Statistical Significance Tests Research Articles

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;43:1161-1172. © 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.

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.

Appraisal of EnMAP hyperspectral imagery use in LULC mapping when combined with machine learning pixel-based classifiers

The recent availability of satellite hyperspectral imaging combined with the developments in the classification techniques have paved the way towards improving our ability to obtain information on the spatiotemporal distribution of land use/land cover (LULC) at improved accuracy. In this context, the present study aims at evaluating the combined use of the recently launched Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission with two powerful machine learning (ML) classifiers. In particular, the Support Vector Machines (SVM) and Random Forest (RF) are used synergistically with EnMAP imagery in performing LULC mapping at a typical Mediterranean setting located in northern Greece. Evaluation of the derived LULC maps is based on the computation of a series classification accuracy metrics. The McNemar's chi-square statistical significance testing was also computed to confirm the statistical significance of the differences between the classifiers. In overall, results showed that SVM slightly outperformed RF, exhibiting a higher overall accuracy, of 92.6% and 88.1%, respectively, whereas the statistical significance of the findings was also attested by the McNemar's statistical test results. To our knowledge, this study is one of the first published so far focusing on exploring the capabilities of ENMAP imagery when combined with different ML pixel-based classifiers in the context of LULC mapping. Our results, indeed provided useful insights on the potential of EnMAP datasets in deriving information on the spatiotemporal distribution of LULC at a highly fragmented Mediterranean landscape, evidencing the EnMAP promising potential in this field.

Assessment of public awareness about Semaglutide (Ozempic) use, safety, and adverse drug reaction in Saudi Arabia

Background: Obesity is a growing epidemic in Saudi Arabia, and Semaglutide has emerged as a promising pharmacotherapeutic option for weight management in obese patients. This study aims to assess public knowledge about Ozempic (semaglutide) use, safety, and adverse drug reactions in Saudi Arabia. Methods: We conducted a cross-sectional survey to collect data among the adult population in Saudi Arabia. Statistical tests of significance, including the chi-squared test and Spearman\&amp;apos;s rho correlation test, were employed to examine differences between groups and assess correlations between variables. Results: Of the 739 participants, 50.7% were females, and most participants had a bachelor&amp;apos;s degree level of education. Study respondents reported that they had heard of Ozempic, but only 244 (58%) knew its indication (Type 2 DM). The analysis showed that good knowledge was observed among the 31–40 and 18–30 age groups compared to other age groups (p = 0.020); no other sociodemographic variables showed a significant association with the knowledge level (p &amp;gt; 0.05).The questionnaire was distributed to a representative sample with a minimum of 385 participants among the Saudi population, with a 95% confidence level and a margin of error of 5%, through online platforms. Data were collected and analyzed using descriptive and inferential statistical methods. Conclusion: Semaglutide is a valuable medication for the treatment of type 2 diabetes and, under appropriate circumstances, for weight loss management. The study showed that the majority of the participants used it for weight loss management, and their awareness regarding its indications, contraindications, and adverse effects was not adequate.

Wingate normative-reference values for a large cohort of Canadian university students

Abstract Study aim: The Wingate Anaerobic Test evaluates anaerobic power and capacity. Used to assess performance, historically among athletic populations, its evaluative capacity for individuals of varying athletic abilities is limited by a lack of normative data based on large participant populations. This study developed Wingate normative-reference values based on a large-scale cohort that is representative of the Canadian university student population. Material and methods: Data were collected from 872 participants, aged 20 to 29 years (mean body mass index [BMI]: 24.44 kg/m2). Testing was completed on a cycle ergometer using a widely recognized protocol, with resistance set at 7.5% of participants’. An independent samples t-test was used to compared the means of dependent variables (i.e., peak power [PP], mean power [MP], and fatigue index fi) and test for statistical significance (p &lt; 0.05) between sexes, and Cohen’s d determined effect size. Results: Males had higher PP and MP (W and W · kg−1), whereas females exhibited lower FI (%). Statistically significant differences between sexes were observed for all variables. Conclusions: Collected data yielded normative-reference standards, including percentile rankings and performance classifications. These norms will allow for significant practical applications, including an effective method to assess anaerobic performance and health.

Geographic atrophy (GA) secondary to AMD: Online medical education can significantly improve ophthalmologists' knowledge and confidence on evidence‐based guidance for patient management

Aims/Purpose: To determine if online medical education for ophthalmologists (Ophth) could improve knowledge for GA disease impact, patient assessment and confidence in emerging therapies.Methods: Ophth participated in an online 30 min video, 3 expert roundtable discussion with accompanying slides. A repeated pairs pre−/post‐assessment study design was used, assessing three multiple choice knowledge and one confidence question. Statistical significance tests: McNemar's test for individual questions (5% significance level, p &lt; 0.05), paired sample t‐test for overall average correct responses and confidence rating; data collected 16 December 2022 to 22 March 2023. Test completers: 230.Results: At baseline, 3% Ophth answered all three questions correctly, increasing to 37% (p &lt; 0.01) post‐assessment. 58% Ophth reported improved confidence understanding the rationale for targeting the complement system with GA therapies. The activity significantly increased knowledge on impact of GA on time to loss of ability to drive (pre: 7%, post: 52%, p &lt; 0.001), on recall of detrimental lesion characteristics (pre: 40%, post: 67% p &lt; 0.01) as well as capability of automated OCT analysis algorithms to predict GA progression (pre: 51%, post: 69%, p &lt; 0.001). Only 24% Ophth polled currently document &gt;75% patients with advanced atrophic AMD with OCT/FAF imaging.Conclusions: Online medical education in the form of expert roundtable discussions with accompanying slides can significantly improve knowledge on assessing patients with GA using imaging, understand the rationale to treat and increase confidence in understanding the role of complement inhibition. Polling showed a lack in documenting patients for GA progression. Complementary education is warranted during the introduction of a novel therapeutic horizon in GA management in clinical practice to support evidence‐based patient selection guidance for emerging therapies.

Therapeutic Measures from Cardiovascular Damage using Statins, Acetylsalicylic Acid during the Abuse of Irrational Cycles of Anabolic Steroids in Bodybuilder Who Were Infected with the COVID-19

Introduction: We are increasingly witnessing the self-initiated, uncontrolled stacking of incompatible anabolic substances in various cycles in the young Balkan Recreational Bodybuilding population group, increasingly frequent acts of violence in our country and the emergence of a neglected and very silent epidemic that is taking on an increasingly aggressive course of illness and psychological behavior. Black market anabolic steroids are associated with many generally health problems and uncontrolled aggression behavior by abusers. Young unsolicited recreate bodybuilding are also associated with generalized atherosclerosis and potential increased high risk for heart disease that can structural and functional damage the cardiovascular system during muscle mass steroid cycles. Besides high calorie intake, elevating serum blood lipid cholesterol levels LDL lowering HDL levels, elevating, systolic and diastolic blood pressure take a certain participation in cardiovascular risk and therefor need a development of certain cardiovascular protection strategies for this avoiding medical supervision anabolic steroids abuser group which are not yet appropriate established. The aim of the short study was to estimate Cardiovascular Protective Strategy Measures on lipid levels, and blood pressure status in a young recreational bodybuilder with different ethnic groups who Abuse Anabolic Steroids during controversial and different mass Cycles with and without taking Polyunsaturated Fatty Acids and Acetylsalicylic Acid (ASA) and Statins. Subjects and methods: This study was conducted from beginning of January, 2022 till end of July, 2022. 140 subjects are included age 17-30 (74 male Recreational Bodybuilder Who Abuse Anabolic Steroids on Simvastatin’s 10mg and Acetylsalicylic Acid 150mg doses of (ASA) and 10 grams of Polyunsaturated Fatty Acids during mass Steroid Cycles and 66 impellers group of male Recreational Bodybuilder Who Abuse Anabolic Steroids consisting without consume Statins and Acetylsalicylic Acid (ASA) during mass Steroid Cycles). For the testing of statistical significance of differences between the exanimated groups non-parameter and parameter tests were used. The difference at a level of p&lt; (0,001) was statistically significant. Results: In all the tested subjects we investigated increasing lipid levels and blood pressure after six months of study. Analysis shows the statistically insignificant influence of antiplatelet therapy (150mg Acetylsalicylic acid) (ASA), combined with Simvastatin 10mg p&lt;(0,001) in steroid abuse subjects during irrational muscle mass steroid cycles. Conclusion: Lack of familiarity with combinations of anabolic substances, as well as the danger of oral applications of very harmful steroids from the former Soviet Union and German Democratic Republic (GDR), this study showed. Avoidance of physician supervision by recreate bodybuilders by deceiving of the athletic subjectivity look, seams appears almost always in gym. This cardiovascular protective measures have poor benefit appeared to be usefully blind preventing strategy of controlling blood lipid levels during muscle mass steroid cycles, with no guaranty of worsen cardiovascular condition by abusing anabolic steroids. The influence of the reflection of the COVID 19 virus on such dramatic therapeutic results in this population group of two groups cannot be ruled out for sure.

Risk assessment and spatio-temporal characteristics analysis of water bloom in three large-scale eutrophic reservoirs in Fujian Province, China

The occurrence of water blooms in reservoirs poses a serious threat to water safety, so the assessment of water bloom risk is crucial. In this study, we constructed a reservoir water bloom risk index (RWBRI) using the trophic state index (TSI), floating algae index (FAI), temperature, precipitation, and wind speed datasets, and estimated and validated the water bloom risk levels of three large-scale eutrophication reservoirs in Fujian Province, China, from 2019 to 2023 based on the Sentinel-2 MSI dataset at the pixel scale. The results showed that there is no positive correlation between the TSI and FAI parameters of the three reservoirs (the significance test coefficients for Shanzai Reservoir, Dongzhen Reservoir, and Shanmei Reservoir were −0.455**, −0.371**, and −0.049, respectively, and ** indicates that the reservoir passed the statistical significance test at the 0.01 significance level). The TSI of Shanmei Reservoir was less fluctuating and more stable than that of Shanzai Reservoir and Dongzhen Reservoir, but the average value of FAI was higher. The average value of water bloom risk in Shanzai Reservoir and Dongzhen Reservoirs is higher than Shanmei Reservoir (0.47, 0.46, and 0.41, respectively), but the trend of annual average changes indicates that the overall risk of Dongzhen Reservoir is decreasing, while the risk of Shanzai Reservoir and Shanmei Reservoir is increasing. Due to the influence of weather, the water bloom risk in spring and summer is higher than that in fall and winter in the three reservoirs, and the highest risk value usually occurs in summer. The high values in Shanzai Reservoir were mainly distributed in the two inlet channels in the northern and southwestern parts of the reservoir, the bank risk was higher than in the other areas of Dongzhen Reservoir, and the high value of the risk in Shanmei Reservoir was mainly distributed in the center of the reservoir. The RWBRI method can be used for water bloom risk identification in reservoirs with eutrophication trends by comparing it with four-hourly and monthly water quality level data.

A Regression Model of Dry Bulk Carriers’ Loading Time

Abstract Although optimization of Vessel Turnaround Time (VTT) is a well-known research problem and its importance has long been understood, research on dry bulk carriers VTT has no priority in the available literature. It is one of the initial motives of the author to write this paper. After a general theoretical introduction, this paper presents research on dry bulk carriers’ loading time, as a component with the dominant share in the total VTT. Through the research, a mathematical model of interdependencies between dry bulk carrier‘s loading time and selected independent variables is defined using a multiple linear regression model. Results of statistical significance tests confirmed that the best-fit regression model is the one which adequately describes the correlation between the dry bulk carrier‘s loading time and the following independent variables: cargo quantity (loaded), number of used cranes per vessel and loading process interruptions. The presented results establish important bases for the author’s further research in this field as well as reliable planning bases for cargo handling management processes at dry bulk cargo terminals where cargo loading/unloading to/from vessels is realized by gantry cranes and/or mobile harbour cranes.

The Belt and Road Initiative's Construction Projects Exert a Substantial Influence on Participating Countries' Economic Landscapes, Eliciting A Noteworthy Impact on their Financial Stability, Infrastructure Investments, and Fiscal Growth

The one belt one road initiative, spearheaded by China, stands as a monumental foreign policy endeavour designed to strengthen ties with 146 nations. The construction projects within this initiative wield a substantial influence on the economic landscapes of participating countries, significantly affecting financial stability, infrastructure investments, and fiscal expansion. Studies utilizing CO2 emissions as a function of financial data (FD), Foreign Direct Investment (FDI), Gross Domestic Product (GDP), squared Gross Domestic Product (GDP2), economic indicators (EC), and trade-related factors (TR) have demonstrated a statistically positive relationship between these variables. The study spans from 2014 to 2021, employing panel data regression techniques to analyze the impact of the One Belt One Road initiative on participating countries' economic landscapes. Utilizing dynamic seemingly unrelated regression (DSUR) panel estimation, the study assesses the initiative's impact on macroeconomic factors such as GDP, FDI, EPI, trade, and FDPS. Statistical significance tests, including p-values and F-statistics, are employed to gauge the significance of the relationship between these factors and the initiative. Null hypothesis testing is utilized to determine whether a variable Granger causes another variable, providing further insights into causal relationships. R2 values are employed to quantify the proportion of variance in the dependent variable attributable to the independent factors. These statistical methodologies offer a robust and empirical approach to comprehending the one belt one road initiative's profound impact on the economic landscapes of participating nations.

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care. Using the Azure OpenAI application interface, this model evaluation study tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain-namely, medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in health care. GPT-4 estimates of the demographic distribution of medical conditions were compared with true US prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups. We found that GPT-4 did not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardised clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and genders. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception. Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools such as GPT-4 for intended use cases before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies before clinical implementation. Priscilla Chan and Mark Zuckerberg.