Statistical Significance Tests Research Articles

PSIV-A-8 Effects of meal frequency on heart rate in horses

Abstract In humans, meal size has been shown to affect heart function with meal ingestion being associated with an increase in heart rate (HR) and cardiac output lasting significantly longer when larger meals are consumed. In horses, HR spikes at the sight of feed due to anticipation, but no studies have specifically evaluated the effect of meal frequency or size on HR. Heart rate has also been used as a measure both positive and negative stress. Domesticated horses are often meal fed, with small frequent meals requiring more labor than large infrequent meals. Fewer larger meals may have negative welfare implications for a grazing species. As part of a larger project investigating the effects of meal frequency on the behavior and metabolism of horses, the objective of this study was to evaluate equine HR in response to feeding frequencies commonly practiced in the field [once (1x), twice (2x), or thrice (3x) per day]. Feeding times were at 0800 (1x, 2x, and 3x), 1300 (3x), and 1800 (2x and 3x) h. The 12 adult clinically healthy horses [average ± SD; 14 ± 6 yr and 572 ± 32 kg body weight (BW)] were separated into two equal groups before completing a 14-d acclimation period to the diet during which they were fed 2x. The diet was provided at 2% BW on a dry matter (DM) basis (1.55% BW grass hay in a slow feed hay net plus 0.45% BW complementary fortified feed). During the study the same diet was fed but the meal frequency, and therefore meal size, differed between the treatments. The order of treatments, lasting 1 wk each, was decided at random for each group. On d 6 of each treatment HR was measured using individual H10 Polar HR monitors, which recorded every second from 0700 to 1900 h yielding 1,514,799 data points. During this time the aisleways housing the stalled horses were blocked off to minimize exposure to human presence other than at feeding time(s). After removing outliers, the mode was calculated for HR of each horse, which was considered to be the resting HR for that horse and ranged from 31 to 46 beats/min. The percentage of data above, below, or at the mode was calculated for each horse over the 12 h period and evaluated using Chi Square tests for statistical significance. The proportion of HR values above, below, or at the mode differed between treatments (P = 0.02). When fed 1x, 69 ± 14% of the HR readings were above the mode on average, as compared with 42 ± 18% and 44 ± 18% for 2x and 3x, respectively. These results suggest that the increase in HR was more than anticipatory and that there may be sustained stress when horses are fed at fewer frequency, such as once daily.

Lost in Statistics

The present paper investigates one aspect of questionable research practices relating to Arabic L1 learners of foreign languages, namely the use of statistics. The objective of the paper is to argue that reproducible research requires adopting wise practices in linguistics and that the excessive focus on quantification does not seem to serve this purpose. Statistical significance tests in quantitative research are routinely used in linguistic inquiry as well as language teaching and learning studies with a view to supporting the relevant explanatory insights in linguistics. In this article, I will expose the misuse of statistics by doctoral students in English departments of Morocco working on Arabic L1 learners’ data, by highlighting some practices that are at odds with international good practices in academic research in linguistics. I will take stock of the current questionable practices in this regard to dispel some of the misunderstanding about the use of statistics which is now gaining grounds lest this becomes an orthodoxy. I will argue that research on Arabic L1 learners’ data should be focused more on exploration and discovery, as well as the validation of epistemological insights than on mere descriptive quantification geared to hypothesis verification. These areas of focus constitute the crux of academic research in linguistics, but they seem to be lost in statistics in doctoral students’ theses. Recommendations and solutions are provided for enhancing transparency and improving reproducibility of doctoral research outcomes to advance theory building and the delivery of new research lines in linguistics as well as to avoid the risk of research waste, in line with the requirements of open science.

Association Between Medicare's Sepsis Reporting Policy (SEP-1) and the Documentation of a Sepsis Diagnosis in the Clinical Record.

Interrupted time series analysis of a retrospective, electronic health record cohort. To determine the association between the implementation of Medicare's sepsis reporting measure (SEP-1) and sepsis diagnosis rates as assessed in clinical documentation. The role of health policy in the effort to improve sepsis diagnosis remains unclear. Adult patients hospitalized with suspected infection and organ dysfunction within 6 hours of presentation to the emergency department, admitted to one of 11 hospitals in a multi-hospital health system from January 2013 to December 2017. Clinician-diagnosed sepsis, as reflected by the inclusion of the terms "sepsis" or "septic" in the text of clinical notes in the first two calendar days following presentation. Among 44,074 adult patients with sepsis admitted to 11 hospitals over 5 years, the proportion with sepsis documentation was 32.2% just before the implementation of SEP-1 in the third quarter of 2015 and increased to 37.3% by the fourth quarter of 2017. Of the 9 post-SEP-1 quarters, 8 had odds ratios for a sepsis diagnosis >1 (overall range: 0.98-1.26; P value for a joint test of statistical significance = 0.005). The effects were clinically modest, with a maximum effect of an absolute increase of 4.2% (95% CI: 0.9-7.8) at the end of the study period. The effect was greater in patients who did not require vasopressors compared with patients who required vasopressors (P value for test of interaction = 0.02). SEP-1 implementation was associated with modest increases in sepsis diagnosis rates, primarily among patients who did not require vasoactive medications.

Identifying spatio-temporal pattern of electric vehicles involved traffic accidents

The increasing popularity of electric vehicles (EVs) has given rise to concerns regarding emerging safety challenges. To address this, an enhanced spatio-temporal kernel density estimation (STKDE) method is developed, which leverages GIS technologies to analyze the temporal evolution of EV accidents across different locations. Our approach introduces several innovations: (1) The incorporation of network distance to address overestimation associated with the conventional Euclidean distance; (2) Integration of a Severity Index (SI) to rationalize accident weighting; (3) Utilization of a likelihood cross-validation method to determine optimal bandwidths for simplified computation. Additionally, statistical significance tests and a ranking system based on cluster strength are implemented to enhance the accuracy and stability of hotspot identification compared to the traditional kernel density estimation (KDE) method. Empirical analyses reveal spatial trends, indicating a higher likelihood of EV accidents occurring near city administrative centers and along arterial roads. Temporally, our findings show increased daytime hotspot density with distinct morning and evening peaks compared to nighttime. Furthermore, a total of 209 hazardous locations, containing 907 accidents, representing 23.4% of all accidents are filtered. Our improved STKDE approach demonstrates a mean hit rate of 0.553 and a PAI of 5.573, which are 3.5% and 27.5% higher, respectively, than those achieved with SKDEKDE. These insights can assist transportation agencies in implementing targeted interventions and resource allocation strategies.

Developing statistical and machine learning models for predicting CO2 solubility in live crude oils

CO2 solubility in crude oil primarily affects the viscosity and swelling capacity of oil and determines the displacement efficiency of CO2 flooding during the enhanced oil recovery process. Dissolution of CO2 into crude oil also ensures safe and permanent storage of CO2 in depleted formations via solubility trapping. This study employed statistical and machine learning algorithms to develop precise and stable predictive models for estimating CO2 solubility in crude oils with dissolved gas. Recognizing the limitations of existing experimental data on CO2 solubility for live oils (crude oils with dissolved gas), we introduced a novel approach employing statistical and machine learning techniques for developing accurate and robust predictive models. Our methodology combined regularization, k-fold cross-validation, and statistical significance tests to ensure the robustness of the models, practices not considered in the previous studies. A linear regression model with L2 regularization is developed as the base model and compared with a 2nd order polynomial model and an ensemble feed-forward neural network (FNN) model integrated with gradient boosting (GB). The incorporation of GB into the neural network capitalizes on the additive sequence of FNN models, thereby enhancing the accuracy and reliability of predictions. Moreover, an improved user-accessible and easily implementable correlation is proposed to estimate CO2 solubility in live crude oils at diverse operating conditions as a function of reservoir temperature, saturation pressure, bubble-point pressure, oil molecular weight, and specific gravity. The performances of the developed model and the correlation are compared against the literature models and correlations. The FNN model with an overall R2 score of 0.9978, average absolute relative deviation (AARD) of 1.54 %, and root mean squared error (RMSE) of 0.0083 outperformed the previously published models and correlations. The new correlation, while being relatively simpler than previously published correlations, showed similar predictive performance with an overall R2 score of 0.9817, AARD of 4.98 %, and RMSE of 0.0225. The model and the correlation can be used to estimate CO2 solubility in live crude oils with 0.67 to 0.97 specific gravity for reservoir temperatures and pressures in the range of 28 to 124 °C and 3.23 to 32.76 MPa, respectively.

Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective

AbstractIn the rapidly evolving landscape of manufacturing, the ability to make accurate predictions is crucial for optimizing processes. This study introduces a novel framework that combines predictive uncertainty with explanatory mechanisms to enhance decision-making in complex systems. The approach leverages Quantile Regression Forests for reliable predictive process monitoring and incorporates Shapley Additive Explanations (SHAP) to identify the drivers of predictive uncertainty. This dual-faceted strategy serves as a valuable tool for domain experts engaged in process planning activities. Supported by a real-world case study involving a medium-sized German manufacturing firm, the article validates the model’s effectiveness through rigorous evaluations, including sensitivity analyses and tests for statistical significance. By seamlessly integrating uncertainty quantification with explainable artificial intelligence, this research makes a novel contribution to the evolving discourse on intelligent decision-making in complex systems.

Impact of TV Advertising on Decision-Making of Consumers in Pakistan

Television advertising has proven to be an effective tool for introducing new products and influencing customer behavior and decision-making processes. This study analyzes the "Impact of Television Advertisement on Consumer Decision Making in Pakistan." The researcher collected data using the survey method and a questionnaire. 300 female consumers were chosen from three regions of Lahore (Karim Park, Gulshan e Ravi, and Defense) based on demographics such as age, education, and income. The data was examined using univariate and bivariate methods of statistical analysis, and conclusions were drawn. The hypothesis was verified using the test for statistical significance, Chi-Square, which confirmed that 'More Nesvita TV ads, more acceptances in actual life among customers'. It was discovered that television advertising had a significant impact on consumers' cognitive functions such as searching for information and alternative selection. The findings provide useful information about the efficiency of Nestlé Nesvita TV advertisements and their impact on consumer decision-making processes in the Pakistani market.  

Comparison of Functional Outcomes of Two Different Rubber Dam Systems in Dental Practices

<p><strong>Background and Objective:</strong> The rubber dam is the only method that can ensure complete isolation during dental procedures. During endodontic and restorative procedures, it improves patient protection, treatment efficacy, and infection control. A novel rubber dam system called Optradam has introduced a remarkably comfortable, user-friendly clamp-free technique. Therefore, this study was conducted to compare conventional rubber and OptraDam®Plus (Ivoclar Vivadent) based on patients’ and dentist preferences.</p>
 <p><strong>Methods: </strong>After receiving ethics board clearance, this trial was carried out at the Department of Operative Dentistry at a tertiary care hospital in Islamabad from June to December 2022. A total of 30 participants were assigned to each group; conventional rubber dam (Group-1) and OptraDam®Plus (Group-2). Two questionnaires were designed and validated one to be completed by dental practitioners and the other by patients. The gathered information was analyzed using statistical tests of significance.</p>
 <p><strong>Results:</strong> A total of 60 patients were treated; 31 (52%) males and 29 (28%) females with a mean age of 32.35 years (SD = 4.59). Both the dentists (80% ) and the patients (73.3%) favored the conventional rubber dam system (Group-1). Concerning patient comfort, the conventional rubber dam (96.7%) was considered more comfortable than the OptraDam®Plus (Group-2) (86.7%). More effective moisture management was seen in Group-1 patients (90%). Other factors, such as time, convenience of application, and imaging radiography, had statistically insignificant results among both groups.</p>
 <p><strong>Conclusion:</strong> The results indicate that both patients as well as operators, preferred the use of conventional rubber dams over OptraDam®Plus during an endodontic and restorative procedure.</p>

Clinical Manifestations of Infections with the Omicron Sub-Lineages BA.1, BA.2, and BA.5: A Retrospective Follow-Up Analysis of Public Health Data from Mecklenburg-Western Pomerania, Germany.

The Omicron variants BA.1, BA.2, and BA.5 caused several waves of SARS-CoV-2 in Germany in 2022. In this comparative study, public health data on SARS-CoV-2 infections from Mecklenburg-Western Pomerania, Germany, between January and October 2022 were examined retrospectively using Pearson's chi-squared tests and Fisher's exact tests for testing for statistical significance. Compared to BA.5 infections, BA.1 and BA.2 infections affected younger individuals aged up to 19 years significantly more often, whereas BA.5 infections occurred significantly more frequently in patients between 40 and 59 years of age when compared to BA.1 and BA.2. Infections with all three variants predominantly caused flu-like symptoms; nevertheless, there were significant differences between the reported symptoms of BA.1, BA.2, and BA.5 infections. Especially, the symptoms of 'fever', 'severe feeling of sickness', 'loss of taste', and 'loss of smell' were significantly more often present in patients with BA.5 infections compared to BA.1 and BA.2 cases. Additionally, BA.2 and BA.5 cases reported significantly more often the symptoms of 'runny nose' and 'cough' than BA.1-infected cases. Our findings indicate remarkable differences in the clinical presentations among the sub-lineages, especially in BA.5 infections. Furthermore, the study demonstrates a powerful tool to link epidemiological data with genetic data in order to investigate their potential impact on public health.

Machine learning-based multimodal fusion recognition of passenger ship seafarers’ workload: A case study of a real navigation experiment

Passenger ships have complex transportation systems and seafarers face high workloads, making them susceptible to serious injuries and fatalities in the event of accidents. Existing unimodal workload recognition for seafarers mainly focuses on fixed load induction in bridge simulators, whereas a multimodal approach using multi-sensor data fusion can overcome the reliability and sensitivity limitations of a single sensor. To accurately identify the workload of seafarers, we propose a machine learning-based multimodal fusion method at the feature layer and utilise the Gini index to determine the feature weight of the multimodal data. Through a real ship navigation experiment, the subjective workload assessment technique (SWAT) was employed to collect the continuous workload scores of 24 seafarers in daily tasks. Further, the Dempster–Shafer evidence theory was used to integrate these scores with the unsafe behavior probability of seafarers to obtain a calibrated workload. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals were collected in real time, and a high-dimensional feature matrix was extracted to construct the workload recognition model. Random forest, XGBoost, and back-propagation neural networks were used to establish multimodal fusion workload recognition models at the feature-fusion stage, and the model performances were compared. The results showed that the multimodal fusion based on EEG, ECG, and EDA had an excellent recognition accuracy. The XGBoost algorithm has better performance with an accuracy of 85.72%, which is an increment of 9.49% compared to that of the unimodal algorithm, and this improvement passed the statistical significance test. Important features suitable for multimodal fusion recognition were also analysed. The findings of this study have profound significance for enhancing risk control measures pertaining to human factors and ensuring navigation safety.

A probability-exponential method of converting Z-numbers and its systematic applications in multi-attribute decision making

Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM.

Associations between paramedics' salutogenic health experience and the intention to stay in the profession: A cross-sectional survey.

To investigate the association between paramedics' salutogenic health and their intentions to stay in their profession. A cross-sectional survey. Paramedics from eight purposively selected organizations participated in this study (n = 433). The data were collected in 2022 with the Salutogenic Health Indicator Scale and validated single questions that assessed work ability, recovery and self-rated stress. Intention to stay was measured using two questions about the intention to leave prehospital emergency medical service work and leave the nursing sector completely during the next 2 years. Data were analysed using logistic regression and statistical significance tests. Men had higher mean salutogenic health scores than women. No significant differences in salutogenic health were observed across work experience or occupational groups. Paramedics intending to stay in their profession exhibited higher salutogenic health scores than those intending to leave. In the logistic regression models, increasing levels of salutogenic health and work ability were associated with intention to stay in the profession.

Human Activity Recognition System with Smartphones Using Machine Learning

Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.

In Silico Acute Aquatic Hazard Assessment and Prioritization Using a Grouped Target Site Model: A Case Study of Organic Substances Reported in Permian Basin Hydraulic Fracturing Operations.

Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;00:1-12. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Dynamic Voltage and Frequency Scaling as a Method for Reducing Energy Consumption in Ultra-Low-Power Embedded Systems

Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%.

Verification of the Applicability of Obstacle Recognition Distance as a Measure of Effectiveness of Road Lighting on Rainy and Foggy Roads

Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects’ obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.

The Spanish subjunctive mood and its English correspondences

Abstract Corpus-based contrastive studies have successfully addressed the empirical study of crosslinguistic similarities and differences and may also contribute to understanding complexity across languages. This paper aims at (dis)proving whether the Spanish subjunctive mood shows greater complexity than its English correspondences as translations or sources of the Spanish subjunctive forms. It also explores a trade-off between language levels, i.e., whether higher morphological complexity is linked to syntactic and lexical complexity. The data come from a bidirectional English-Spanish corpus, and the word-alignment-based metric system has been used to quantify morphological complexity. Syntactic and lexical complexity were also investigated and computed using tests of statistical significance. Results corroborate that Spanish presents higher complexity in this verbal area in all three levels, morphological, syntactic, and lexical: the hypothesized trade-off between higher morphological complexity on the one hand, and lower syntactic and lexical complexity on the other is not validated by our data.

Autogenic masking of allogenic inputs: Numerical modelling of signal preservation in deep-water fan strata

Variations in climate, sea-level and tectonic processes can be recorded in various ways in strata, and recognising and measuring such external signals preserved in strata is an important aspect of deciphering Earth history. Key questions are what type of signals are likely to be preserved; how input signals may be shredded, over-printed or otherwise reduced before preservation, by stochastic or autogenic processes; and how allocyclic products may be mimicked and masked by autogenic processes. A reduced-complexity numerical forward model of down-slope sediment erosion, transport and dispersive deposition of submarine fan strata is the simplest-possible formulation that produces reasonably realistic strata that can be robustly analysed for signal content across model realisations with a range of external forcing amplitudes and periods. Three different initial topographic surfaces that include increasing influence of random noise allow comparison of the signal preserved with different levels of random influence on the deterministic model. Spectral analysis, with rigorous testing for statistical significance, shows that the signal recorded depends strongly on the level of noise present in the underlying topography, that short-period high-amplitude allocyclic signals are the most likely to be preserved in this case, and that autogenic dynamics in the modelled system can effectively mask even long-period high-amplitude input signals because they have similar periodicities. Autogenic processes in this model can also produce 5:1 bundling of cyclical strata commonly assumed to be strong evidence for orbital forcing. This analysis suggests that we might be substantially underestimating the complexity involved in extracting a signal of external forcing from strata where any autogenic processes operate, to the extent that the two different effects may often be practically indistinguishable, indicating that a multiple hypothesis approach is important to account for the resulting uncertainty.

Effects of beetroot-pineapple juice on hypertensive elderly in Depok

The prevalence of hypertension, based on measurement results among the population aged ? 18 in Indonesia, increased by 8.3% from 2013 to 2018. Due to this rise, there is an evident need for treatments to decrease this rate. Introducing non-pharmacological therapies, such as beetroot and pineapple juice, maybe a viable solution. This study aimed to assess the difference in blood pressure before and after the consumption of beetroot and pineapple juice among elderly hypertensive patients in the Abadijaya Public Health Center region of Depok City. The research followed an experimental design, specifically a pretest-posttest one-group format. The study involved 15 elderly individuals aged between 60-80 years with blood pressure readings of ? 140/90 mmHg. They consumed 300 ml of the juice daily for seven consecutive days. Data analysis included the Shapiro-Wilk test for normality, the paired t-test, and the Wilcoxon test for statistical significance. The findings revealed a significant reduction in blood pressure after the consumption of beetroot and pineapple juice, with a p-value for systolic pressure at 0,000 and diastolic pressure at 0,020 (p<0,05). The average decrease in systolic blood pressure was 13,03 mmHg, and for diastolic blood pressure, it was 4,8 mmHg. There was a discernible difference in blood pressure measurements before and after juice consumption among the elderly hypertensive patients.

Association between Psychological Distress and Possible, Probable, and Definite Sleep Bruxism-A Comparison of Approved Diagnostic Procedures.

(1) Background: The relationship between sleep bruxism (SB) and psychological distress has been investigated in numerous studies and is heterogeneous. Different diagnostic procedures have been applied to determine SB. The aim of this study was to directly compare the association between psychological distress and SB diagnosed by different accepted methods. (2) Methods: Data of N = 45 subjects were analyzed, including group comparisons and correlation analyses. Following diagnostic methods for the determination of SB were used in one sample: self-report, clinical assessment, polysomnography with audio-video recording and a novel diagnostic sheet with analyzing software. Psychological distress was measured using the global severity index (GSI) of the Symptom Checklist-90-Standard (SCL-90-S). (3) Results: The GSI did not differ significantly between subjects with and without SB, regardless of the underlying diagnostic classification (p > 0.05). In-depth correlation analyses of self-report and clinical data revealed a weak-to-medium correlation with the GSI (r = 0.12-0.44). Due to non-normally distributed data, a test of statistical significance was not possible. Variables of instrumental methods such as the SB index (amount of SB activity per hour) of polysomnography (PSG) showed almost no correlation with psychological distress (r = -0.06-0.05). (4) Conclusions: Despite these limitations, the results provide an indication that the choice of diagnostic procedure may elucidate the variance in the correlation between SB and psychological distress.