Search Results (7)

Background and Objectives: Breast cancer is the leading oncological diagnosis among women in Kazakhstan, yet a substantial proportion of cases are still detected beyond the earliest stages, particularly in peripheral regions such as Aktobe. Despite a national mammography screening programme covering women aged 40–70 years since 2018, structural differences in access to early diagnosis—related to geography, socioeconomic circumstances, and the diagnostic pathway—may compromise outcomes. We aimed to identify factors independently associated with early-stage presentation and to characterise the temporal pattern of early-stage presentation without assuming a monotonic trend. Methods and Materials: We conducted a retrospective, population-based analytical study of all confirmed breast cancer cases (ICD-10 C50) registered in the Aktobe regional cancer registry and diagnosed between 1 January 2015 and 31 December 2025 (n = 2232). The outcome was early-stage presentation, defined literally as Stages I–IIa at diagnosis, with Stages IIb–IV as the comparator; this is a stage-at-presentation classification and is not intended to indicate surgical operability or treatment sequence. Multivariable logistic regression estimated adjusted odds ratios (aORs). Two models were used: Model A included age, sex, residence, administrative nationality, employment/social status, and calendar year; Model B additionally included the diagnostic pathway, which may lie on the causal pathway between structural determinants and stage. Calendar year was modelled as a categorical variable, and a complementary phase-based model (2015–2017, 2018–2019, 2020–2022, 2023–2025) was fitted. A residence-by-year interaction; a multinomial sensitivity analysis separating Stages IIb, III, and IV; discrimination (AUC, Brier score); and calibration (Hosmer–Lemeshow test, calibration plot) were also assessed. Results: Of 2232 patients (99.1% female; mean age 57.0 ± 12.5 years), 1323 (59.3%) presented at Stages I–IIa. In Model A, rural residence (aOR 0.77, 95% CI 0.64–0.93; p = 0.006), unemployment relative to employment (aOR 0.69, 95% CI 0.52–0.90; p = 0.007), Russian administrative nationality (aOR 0.64, 95% CI 0.50–0.82; p < 0.001), and other non-Kazakh nationalities (aOR 0.73, 95% CI 0.58–0.94; p = 0.012) were independently associated with lower odds of Stage I–IIa presentation. In Model B, patient-initiated (self-referral) presentation was associated with lower odds relative to clinical examination room detection (aOR 0.46, 95% CI 0.30–0.72; p < 0.001), whereas organised screening was not significantly associated (aOR 1.52, 95% CI 0.95–2.43; p = 0.082). The calendar-year pattern was clearly non-linear (categorical vs. linear year: likelihood-ratio χ² = 76.1, df = 9, p < 0.001): odds of Stage I–IIa presentation peaked in 2018 (aOR 2.49, 95% CI 1.57–3.93 vs. 2015), were lowest in 2022 (aOR 0.64, 95% CI 0.42–0.97), and partially recovered thereafter. In the phase-based model (reference 2015–2017), the aORs were 1.62 (95% CI 1.22–2.15) for 2018–2019, 0.54 (95% CI 0.41–0.69) for 2020–2022, and 0.62 (95% CI 0.46–0.84) for 2023–2025. Model discrimination was limited (AUC 0.648, 95% CI 0.625–0.671; Brier score 0.226) with acceptable calibration (Hosmer–Lemeshow χ² = 8.38, df = 8, p = 0.40). Conclusions: In this registry-based cohort, rural residence, unemployment, non-Kazakh administrative nationality, and patient-initiated presentation were independently associated with lower odds of early-stage breast cancer presentation. The temporal pattern was non-monotonic, with the highest odds around the 2018 screening expansion, a marked reduction during 2020–2022, and only partial recovery thereafter. These are observational associations rather than causal or programme-evaluation findings; they should be interpreted as hypothesis-generating and require confirmation with screening-process, service-capacity, and patient-level access data. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

This work focuses on the advanced modeling of the thixotropic nature of blood, coupled with an elasto-visco-plastic formulation by invoking a consistent and validated model for TEVP materials. The proposed model has been verified for the adequate description of the rheological behavior of suspensions, introducing a scalar variable that describes dynamically the level of internal microstructure of rouleaux at any instance, capturing accurately the aggregation and disaggregation mechanisms of the RBCs. Also, a non-linear fitting is adopted for the definition of the model’s parameters on limited available experimental data of steady and transient rheometric flows of blood samples. We present the predictability of the new model in various steady and transient rheometric flows, including startup shear, rectangular shear steps, shear cessation, triangular shear steps and LAOS tests. Our model provides predictions for the elasto-thixotropic mechanism in startup shear flows, demonstrating a non-monotonic relationship of the thixotropic index on the shear-rate. The intermittent shear step test reveals the dynamics of the structural reconstruction, which in turn is associated with the aggregation process. Moreover, our model offers robust predictions for less examined tests such as uniaxial elongation, in which normal stress was found to have considerable contribution. Apart from the integrated modeling of blood rheological complexity, our implementation is adequate for multi-dimensional simulations due to its tensorial formalism accomplished with a single time scale for the thixotropic effects, resulting in a low computational cost compared to other TEVP models. Full article

In this paper, we propose, analyze, and test an alternative method for solving the ${\ell }_1$ -norm regularization problem for recovering sparse signals and blurred images in compressive sensing. The method is motivated by the recent proposed nonlinear conjugate gradient method of Tang, Li and Cui [Journal of Inequalities and Applications, 2020(1), 27] designed based on the least-squares technique. The proposed method aims to minimize a non-smooth minimization problem consisting of a least-squares data fitting term and an ${\ell }_1$ -norm regularization term. The search directions generated by the proposed method are descent directions. In addition, under the monotonicity and Lipschitz continuity assumption, we establish the global convergence of the method. Preliminary numerical results are reported to show the efficiency of the proposed method in practical computation. Full article

The present paper deals with nonlinear, non-monotonic data regression. This paper introduces an efficient algorithm to perform data transformation from non-monotonic to monotonic to be paired with a statistical bivariate regression method. The proposed algorithm is applied to a number of synthetic and real-world non-monotonic data sets to test its effectiveness. The proposed novel non-isotonic regression algorithm is also applied to a collection of data about strontium isotope stratigraphy and compared to a LOWESS regression tool. Full article

Measuring and comparing student performance have been topics of much interest for educators and psychologists. Particular attention has traditionally been paid to the design of experimental studies and careful analyses of observational data. Classical statistical techniques, such as fitting regression lines, have traditionally been utilized and far-reaching policy guidelines offered. In the present paper, we argue in favour of a novel technique, which is mathematical in nature, and whose main idea relies on measuring distance of the actual bivariate data from the class of all monotonic (increasing in the context of this paper) patterns. The technique sharply contrasts the classical approach of fitting least-squares regression lines to actual data, which usually follow non-linear and even non-monotonic patterns, and then assessing and comparing their slopes based on the Pearson correlation coefficient, whose use is justifiable only when patterns are (approximately) linear. We describe the herein suggested distance-based technique in detail, show its benefits, and provide a step-by-step implementation guide. Detailed graphical and numerical illustrations elucidate our theoretical considerations throughout the paper. The index of increase, upon which the technique is based, can also be used as a summary index for the LOESS and other fitted regression curves. Full article