Non-parametric Statistical Tests Research Articles

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

Clinical usability study of a home-based self-administration transcranial direct current stimulation for primary dysmenorrhea: A randomized controlled trial.

This study tested the usability of a home-based self-administration transcranial direct current stimulation (tDCS) device designed specifically for women's health needs. This is a single center triple blinded clinical usability study for a new wireless, Bluetooth-controlled wearable tDCS device for women's health. The study aims to evaluate the usability and effective blinding of a home-based tDCS system. A total of forty-nine women of reproductive age were randomly allocated (1:1) to receive one session of active tDCS (n = 24) or sham tDCS (n = 25) over the motor and dorsolateral prefrontal cortex. Each participant self-administered one 20-minute session without supervision following guidance on a software application alone. The System Usability Scale (SUS) and the Patient Global Impression of Change (PGIC) were used to evaluate the usability of the system. Regardless of sham or active conditions, all users found the system easy to use without the support of researchers. Usability scores were considered to be "excellent" in both groups and no significant difference was found between sham and active groups showing effective blinding of the device (Active group: 93.7 (83.1-97.5); Sham group 90 (86.2-95) p = 0.79) and PGIC (Active group: 2 (1-2.75); Sham group 2 (1-2) p = 0.99) using an unpaired t-test or non-parametric statistical tests accordingly. The new Bluetooth-controlled wearable tDCS device is easy, safe to use and completely controlled by a smartphone app. This device is focused on women's health and will be tested as an alternative treatment for chronic pelvic pain and mood disturbance associated with menstrual cycles in further research.

Ecological risk of metals in Andean water resources: A framework for early environmental assessment of mining projects in Peru

Metallic contaminants in Andean water resources influenced by mining activities poses risks to aquatic ecosystems and a challenge to regulatory agencies responsible for environmental compliance. In this study, the Ecological Risk Assessment (ERA) framework was adapted to assess dissolved heavy metal concentrations at 283 surface water monitoring stations near to six mining projects during the dry and wet seasons. Reports from OEFA-Peru on Early Environmental Assessment (EEA) were used to apply various criteria and non-parametric statistical tests. They included ecological, ecotoxicological, chemical, and regulatory factors. The main goal of this research was to identify, analyze, characterize, and compare the risks present at different trophic levels. These levels were categorized as T1 (Microalgae), T2 (Zooplankton and Benthic invertebrates), and T3 (Fish). Individual risk (IR) was estimated using the quotient model, while total risk (TR) was assessed using the additive probability rule. Rainbow trout (Oncorhynchus mykiss), representing trophic level T3, showed the highest sensitivity to Fe and Cu. Statistical tests ranked the IR as Fe > Cu > Zn > Mn > Pb (p < 0.01). The TR was more prevalent during the wet season compared to the dry season (p < 0.01). Notably, around 50 % of the monitoring stations (n = 142) were classified as high risk, and 9 % (n = 13) showed extremely high-risk values for Cu and Fe. The adapted ERA framework demonstrated great effectiveness in identifying critical points of metal contamination in high Andean aquatic ecosystems under mining influence. However, specialized studies are suggested that allow the sources of pollution to be associated with specific regulatory actions.

ADHD Patients with Suicidal Behaviour: Risk Factors, Comorbidities, and Clinical Profile: A Naturalistic Exploratory Study

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that disrupts brain functioning and is characterized by hyperactivity, impulsivity, and inattention. With varying presentations and complex etiological factors contributing to the development of ADHD, along with its persistence into adulthood, ADHD must be studied. Researchers have taken an interest in the relationship between ADHD and suicide, which is a serious public health concern with increasing prevalence rates in the Americas. The current literature reveals conflicting views on the importance of psychiatric comorbidities in the development of suicidal behaviours in ADHD patients. Therefore, this study aimed to determine whether there were significant differences between adult ADHD patients with suicide risk and adult ADHD patients without suicide risk. This study was a naturalistic retrospective chart review pilot study that used a sample of adults with a confirmed diagnosis of ADHD from January 2023 to August 2023. Using convenience sampling and sets of inclusion and exclusion criteria, patient data were sequentially collected from Med Access electronic medical records. The control and experimental groups each consisted of 50 patients (100) ranging from 19 to 58 years old. Our quantitative data were analyzed using non-parametric statistical tests, including the Chi-Square test and the Mann–Whitney U test. The results showed significant associations between ADHD patients with suicide risk and (1) borderline personality disorder; (2) binge eating disorder; (3) seven specific psychosocial risk factors; and (4) a higher number of antidepressant medication trials. No significant associations were found with other psychiatric disorders; however, there are important sex differences in terms of the risk factors. Our pilot study reveals several significant differences between adult ADHD patients with suicide risk and those without suicide risk. However, given our limited sample size and limitations, we hope our study encourages larger-scale studies to further investigate this relationship to improve its generalizability.

A variable neighborhood search for the median location problem with interconnected facilities

AbstractThe ‐median problem has been widely studied in the literature. However, there are several variants that include new constraints to the classical problem that make it more realistic. In this work, we study the variant that considers interconnected facilities, that is, the distances between each pair of facilities are less than or equal to a certain threshold . This optimization problem, usually known as the median location problem with interconnected facilities, consists of locating a set of interconnected facilities to minimize the distance between those interconnected facilities and the demand points. The large variety of real‐world applications that fit into this model makes it attractive to design an algorithm able to solve the problem efficiently. To this end, a procedure based on the variable neighborhood search methodology is designed and implemented by using problem‐dependent neighborhoods. Experimental results show that our proposal is able to reach most of the optimal solutions when they are known. Additionally, it outperforms previous state‐of‐the‐art methods in those instances where the optima are unknown. These results are further confirmed by conducting nonparametrical statistical tests.

Empowering Structural Integrity: YOLO-Based Crack Detection and ML-Driven Concrete Strength Prediction of Critical Infrastructure Caused due to Mining Operation.

In this paper, YOLO algorithm is used for detecting crack by incorporating machine learning approach utilized for predicting concrete strength and possible crack in critical infrastructure caused during mining operation. Over time, those fissures become more noticeable until they finally collapse. Big data is accumulating huge amount of data by utilizing remote sensing, earth penetrating RADAR, LiDAR, GPS, and satellite image data for large-scale operations. The performance of the proposed approach is compared with other state-of-the-art algorithms, such as, YOLO V3 and YOLO V8. To investigate the performance, four performances metric like mean absolute error (MAE), root mean square error (RMSE),  relative standard error (RSE) and root relative squared error (RRSE) has considered. It is found that our approach is giving superior results in term of the compared performance metrics. Moreover, non-parametric statistical testing, such as, signed test, Wilcoxon Rank test and Friedman's test has been conducted to verify the dominance of proposed approach over others.

To Stage or Not to Stage? Direct-to-Implant Versus Staged Reconstruction for Implant-Based Breast Reconstruction

Abstract Goals/Purpose Implant-based breast reconstruction remains the common method of reconstruction after mastectomy in the US. An increasing number of centers are now offering direct-to-implant (DTI) breast reconstruction, avoiding staged reconstructions with tissue expander placement first. However, little is known regarding the need for revisions following DTI. In this study we sought to investigate short- and long-term complications and revisions following DTI versus staged reconstruction. Methods/Technique We retrospectively reviewed all patients who underwent bilateral nipple-sparing-mastectomies seeking implant-based breast reconstruction between September 2016 to September 2021. We extracted demographic and clinical information for the selected patients. Patients were divided in two cohorts: the DTI cohort, and the staged cohort. Baseline differences among the two groups were assessed by performing non-parametric statistical tests. Number of complications and revisions were assessed by employing chi-square. Logistic regression was then employed to adjust for possible confounders. Results/Complications During the study period, we identified 143 patients, 69 in the DTI cohort and 74 in the staged cohort. Patients in the staged cohort were more likely to have higher BMI (mean 26 kg/m2 versus 24.3 kg/m2 in the DTI cohort) and have undergone prior radiotherapy than those in the DTI cohort. No differences based on age, race/ethnicity or comorbidities were present. According to our analysis and within the follow up period time of average 21 months, the two cohorts had similar rates of minor and major short- and long-term surgical complications. After adjusting for potential confounding factors, the DTI group had a significantly higher rate of overall revisions (OR 2.73, 95% CI 1.23-6.07, p-value 0.01) and specifically revisions with implant exchange (0R 3.06, 95% CI 1.06-8.85, p-value 0.03). Most common reason for reason was asymmetry or contour deformity. However, the DTI cohort had significantly lower charges associated with their care during the follow up period (d=-100,178.14 US dollars, p-value=0.001). Complications rates were similar among the two groups. Conclusion An increasing number of patients are now interested in DTI breast reconstruction. According to the results of our study, DTI patients may have increased number of revisions but share similar rates of complications. DTI breast reconstruction is also associated with lower charges, even after accounting for the additional necessary revision procedures. Careful selection of patients for DTI and appropriate education on the need for revisions in the future is paramount.

Targeting transitioning lung monocytes/macrophages as treatment strategies in lung disease related to environmental exposures

BackgroundEnvironmental/occupational exposures cause significant lung diseases. Agricultural organic dust extracts (ODE) and bacterial component lipopolysaccharide (LPS) induce recruited, transitioning murine lung monocytes/macrophages, yet their cellular role remains unclear.MethodsCCR2 RFP+ mice were intratracheally instilled with high concentration ODE (25%), LPS (10 μg), or gram-positive peptidoglycan (PGN, 100 μg) for monocyte/macrophage cell-trafficking studies. CCR2 knockout (KO) mice and administration of intravenous clodronate liposomes strategies were employed to reduce circulating monocytes available for lung recruitment following LPS exposure. Lung tissues and bronchoalveolar lavage fluid (BALF) were collected. Pro-inflammatory and/or pro-fibrotic cytokines, chemokines, and lung extracellular matrix mediators were quantitated by ELISA. Infiltrating lung cells including monocyte/macrophage subpopulations, neutrophils, and lymphocytes were characterized by flow cytometry. Lung histopathology, collagen content, vimentin, and post-translational protein citrullination and malondialdehyde acetaldehyde (MAA) modification were quantitated. Parametric statistical tests (one-way ANOVA, Tukey’smultiple comparison) and nonparametric statistical (Kruskal–Wallis, Dunn’s multiple comparison) tests were used following Shapiro–Wilk testing for normality.ResultsIntratracheal instillation of ODE, LPS, or PGN robustly induced the recruitment of inflammatory CCR2+ CD11cintCD11bhi monocytes/macrophages and both CCR2+ and CCR2− CD11c−CD11bhi monocytes at 48 h. There were also increases in CCR2+ CD4+ and CD8+ T cells and NK cells. Despite reductions in LPS-induced lung infiltrating CD11cintCD11bhi cells (54% reduction), CCR2 knockout (KO) mice were not protected against LPS-induced inflammatory and pro-fibrotic consequences. Instead, compensatory increases in lung neutrophils and CCL2 and CCL7 release occurred. In contrast, the depletion of circulating monocytes through the administration of intravenous clodronate (vs. vehicle) liposomes 24 h prior to LPS exposure reduced LPS-induced infiltrating CD11cintCD11bhi monocyte-macrophage subpopulation by 59% without compensatory changes in other cell populations. Clodronate liposome pre-treatment significantly reduced LPS-induced IL-6 (66% reduction), matrix metalloproteinases (MMP)-3 (36%), MMP-8 (57%), tissue inhibitor of metalloproteinases (61%), fibronectin (38%), collagen content (22%), and vimentin (40%). LPS-induced lung protein citrullination and MAA modification, post-translational modifications implicated in lung disease, were reduced (39% and 48%) with clodronate vs. vehicle liposome.ConclusionHighly concentrated environmental/occupational exposures induced the recruitment of CCR2+ and CCR2− transitioning monocyte-macrophage and monocyte subpopulations and targeting peripheral monocytes may reduce the adverse lung consequences resulting from exposures to LPS-enriched inhalants.

The relationship between patient characteristics and analgesic self-medication behavior in pharmacies in Kesesi District, Pekalongan Regency

Self-medication is an effort to self-medicate without a doctor's prescription. Analgesics are medications used to reduce or relieve pain. The purpose of this study was to determine the relationship between patient characteristics and analgesic self-education behavior in pharmacies in Kesesi District, Pekalongan Regency. This study used an analytical observational method with a cross-sectional approach, the research instrument used a questionnaire involving 97 respondents in 3 pharmacies in Kesesi District, Pekalongan Regency. The sampling technique uses the consecutive sampling method. The independent variable is the characteristic and the dependent variable is the analgesic self-medication behavior. Data analysis using univariate and bivariate analysis with non-parametric rank spearman statistical test. The results showed that the age of 26-45 years was 50.5%, women were 66%, secondary education was 61.9%, self-employed by 47.7% and 32% of moderate-income people were more self-medicated analgesics. Some patients had good behavior in self-medication analgesics by 71.1%. The conclusion of this study was that patient characteristics including age (p = 0.014), education (p = 0.000), occupation (p = 0.000) and income (p = 0.000) had a relationship with analgesic self-medication behavior in pharmacies Kesesi District, Pekalongan Regency, while unrelated patient characteristics were gender (p = 0.237).

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PurposeFurther acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. MethodsThe effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. ResultsThe simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). ConclusionADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Using a Bone Density Measuring Micromotor to Distinguish Different Cortical/Cancellous Combinations in Vitro: A Study Using Polyurethane Foam Blocks.

To assess whether a recently introduced implant placement micromotor that provides site-specific, operator independent cancellous bone density measurements can detect the presence of a cortical bone layer and measure its thickness and density. An in vitro experiment was performed on six double-layer polyurethane foam blocks mimicking the jawbone with different cortical thickness/cancellous density combinations. The densities were measured using the micromotor after either removing the cortical layer or leaving it intact, both with and without irrigation. The results were compared by means of nonparametric statistical tests. Independent of irrigation, the micromotor detected the cortical layer when it was left intact and accurately estimated its thickness. The micromotor did not discriminate between blocks when they were considered separately, but it did when they were grouped into four or three classes. The present study suggests that the micromotor may represent a valid device to quantitatively assess bone quality and density. If the micromotor can quantitatively distinguish different cortical/cancellous bone combinations in humans, it may be a helpful tool for performing finely tuned, patient-tailored preparations of the implant seat, making tooth rehabilitation in challenging clinical conditions more predictable.

Differences in dietary intake in early postmenopausal women with different levels of areal and volumetric bone mineral density: a cross-sectional analysis of baseline data from an intervention study

Osteoporosis is a degenerative disease of the bone. The rate of bone loss is accelerated during the first postmenopausal years in women which results in their disproportionate prevalence of osteoporosis(2). Some of the factors contributing to the development and maintenance of bone mineral density (BMD) relate to diet, particularly the intake of protein, calcium and other micronutrients that play a crucial role in bone composition(3). The most common method of measuring BMD is dual-energy X-ray absorptiometry (DXA) which generates a two-dimensional image of the scan site (typically spine, hip and/or forearm) to determine areal BMD (aBMD). However, new methods have recently emerged, including High Resolution peripheral Quantitative Computed Tomography (HRpQCT), that offer more accurate three-dimensional measurements of volumetric BMD (vBMD) and microstructure of distal tibia and radius(4). The aim of this study was to examine the differences in the dietary intake of nutrients that represent organic or inorganic components of the bone, in early postmenopausal women with different spine aBMD and tibia and radius vBMD levels. One hundred and fourteen healthy early postmenopausal women with a lumbar spine or total hip BMD T score &gt; −2.5 (measured by DXA) were recruited as part of a larger interventional study. Dietary intake was recorded using a 297-point self-reported validated food frequency questionnaire(5) for assessing the intake of energy, macro and micronutrients. Physical activity was self-reported using the validated Active Australia Questionnaire. Years since menopause were self-reported. DXA and HRpQCT scans measured L1-L4 spine, proximal femur aBMD, and distal tibia and radius vBMD respectively. Non-parametric statistical tests examined differences in dietary intake and physical activity levels between women at different levels of aBMD and vBMD. Data reported as median and interquartile ranges. There were no significant differences observed in the total sample between tertiles of aBMD and vBMD, regarding nutrient intake. However, for women with less than 3 years since menopause (i.e., the time-period of accelerated bone loss), lower dietary intakes of energy [8,658(3,324) vs 10,068(3,688) kJ/day; p = 0.047], protein [94(29) vs 103(32) g/day; (p = 0.044)], sodium [1,927(992) vs 2,625(2,185) mg/d; (p = 0.044)], potassium [4,064(1,373) vs 5,121(2,377) mg/d; (p = 0.041)], calcium [969(325) vs 1,214(652) mg/d; (p = 0.028)] and zinc [10(3) vs 12(4) mg/d; (p = 0.005)] were observed for women with osteopenia (−1&lt; L1-L4 aBMD T-score &lt;2.5) compared to those with normal L1-L4 aBMD (i.e., T-score &gt; −1). No significant differences were observed for women with more than 3 years since menopause, with the only exception of alcohol intake (p = 0.033), which was found to be lower in women with osteopenia compared to those with normal aBMD. These findings highlight the importance of targeting osteopenic women within the first 3 years following menopause as candidates for tailored dietary intervention programs for preventing osteoporosis.

Organizational Commitment and Employee Performance during the COVID-19 Pandemic: Evidence from a BPO Company

The pandemic necessitated remote workplace setup, and as employees’ homes doubled as their workplaces, it became difficult to distinguish between the two. With this reality came the challenge of enhancing employee performance and organizational commitment. This study investigated the correlation between organizational commitment and employee performance during the COVID-19 pandemic. This study involved 158 employees of a BPO (Business Process Outsourcing) company situated in Quezon City, Philippines. Non-parametric statistical testing such as Mann–Whitney U and Kruskal-Wallis tests were employed to examine the significant differences in the respondent's ratings when their demographic profiles were considered. Spearman’s rank correlation was used to measure the strength of the relationship between the constructs of organizational commitment and dimensions of employee performance. Results of the study revealed a high positive relationship between continuance commitment and employee performance. Furthermore, this study also explored organizational commitment along four demographic variables, resulting in the detection of significant differences in the perception of continuance commitment on account of gender, age, civil status, and length of service. A significant difference was also detected in affective commitment considering the length of service. Single, married, and widowed respondents also differed in their opinions on normative commitment. While research on organizational commitment exist, few studies have explored the role of demographic factors in shaping employee responses to pandemics. This study demonstrates value by examining the influence of demographic variables on organizational commitment and employee performance during the pandemic, adopting an intersectional perspective, and offering practical implications for organizational management, theoretical advancement, and future research directions.

Association Between Obstructive Sleep Apnea (OSA) Severity and Optimal Continuous Positive Airway Pressure (CPAP); Do Severe OSA Patients Always Need High CPAP Pressure?

Objective: Obstructive sleep apnea (OSA) is a prevalent sleep disorder associated with an increased risk of cardiovascular disease. Severity of OSA is assessed using the apnea-hypopnea index (AHI), but the optimal continuous positive airway pressure (CPAP) does not always correlate proportionally with AHI. This study aims to identify factors leading to the need for high CPAP pressure despite low AHI or vice versa.Methods: The study includes 91 patients diagnosed with OSA through polysomnography. Participants undergoing CPAP titration were categorized in three groups based on AHI (30/h) and optimal CPAP pressure (10 cm H&lt;sub&gt;2&lt;/sub&gt;O); low AHI and high CPAP pressure group (LH group), high AHI and low CPAP pressure group (HL group), direct proportion group (DP group). The Kruskal–Wallis nonparametric statistical test is used to identify significant differences between the three groups.Results: Among the subjects, 78% were male, with no significant age or BMI differences between groups. There were 36 (39.6%) subjects with mild to moderate OSA and 55 (60.4%) subjects with severe OSA. The study revealed that patients requiring high CPAP pressure despite low AHI (LH group) had lower AHI, oxygen desaturation index, and various other parameters compared to other groups as expected. However, certain factors like the percentage of hypopnea in AHI, limb movement (LM) arousal index, and spontaneous arousal index were higher in the LH group.Conclusion: Patients with mild to moderate OSA requiring high CPAP pressure are more likely associated with specific aspects such as hypopnea in AHI, LM arousal, and spontaneous arousal index, presenting unpredictable outcomes.

Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors.

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes. We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance. By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score. A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.

Temporal and Spatial Analyses of Forest Burnt Area in the Middle Volga Region Based on Satellite Imagery and Climatic Factors

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen’s slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (&gt;84%) and F-score value (&gt;82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed.

Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam

Climate risks have posed a major threat to many local communities living in low-lying coastal megacities across the globe, including Ho Chi Minh City, Vietnam. Hence, this study first aimed to contribute towards a comprehensive understanding of temporal trend patterns of annual rainfall and absolute extremes in Ho Chi Minh City over the last 4 decades (1980-2022) through multiple non-parametric statistical trend tests. We employed the quantile delta mapping (QDM) method to develop daily bias-corrected rainfall data based on the outputs in the latest Coupled Model Intercomparison Project phase 6 (CMIP6) under 8 shared socio-economic pathway (SSP) greenhouse gas emission scenarios. Evaluation of model performance was implemented by repeatedly omitting 5 successive years in turn for estimating testing errors. The outcomes indicate the high applicability of well-calibrated transfer functions, even for high quantiles, to the production of future rainfall scenarios. The projected changes in annual rainfall and absolute extremes were obtained by estimating multi-model medians from CMIP6 models for future periods (i.e. 2021-2040, 2041-2060, 2061-2080, and 2081-2100), with reference to the base period (1995-2014). In general, annual rainfall in Ho Chi Minh City is projected to increase substantially, and Thu Duc station consistently shows the highest increases in annual rainfall. Projected changes are approximately 30.9% (8.3 to 77.8%) under the high-end scenario (i.e. SSP5-8.5) by the end of the 21st century. It is expected that these findings will yield several solid arguments for mitigating climate-related risks in Ho Chi Minh City.

Relationship between the Level of Anxiety and Coping Strategies among Boarding Students of Cavite State University

The study entitled “Relationship between the Level of Anxiety and Coping Strategies among Boarding Students of Cavite State University” was conducted to distinguish the relationship between the level of anxiety and coping strategies of boarding Cavite State University’s students. Kendall’s Tau-B, a nonparametric statistical test, was used to determine the correlation between the two factors using SPSS, a statistical software, for fast computation. The inquiry is carried out by handing out 100 survey-modified questionnaires that consist of two factors, the Beck Anxiety Inventory and Coping Inventory for Stressful Situation-21, to boarding students in or near Cavite State University – Indang Campus. The study’s findings found a weak but significant relationship (????=0.293) between the level of anxiety and coping strategies among boarding students of Cavite State University – Indang Campus with a p-value of 0.002. This underscores a meaningful association, indicating that as the level of anxiety increases, coping strategies tend to increase as well. Whereas students should prioritize self-care and ask for professional help to develop coping strategies to improve their well-being.

Effect of Long Suffering Diabetes Mellitus on Blood Ureum Levels

Diabetes mellitus is a disease characterized by increasing blood sugar levels caused by defective insulin secretion or action. Increased levels of sugar in the blood have a direct impact on the blood vessels in the kidneys. Such as decreasing the function of the kidney, the disturbing kidney function will increase the level of urea in the blood.To find out whether there is an effect of long-suffering from Diabetes mellitus on blood urea levels. This research is analytical observational research with a cross-sectional approach. The samples used Diabetes mellitus patients with variations in length suffering of 1-3 years, 4-6 years, and &gt; 6 years. Data were collected and analyzed using Kruskal Wallis non-parametric statistical test. The average blood urea level of Diabetes mellitus patients who suffer 1-3 years, 4-6 years, and &gt; 6 years is 29.17 mg/dL, 53.25 mg/dL, and 118.11 mg/dL. The Kruskal Wallis test showed a significant value = 0.000 &lt; 0.05 (Pα) which indicated that the duration of suffering from Diabetes mellitus affected increasing blood urea levels