of poor neurological outcome when compared to other methods in adult CA survivors (CRD42019111021).

The present study identifies the dimensions and components of organizational resilience in Iranian banks. Regarding the purpose of the research, first, the research literature on the resilience of the banking industry was thoroughly reviewed and the bank resilience indicators were analyzed. Since few studies which were conducted in this area took a partial look at resilience, this study has tried to take a more comprehensive look at the dimensions and components. Therefore, 12 experts in the banking industry were interviewed and the initial model was identified using expert opinions. The technique to analyze the interviews was thematic analysis, with three stages of encoding. The model was approved by 35 experts in the field of banking, using the obtained confirmatory factor analysis (CFA) technique. The results showed that in order to create and increase organizational resilience, it is necessary to consider several factors in the two areas of microeconomic management and macroeconomic management of Iranian banks.

Abstract Background The aim of this study was to explore the performance and outcomes for intravascular (IC) versus surface cooling devices (SFC) for targeted temperature management (TTM) after out-of-hospital cardiac arrest. Methods A retrospective analysis of data from the Time-differentiated Therapeutic Hypothermia (TTH48) trial (NCT01689077), which compared whether TTM at 33 °C for 48 h results in better neurologic outcomes compared with standard 24-h duration. Devices were assessed for the speed of cooling and rewarming rates. Precision was assessed by measuring temperature variability (TV), i.e., the standard deviation (SD) of all temperature measurements in the cooling phase. Main outcomes were overall mortality and poor neurological outcome, including death, severe disability, or vegetative status. Results A total of 352 patients had available data and were included in the analysis; of those, 218 (62%) were managed with IC. A total of 114/218 (53%) patients with IC and 61/134 (43%) with SFC were cooled for 48 h (p = 0.22). Time to target temperature (≤ 34 °C) was significantly shorter for patients treated with endovascular devices (2.2 [1.1–4.0] vs. 4.2 [2.7–6.0] h, p

Objectives Economic constraints are a common explanation of why patients with low socioeconomic status tend to experience less access to medical care. We tested whether the decreased care extends to medical assistance in dying in a healthcare system with no direct economic constraints.Design Population-based case–control study of adults who died.Setting Ontario, Canada, between 1 June 2016 and 1 June 2019.Patients Patients receiving palliative care under universal insurance with no user fees.Exposure Patient’s socioeconomic status identified using standardised quintiles.Main outcome measure Whether the patient received medical assistance in dying.Results A total of 50 096 palliative care patients died, of whom 920 received medical assistance in dying (cases) and 49 176 did not receive medical assistance in dying (controls). Medical assistance in dying was less frequent for patients with low socioeconomic status (166 of 11 008=1.5%) than for patients with high socioeconomic status (227 of 9277=2.4%). This equalled a 39% decreased odds of receiving medical assistance in dying associated with low socioeconomic status (OR=0.61, 95% CI 0.50 to 0.75, p

Objectives The Norwegian Cardiac Arrest Registry (NorCAR) was established in 2013 when cardiac arrest became a mandatory reportable condition. The aim of this cohort study is to describe how the world’s first mandatory, population-based cardiac arrest registry evolved during its first 6 years.Setting Norway has a total population of 5.3 million inhabitants with a population density that varies considerably. All residents are assigned a unique identifier number, giving nationally approved registries access to information about all births and deaths in the country. Data in the registry are entered by data processors; public employees with close links to the emergency medical services. All data processors undergo a standardised training and meet for yearly retraining and updates.Participants All events of cardiac arrest where bystanders or healthcare professionals have started cardiopulmonary resuscitation or performed defibrillation are included into the NorCAR.Primary and secondary outcome measures Since the establishment of the registry, the number of reporting health trusts, the number of reported events and the corresponding population at risk were followed year by year. Outcome is measured as changes in inclusion rate, incidence per 100 000 inhabitants and survival to 30 days after cardiac arrest.Results In total, 14 849 cases were registered over 6 years, between 2013 and 2018. The number of health trusts reporting rose steadily from 2013. Within 3 years, all trusts reported to the registry with an increasing number of events reported; going from 1101 to 3400 per year. The prevalence of bystander cardiopulmonary resuscitation increased slightly, but the population incidence of survival did not change.Conclusion Declaring cardiac arrest as a reportable condition and close follow-up of all reporting areas is essential when building a national registry.

Abstract Background The modifiable prehospital system factors, bystander cardiopulmonary resuscitation (CPR), emergency medical services (EMS), response time, and EMS physician attendance, may affect short- and long-term survival for both rural and urban out-of-hospital cardiac arrest (OHCA) patients. We studied how such factors influenced OHCA survival in a mixed urban/rural region with a high survival rate after OHCA. Methods We analyzed the association between modifiable prehospital factors and survival to different stages of care in 1138 medical OHCA patients from an Utstein template-based cardiac arrest registry, using Kaplan-Meier type survival curves, univariable and multivariable logistic regression and mortality hazard plots. Results We found a significantly higher probability for survival to hospital admission (OR: 1.84, 95% CI 1.43–2.36, p

The purpose of this study is identifying factors affecting the probability of loan default and forecasting default probability of non-corporate (natural) customers of Pasargad bank by means of neural networks method (NNM). Variables influencing creation of default were identified through investigating background studies and literature review. At the next step, data related to 470 customers were collected from a statistical population of 25342 people who received loans from Pasargad bank in Tehran region from 2013 to 2014. Results show that NNM could accurately forecast 92% of applicants default probability. According to NNM results, bad financial history or type of collateral have had more significant effect on default probability than the other input variables.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

Gravel pavement has lower construction costs but poorer performance than asphalt surfaces on roads. It also emits dust and deforms under the impact of vehicle loads and ambient air factors; the resulting ripples and ruts constantly deepen, and therefore increase vehicle vibrations and fuel consumption, and reduce safe driving speed and comfort. In this study, existing pavement quality evaluation indexes are analysed, and a methodology for adapting them for roads with gravel pavement is proposed. We report the measured wave depth and length of gravel pavement profile using the straightedge method on a 160 m long road section at three stages of road utilization. The measured pavement elevation was processed according to ISO 8608, and the frequency response of a vehicle was investigated using simulations in MATLAB/Simulink. The international roughness index (IRI) analysis showed that a speed of 30–45 km/h instead of 80 km/h provided the objective results of the IRI calculation on the flexible pavement due to the decreasing velocity of a vehicle’s unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement was explored, identifying specific driving safety and comfort cases. Finally, an increase in the dynamic load coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrated the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers. The main relevant objectives of this study are the adaptation of a road roughness indicator to gravel pavement, including the evaluation of vehicle dynamic responses at different speeds and pavement deterioration states.

The Collatz dynamic is known to generate a complex quiver of sequences over natural numbers for which the inflation propensity remains so unpredictable it could be used to generate reliable proof-of-work algorithms for the cryptocurrency industry; it has so far resisted every attempt at linearizing its behavior. Here, we establish an ad hoc equivalent of modular arithmetics for Collatz sequences based on five arithmetic rules that we prove apply to the entire Collatz dynamical system and for which the iterations exactly define the full basin of attractions leading to any odd number. We further simulate these rules to gain insight into their quiver geometry and computational properties and observe that they linearize the proof of convergence of the full rows of the binary tree over odd numbers in their natural order, a result which, along with the full description of the basin of any odd number, has never been achieved before. We then provide two theoretical programs to explain why the five rules linearize Collatz convergence, one specifically dependent upon the Axiom of Choice and one on Peano arithmetic.

Abstract This paper presents a description of, and some reflections around, the experience of implementing simulation-based education within a nursing education programme in a low-income context. The students in the nursing education programme found the simulation sessions to be useful, motivating and a realistic learning method. Our experience may provide useful insight for other nursing education programmes in low-income contexts. It looks like a deeper knowledge about the feasibility of simulation-based education from both the teacher and student perspective is necessary.

Objectives Globally, perinatal mortality remains high, especially in sub-Saharan countries, mainly because of inadequate obstetric and newborn care. Helping Babies Breathe (HBB) resuscitation training as part of a continuous quality improvement (CQI) programme may improve outcomes. The aim of this study was to describe observed changes in perinatal survival during a 6-year period, while adjusting for relevant perinatal risk factors.Setting Delivery rooms and operating theatre in a rural referral hospital in northern-central Tanzania providing comprehensive obstetric and basic newborn care 24 hours a day. The hospital serves approximately 2 million people comprising low social-economic status.Participants All newborns (n=31 122) born in the hospital from February 2010 through January 2017; 4893 were born in the 1-year baseline period (February 2010 through January 2011), 26 229 in the following CQI period.Interventions The HBB CQI project, including frequent HBB training, was implemented from February 2011. This is a quality assessment analysis of prospectively collected observational data including patient, process and outcome measures of every delivery. Logistic regression modelling was used to construct risk-adjusted variable life adjusted display (VLAD) and cumulative sum (CUSUM) plots to monitor changes in perinatal survival (primary outcome).Results During the 6-year CQI period, the unadjusted number of extra lives saved according to the VLAD plot was 150 despite more women admitted with pregnancy and labour complications and more caesarean deliveries. After adjusting for these risk factors, the risk-adjusted VLAD plot indicated that an estimated 250 extra lives were saved. The risk-adjusted CUSUM plot confirmed a persistent and steady increase in perinatal survival.Conclusions The risk-adjusted statistical process control methods indicate significant improvement in perinatal survival after initiation of the HBB CQI project with continuous focus on newborn resuscitation training during the period, despite a concomitant increase in high-risk deliveries. Risk-adjusted VLAD and CUSUM are useful methods to quantify, illustrate and demonstrate persistent changes in outcome over time.

This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.