Nevin Manimala

Sci Rep. 2025 Dec 12. doi: 10.1038/s41598-025-31174-z. Online ahead of print.

ABSTRACT

Control charts are widely used in manufacturing and quality management to monitor process variability. Although easy to implement, the Shewhart control chart lacks sensitivity to small process shifts. The exponentially weighted moving average (EWMA) control chart is effective in detecting minor shifts, but responds slowly to sudden changes. To enhance the detection capability, this study adopts the adaptive EWMA (AEWMA) control chart, which features dynamic adjustment mechanisms to improve the monitoring performance. This is applicable for skewed process data, particularly those following a Gamma distribution such as lifetime data, waiting times, and current stability indicators in semiconductor manufacturing. The Wilson-Hilferty transformation was applied to approximate the normality before constructing the AEWMA control chart. Monte Carlo simulations were employed to design and evaluate both the fixed sampling interval (FSI) and variable sampling interval (VSI) schemes under various combinations of shapes, scale parameters, and smoothing constants. To address the limitations of the traditional average run length (ARL) in reflecting the differences in sampling schemes, this study also adopted the average time-to-signal (ATS) as a performance metric. The simulation results demonstrated that the AEWMA VSI chart outperformed both the AEWMA FSI and EWMA charts in terms of sensitivity and stability when detecting small process shifts.

PMID:41388064 | DOI:10.1038/s41598-025-31174-z

Sci Rep. 2025 Dec 12. doi: 10.1038/s41598-025-29755-z. Online ahead of print.

ABSTRACT

Earthquake early warning systems (EEWS) aim to warn end-users of impending ground shaking. They can be most impactful in large earthquakes occurring close to large population centers, where exposure to strong ground shaking is extensive. However, such earthquakes are rare, and EEWS performance expectations remain uncertain. The February 2023 Kahramanmaraş, Türkiye sequence, including the M7.8 Pazarcık and M7.5 Elbistan events, exposed millions to strong ground shaking and produced a rich waveform dataset, offering a test case. We use this data to produce a realistic simulation of warning times. We use the EPIC point source algorithm for real-time earthquake characterization, and incorporate alert delivery latency using a statistical model driven by real-world alert delivery data from California, collected by the MyShake smartphone app. We show EPIC would produce solutions very quickly (4 s for Pazarcık, 10 s for Elbistan). Despite EPIC’s expected magnitude underestimation (peak M6.7 for Pazarcık, M7.2 for Elbistan), we show that its magnitude estimate grows large quickly enough to provide areas of MMI 6+ shaking with up to 20 s of warning time, even with alert delivery latencies included, provided that low alerting thresholds of MMI 3 or 4 are used.

PMID:41388057 | DOI:10.1038/s41598-025-29755-z

Sci Rep. 2025 Dec 12;15(1):43713. doi: 10.1038/s41598-025-27466-z.

ABSTRACT

The use of a plough is characterized by the presence of a turning moment acting in a clockwise direction. When placing the plough resistance center to the left of the tractor’s longitudinal symmetry axis, it becomes possible to use the plough with one landside placed on its last bottom. The article presents a new analytical dependence that allows determining the design and technological parameters of the plow using the transverse displacement of the center of resistance to the left of the tractor’s longitudinal axis of symmetry. Studies have shown that using a plough with one landside instead of five does not affect the ploughing depth and the operating width. At the statistical significance level of 0.05, the null hypothesis about the equality of the mean values and variances of these parameters’ oscillations is not rejected. The normalized correlation functions and spectral densities of these processes’ oscillations are approximately the same. Using a plough with one landside instead of five leads to a 10.3% decrease in draft resistance, accompanied by a 10% decrease in specific fuel consumption. This study fills a gap in the practical operation of plows, aimed at reducing their draft resistance, which is key in reducing fuel consumption.

PMID:41388049 | DOI:10.1038/s41598-025-27466-z

Sci Rep. 2025 Dec 12. doi: 10.1038/s41598-025-31610-0. Online ahead of print.

ABSTRACT

This study presents a multi-temporal geospatial modeling approach to identify and map land degradation and desertification hotspots in the aeolian-dominated semi-arid regions of Bommanahal, Beluguppa, and Kanekal Mandals of Anantapur District, Andhra Pradesh, India. Landsat 4-5 TM (1990), Landsat 7 ETM+ (2000, 2010), and Landsat 8 OLI/TIRS (2020) datasets were processed through a standardized workflow comprising radiometric calibration, atmospheric correction, LST retrieval, and spectral index computation. Three diagnostic indices: Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI), and Normalized Difference Salinity Index (NDSI), were integrated with Land Surface Temperature (LST) to quantify vegetation stress, soil texture variability, and salinity conditions. Correlation and regression analyses were employed to evaluate the relationships between LST and index-derived DN values, after which stratified sample extraction and buffer-based zonal statistics were used to delineate surface degradation intensity. A composite hotspot map was generated using mask extraction and cell statistics to merge the most degraded pixel clusters, identifying approximately 192 km² as severe degradation zones. Model performance was validated using ROC-AUC analysis, yielding an accuracy of 0.851. The study demonstrates a reproducible GIS workflow for semi-arid degradation assessment and provides a robust spatial framework for targeted land restoration, sustainable resource planning, and long-term environmental management in vulnerable dryland ecosystems.

PMID:41388010 | DOI:10.1038/s41598-025-31610-0

J Clin Periodontol. 2025 Dec 12. doi: 10.1111/jcpe.70075. Online ahead of print.

ABSTRACT

AIM: Effective prevention of periodontitis requires addressing multiple modifiable health behaviours (MHBs). This study aimed to examine how MHBs interact and to identify the key behaviours that could serve as targets for intervention.

MATERIALS AND METHODS: We analysed data from a large, population-based survey using questionnaires conducted in China during 2021-2023. Logistic regression was used to examine associations between self-reported periodontitis and multiple MHBs. Network analysis was applied to assess interrelationships among MHBs and identify central MHBs.

RESULTS: Among 85,483 participants, 12.8% reported periodontitis. Ten MHBs were associated with self-reported periodontitis. Ever smoking, ever drinking, unhealthy bedtime, frequent consumption of sugar-sweetened snacks and beverages, insufficient vegetable or fruit intake and toothbrushing less than twice daily were linked to higher prevalence, while insufficient physical activity, no daily flossing and absence of annual dental checkups were linked to lower prevalence. Network analysis identified ever smoking, ever drinking, toothbrushing frequency and dental checkups as the most central behaviours, with variations observed across different subgroups.

CONCLUSION: This study identified key MHBs and revealed differences in central behaviours across subgroups. Ever smoking, ever drinking, toothbrushing frequency and annual dental checkups may represent priority targets for tailored prevention strategies. Future research is warranted to inform integrated interventions.

PMID:41387986 | DOI:10.1111/jcpe.70075

J Oral Rehabil. 2025 Dec 12. doi: 10.1111/joor.70136. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare clinical examination findings, functional limitations, and psychological profiles between patients with subclinical temporomandibular disorders (TMD) and those with self-reported painful TMD.

MATERIALS AND METHODS: This cross-sectional study investigated 98 subclinical TMD patients and 262 self-reported painful TMD patients attending a specialised orofacial pain clinic. Subclinical TMD was defined as positive palpation findings without muscle or joint pain complaints. Self-reported painful TMD required both pain complaints and positive clinical findings. Validated questionnaires assessed jaw functional limitations (JFLS-20), depression (PHQ-9), stress (SPST-20), and oral behaviours (OBC). Statistical analysis used Mann-Whitney U tests and chi-square tests with Bonferroni correction.

RESULTS: Self-reported painful TMD patients were significantly older (median age 39 vs. 31 years, p = 0.003), showed greater jaw functional limitations (JFLS global score: 1.88 vs. 0.75, p < 0.001), and higher depression scores (PHQ-9: 1.0 vs. 0.0, p < 0.001). Sleep bruxism frequency differed significantly between groups (p < 0.001), with subclinical patients more commonly reporting frequent sleep bruxism. No significant differences were found in stress levels, overall oral behaviours, or daytime clenching frequency.

CONCLUSIONS: Patients with self-reported painful TMD demonstrate distinct clinical profiles characterised by greater functional impairment and psychological distress compared to subclinical cases.

CLINICAL RELEVANCE: These findings suggest different phenotypes within the TMD spectrum that may require tailored clinical approaches. Longitudinal studies are needed to determine whether subclinical TMD represents an early stage or a distinct entity.

PMID:41387980 | DOI:10.1111/joor.70136

Sci Rep. 2025 Dec 12;15(1):43721. doi: 10.1038/s41598-025-27511-x.

ABSTRACT

Income-based disparities has a profound impact on health outcomes by limiting access to preventive care, early diagnosis, and effective treatment with chronic conditions such as diabetes. Our aim was to investigate the impact of income-based disparities on diabetic foot amputation rates, focusing on disparities in outcomes among income levels. This retrospective cohort study used data from the Korean National Health Insurance Service database that tracked 2,580,585 diabetic patients between 2009 and 2012 (baseline) and followed them through 2019 using claims and mortality data to identify incident lower extremity amputation. Participants were categorized into income quartiles (Q1-Q4) with Q1 being the lowest-income group including Medical Aid beneficiaries. The primary outcome was the first occurrence of diabetes-related lower extremity amputation (LEA). LEA performance rates were inversely related to income, with Q1 + Medical Aid having the highest rate (0.64 per 1,00 person-years) and Q4 the lowest (0.45 per 1000 person-years). Adjusted models revealed a protective effect of higher income on LEA. The difference appeared to be more pronounced in individuals younger than 65 years, based on stratified analysis. Income-based disparities were observed in diabetic foot outcomes, with higher income groups showing improved limb preservation, especially among younger individuals. Given the observational design, these findings should be interpreted as associations rather than causal relationships. These findings suggest that targeted interventions may help reduce income-based disparities, alleviate the socioeconomic burden of diabetes, and improve quality of life in vulnerable populations.

PMID:41387978 | DOI:10.1038/s41598-025-27511-x

Sci Rep. 2025 Dec 12. doi: 10.1038/s41598-025-29171-3. Online ahead of print.

ABSTRACT

Understanding the movement behavior of Japanese sea cucumbers (Apostichopus japonicus) is essential for ecological research and fisheries management. However, tracking their locomotion is challenging due to their slow movement and environmental variability. In this study, we employed acoustic telemetry combined with a data assimilation approach using the Kalman filter to estimate movement trajectories with high accuracy, overcoming the limitations of traditional visual tracking methods. To characterize movement complexity, we applied fractal dimension analysis, quantifying the randomness and variability of individual locomotion across different environmental conditions. Additionally, we examined the influence of key environmental factors, including water temperature, diel cycles, and boulder presence, using Generalized Linear Models (GLM). The results indicate that during the growing stage, higher water temperatures significantly increased movement activity, while boulder zones influenced movement differently depending on the season. This study also provides long-term tracking data on released sea cucumbers, offering new insights into their settlement and dispersal patterns. By combining acoustic telemetry, data assimilation, fractal analysis, and statistical modeling, we established a framework to investigate the behavioral dynamics of slow-moving benthic organisms. These findings enhance our understanding of sea cucumber ecology and provide a quantitative framework for future studies on marine invertebrate movement.

PMID:41387973 | DOI:10.1038/s41598-025-29171-3

Sci Data. 2025 Dec 12. doi: 10.1038/s41597-025-06287-9. Online ahead of print.

ABSTRACT

India has set an ambitious target of achieving net-zero carbon emissions by 2070. Road transport, contributing to 12% of India’s CO₂ emissions, plays a significant role in exacerbating urban air pollution. Given India’s swift urbanization, CO₂ emissions from this sector could potentially double by 2050, jeopardizing climate goals. We present CHETNA-Road, a comprehensive 500-meter gridded city traffic emissions dataset for 15 Indian cities derived from street-level floating car data (FCD) based on GPS position and speed of individual vehicles. We applied statistical and machine-learning techniques to improve data quality and extrapolated data to cover all city traffic instead of only the vehicles equipped with GPS using fuel consumption data. We estimated daily CO₂ and ten major pollutant emissions using the COPERT model, which includes speed and vehicle-type dependent emission factors. Finally, we evaluated our dataset against global coarser resolution emission datasets, including Carbon-Monitor cities, EDGAR, and CAMS. Our dataset provides critical insights into India’s road traffic emissions and serves as a foundation for targeting congestion and pollution reduction strategies.

PMID:41387954 | DOI:10.1038/s41597-025-06287-9

Cardiooncology. 2025 Dec 12. doi: 10.1186/s40959-025-00414-6. Online ahead of print.

ABSTRACT

BACKGROUND: Cancer survivors (CSs) are at increased risk of atrial fibrillation (AF), potentially due to cancer-related inflammation and treatment effects. While inflammation has been implicated in both cancer and AF, the association between C-reactive protein (CRP) and AF risk in CSs remains unclear.

METHODS: We analyzed data from 19,677 UK Biobank participants (mean age 60; 34.2% male) with a prior cancer diagnosis. Incident AF was evaluated using competing-risk Cox proportional hazards models, adjusting for sociodemographic, lifestyle, and clinical factors.

RESULTS: Over a median follow-up of 10.4 years, 836 CSs (4.2%) developed AF. Competing risk analysis revealed that the significant association between elevated CRP (> 2 mg/L) and AF risk in CSs, observed in models adjusted for sociodemographic and clinical factors (HR 1.21, 95% CI 1.06-1.37; P = 0.005), progressively attenuated with further adjustment for lifestyle factors (HR 1.14, 95% CI 0.99-1.31; P = 0.076). Despite losing statistical significance in the fully adjusted model, a consistent, suggestive trend was observed. This association was particularly pronounced in individuals not receiving radiotherapy.

CONCLUSIONS: Our findings suggest that systemic inflammation is associated with an increased risk of AF among CSs, particularly in individuals without a history of radiotherapy. Further studies are needed to explore underlying mechanisms and therapeutic implications.

PMID:41387931 | DOI:10.1186/s40959-025-00414-6