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Background: Accurate measurement of polyps size is crucial in predicting malignancy, planning relevant intervention strategies and surveillance schedules. Endoscopists' visual estimations can lack precision. This study builds on our prior research, with the aim to evaluate a recently developed quantitative method to measure the polyp size and location accurately during a simulated endoscopy session. Methods: The quantitative method merges information about endoscopic positions obtained from an electromagnetic tracking sensor, with corresponding points on the images of the segmented polyp border. This yields real-scale 3D coordinates of the border of the polyp. By utilising the sensor, positions of any anatomical landmarks are attainable, enabling the estimation of a polyp's location relative to them. To verify the method's reliability and accuracy, simulated endoscopies were conducted in pig stomachs, where polyps were artificially created and assessed in a test–retest manner. The polyp measurements were subsequently compared against clipper measurements. Results: The average size of the fifteen polyps evaluated was approximately 12 ± 4.3 mm, ranging from 5 to 20 mm. The test–retest reliability, measured by the Intraclass Correlation Coefficient (ICC) for polyp size estimation, demonstrated an absolute agreement of 0.991 (95% CI 0.973–0.997, p < 0.05). Bland & Altman analysis revealed a mean estimation difference of − 0.17 mm (− 2.03%) for polyp size and, a mean difference of − 0.4 mm (− 0.21%) for polyp location. Both differences were statistically non-significant (p > 0.05). When comparing the proposed method with calliper measurements, the Bland & Altman plots showed 95% of size estimation differences between − 1.4 and 1.8 mm (− 13 to 17.4%) which was not significant (p > 0.05). Conclusions: The proposed method of measurements of polyp size and location was found to be highly accurate, offering great potential for clinical implementation to improve polyp assessment. This level of performance represents a notable improvement over visual estimation technique used in clinical practice. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in today's urbanized world, necessitating efficient and accurate methods for air quality monitoring. The proliferation of IoT devices has led to a surge in the generation of time-series data. With its high volume and complexity, this surge in time-series data necessitates cloud-based solutions for handling and analyzing this data effectively. However, existing methods for air quality monitoring face challenges in capturing the complex patterns and dynamics of air pollution, which often exhibit both linear and nonlinear characteristics. Air pollution data often exhibit both linear and nonlinear characteristics. Linearity and nonlinearity refer to the nature of the relationships within the data. Some aspects of air quality, such as pollutant concentrations, may follow linear patterns, while other factors, like the interaction of multiple pollutants and environmental conditions, exhibit nonlinear relationships. This complexity arises from the multifaceted nature of air quality dynamics, which various factors and interactions can influence. To address these challenges, this study introduces a novel hybrid time-series approach that combines the proven strengths of two well-established techniques: traditional time-series autoregressive integrated moving average (ARIMA) and soft computing adaptive neuro-fuzzy inference system (ANFIS). The hybrid model is designed to provide a comprehensive solution that accommodates the diverse characteristics of air quality time-series data. To assess the efficacy of our proposed model, we conducted extensive experiments using real-world air pollution datasets obtained from the Ministry of Environment, Forest and Climate Change of India, covering the period from January 2015 to July 2020. Our evaluation includes a range of performance metrics such as root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared logarithmic error (MSLE). Specifically, our model demonstrates exceptional accuracy, with notably low error values for key metrics such as air quality index (AQI) and PM2.5. Furthermore, we subjected our innovative hybrid model to rigorous statistical testing using the Diebold-Mariano test, establishing the significance and superiority of our approach. This research advances our understanding of air quality prediction and offers a valuable solution for mitigating the detrimental effects of air pollution on public health and the environment. [ABSTRACT FROM AUTHOR]

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios. [ABSTRACT FROM AUTHOR]

The present in vitro study aimed to evaluate the influence of various surface treatments on the micro-shear bond strength (μSBS) of calcium silicate–based materials (CSMs) to resin composite. Two hundred and forty cylindrical acrylic molds were filled with Biodentine, NeoMTA Plus, NeoPUTTY MTA, TheraCal LC, and Well-Root PT. The specimens were divided into four groups: control with no surface treatment, acid-etching, tribochemical silica coating (TSC), sandblasting with aluminum oxide (AL). Resin composite was applied and photo-polymerized. The μSBS was measured and surface alterations were evaluated using a SEM. Data were examined using two-way ANOVA and Tukey tests at a significance level of 5%. The lowest bond strength values were detected in the controls. Moreover, the lowest μSBS was demonstrated in the untreated NeoMTA Plus group, whereas the aluminum oxide–treated TheraCal LC group exhibited the highest bond strength (p < 0.05). Cohesive failures were observed dominantly for Biodentine and TheraCal LC, adhesive failures in Well-Root PT, and mixed failures for NeoPUTTY MTA. Surface treatments increased the μSBS values of CSMs and affected the micromorphology of the sample surfaces. The study suggests that surface treatments would be advantageous on CSMs. [ABSTRACT FROM AUTHOR]

Background: Endoscopic submucosal dissection (ESD) was widely used for the removal of esophageal tumors, and post-endoscopic submucosal dissection electrocoagulation syndrome (PEECS) was one of the postoperative adverse events. The aim of this research was to develop and validate a model to predict electrocoagulation syndrome after endoscopic submucosal dissection of esophageal tumors. Materials and methods: Patients who underwent esophageal ESD in our hospital were retrospectively included. A predictive nomogram was established based on the results of multivariate logistic regression analysis, and bootstrapping resampling was used for internal validation. Besides, the clinical usefulness of the nomogram was evaluated using decision curve analysis (DCA) and clinical impact curve. Results: A total of 552 patients who underwent esophageal ESD were included in the study, and the incidence of PPECS was 12.5% (69/552). Risk factors associated with PEECS (p < 0.1) were analyzed by multivariate logistic regression analysis, and the final model included four variables, namely gender, diabetes, tumor size and operation time. The predictive nomogram was constructed based on the above four variables, and the area under the ROC curve (AUC) was 0.811 (95% CI 0.767–0.855). The calibration curve of the nomogram presented good agreement between the predicted and actual probabilities. DCA showed that the model improved patient outcomes by helping to assess the risk of PEECS in patients compared to an all-or-no treatment strategy. In addition, the clinical impact curve of the model also indicates that the nomogram has a high clinical net benefit. Conclusion: In conclusion, we have developed a predictive nomogram for PEECS after ESD for esophageal tumors with good predictive accuracy and discrimination. This predictive nomogram can be effectively used to identify high-risk patients with PEECS, which will help clinicians in clinical decision-making and early intervention. [ABSTRACT FROM AUTHOR]

Background: Psychological Clearance level (PCL) for patients undergoing metabolic and bariatric surgery (MBS) is a critical step for successful postoperative outcomes. This study aims to assess the relationship between the level of psychologic fitness and postoperative outcomes in patients undergoing MBS. Methods: We retrospectively analyzed the data of patients who underwent MBS (laparoscopic sleeve gastrectomy [LSG] and laparoscopic Roux-en-Y Gastric Bypass [LRYGB]) and completed two years follow-up, between 2012 and 2019, in a single medical center. The patients were divided into four groups based on PCL, suggesting level of readiness for surgery: Group A (PCL-0: guarded), group B (PCL-1: Fair/reasonable), group C (PCL-2: Good/appropriate), and group D (PCL-3: Strong/excellent). Primary outcome was the percent of total body weight loss (%TWL), and the absolute change in BMI units. Secondary outcomes were missed postoperative visits and patient compliance. Differences between the groups were analyzed using a generalized linear model (GLM), chi-squared and exact Fisher tests, as appropriate. Results: Of 1411 total patients, 607 (43.20%) had complete data at two years follow-up. 512 (84.34%) were females. LSG was performed in 361 (59.5%). No difference was found in %TWL between the four groups (22.14% vs. 28.0% vs. 26.0% vs. 24.8%, p = 0.118). We found a small difference in the mean (SD) of absolute change in BMI between the groups, and on post-hoc analysis it was found between groups B (PCL-1) and D (PCL-3). Overall, no difference between the groups in number of follow-up visits, or compliance issues. However, patients who attended more follow-up visits had less compliance issues (p < 0.001). PCL is inversely correlated with number of psychologic diagnoses (r = − 0.41, p < 0.001) and medical comorbidities (r = − 0.20, p < 0.001). Conclusion: We found no difference in the percent of TWL in patients who underwent MBS based on PCL at two -years follow-up. Medical comorbidities and psychiatric diagnoses impact the PCL. [ABSTRACT FROM AUTHOR]

Objectives: More than half of women do not achieve appropriate gestational weight gain. Maternal body image may be an important factor associated with gestational weight gain. However, this association has not been thoroughly evaluated. We aimed to elucidate whether body image parameters before and during pregnancy are associated with gestational weight gain in Japanese women. Methods: This prospective cohort study was conducted at a hospital in Osaka, Japan from March 2020 to March 2021. We recruited women with singleton pregnancies in their second and third trimesters. Body image was assessed using the Pregnancy and Weight Gain Attitude Scale and additional questions. Gestational weight gain was classified as insufficient, appropriate, or excessive based on recommended ranges in Japan. One-way analysis of variance, chi-square tests, and multinomial logistic regression analyses were performed to identify factors associated with insufficient or excessive weight gain. Results: Of 266 enrolled women, 47 had insufficient weight gain and 100 had excessive weight gain during pregnancy. Risk factors for excessive gestational weight gain included a history of dietary restriction before pregnancy, negative attitudes toward gestational weight gain, and perception of body shape as fat and body shape dissatisfaction during pregnancy. Perception of body shape as thin during pregnancy was identified as a risk factor for insufficient gestational weight gain. Conclusions: Body image before and during pregnancy may be an important factor in preventing insufficient or excessive gestational weight gain in Japanese women. Healthcare professionals should consider body image when providing health guidance on weight management to pregnant women. Significance: What is Already Known? Body image is a possible factor in gestational weight gain. Compared to women in other countries, Japanese women of reproductive age tend to have a negative body image. What this Study adds? A history of dietary restriction before pregnancy and negative body image during pregnancy were associated with excessive gestational weight gain among Japanese women. Perception of body shape during pregnancy as thin was associated with insufficient gestational weight gain. Thus, even in a Japanese population, characterized by a majority of women with pre-pregnancy underweight or normal body mass index status, associations between negative body image and gestational weight gain were similar to those in other countries. [ABSTRACT FROM AUTHOR]

On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize (Zea mays L.) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error < 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in > 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54). [ABSTRACT FROM AUTHOR]

This paper aims to interpret the connotation of high-quality development of tourism economy (HQTE) from the perspective of the new development concepts of innovation, coordination, green, openness and sharing, and then to evaluate the spatial differentiation of China's HQTE based on provincial panel data from 2009 to 2018. Specifically, we employ the spatial convergence model to explore the absolute and conditional β convergence trends of HQTE in the whole country and the eastern, central and western regions of China. Our empirical results reveal that: 1) within the decade, from 2009 to 2018, regions of China with the highest HQTE index is its eastern region followed by the central region and then the western region, but the fastest growing one is the western region of China followed by the central region and then the eastern region. 2) Whether or not the spatial effect is included, there are absolute and conditional β convergence in HQTE in the whole country and aforementioned three regions. 3) The degree of government attention as well as the level of economic development and location accessibility are the positive driving factors for the convergence of HQTE in the whole country and the three regions. The degree of marketization and human capital have not passed the significance test either in the whole country or in the three regions. The above conclusions could deepen the understanding of the regional imbalance and spatial convergence characteristics of HQTE, clarify the primary development objects, and accomplish the goal of China's HQTE. [ABSTRACT FROM AUTHOR]

Previous studies on Bayesian situations, in which probabilistic information is used to update the probability of a hypothesis, have often focused on the calculation of a posterior probability. We argue that for an in-depth understanding of Bayesian situations, it is (apart from mere calculation) also necessary to be able to evaluate the effect of changes of parameters in the Bayesian situation and the consequences, e.g., for the posterior probability. Thus, by understanding Bayes' formula as a function, the concept of covariation is introduced as an extension of conventional Bayesian reasoning, and covariational reasoning in Bayesian situations is studied. Prospective teachers (N=173) for primary (N=112) and secondary (N=61) school from two German universities participated in the study and reasoned about covariation in Bayesian situations. In a mixed-methods approach, firstly, the elaborateness of prospective teachers' covariational reasoning is assessed by analysing the arguments qualitatively, using an adaption of the Structure of Observed Learning Outcome (SOLO) taxonomy. Secondly, the influence of possibly supportive variables on covariational reasoning is analysed quantitatively by checking whether (i) the changed parameter in the Bayesian situation (false-positive rate, true-positive rate or base rate), (ii) the visualisation depicting the Bayesian situation (double-tree vs. unit square) or (iii) the calculation (correct or incorrect) influences the SOLO level. The results show that among these three variables, only the changed parameter seems to influence the covariational reasoning. Implications are discussed. [ABSTRACT FROM AUTHOR]

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems. [ABSTRACT FROM AUTHOR]

Air pollution is a significant environmental issue affecting public health and ecosystems worldwide, resulting from various sources such as industrial activities, vehicle emissions, and fossil fuel burning. Air pollution contributes to climate change and can cause several health problems, such as respiratory illnesses, cardiovascular disease, and cancer. A potential solution to this problem has been proposed by using different artificial intelligence (AI) and time-series models. These models are implemented in the cloud environment to forecast the Air Quality Index (AQI) utilizing Internet of things (IoT) devices. The recent influx of IoT-enabled time-series air pollution data poses challenges for traditional models. Various approaches have been explored to forecast AQI in the cloud environment using IoT devices. The primary objective of this study is to assess the efficacy of an IoT-Cloud-based model for forecasting the AQI under different meteorological conditions. To achieve this, we proposed a novel BO-HyTS approach that combines seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) and fine-tuned it by using Bayesian optimization to predict air pollution levels. The proposed BO-HyTS model can capture both linear and nonlinear characteristics of the time-series data, thus augmenting the accuracy of the forecasting process. Additionally, several AQI forecasting models, including classical time-series, machine learning, and deep learning, are employed to forecast air quality from time-series data. Five statistical evaluation metrics are incorporated to evaluate the effectiveness of models. While comparing the various algorithms among themselves becomes difficult, a non-parametric statistical significance test (Friedman test) is applied to assess the performance of the different machine learning, time-series, and deep learning models. The findings reveal that the proposed BO-HyTS model produced significantly better results than their competitor's, providing the most accurate and efficient forecasting model, with an MSE of 632.200, RMSE of 25.14, Med AE of 19.11, Max Error of 51.52, and MAE of 20.49. The results of this study provide insights into the future patterns of AQI in various Indian states and set a standard for these states as governments develop their healthcare policies accordingly. The proposed BO-HyTS model has the potential to inform policy decisions and enable governments and organizations to protect better and manage the environment beforehand. [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Recent research has highlighted the statistical significance and usefulness of trigonometric distribution models for simulating a variety of phenomena. A new set of distributions generated by exponentiated cosine and tangent functions has been introduced. Consequently, two families of distributions called the exponentiated cosine and tan Stacy distributions are derived. Some vital statistical and reliability characteristics are studied. The finite sample characteristics of parameter estimations are compared to the exponentiated cosine and tan distribution generated by four estimation methods: maximum likelihood, ordinary least squares, weighted least squares, and Cramer–von Mises. The methodologies are evaluated using simulation studies. Finally, the practical applications of the proposed trigonometric versions of the Stacy model are examined and applied on two lifetime reliability engineering datasets and another lifetime medical dataset concerning bladder cancer data. These real applications serve to highlight the effectiveness and adaptability of this innovative families within the respective fields compared with other lifetime well-known distribution models. [ABSTRACT FROM AUTHOR]

We conducted three studies to explore how job candidates perceive and respond to hiring managers who commit gender role violations during salary negotiations, extending previous studies restricted to economic outcomes and backlash effects by focusing on a range of job candidate reactions. We apply expectancy violation and role incongruity theories to suggest that job candidates respond more strongly to hiring managers who violate gender role expectations than those who conform to expectations. We suggest that this reaction is more negative in response to agentic female hiring managers than any other type and more positive in response to communal male hiring managers than any other type. Across the three studies, the pattern of mean differences supports our predictions, although significance tests were not fully supportive across all outcomes and studies. Job candidates perceived hiring managers as more extreme on characteristics that violate gender role expectations (agentic female hiring managers were perceived to be more agentic than male counterparts, communal male hiring managers were perceived to be more communal than female counterparts). Job candidates were also less engaged with an agentic female hiring manager than an agentic male hiring manager. Significance tests were mixed for negative affect, self-efficacy, and trust, but mean differences were mostly consistent with the expectation that those negotiating with an agentic female hiring manager would experience the least favorable outcomes, whereas those negotiating with a communal male hiring manager would experience the most favorable outcomes. These results demonstrate that gender role violations extend beyond their impact on the violator (e.g., backlash), dramatically influencing job candidates on a range of outcomes as well. [ABSTRACT FROM AUTHOR]

A rapid expansion in the world's population needs an enormous supply of food grains to satisfy agricultural needs. Unfortunately, crop diseases adversely impact food production and disrupt the supply chain. To circumvent the limitations of continuous human monitoring, machine learning techniques can automatically diagnose leaf diseases in their early stages based on image data. In this paper, an Interpretable Leaf Disease Detector (I-LDD) framework for image-based leaf disease detection using Extreme Learning Machine (ELM) is proposed. The choice of ELM for this work has been motivated by its quicker convergence, good generalization capability, and shorter learning time compared to the standard gradient-based learning algorithms. Experiments have been carried out using a publicly available PlantVillage dataset comprising healthy and diseased leaf images for 32 categories. In the first phase, the leaf images are preprocessed and segmented using the k-means clustering algorithm. In the second phase, textural and frequency-based features are extracted from the segmented images. In the third phase, several machine learning classifiers are trained using tenfold cross-validation. It is observed that I-LDD achieves an accuracy of 0.9322 ± 0.0088 at a 95% confidence level, outperforming the state-of-the-art methods. Moreover, a statistical significance test on the classification performance metrics also revealed the superiority of I-LDD over the state-of-the-art classifiers. Further, Local Interpretable Model-agnostic Explanations (LIME) is used to obtain the top 10 superpixels that contributed most to the class predicted by I-LDD. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, therefore understanding arterial stiffness is essential to developing innovative technologies to detect, monitor and treat them. The ubiquitous spread of photoplethysmography (PPG), a completely non-invasive blood-volume sensing technology suitable for all ages, highlights immense potential for arterial stiffness assessment in the wider healthcare setting outside specialist clinics, for example during routine visits to a General Practitioner or even at home with the use of mobile and wearable health devices. This study employs a custom-manufactured in vitro cardiovascular system with vessels of varying stiffness to test the hypothesis that PPG signals may be used to detect and assess the level of arterial stiffness under controlled conditions. Analysis of various morphological features demonstrated significant (p < 0.05) correlations with vessel stiffness. Particularly, area related features were closely linked to stiffness in red PPG signals, while for infrared PPG signals the most correlated features were related to pulse-width. This study demonstrates the utility of custom vessels and in vitro investigations to work towards non-invasive cardiovascular assessment using PPG, a valuable tool with applications in clinical healthcare, wearable health devices and beyond. [ABSTRACT FROM AUTHOR]

It is well known that the urban systems, in particular cities, display scaling behaviour regarding socio-economic, infrastructural and individual basic services indicators. However, understanding urbanisation and the links between rural and urban areas is fundamental to making the most of the global transformations happening around the world. In this context, it is important to study the scaling laws based on both the urban and rural regions, going beyond cities. This paper explores the extension of the idea of allometric urban scaling law to study the scaling behaviour of Indian districts, with both the urban and rural population. To proceed, we have chosen districts (both rural and urban) of India, a relatively larger local administrative units, which are more or less independently functional within a country. This interdisciplinary work focus on the scaling analysis of various socio-economic indicators (SEIs) corresponding to the size (population) of four distinct urbanisation classes, namely rural, semi-rural, semi-urban and urban districts. The scaling exponents (β ) were estimated for each classes for the years 2001 and 2011 along with their goodness-of-fit measured by the R 2 values. Our rigorous statistical analysis indicates that the scaling laws indeed exist even at the district level for most of the SEIs considered, related to education, employment, housing, health, etc.; the R 2 values obtained for these SEIs are very high (often greater than 0.8 or 0.9) in both the years. Moreover, linearity of the scaling factors have been statistically tested and it has been found, at 95% level of confidence, that not all the SEIs behave linearly ( β = 1 ); some of them are characterised by super-linear ( β > 1 ) behaviour and some behave sub-linearly ( β < 1 ). Statistical hypothesis tests have also been performed to test the equality of two scaling factors corresponding to two distinct classes and two different years to understand the differences in scaling relationships amongst increasing urbanisation classes and their changes over time. [ABSTRACT FROM AUTHOR]

The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman's Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen's Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies. [ABSTRACT FROM AUTHOR]

Purpose: To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs). Methods: For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture. Results: In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907–0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963–0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707–0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642–0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax. Conclusions: Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability. [ABSTRACT FROM AUTHOR]

Objective: This study assessed the outcome of shaping the mesial roots of mandibular first molars containing band-shaped isthmuses with an isthmus floor located at the apical third with Reciproc Blue and Rotate instruments in terms of canal transportation, canal aberrations and unprepared apical canals. Materials and methods: Pair-matched mesial roots of mandibular first molars according to their isthmus location (distance from apical foramina) and size were constructed based on quantitative measurements (length, major and minor diameter at isthmus roof and floor) on preoperative micro-CT scanning (n = 10). The pair-matched specimens were prepared either using Reciproc Blue R25 (25.08v) or Rotate (15.04, 20.05, 25.06) instruments and then subjected to a second micro-CT scanning. Micro-CT scans were analysed to evaluate the canal transportation at the apical foramen and isthmus floor levels and the frequency of procedural errors (separated instruments, artificial canal preparation, cross shaping and unprepared apical canals). Canal transportation values were compared using Mann–Whitney U, and the distribution of procedural errors was compared with chi-square tests with a 5% significance threshold. Results: Greater transportation values were detected in the apical foramen level compared to the isthmus floor in both groups. No significant difference was detected between Rotate and Reciproc Blue groups in terms of absolute transportation values and the incidence of procedural errors (p >.05). Conclusion: Preparation of mesial root canals containing a band-shaped isthmus at the apical portion with Reciproc Blue and Rotate instruments caused transportation of the original canal position at the isthmus floor and the apical foramen levels and resulted in procedural errors. Clinical relevance: This ex vivo micro-CT study using controlled pair-matched specimens showed that presence of a band-shaped isthmus complicates canal preparation procedure irrespective of the preparation systems. [ABSTRACT FROM AUTHOR]

The Encontro das Águas State Park (EASP), renowned as the world's largest refuge for Jaguars (Panthera onca), is located within the Brazilian portion of the Pantanal biome, and it covers a vast area of approximately 1,080 square kilometers. This ecologically rich region suffered significant devastation from extensive fires in 2020. Given that the ongoing monitoring of wildfires is a crucial task for the preservation of fauna and flora in legally protected environments such as the Pantanal biome, this paper investigates the catastrophic 2020 fire incidents in the EASP reserve through a fully automated methodology capable of detecting and characterizing fire-devastated areas. By taking updated and accurate data from the Google Earth Engine platform, our approach integrates a comprehensive collection of MODIS sensor images, spectral indices, and filtering processes to generate a spatial map of fire-affected areas in a given period of analysis. Specifically, given a surface reflectance and atmospheric corrected MODIS (MOD09Q/A1) image series, the NBR index is computed from each image and then processed through Savitzky-Golay filtering to remove noisy and missing data. Next, the Δ NBR index is calculated for each consecutive pair of images so as to produce a frequency map of burned areas. In order to quantify and analyze the recent changes due to these successive wildfires that took place in this Pantanal portion, we focused on the devastating fire events that occurred in the EASP park from July to September 2020. The fire mappings were assessed and statistically validated using the kappa coefficient and significance tests computed through reference samples collected from official databases and visual inspection. The findings revealed that, tragically, 84% of the study area experienced at least one instance of fire during the three-month investigation period. The high temporal resolution of MODIS sensors proves to be extremely valuable in promptly and effectively detecting changes in land use. [ABSTRACT FROM AUTHOR]

Statistical inference is the process of drawing conclusions about underlying population(s) using sample data to either confirm or falsify hypotheses. However, the complexity of real-life problems often makes the underlying statistical models inadequate, as information is often imprecise in many respects. To address this common problem, some papers have been published on modifications and extensions of test concepts by employing tools of fuzzy statistics. In this paper, we present a non-parametric test for the difference between quantiles of two independent populations based on fuzzy random variables. For this purpose, we consider the fuzzy quantile function and its estimation based on α -values of fuzzy random variables. We then provide a fuzzy test based on the fuzzy empirical distribution function for the difference of fuzzy order statistics from these independent populations. We also suggest a specific degree-based criterion to compare the fuzzy test statistics at a specific significance level to decide whether the underlying fuzzy null hypothesis can be rejected or not. The effectiveness of the proposed two-sample test on quantiles is investigated via numerical examples. [ABSTRACT FROM AUTHOR]

Purpose: The European and Japanese system for cholesteatoma classification proposed an anatomical differentiation in five sites. In stage I disease, one site would be affected and in stage II, two to five. We tested the significance of this differentiation by analyzing the influence of the number of affected sites on residual disease, hearing ability and surgical complexity. Methods: Cases of acquired cholesteatoma treated at a single tertiary referral center between 2010-01-01 and 2019-07-31 were retrospectively analyzed. Residual disease was determined according to the system. The air–bone gap mean of 0.5, 1, 2, 3 kHz (ABG) and its change with surgery served as hearing outcome. The surgical complexity was estimated regarding the Wullstein's tympanoplasty classification and the procedure approach (transcanal, canal up/down). Results: 513 ears (431 patients) were followed-up during 21.6 ± 21.5 months. 107 (20.9%) ears had one site affected, 130 (25.3%) two, 157 (30.6%) three, 72 (14.0%) four and 47 (9.2%) five. An increasing number of affected sites resulted in higher residual rates (9.4–21.3%, p = 0.008) and surgical complexity, as well poorer ABG (preoperative 14.1 to 25.3 dB, postoperative 11.3–16.8 dB, p < 0.001). These differences existed between the means of cases of stage I and II, but also when only considering ears with stage II classification. Conclusion: The data showed statistically significant differences when comparing the averages of ears with two to five affected sites, questioning the pertinence of the differentiation between stages I and II. [ABSTRACT FROM AUTHOR]

Quickly intensifying tropical cyclones (TCs) are exceptionally hazardous for Atlantic coastlines. An analysis of observed maximum changes in wind speed for Atlantic TCs from 1971 to 2020 indicates that TC intensification rates have already changed as anthropogenic greenhouse gas emissions have warmed the planet and oceans. Mean maximum TC intensification rates are up to 28.7% greater in a modern era (2001–2020) compared to a historical era (1971–1990). In the modern era, it is about as likely for TCs to intensify by at least 50 kts in 24 h, and more likely for TCs to intensify by at least 20 kts within 24 h than it was for TCs to intensify by these amounts in 36 h in the historical era. Finally, the number of TCs that intensify from a Category 1 hurricane (or weaker) into a major hurricane within 36 h has more than doubled in the modern era relative to the historical era. Significance tests suggest that it would have been statistically impossible to observe the number of TCs that intensified in this way during the modern era if rates of intensification had not changed from the historical era. [ABSTRACT FROM AUTHOR]

Equine piroplasmosis is reported to be mostly endemic in tropical and subtropical regions of the world including Africa. The disease is of major negative economic significance by affecting the trade of horses as well as leading to poor performance of horses and death. The study was aimed at ascertaining the prevalence of equine piroplasmosis and its associated hematological changes in horses in the Accra Metropolis. Simple random sampling technique was used to sample a total of 101 horses from the Shangrilla, Osu, and the Burma Camp. Whole blood samples were obtained from the jugular vein of all sampled horses and subjected to microscopy and complete blood count analysis. Data collected was subjected to descriptive statistics and independent t-test using Statistical Package for Social Sciences (SPSS) version 26.0. Statistical significance was tested at a 5% significance level (P-value ≤ 0.05). The overall prevalence of equine piroplasmosis was 3.96% of which Theileria spp. was the only piroplasm identified. The mixed breed of horses was most infected as compared to the foreign and indigenous breeds. The location of horses had a significant effect on the prevalence of equine piroplasmosis. With respect to the blood parameters, there was a significant reduction in the packed cell volume (PCV) for the infected horses as compared to the uninfected horses. Equine piroplasms exist in horses in Ghana; hence, further studies should be carried out in order regions to obtain a nationwide view of equine piroplasms in Ghana using serology and molecular diagnostic techniques. [ABSTRACT FROM AUTHOR]

Purpose: To assess the influence of time of day when a study is interpreted on discrepancy rates for common and advanced studies performed in the acute community setting. Methods: This retrospective study used the databank of a U.S. teleradiology company to retrieve studies between 2012 and 2016 with a preliminary report followed by a final report by the on-site client hospital. Neuroradiology, abdominal radiology, and musculoskeletal radiology studies were included. Teleradiologists were fellowship trained in one of these subspecialty areas. Daytime, evening, and overnight times were defined. Associations between major and minor discrepancies, time of day, and whether the study was common or advanced were tested with significance set at p =.05. Results: A total of 5,883,980 studies were analyzed. There were 8444 major discrepancies (0.14%) and 17,208 minor discrepancies (0.29%). For common studies, daytime (0.13%) and evening (0.13%) had lower major discrepancy rates compared to overnight (0.14%) (daytime to overnight, RR = 0.57, 95%CI: 0.45, 0.72, p < 0.01 and evening to overnight, RR = 0.57, 95%CI: 0.49,0.67, p < 0.01). Minor discrepancy rates for common studies were decreased for evening (0.29%) compared to overnight (0.30%) (RR = 0.89, 95%CI: 0.80,0.99, p = 0.029). For advanced studies, daytime (.15%) had lower major discrepancy rates compared to evening (0.20%) and overnight (.23%) (daytime to evening, RR = 0.77, 95%CI: 0.61, 0.97, p = 0.028 and daytime to overnight, RR = 0.66, 95%CI: 0.50, 0.87, p ≤ 0.01). Conclusion: Significantly higher major discrepancy rates for studies interpreted overnight suggest the need for radiologists to exercise greater caution when interpreting studies overnight and may require practice management strategies to help optimize overnight work conditions. The lower major discrepancy rates on advanced studies interpreted during the daytime suggest the need for reserving advanced studies for interpretation during the day when possible. [ABSTRACT FROM AUTHOR]

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. Critical relevance statement: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. Key points: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the impact of unobserved heterogeneity on microfinance social efficiency analysis. Based on recent nonparametric techniques and directional distances, we identify a latent heterogeneity factor related to the microfinance institute (MFI) manager's ability to promote women, independent of MFI size. We test for the significance of this unobserved factor and analyze the impact of MFI social inefficiency measures. Using a cross-country sample of 501 MFIs in 2011 from six main regions of the world, our findings reveal a significant effect of unobserved heterogeneity on the frontier and hence stress the importance of subjective factors in defining the set of production possibilities. We assess the robustness of our findings with the considered profit-oriented status and analyze the link between our unobserved heterogeneity factor and institutional and socioeconomic indicators. [ABSTRACT FROM AUTHOR]

With the emergence of digitilization, numeric prediction has become a prominent problem in various fields including finance, engineering, industry, and medicine. Among several machine learning methods, regression tree is a widely preferred method due to its simplicity, interpretability and robustness. Motivated by this, we introduce a novel ensemble regression tree based methodology, namely LOF-BRT+OR. The proposed methodology is an integrated solution approach with outlier removal, regression tree and ensemble learning. First, irregular data points are removed using local outlier factor (LOF), which measures the degree of being an outlier for each point. Next, a novel regression tree with LOF weighted node model is introduced. In the proposed node model, the weights of the points in the nodes are determined according to their surrounding neighborhood, as a function of LOF values and neighbor ranks. Finally, in order to increase the prediction performance, ensemble learning is adopted. In particular, bootstrap aggregation is used to generate multiple regression trees with LOF weighted node model. The experimental study shows that the proposed methodology yields the best root mean squared error (RMSE) values in five out of nine data sets. Also, the non-parametric tests demonstrate the statistical significance of the proposed approach over the benchmark methods. The proposed methodology can be applicable to various prediction problems. [ABSTRACT FROM AUTHOR]

Preoperative homeostasis of sex hormones in testicular germ cell tumor (TGCT) patients is scarcely characterized. We aimed to explore regulation of sex hormones and their implications for histopathological parameters and prognosis in TGCT using a data-driven explorative approach. Pre-surgery serum concentrations of luteinizing hormone (LH), follicle-stimulating hormone (FSH), testosterone (T), estradiol (E2) and prolactin were measured in a retrospective multicenter TGCT cohort (n = 518). Clusters of patients were defined by latent class analysis. Clinical, pathologic and survival parameters were compared between the clusters by statistical hypothesis testing, Random Forest modeling and Peto-Peto test. Cancer tissue expression of sex hormone-related genes was explored in the publicly available TCGA cohort (n = 149). We included 354 patients with pure seminoma and 164 patients with non-seminomatous germ cell tumors (NSGCT), with a median age of 36 years. Three hormonal clusters were defined: 'neutral' (n = 228) with normal sex hormone homeostasis, 'testicle' (n = 91) with elevated T and E2, low pituitary hormones, and finally 'pituitary' subset (n = 103) with increased FSH and LH paralleled by low-to-normal levels of the gonadal hormones. Relapse-free survival in the hormonal subsets was comparable (p = 0.64). Cancer tissue expression of luteinizing hormone- and follicle-stimulating hormone-coding genes was significantly higher in seminomas, while genes of T and E2 biosynthesis enzymes were strongly upregulated in NSGCT. Substantial percentages of TGCT patients are at increased risk of sex hormone dysfunction at primary diagnosis before orchiectomy. TGCT may directly influence systemic hormonal homeostasis by in-situ synthesis of sex hormones. [ABSTRACT FROM AUTHOR]

Sexual dysfunction (SD) is a common symptom for many with multiple sclerosis (MS). However, most studies researching this topic have predominantly White samples. While this matches epidemiological estimates, research has shown that those of other ethnic groups, such as African Americans/Blacks and Hispanics/Latinos, tend to exhibit more symptomology. This study aimed to determine if this greater symptomology in other ethnic groups with MS holds for SD. Study participants were recruited from the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. Participants completed the MSISQ-19 and the PDDS and provided demographic information. Study participants were 5445 respondents (Mage = 51 years, SD = 9.56, 75% female, 93% White). White, African American/Black, and Hispanic/Latino groups were compared on sexual dysfunction. Linear mixed effects modeling revealed that the MSISQ-19 subscale scores were significantly different for the African American/Black group (t = − 2.48, p < 0.013) and all female participants (t = − 2.58, p < 0.010). African American/Black participants had the least amount of primary SD (M = 10.56, 95% CI [9.09, 12.03]). African American/Black participants had the highest amount of secondary SD (M = 18.62, 95% CI [17.15, 20.09]), though the result was not statistically significant. Additionally, African American/Black females scored higher in all subscale scores when compared to African American/Black males, with a 3-point and 2-point difference in primary and tertiary symptoms. Our study has highlighted the importance of clinicians assessing for SD within their patients and to consider potential differences in symptom presentations of racial groups. [ABSTRACT FROM AUTHOR]

The task of comparison testing of time series power spectrum parametric estimates is considered. It is shown that an urgent problem that arises in solving it is the optimization of the parameters of spectral estimates under conditions of small samples of observations. To overcome this problem, it is proposed to use the system-wide concept of analysis through time series synthesis. Based on this concept, a regular method was developed for comparison testing of parametric estimates of the power spectrum that are obtained from a time series of finite duration. Within the method, solutions are accepted based on the results of testing statistical hypotheses about the homogeneity of two samples: the finite empirical one, compiled based on the results of observations, and an infinite virtual one, mathematically synthesized according to each individual parametric estimate in the series of spectral alternatives being examined. The principle of minimum informational divergence between samples according to the Kullback–Leibler criterion. An example of the practical application of the developed method is presented in the problem of discrete spectral modeling of speech signals. The ability of the method to identify patterns of unstable parametric estimates of the autoregressive type has been shown. The results obtained are intended for use in the field of speech acoustics, as well as technical and medical diagnostics, where parametric methods of spectral analysis are increasingly used in practice. [ABSTRACT FROM AUTHOR]

Background: Primary headache disorders are a group of highly prevalent and disabling neurological diseases that mainly consist of migraine and tension-type headache (TTH). A previous study showed that the burden of headaches peaked at a working age that ranged from 15 to 49, particularly among females, affecting their productivity and severely damaging their social interactions. Methods: The latest dataset was retrieved from the Global Burden of Disease (GBD) Study 2019. Three indicators, including prevalence, incidence, and years lived with disability (YLDs), were adopted for evaluation. The overall and specific headache burdens were fully compared and analysed at global, regional, and national levels. The ratio of female YLD rates to male YLD rates due to headaches was calculated to estimate the sex pattern. Finally, we utilized the two-tailed Spearman test to explore the potential association between socioeconomic background and headaches among young people. Results: Globally, for overall headache disorders, a total of 2,049,979,883 prevalent cases (95% uncertainty interval (UI): 1,864,148,110 to 2,239,388,034), 601,229,802 incident cases (95% UI: 530,329,914 to 681,007,934), and 38,355,993 YLDs (95% UI: 7,259,286 to 83,634,503) were observed for those aged 10 to 54 in 2019. Sex differences were widely found for all headache types among adolescents and young adults, especially migraine. However, the most interesting finding was that the associations we tested between the socioeconomic environment and young headache patients were positive, regardless of region or specific country or territory. Conclusions: Overall, the global burden of headaches in adolescents and young adults largely increased from 1990 to 2019. Although slight declines were observed in sex differences, they remained significant and challenging. The positive correlations between headache and socioeconomic background among young people were relatively inconsistent with previous investigations, and several related hypotheses were proposed for explanation. Interdisciplinary actions involving education, policy- and law-making, and basic medical practice are desperately needed to further fight against the headache burden, promote gender equality in headache care, and eliminate the stigmatization of headache patients in student and working groups. [ABSTRACT FROM AUTHOR]

The ensemble empirical mode decomposition (EEMD) has become a preferred technique to decompose nonlinear and non-stationary signals due to its ability to create time-varying basis functions. However, current EEMD signal cleaning techniques are unable to deal with situations where a signal only occurs for a portion of the entire recording length. By combining change point detection and statistical hypothesis testing, we demonstrate how to clean a signal to emphasize unique local changes within each basis function. This not only allows us to observe which frequency bands are undergoing a change, but also leads to improved recovery of the underlying information. Using this technique, we demonstrate improved signal cleaning performance for acoustic shockwave signal detection. [ABSTRACT FROM AUTHOR]

Background: Criteria to diagnose gastroesophageal reflux disease (GERD) vary. The American Gastroenterology Association (AGA) 2022 Expert Review on GERD focuses on acid exposure time (AET) rather than DeMeester score from ambulatory pH testing (BRAVO). We aim to review outcomes following anti-reflux surgery (ARS) at our institution, grouped by differing criteria for the diagnosis of GERD. Methods: A retrospective review of a prospective gastroesophageal quality database was performed for all patients undergoing evaluation for ARS with preoperative BRAVO ≥ 48 h. Group comparisons were made using two-tailed Wilcoxon rank-sum and Fisher's exact tests and two-tailed statistical significance of p < 0.05. Results: Between 2010 and 2022, 253 patients underwent an evaluation for ARS with BRAVO testing. Most patients (86.9%) met our institution's historical criteria: LA C/D esophagitis, Barrett's, or DeMeester ≥ 14.72 on 1+ days. Fewer patients (67.2%) met new AGA criteria: LA B/C/D esophagitis, Barrett's, or AET ≥ 6% on 2+ days. Sixty-one patients (24%) met historical criteria only, with significantly lower BMI, ASA, less hiatal hernias, and less DeMeester and AET-positive days, a less severe GERD phenotype. There were no differences between groups in perioperative outcomes or % symptom resolution. Objective GERD outcomes (need for dilation, esophagitis, and postop BRAVO) were equivalent between groups. Patient-reported quality of life scores, including GERD-HRQL, RSI, and Dysphagia Score did not differ between groups from preop through 1 year postop. Those who met our historical criteria only reported significantly worse RSI scores (p = 0.03) and worse GERD-HRQL scores at 2 years postop, though not statistically significant (p = 0.07). Conclusion: Updated AGA GERD guidelines exclude a portion of patients who historically would have been diagnosed with and surgically treated for GERD. This cohort appears to have a less severe GERD phenotype but equivalent outcomes up to 1 year, with more atypical GERD symptoms at 2 years postop. AET may better define who should be offered ARS than DeMeester score. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the influence of antenatal factors such as anxiety, depression, perceived stress, marital satisfaction, maternal antenatal attachment, and social support on postnatal maternal attachment and competence in women who received assisted reproductive treatment. A prospective longitudinal cohort design was adopted with two groups—50 women who received assisted reproductive treatment and 50 women who had natural conception. Both the groups were assessed using self-report measures over three time points: T1, 7th month of pregnancy; T2, 2 weeks postpartum; and T3, 3 months postpartum. A final sample of 44 women who had assisted conception and 47 women who had natural conception completed assessments across all three time points. Descriptive, bivariate analyses, and stepwise multiple linear regression analyses were carried out. In the assisted conception group, maternal antenatal attachment, depression, and marital satisfaction significantly predicted postnatal maternal-infant attachment. Perceived social support, depression, and duration of marriage significantly predicted postnatal maternal competence. In the naturally conceived group, maternal antenatal attachment and social support significantly predicted postnatal maternal-infant attachment; perceived stress significantly predicted postnatal maternal competence. Antenatal depressive symptoms and relational factors significantly influenced postnatal maternal attachment and competence highlighting the need for screening and targeted psychological interventions during pregnancy. [ABSTRACT FROM AUTHOR]

Trend analysis benefits collecting and analyzing reliable data in climate change studies. In this context, long-term temperature and precipitation data analysis, two variables sensitive to climate change, is essential. This study aims to holistically and partially determine the annual trends of precipitation and temperature time series for 1970–2019 at nine selected stations in the Susurluk Basin, Turkey. The innovative trend significance test (ITST), innovative crossing trend analysis (ICTA) method, and Mann-Kendall (MK) test were used to determine holistic trends. Also, partial trends were determined using successive average methodology (SAM). For the precipitation variable, while an increasing trend was determined for the ITST, there was mostly no trend for the other methods. While a strong increasing trend was detected for the temperature according to the ITST and MK methods, no trend was observed in any station according to the ICTA method. According to the SAM results, the maximum trend durations for the peak and trough change points were 4.9 (10.3) and 5.3 (8.4) years, respectively, for precipitation (temperature). The strong temperature trends in the basin will likely continue, requiring precautions against extreme events such as drought. [ABSTRACT FROM AUTHOR]

Introduction and hypothesis: Predictors of surgical outcomes in patients with an obstetric fistula who have been operated before should be identified in order to guide surgical strategy and optimize counseling of the patient. Methods: This retrospective study is aimed at identifying predictors of outcomes for repeat surgery in 346 patients who had been operated on before for an obstetrics fistula at the Fistula Care Center (FCC) in Lilongwe, Malawi. Repeat cases were only undertaken by advanced and expert surgeons. The primary outcome was successful anatomical closure, based on a negative postoperative dye test. The secondary outcomes involved urinary continence, based on a patient-reported questionnaire and an objective 1-h pad weight test. Logistic regression models were used to test the predictors for statistical significance. Results: Successful fistula closure was achieved in 288 (83%) patients and continence was achieved in 185 (64%) patients after the first repeat attempt at the FCC. Lack of urethral involvement (Goh classification: proximity to the urethra) was shown to be a good predictor of the outcomes: fistula closure and subjective and objective continence. Conclusions: Absence of urethral involvement is an independent predictor for successful outcomes in repeat surgery for obstetric fistulas. Even in the hands of an expert surgeon, the risk of another failure in achieving anatomical closure or subjective or objective continence is between 4 and 5 times higher than when the urethra is not involved. [ABSTRACT FROM AUTHOR]

Introduction: Interpretation and analysis of NMR-based metabolic profiling studies is limited by substantially incomplete commercial and academic databases. Statistical significance tests, including p-values, VIP scores, AUC values and FC values, can be largely inconsistent. Data normalization prior to statistical analysis can cause erroneous outcomes. Objectives: The objectives were (1) to quantitatively assess consistency among p-values, VIP scores, AUC values and FC values in representative NMR-based metabolic profiling datasets, (2) to assess how data normalization can impact statistical significance outcomes, (3) to determine resonance peak assignment completion potential using commonly used databases and (4) to analyze intersection and uniqueness of metabolite space in these databases. Methods: P-values, VIP scores, AUC values and FC values, and their dependence on data normalization, were determined in orthotopic mouse model of pancreatic cancer and two human pancreatic cancer cell lines. Completeness of resonance assignments were evaluated using Chenomx, the human metabolite database (HMDB) and the COLMAR database. The intersection and uniqueness of the databases was quantified. Results: P-values and AUC values were strongly correlated compared to VIP or FC values. Distributions of statistically significant bins depended strongly on whether or not datasets were normalized. 40–45% of peaks had either no or ambiguous database matches. 9–22% of metabolites were unique to each database. Conclusions: Lack of consistency in statistical analyses of metabolomics data can lead to misleading or inconsistent interpretation. Data normalization can have large effects on statistical analysis and should be justified. About 40% of peak assignments remain ambiguous or impossible with current databases. 1D and 2D databases should be made consistent to maximize metabolite assignment confidence and validation. [ABSTRACT FROM AUTHOR]

Metaheuristics are intelligent optimization techniques that lead the searching procedure through utilizing exploitation and exploration. Increasing the number of hard problems with big data sets has encouraged researchers to implement novel metaheuristics and hybrid the existing ones to improve their performance. Hence, in this work, a novel metaheuristic called group learning algorithm is proposed. The main inspiration of the algorithm emerged from the way individuals inside a group affect each other, and the effect of group leader on group members. The two main steps of optimization, exploration and exploitation are outlined through integrating the behaviors of group members and the group leader to complete the assigned task. The proposed work is evaluated against a number of benchmarks. The produced results of classical benchmarks are compared against PSO, GWO, TLBO, BA, ALO, and SSA. In general, compared to other participated algorithms, out of 19 classical benchmarks, the proposed work showed better results in 11. However, the second best algorithm which is SSA performed better in 4 out of 19 benchmarks. To further evaluate the ability of the algorithm to optimize large scale optimization problems CEC-C06 2019 benchmarks are utilized. In comparison to other participated algorithms, the proposed work produced better results in most of the cases. Additionally, the statistical tests confirmed the significance of the produced results. The results are evidence that the proposed algorithm has the ability to optimize various types of problems including large scale optimization problems. [ABSTRACT FROM AUTHOR]

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further. Specifically, we used Spectral embedding, Multidimensional scaling, Isomap, t-Distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation and Projection, and sparse denoising autoencoder based manifolds on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We then determine the clustering potential of the learned embeddings and then determine if category sub-groupings or sub-categories can be found. We then used a Kruskal–sWallis H test to determine the statistical significance of the discovered AD subcategories. Our results show that the existing AD categories do exhibit sub-groupings, especially in mild cognitive impairment transitions in many of the tested manifolds, showing there may be a need for further subcategories to describe AD progression. [ABSTRACT FROM AUTHOR]

Some data are collected on circular (rather than linear) scales. Often researchers are interested in comparing two samples of such circular data to test the hypothesis that they came from the same underlying population. Recently, we compared 18 statistical approaches to testing such a hypothesis, and recommended two as particularly effective. A very recent publication introduced a novel statistical approach that was claimed to outperform the methods that we had indicated were highest performing. However, the evidence base for this claim was limited. Here we perform simulation studies to offer a more detailed comparison of the new "Angular Randomisation Test" (ART) with existing tests. We expand previous evaluations in two ways: exploring small and medium sized samples, and exploring a range of different shapes for the underlying distribution(s). We find that the ART controls type I error rates at the nominal level. The ART had greater power than established methods in detecting a difference in underlying distribution caused by a shift around the circle. Its performance advantage in this case was strongest when samples where small and unbalanced in size. When the difference between underlying unimodal distributions was in shape rather than central tendency, then the ART was at least as good (and sometimes considerably more powerful) than the established methods, except when distributions samples were small and uneven in size, and the smaller sample came from a more concentrated underlying distribution. In such cases its power could be markedly inferior to established alternatives. The ART was also inferior to alternatives in dealing with axially distributed data. We conclude that under widely-encountered circumstances the ART test can be recommended for its simplicity of implementation, but researchers should be aware of situations where it cannot be recommended. [ABSTRACT FROM AUTHOR]

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use single-channel structure to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the concept of dual channel and design the algorithm model as two independent modules. The algorithm obtained different feature correlation sequences, thus avoided relevant feature loss. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common neurological diseases, affecting approximately 50 million people. This illness can be diagnosed by electroencephalogram (EEG), whose analysis depends on human interpretation, which may lead to divergent results and be imprecise and error-prone. Moreover, one estimates more than 80% of the epilepsy examinations return no anomalies at all, wasting the effort of analysis. In this context, machine learning (ML) methods are used to aid in epilepsy detection. An essential task for classifier building is feature extraction, for which there are many measures that can be computed. However, part of these features can be irrelevant or hinder the performance of ML methods. This paper proposes an automatic way to classify EEG segments by using a reduced set of features. The proposed method combines multispectral analysis, feature selection, and classifier building approaches. As our main contribution, a methodology to minimize the number of features for classifier building is proposed. A total of 285 measures are extracted. Afterward, two attribute selection approaches are used: Pearson's correlation coefficient filter and wrapper based on the genetic algorithm. Then, five well-known ML methods (k-nearest-neighbor, support vector machine, naive Bayes, artificial neural network, and random forest) were used to build 281 different classifiers. As a result, the proposed classifiers reached an accuracy between 87.2 and 90.99% and considerably reduced the number of features from 285 to 30, keeping competitive scores. Additionally, statistical hypothesis tests prove that our proposed approach is as efficient as using the complete feature dataset for classifier building. [ABSTRACT FROM AUTHOR]

In the linear regression model with possibly autoregressive errors, we construct a family of nonparametric tests for significance of regression, under a nuisance autoregression of model errors. The tests avoid an estimation of nuisance parameters, in contrast to the tests proposed in the literature. A simulation study illustrate their good performance. [ABSTRACT FROM AUTHOR]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite scores is preferred for prediction and diagnosis of individuals. While path analysis with composite scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally weighted composites. However, path analysis with Bartlett-factor scores and the partial least-squares approach to SEM perform the best with respect to effect size and power. [ABSTRACT FROM AUTHOR]

Traffic accidents have become a threatening socioeconomic concern worldwide. The global annual deaths and injuries toll from traffic accidents are estimated to be around 1.35 and 50 million, respectively. The Kingdom of Saudi Arabia (KSA) has witnessed alarming traffic safety issues during the past few decades. Recently, the government has introduced several proactive traffic safety interventions and policies, such as the induction of SAHER traffic management; however, the situations seem to have marginally improved. The main objective of this study is to analyze the pattern of traffic accidents in KSA and to assess the safety evaluation of the proposed SAHER traffic enforcement initiative. An analytic framework consisting of descriptive statistical analysis, geographic information system-based spatial analysis, statistical significance tests such as analysis of variance, and paired t-tests were conducted to evaluate the variations in traffic safety conditions in before-and-after SAHER deployment scenarios. Statistical analysis revealed that distracted driving and overspeeding consistently accounted for a significant proportion of the reported accidents, and variations in traffic accident frequencies over the years were statistically significant. This analysis also showed that the predominant accident types in KSA are vehicular collisions, fixed objects, and run-off accidents. For severity analysis, normalization of accident data was done based on the population of respective regions in KSA. The results indicated that the mean values of various traffic safety measures or accident attribute categories, such as accident occurrence, injury, and fatality rates, tend to reduce steadily in the latter part of the study period. It was found that the Northern border and Eastern regions had the highest accident rates, while Aseer, Al-Baha, and Tabouk Provinces had the most prominent injury and fatality rates. Analysis within and outside the major cities revealed that although accidents were concentrated more inside the cities, injury and fatality rates for accidents that occurred outside the cities were significantly high. A before-and-after study for SAHER effectiveness evaluation indicated that while the average reduction in different accident patterns was not statistically significant, the upward trend of both accident rates and fatalities was controlled to some extent. Several suitable policy recommendations are suggested based on the findings of the study. [ABSTRACT FROM AUTHOR]

In classical statistics, there is a close link between null hypothesis significance testing (NHST) and parameter estimation via confidence intervals. However, for the Bayesian counterpart, a link between null hypothesis Bayesian testing (NHBT) and Bayesian estimation via a posterior distribution is less straightforward, but does exist, and has recently been reiterated by Rouder, Haaf, and Vandekerckhove (2018). It hinges on a combination of a point mass probability and a probability density function as prior (denoted as the spike-and-slab prior). In the present paper, it is first carefully explained how the spike-and-slab prior is defined, and how results can be derived for which proofs were not given in Rouder, Haaf, and Vandekerckhove (2018). Next, it is shown that this spike-and-slab prior can be approximated by a pure probability density function with a rectangular peak around the center towering highly above the remainder of the density function. Finally, we will indicate how this 'hill-and-chimney' prior may in turn be approximated by fully continuous priors. In this way, it is shown that NHBT results can be approximated well by results from estimation using a strongly peaked prior, and it is noted that the estimation itself offers more than merely the posterior odds on which NHBT is based. Thus, it complies with the strong APA requirement of not just mentioning testing results but also offering effect size information. It also offers a transparent perspective on the NHBT approach employing a prior with a strong peak around the chosen point null hypothesis value. [ABSTRACT FROM AUTHOR]

Due to its low dependency on the control parameters and straightforward operations, the Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow convergence and low solution precision. In this research, a hybrid Artificial Electric Field Employing Cuckoo Search Algorithm with Refraction Learning (AEFA-CSR) is suggested as a better version of the AEFA to address the aforementioned issues. The Cuckoo Search (CS) method is added to the algorithm to boost convergence and diversity which may improve global exploration. Refraction learning (RL) is utilized to enhance the lead agent which can help it to advance toward the global optimum and improve local exploitation potential with each iteration. Tests are run on 20 benchmark functions to gauge the proposed algorithm's efficiency. In order to compare it with the other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests and Friedman tests with 5% significance level are used. In order to evaluate the algorithm's efficiency and usability, some significant tests are carried out. As a result, the overall effectiveness of the algorithm with different dimensions and populations varied between 61.53 and 90.0% by overcoming all the compared algorithms. Regarding the promising results, a set of engineering problems are investigated for a further validation of our methodology. The results proved that AEFA-CSR is a solid optimizer with its satisfactory performance. [ABSTRACT FROM AUTHOR]