Degree Of Certainty Research Articles

Handedness and religiosity, a two-nation study: Evidence that hemispheric functioning may influence religious beliefs

Prior research has reported that so-called consistent-handers are more religious than non-consistent-handers. This study analyzed data bearing on this possibility using large samples of college students from two different countries; Malaysia and the United States. When samples from both countries were separated by sex, no significant support for this prior research was found. However, when we analyzed our data with handedness dichotomized between right-handers and non-right-handers, some significant relationships were found. Among females in both countries, left- and mixed-handers expressed lower degrees of certainty about the existence of God and life-after-death. Also, right-handedness and religious service attendance were positively and significantly associated among Malaysian males. Among US males, however, belief in God was actually significantly stronger among right-handers than among left and mixed-handers. Assuming that right handedness is a rough proxy for left hemispheric dominance, our findings provide some support for the hypothesis that right hemispheric dominance is associated with orthodox religiosity at least among females.

A novel approach to determine the optimal annual case volume for cardiovascular interventions in need of centralization

Abstract Background There is an incentive to centralize specialized health care, which particularly applies to high-risk surgical and trans-catheter cardiovascular procedures. In health care policy, annual hospital case volume is the main criterion to define expertise and determine centralization. Purpose We propose a novel methodology to evaluate the volume-outcome relation for high-risk and infrequent cardiovascular procedures, by use of a meta-analytical approach, and aim to determine a specific annual case volume threshold, using surgery for acute type A aortic dissection (ATAAD) as an example. Methods This study was designed as a systematic review and meta-analysis (PROSPERO; registration number CRD42022345024). We included studies reporting on (i) consecutive ATAAD patients, (ii) years of inclusion and (iii) early mortality. The primary outcome was early mortality in relation to annual hospital case volume. Secondary outcome was long-term survival. Data were presented and compared in volume quartiles (Q's, Q1 1-12, Q2 12-17, Q3 17-28, and Q4 28-241 cases/year). Restricted cubic spline analysis was used to demonstrate the volume-outcome relation. Derived from optimization statistics, we introduce the concept of the 'elbow-method' to determine the optimal annual case volume (see Figure 1). For long-term survival, individual patient data derived from Kaplan-Meier curves were integrated. For clinical interpretation, numbers needed to treat (NNT) were calculated. Results 125 studies from 125 unique centers were included (25 countries, 5 continents, see Figure 1), comprising 33 027 patients. A significant between-quartile difference for early mortality was observed (10.5% [Q4] vs. 16.8% [Q1], p<0.001, I2=72%), which persisted after correction for age, sex, and dissection type (p<0.001). Furthermore, a significant non-linear volume-outcome association was observed as well (p<0.001, Figure 1). The optimal annual case volume after which results did not improve any further, was determined at 41 cases/year (95% CI 39-45, relative risk reduction 37.1%, NNT 16.1, compared to 10 cases/year, Figure 1). Even more pronounced between-quartile survival differences were observed for long-term survival (43 studies, n=11 704, 10-year survival Q4 69% vs Q1 56%, p<0.001, HR=0.87, 95%CI=0.84-0.90, p<0.001, NNT 6.7, Figure 2). This notable long-term survival difference persisted after correction for age, sex, and dissection type (Figure 2). Conclusions High volume centers exhibit superior results in surgery for ATAAD, compared to lower volume centers. Using a novel approach, the optimal annual hospital ATAAD case volume threshold was statistically determined at 41 cases/year, with a high degree of certainty. Despite its acute character, rerouting of patients and centralization of ATAAD care in high-volume centers may lead to improved outcomes. This novel method has the potential to be applied to various other cardiovascular procedures requiring centralization.Figure 1Early mortalityFigure 2Long-term survival

Detection of Breast Cancer using Deep Learning Techniques

Because of the current population boom in health research, early sickness diagnosis has become a vital concern. As the population expands, the risk of dying from breast cancer rises dramatically. Breast carcinoma has been identified whenever the second most dangerous of the previously described malignancies. The researcher automated illness detection system assists medical practitioners in disease diagnosis, provides consistent, effective, and punctual intervention, and reduces the risk of death. Any disease that is diagnosed appropriately and promptly may be treated with minimal human intervention. An overwhelming majority of people are unaware of their illness until it becomes chronic. It increases the world mortality rate. Breast carcinoma has emerged as one of the increasingly rare diseases that may be treated if detected early enough and before it spreads to other regions of the body. Breast carcinoma constitutes one of the most frequent malignancies in women globally, and early identification is critical for improving survival and treatment success. Breast cancer detection technologies in areas like mammography and ultrasound have limits outside in the sense of preciseness as well as sensitivity. Deep learning algorithms have begun to emerge as a potential strategy for enhancing the degree of certainty and efficiency belonging to breast cancer diagnosis in recent years. Deep learning is an artificial intelligence area that focuses down training multi-layer neural networks to gain knowledge of and extract complicated patterns from big datasets. Researchers have developed sophisticated models suited to successfully diagnosing breast cancer from several medical imaging modalities, which might involve mammograms, MRI scans, additionally histopathological images, by utilizing the power throughout deep learning algorithms. Breast carcinoma detection is an important subject of study with significant public health implications. Deep learning techniques, a subset of computational neuroscience (AI), demonstrate excellent results in identifying and identifying cases of breast cancer. Deep learning breast cancer detection technologies have significant research repercussions due to the fact that they enable early diagnosis, enhance exactness, automate screening processes, give personalized treatment, and together with expand healthcare services to underserved areas. Persevered research in this area has the potential to change breast cancer diagnostics, resulting in better patient outcomes and, eventually, lifesaving. In this research we will be using The Weighted Product Model. The Weighted Product Model (WPM) represents a decision-making approach that uses numerous criteria to evaluate and rank options. It applies a multiple-criteria analysis approach that considers the value or weight assigned to every criterion as well as the effectiveness or score residing in every possible outcome on those criteria. Taken of Alternative Parameters SVM, Random Forest, Logistic Regression, KNN, Naive Bayes. Taken of Evaluation Parameters Accuracy, Recall, Precision, FI-Score, ROC AUC. As per Weighted Normalized Decision Matrix we get to know that SVM got more value were Random Forest, Logistic Regression, Naive Bayes got less value. From the above results I conclude that as per Weighted Normalized Decision Matrix we get to know that SVM got more value than others.

Comparison of analgesic effects of pericapsular nerve group block and fascia iliaca compartment block during hip arthroplasty: A systematic review and meta-analysis of randomised controlled trials

Background and Aims: Postoperative pain for patients having hip arthroplasty ranges from moderate to severe. Many regional anaesthesia procedures treat postoperative pain to improve functional ability and quality of life. Evidence comparing the analgesic effects of the pericapsular nerve group (PENG) block and fascia iliaca compartment block (FICB) remains unclear. The analgesic efficacies of PENG and FICB in hip arthroplasty were compared to determine which technique is associated with superior analgesia. Methods: The electronic databases (PubMed, Cochrane Library, Google Scholar and Web of Sciences) were searched for published randomised controlled trials (RCTs) till 5 April 2023 comparing PENG block vs. FICB following hip arthroplasty. The primary outcome was pain scores [numerical rating scale (NRS) or visual analogue scale (VAS)] between 0 and 10 at rest and during movement at 24 h. Secondary outcomes included pain scores at rest and during movement within 30 min, at 6 h and 12 h, time to first rescue analgesia and cumulative postoperative opioid use in 24 h. We assessed the risk of bias using the Cochrane Collaboration Risk-of-Bias 2 tool. Using Grading of Recommendations Assessment, Development, and Evaluation (GRADE), the certainty of the evidence was assessed. Subgroup analysis was performed to explore the source of heterogeneity. Results: We included 12 RCTs examining 644 patients. Pain scores at rest at 24 h (standardised mean differences (SMDs): 0.17; 95% confidence interval (CI): -0.90 to 1.23; P = 0.76, moderate certainty) and during movement at 24 h (SMD: -0.58, 95% CI: -1.53 to 0.38, P = 0.24, moderate certainty) were not different in both PENG block and FICB. Pain scores at rest and during movement within 30 min may be lower with PENG block than FICB. However, the pain score at rest and during movement at 6 h and the time to first rescue analgesia were not different between the two treatment arms. The mean opioid consumption in oral morphine equivalents (mg) in 24 h may be lower with PENG than FICB. Conclusion: We observed no difference between the PENG block and the FICB at 24 h for pain at rest and movement with a moderate degree of certainty. However, PENG block showed improved analgesia within 30 min at rest and during movement, and reduce postoperative opioid consumption in 24 h with moderate certainty of evidence. Further large-scale and high-quality RCTs are required to supplement the present findings.

Classification of intracranial tumors based on optical-spectral analysis

The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combining various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceeding a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifying statistically significant spectral ranges for analysis of diagnostically important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementation of the automatic classification method. Importantly, the classification task can be used in two applications – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.

Safety Evaluation of Reinforced Concrete Structures Using Multi-Source Fusion Uncertainty Cloud Inference and Experimental Study.

Structural damage detection and safety evaluations have emerged as a core driving force in structural health monitoring (SHM). Focusing on the multi-source monitoring data in sensing systems and the uncertainty caused by initial defects and monitoring errors, in this study, we develop a comprehensive method for evaluating structural safety, named multi-source fusion uncertainty cloud inference (MFUCI), that focuses on characterizing the relationship between condition indexes and structural performance in order to quantify the structural health status. Firstly, based on cloud theory, the cloud numerical characteristics of the condition index cloud drops are used to establish the qualitative rule base. Next, the proposed multi-source fusion generator yields a multi-source joint certainty degree, which is then transformed into cloud drops with certainty degree information. Lastly, a quantitative structural health evaluation is performed through precision processing. This study focuses on the numerical simulation of an RC frame at the structural level and an RC T-beam damage test at the component level, based on the stiffness degradation process. The results show that the proposed method is effective at evaluating the health of components and structures in a quantitative manner. It demonstrates reliability and robustness by incorporating uncertainty information through noise immunity and cross-domain inference, outperforming baseline models such as Bayesian neural network (BNN) in uncertainty estimations and LSTM in point estimations.

Doctoral Student Experiences in Indonesian Postgraduate Programs: A Phenomenological Investigation of Challenges

This research answers academic anxiety that departs from a study of the complexity of the problems faced by doctoral students in Indonesia. The existing phenomenon is that doctoral students in Indonesia who are scattered in various public and private universities with various fields of knowledge still face many technical and non-technical problems caused by problems that are both academic and non-academic in nature. This research uses a qualitative descriptive approach with the phenomenological study method developed by Edmund Husserl. Data collection techniques use literature and field studies through interviews, documentation and observation. The primary data source is postgraduate students, and the secondary data source is the Postgraduate Director, Head of Study Program and administrative staff. Data analysis in this qualitative research consists of three streams of activities that occur simultaneously, namely data reduction, data presentation, drawing conclusions or verification. Data checking in this research is based on four criteria, namely, degree of trustworthiness, transferability, dependability, and certainty. The findings from this study include the theoretical level regarding the conceptualization of the Portrait of Doctoral Programs in Indonesia as an Educational Phenomenon and dissertation as Research Identity of Doctoral Programs (Standards and Characteristics). This research also found academic and non-academic problems for doctoral students in Indonesia. The implications of the results of this research are to provide recommendations for ideas for the academic community for making decisions and academic policies as well as formulating a quality education system and improving the quality assurance of postgraduate doctoral education in Indonesia.

Secondary sneaker market: Investigating the motives, activities, resources and capabilities of the triadic framework

PurposeThe purpose of this study was to investigate the elements of the triadic framework (e.g. consumer motives, activities, resources and capabilities) and their influence on consumer attitudes and the theory of reasoned action (TRA) elements of subjective norms and eventual purchase intention of secondary sneakers.Design/methodology/approachThe framework draws from two models, the triadic framework for collaborative consumption and the TRA. Data was collected using an online survey, which consisted of demographic questions and reliable scales. The hypothesized relationships were tested through structural equation modeling.FindingsThe findings indicate that reducing risks and the customer activity of providing information was significant in influencing consumers' attitudes. However, the customer motives of economic and environmental as well as technical skills were not significant in influencing attitudes.Practical implicationsThis study attempts to inform secondary and full-priced retailers offering used products strategies to provide the best experience to millennial and generation Z consumers to ensure they can attract and retain them.Originality/valueIt can be stated with a high degree of certainty that sneaker resale remains one of the most permanent trends in the market today with sneaker resale is significantly outperforming the broader ecommerce ecosystem suggesting more research into the motivations of secondary sneaker consumption.

ECMO in adult patients with severe trauma: a systematic review and meta-analysis

BackgroundSevere trauma can result in cardiorespiratory failure, and when conventional treatment is ineffective, extracorporeal membrane oxygenation (ECMO) can serve as an adjunctive therapy. However, the indications for ECMO in trauma cases are uncertain and clinical outcomes are variable. This study sought to describe the prognosis of adult trauma patients requiring ECMO, aiming to inform clinical decision-making and future research.MethodsA comprehensive search was conducted on Pubmed, Embase, Cochrane, and Scopus databases until March 13, 2023, encompassing relevant studies involving over 5 trauma patients (aged ≥ 16 years) requiring ECMO support. The primary outcome measure was survival until discharge, with secondary measures including length of stay in the ICU and hospital, ECMO duration, and complications during ECMO. Random-effects meta-analyses were conducted to analyze these outcomes. The study quality was assessed using the Joanna Briggs Institute checklist, while the certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.ResultsThe meta-analysis comprised 36 observational studies encompassing 1822 patients. The pooled survival rate was 65.9% (95% CI 61.3–70.5%). Specifically, studies focusing on traumatic brain injury (TBI) (16 studies, 383 patients) reported a survival rate of 66.1% (95% CI 55.4–76.2%), while studies non-TBI (15 studies, 262 patients) reported a survival rate of 68.1% (95% CI 56.9–78.5%). No significant difference was observed between these two survival comparisons (p = 0.623). Notably, studies utilizing venoarterial extracorporeal membrane oxygenation (VA ECMO) (15 studies, 39.0%, 95% CI 23.3–55.6%) demonstrated significantly lower survival rates than those using venovenous extracorporeal membrane oxygenation (VV ECMO) (23 studies, 72.3%, 95% CI 63.2–80.7%, p < 0.001). The graded assessment of evidence provided a high degree of certainty regarding the pooled survival.ConclusionsECMO is now considered beneficial for severely traumatized patients, improving prognosis and serving as a valuable tool in managing trauma-related severe cardiorespiratory failure, haemorrhagic shock, and cardiac arrest.

System identification and finite element model updating of a 6 MW offshore wind turbine using vibrational response measurements

Offshore wind energy is playing an increasingly vital role in the clean energy transition around the world, and improved reliability of wind turbine structures is necessary for the long-term success and efficiency of renewable energy. Increased reliability would reduce costs associated with maintenance due to breakages and in turn reduce the levelized cost of energy for offshore wind energy sources. Structural health monitoring methods can be used to predict breakages and extend lifetimes by continuously monitoring instrumented structures. This paper presents system identification and model updating of a 6 MW offshore wind turbine using vibration measurements under varying operational conditions. The turbine is monopile-supported and instrumented with strain gauges and accelerometers at several elevations along the tower and monopile. Effective stiffness of soil springs in the model are updated to match modal-predicted natural frequencies and mode shapes of the first two modes with those identified from measurements at different operating conditions. A deterministic and probabilistic (Bayesian) approach to model updating are compared. The sensitivity of identified modal parameters and the updated model parameters are investigated with respect to operational and environmental conditions such as wind speed. Results show that deterministic model updating can match modal parameters with high accuracy across datasets and environmental conditions. Bayesian model updating results successfully estimate the posterior distribution of updating model parameters with an increasing degree of certainty as more data is used.

Expert System To Determine Psychological Disorders In Chronic Kidney Failure (CKD) Patients Undergoing Hemodialysis Therapy Using Certainty Factor Method

Chronic Kidney Failure (CKD) is damage to the kidneys both in structure and/or function that lasts for 3 months or more. Hemodialysis is a prolonged therapy that can significantly impact the physical and psychological well-being of patients with chronic kidney disease. This therapy has a big effect on sufferers. The psychological impact that appears can affect the success of therapy so it is important to recognize these symptoms and provide appropriate treatment to overcome them. Based on research at Delia General Hospital, patients who will undergo Hemodialysis therapy must come to the hospital to receive comprehensive therapy by a doctor. Long patient queues when undergoing therapy can make patients tired and remember the patient's condition in order to get information and therapy. Handling of these problems can be overcome by building a system that can determine psychological disorders in patients. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques in solving problems that usually can only be solved by an expert in a particular field. Certainty Factor (CF) is a method capable of defining the degree of certainty of a rule or fact in describing an expert's belief in the problem at hand. With an expert system, it can help identify and determine early on psychological disorders in patients. From the results of trials conducted by expert systems to determine psychological disorders in patients with kidney failure using the Certainty Factor method, the highest value is depression with a percentage of 94.59%.&#x0D;

Safety and efficacy of enhanced recovery after surgery among patients undergoing percutaneous nephrolithotomy: protocol for a systematic review and meta-analysis

IntroductionEnhanced recovery after surgery is widely used in the perioperative period in the field of urology; however, it lacks comprehensive and systematic evidence supporting its efficacy and safety after percutaneous...

Determinants of risk readiness as a resource in women’s entrepreneurship

The growth of the scale of women’s entrepreneurship highlights the interest in studying both the specifics of the phenomenon itself and the resource potential of its main subject – women entrepreneurs. Entrepreneurship is built on risk, and the readiness for risk largely determines the success and stability of a business. The aim of the analysis in this article is to determine how the sufficiency/insufficiency of the resource of risk readiness in women’s entrepreneurship is linked to a range of individually subjectively evaluated dispositions in one or another coordinate system. The task was to assess the productivity of introducing the concept of disposition (status) in risk-related research and to determine the presence or absence of differentiation among holders and those in need of risk readiness as a resource in women’s entrepreneurship. The empirical basis of the analysis is the data from the “Russian Longitudinal Monitoring Survey of HSE (RLMS-HSE)”. The methodology of target group analysis is applied. The results of the exploratory study revealed differentiation in several indicators of emotional status, socialpsychological attitudes, financial status, value profile, and decision-making subjectivity in risk and uncertainty situations. Such results allow for considering them with a reasonable degree of certainty as interconnected with risk readiness. The methodological approach of considering dispositions as existing statuses of individuals in specific coordinate systems proved to be productive. A promising task in this regard is a deeper methodological and conceptual elaboration of the concept in connection with risk-related issues.

Measuring the Phytochemical Richness of Meat: Effects of Grass/Grain Finishing Systems and Grapeseed Extract Supplementation on the Fatty Acid and Phytochemical Content of Beef.

Grass-finished beef (GFB) can provide beneficial bioactive compounds to healthy diets, including omega-3 polyunsaturated fatty acids (n-3 PUFAs), conjugated linoleic acid (CLA), and secondary bioactive compounds, such as phytochemicals. The objective of this study was to compare fatty acids (FAs), micronutrients, and phytochemicals of beef fed a biodiverse pasture (GRASS), a total mixed ration (GRAIN), or a total mixed ration with 5% grapeseed extract (GRAPE). This was a two-year study involving fifty-four Red Angus steers (n = 54). GFB contained higher levels of n-3 PUFAs, vitamin E, iron, zinc, stachydrine, hippuric acid, citric acid, and succinic acid than beef from GRAIN and GRAPE (p < 0.001 for all). No differences were observed in quantified phytochemicals between beef from GRAIN and GRAPE (p > 0.05). Random forest analysis indicated that phytochemical and FA composition of meat can predict cattle diets with a degree of certainty, especially for GFB (5.6% class error). In conclusion, these results indicate that GFB contains higher levels of potentially beneficial bioactive compounds, such as n-3 PUFAs, micronutrients, and phytochemicals, compared to grain-finished beef. Additionally, the n-6:n-3 ratio was the most crucial factor capable of separating beef based on finishing diets.

Uncertainty-driven ensemble classification exploiting unlabeled data

This works investigates the use of margin and diversity, two key concepts in ensemble learning, to develop a versatile uncertainty-driven ensemble classifier, under the scarcity of labeled data. New semi-supervised definitions are proposed for both margin and diversity. To strengthen the semi-supervised classification scheme, classification accuracy estimates are accompanied by new robust ensemble metrics. These measures reflect the degree of certainty associated with the classification outcome of each pattern. They are calculated in a semi-supervised way. The proposed metrics are exploited in an original uncertainty-driven fusion process that combines multiple classification results. The relevance of these new criteria is examined in change detection experiments. The underlying fusion rule significantly improves the change detection performance.

Modal Auxiliaries as Epistemic Devices in Marking Scientific Researchers’ Uncertainty on Covid-19

The early phase of the COVID-19 pandemic caused a lot of pressure to scientists due to the novel nature of the coronavirus. As experts in the field, they were expected to produce only reliable information. Owing to the limited data available at the time, there were many uncertainties surrounding the virus. However, studies that looked into how the uncertainties were navigated are scarce. This corpus-based study investigates this issue using the system value of modal operators by Halliday and Matthiessen (2014), along with Dong et al.’s (2020) classification of COVID-19 research themes to explore levels of scientific researchers’ certainty in presenting information about coronavirus. Specifically, their choices of modal auxiliaries as epistemic devices are analysed. Results demonstrate that researchers mainly conduct studies on epidemiology with the lowest degree of certainty by utilising modals such as may, could and might. Furthermore, while some of the propositions expressed do display researchers’ assumptions of possibilities, they however, are presented with insufficient evidence. These findings help to untangle scientific researchers’ expressions of uncertainty and certainty through their use of epistemic devices and contribute to a better understanding of their intentions in conveying vital information.

An evidenced-based diagnostic tool for superior semicircular canal dehiscence syndrome

PurposeTo construct a symptoms-based prediction tool to assess the likelihood of superior canal dehiscence (SSCD) on high-resolution CT. Materials and methodsMathematical modeling was employed to predict radiologic evidence of SSCD at a tertiary neurotology referral center. ResultsA total of 168 patients were included, of which 118 had imaging-confirmed SSCD. On univariate analysis significant predictors of SSCD presence were: sound/pressure-induced vertigo (p = 0.006), disequilibrium (p = 0.008), hyperacusis (p = 0.008), and autophony (p = 0.034). Multivariate analysis enabled a 14-point symptom-weighted tool to be developed, wherein a score of ≥6 raised the suspicion of SSCD (≥70% likelihood of being present), R2 = 0.853. ConclusionsThe likelihood of SSCD on CT scan can be determined with a high degree of certainty based on symptoms recorded at presentation. Using the evidenced-based diagnostic tool validated herein, a score ≥6 with any symptom combination justifies ordering a CT scan.

Classification of Terms in Patent Law

The paper attempts to explore the complex of patent legal terms as a separate group of terms in civil law through the prism of the elements that make up this set. The classification was carried out in two stages. At the first stage, taking into account the approaches developed in the science of civil law, seven bases of division and the features of their application to patent legal terms are considered. At the second stage, five additional grounds for dividing patent legal terms into groups are proposed. The analysis of the elements identified as a result of the classification made it possible to formulate conclusions about the features of urgent regulation of patent-legal relations: a high degree of certainty and at the same time flexibility of temporary regulation; about the absence of certain deadlines and the presence of specific groups of deadlines in connection with the peculiarities of the subject of regulation of patent law and the focus of patent legislation on the creation of favorable conditions for scientific, scientific-technical and other types of creative activities.

Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang's Camera Calibration Method.

Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang's method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang's camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang's method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.

Does God Comfort You When You Are Sad? Religious Diversity in Children’s Attribution of Positive and Negative Traits to God

Children actively construct their understanding of God in early childhood, an understanding that incorporates affect-laden mental representations often referred to as God images. To explore religious variations in children’s association of positive and negative traits to God, 254 preschool-aged children from Protestant Christian, Catholic, Muslim, and Non-Affiliated religious backgrounds indicated their certainty that God scares them, punishes them, is angry at them, loves them, comforts them, and helps people. Parents indicated the frequency of children’s religious engagement. Older children were more certain than younger children that God did not scare or punish them and that God loved and comforted them, and helped people. Moreover, religious affiliation differences emerged in children’s attribution of both positively and negatively valenced properties to God, and more frequent religious engagement was related to a higher degree of certainty that God loves, comforts, helps, and becomes angry, but was unrelated to the certainty that God scares or punishes. The findings suggest that religious engagement plays an important role in children’s developing God image.