Tag: statistics

Sci Data. 2025 Aug 6;12(1):1371. doi: 10.1038/s41597-025-05723-0.

ABSTRACT

Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China’s crop patterns and can serve as critical input for biogeochemical and agricultural models.

PMID:40770232 | DOI:10.1038/s41597-025-05723-0

Sci Rep. 2025 Aug 6;15(1):28698. doi: 10.1038/s41598-025-14698-2.

ABSTRACT

The adoption of Artificial Intelligence (AI) is reshaping the labor market; however, individuals’ perceptions of its impact remain inconsistent. This study investigates the presence of the Invulnerability Bias (IB), where workers perceive that AI will have a greater impact on others’ jobs than on their own, and Optimism Bias by Type of Impact (OBTI), where individuals perceive AI’s future impact on their own job as more positive than on others’. The study analyzes survey data collected from 201 participants, recruited through social media using convenience sampling. The data were analyzed using a combination of statistical and machine learning methods, including the Wilcoxon test, ordinary least squares regression, clustering, random forests, and decision trees. Results confirm a significant IB, but not OBTI; only 31.8% perceived AI’s future impact on their own job as more positive than on others’. Analysis shows that greater knowledge of AI correlates with lower IB, suggesting that familiarity with AI reduces the tendency to externalize perceived risk. Furthermore, bias levels vary across professional sectors: healthcare, law, and public administration exhibit the highest IB, while technology-related professions show lower levels. These findings highlight the need for interventions to improve workers’ awareness of AI’s potential future impact on employment.

PMID:40770226 | DOI:10.1038/s41598-025-14698-2

Sci Rep. 2025 Aug 6;15(1):28723. doi: 10.1038/s41598-025-14406-0.

ABSTRACT

Alzheimer’s disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transition time from Cognitive Normal (CN) to Mild Cognitive Impairment (MCI) in elderly individuals, considering the predictive value of baseline comorbidities. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) databases, we construct feature sets encompassing demographics, cognitive scores, and comorbidities. Various machine learning and deep learning methods for survival analysis are employed. Our top-performing model, fast random forest, achieves a concordance index of 0.84 when considering all feature modalities, with comorbidity data emerging as a significant predictor. The top features identified by the best-performing model include one demographic feature (age), seven cognitive scores (ADAS13, RAVLT learning, FAQ, ADAS11, RAVLT immediate, CDRSB, ADASQ4), and two comorbidities (Endocrine & Metabolic, Renal & Genitourinary). Age is highlighted as the most influential predictor, while cognitive scores are crucial indicators of Alzheimer’s disease. External validation against the AIBL dataset affirms the robustness of our approach. Overall, our study contributes to a deeper understanding of the role of baseline comorbidities in AD risk prediction and emphasizes the importance of incorporating comprehensive feature assessment in clinical practice for early diagnosis and personalized treatment planning.

PMID:40770222 | DOI:10.1038/s41598-025-14406-0

Sci Rep. 2025 Aug 6;15(1):28719. doi: 10.1038/s41598-025-09508-8.

ABSTRACT

With the instantaneous economic development, air quality keeps on dwindling. Some key factors for the emergence and evolution of air pollution are high-intensity pollution emissions and adverse weather circumstances. In air pollutants, Particulate Matter (PM) possessing less than 2.5Mu is considered the most severe health issue, resulting in respiratory tract illness and cardiovascular disease. Therefore, it is mandatory to predict PM 2.5 concentrations accurately to ward off the general public from the desperate influence of air pollution in advance owing to its complex nature. Aiming at the complexity of air quality prediction, a new method called Hebbian Concordance and Attention-based Long Short-Term Memory (HC-ALSTM) is proposed. The HC-ALSTM method is split into four sections. They are preprocessing using the Statistical Normalization-based Preprocessing model, feature extraction employing the Generalised Hebbian Spatio Temporal Feature extraction model, feature selection using Concordance Correlation function, and Attention-based Long Short-Term Memory for air quality prediction. First, the Statistical Normalization-based Preprocessing model is applied to the raw dataset to normalize the impact of distinct air pollutants on the bordering factor. Second, with the Generalised Hebbian Spatio Temporal Feature extraction algorithm, processed samples are applied to extract the dimensionality-reduced spatio-temporal feature. Third, with the extracted features, essential or significant features are selected using Concordance Correlation analysis that determines the impact of pollutant concentration of bordering places for predicting air quality index involving both city and state, daily and hourly. Finally, Attention-based Long Short-Term Memory is applied to the extracted and selected features to predict air quality accurately. Through evaluation and analysis using two other evaluation methods, the proposed HC-ALSTM method performs better in error and time. Our method has dramatically improved air quality prediction accuracy and overhead compared with other methods.

PMID:40770205 | DOI:10.1038/s41598-025-09508-8

Sci Rep. 2025 Aug 6;15(1):28802. doi: 10.1038/s41598-025-13041-z.

ABSTRACT

Due to the uncertainty ​​of geomechanical parameters, it is necessary to investigate the risks arising from geotechnical hazards in tunnel design from a statistical perspective. In this study, a hybrid quantitative approach incorporating uncertainty in geomechanical parameters, the finite element method (FEM), and the variation coefficient method (VCM) was employed to investigate geotechnical hazards in the Alborz tunnel. At first, by considering five statistical intervals [µ, µ + 0.5(SD), µ-0.5(SD), µ + SD, and µ-SD], different values ​​of geomechanical parameters including uniaxial compressive strength of intact rock, density, depth, Young’s modulus and tensile strength of rock mass, cohesion, friction angle, and in situ stress ratio were generated for three formations of the tunnel. Next, five numerical simulations were performed for each formation using the finite element method. The outputs of the FEM analysis, including the maximum displacement, the maximum axial force in the support systems, and the major principal stress around the tunnel, were used to calculate the three geotechnical hazards namely squeezing potential, seismic activity effects, and stress concentration around the tunnel. The results showed that the vulnerability index for squeezing potential has the highest value in each geological formation. The results of this study and the presented approach can be used as a hybrid model for investigating and predicting hazards in rock tunnels. Additionally, this approach reduces the negative impact of uncertainty in geomechanical parameters on safe and economical design in underground spaces.

PMID:40770202 | DOI:10.1038/s41598-025-13041-z

Sci Rep. 2025 Aug 6;15(1):28693. doi: 10.1038/s41598-025-10837-x.

ABSTRACT

In the current industrial scenario, permanent magnet synchronous motors are widely employed for drive based applications and many other robotics and machine tool applications. With a simple structure and high torque-to-inertia ratio, PMSM are able to be operated even in medical industry and laboratory experimentation set ups. The main limitation of PMSM is the presence of inherent coupled flux and torque which makes it very difficult to control. This paper focuses on fuzzy based PI controllers along with novel neural based controller for speed control of PMSM. A novel slime mould algorithm based Elman spiking neural network (ESNN) model hybridized with fuzzy inference proportional-integral controller is designed in this paper to regulate the speed and torque of permanent magnet synchronous motor drive. Due to the existence of randomness in the proposed soft computing controller, it is tested for its validity and suitability by performing statistical analysis and is observed to be valid to act as a controller model for PMSM drive mechanism. In this paper, this soft computing controller possess randomness during first phase of weight update and during optimal gain value determination Simulation process for the designed new soft computing controller was done in MATLAB.

PMID:40770200 | DOI:10.1038/s41598-025-10837-x

NPJ Breast Cancer. 2025 Aug 6;11(1):85. doi: 10.1038/s41523-025-00804-0.

ABSTRACT

U.S. Black women have disproportionately high breast cancer mortality, partly due to later-stage diagnoses. We examined how social drivers of health (SDOH) relate to stage at diagnosis by analyzing data from 4,995 breast cancer survivors in the Black Women’s Health Study, Carolina Breast Cancer Study, and Women’s Circle of Health Studies. SDOH were self-reported and stage was ascertained from medical records. We used polytomous logistic regression to estimate odds ratios (ORs) for diagnosis at stages III/IV or II versus stage I (referent), adjusting for age, insurance status, and income. Meta-analyzed results indicated that underutilization of screening mammography (OR = 3.21, 95% CI 1.90-5.43) and income below the federal poverty line (OR = 1.91, 95% CI 1.17-3.10) were significantly associated with later stage diagnosis (III/IV). ORs for lack of insurance and lower education were above 1.0, but not consistently statistically significant. These findings substantiate the importance of the affordability and utilization of breast cancer screening.

PMID:40770199 | DOI:10.1038/s41523-025-00804-0

Neuropsychol Rev. 2025 Aug 7. doi: 10.1007/s11065-025-09671-9. Online ahead of print.

ABSTRACT

Dynamic functional connectivity (dFC) methods could shift understandings about brain-behavior relationships. Information processing speed (IPS) may be of particular interest to dFC analyses as dFC is able to capture time-sensitive FC changes. The present systematic review aims to explore the association between IPS and dFC of resting-state functional magnetic resonance imaging (rsfMRI) data in healthy individuals. Included papers were published through July 24, 2023. Searches conducted on ProQuest and ScienceDirect used the search terms processing speed AND fMRI AND resting state AND dynamic functional connectivity OR dynamic functional network connectivity. Studies were eligible based on the following inclusion criteria: empirical research, published in English, use of a well-characterized healthy population (n > 30), use of rsfMRI, calculation of dFC, measurement of IPS, and a statistical test between dFC and IPS. Results reveal mixed findings. Five studies report no relationship between dFC and IPS, whereas eight report mixed or positive findings. We noted several trends in findings that may be driving inconsistencies. Over half of the reviewed studies used the Human Connectome Project data. Second, IPS was more likely to be related to dFC if images were acquired using an eyes open procedure with fixation on a crosshair. As all included IPS measures involved a visual component, IPS and dFC measurement might both be capturing information about visuoperceptual connections. Future work that addresses these biases and trends may illuminate the nature of the relationship between dFC and IPS.

PMID:40770165 | DOI:10.1007/s11065-025-09671-9

J Epidemiol Glob Health. 2025 Aug 6;15(1):103. doi: 10.1007/s44197-025-00446-2.

ABSTRACT

AIM: To describe the epidemiological characteristics of Middle East respiratory syndrome coronavirus (MERS-CoV) human cases since the first reported case in 2012.

METHODS: This is a retrospective descriptive epidemiological analysis of all laboratory-confirmed MERS-CoV human cases reported to the World Health Organization (WHO) from 2012 to May 2025. Cumulative cases globally, along with their demographics, comorbidities, epidemiological exposure, symptoms, hospital admissions, and mortality, were included. Descriptive analysis was used for the data.

RESULTS: Between March 2012 and May 2025, a total of 2,626 laboratory-confirmed MERS-CoV human cases were reported to the WHO, with 947 (36.1%) resulting in deaths. The majority of cases occurred in the Kingdom of Saudi Arabia (KSA), with 2,217 (84.4%) human cases and 866 (39.1%) deaths. Twenty-six other countries reported human cases, with the highest number occurring in South Korea, which reported 186 cases (7.1%). The highest number of cases occurred in 2014, with 662 (29.9%) cases, followed by 2015, with 453 (20.4%) cases. Almost half of the cases in KSA (44.7%) were secondary infections, and most (83%) required hospital admission, with 39.7% requiring admission to intensive care unit. The most common comorbidities were diabetes mellitus, chronic heart disease, and chronic renal failure. Between 2020 and the end of May 2025, 113 new human cases of MERS-CoV infection (4.3%) were reported, with the majority occurring in KSA. In 2025 alone, 10 new cases were reported, with two deaths. Secondary transmission occurred in 60% of these cases. Seven of the 10 cases were reported in April 2025 alone.

CONCLUSION: Between 2012 and May 2025, the majority of MERS-CoV infections occurred in the Kingdom of Saudi Arabia and had a high mortality, reaching 40%. Although most cases were reported between 2014 and 2015, new human cases are still ongoing and are increasing in 2025. Continued epidemiological investigation and surveillance are needed.

PMID:40770164 | DOI:10.1007/s44197-025-00446-2

J Appl Genet. 2025 Aug 7. doi: 10.1007/s13353-025-00998-w. Online ahead of print.

ABSTRACT

The analysis of genotype-by-environment interaction (GEI) in multi-environmental trials (METs) represents a crucial component of breeding programs prior to the release of new commercial cultivars tailored for specific regions or diverse environmental conditions. Moreover, emphasizing individual traits during selection can yield misleading conclusions. Consequently, the implementation of robust selection models is essential for identifying superior genotypes based on multiple traits. The present dataset demonstrates the utility of the multi-trait stability index (MTSI) in identifying high-yielding and stable barley genotypes across ten diverse environments. The evaluated phenological and agronomic traits included days to heading, days to physiological maturity, grain-filling period, plant height, thousand-kernel weight, and grain yield. A combined analysis of variance (ANOVA) revealed significant effects attributable to environments (E), genotypes (G), and their interaction (GEI) across all assessed traits. Correlation analysis further indicated positive associations between all measured traits and grain yield. In the MTSI model, three first factors accounted for 75% of the total phenotypic variation observed across the test environments. The highest selection gain percentages were recorded for thousand-kernel weight and grain yield. Among the genotypes evaluated, G3, G10, and G14, characterized by the lowest values of the MTSI index, were identified as superior in terms of grain yield, stability, and desirable agronomic attributes. In conclusion, the findings highlight the efficacy of the MTSI in reliably identifying superior genotypes in METs. The results demonstrate that the MTSI index not only enhances the efficiency of the selection process but also improves the accuracy of genotype evaluation and ranking across heterogeneous environmental conditions. This underscores the potential of the MTSI index to support informed breeding decisions, ultimately facilitating the development of high-performing plant varieties that exhibit both yield stability and adaptability across diverse environments.

PMID:40770158 | DOI:10.1007/s13353-025-00998-w