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Mathematics

Mathematics is a peer-reviewed, open access journal which provides an advanced forum for studies related to mathematics, and is published semimonthly online by MDPI. 
The European Society for Fuzzy Logic and Technology (EUSFLAT) and International Society for the Study of Information (IS4SI) are affiliated with Mathematics and their members receive a discount on article processing charges.
Quartile Ranking JCR - Q1 (Mathematics)

All Articles (25,310)

  • Feature Paper
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  • Open Access

We introduce a new method for estimating gravitational wave parameters. This approach uses a second-order likelihood optimization framework built into a machine learning system (JimGW). Current methods often rely on first-order approximations, which can miss important details, while our method incorporates the full Hessian matrix of the likelihood function. This allows us to better capture the shape of the parameter space for gravitational waves. Our theoretical framework demonstrates that the trace of the Hessian matrix, when properly normalized, provides a coordinate-invariant measure of the local likelihood geometry that significantly enhances parameter recovery accuracy for gravitational wave sources. We test our second-order method using data from the three gravitational wave events. Take GW150914 as an example; the results show large gains in precision for parameter estimation, with accuracy gains exceeding 93% across all inferred parameters compared to standard first-order implementations. We use Jensen–Shannon divergence to compare the resulting posterior distributions. The JSD values range from 0.366 to 0.948, which correlate directly with improved parameter recovery as validated through injection studies. The method remains computationally efficient with only a 20% increase in runtime. At the same time, it produces seven times more effective samples. Our results show that machine learning methods using only first-order information can lead to systematic errors in gravitational wave parameter estimation. The incorporation of second-order corrections emerges not as an optional refinement but as a necessary component for achieving theoretically optimal inference. It also matters for ongoing gravitational wave analyses, future detector networks, and the broader application of machine learning methods in precision scientific measurement.

17 December 2025

Performance comparison: First-order versus second-order methods.
  • Correction
  • Open Access

There was an error in the original publication [...]

17 December 2025

Accurate detection of concealed items in X-ray baggage images is critical for public safety in high-security environments such as airports and railway stations. However, small objects with low material contrast, such as plastic lighters, remain challenging to identify due to background clutter, overlapping contents, and weak edge features. In this paper, we propose a novel architecture called the Contrast-Enhanced Feature Pyramid Network (CE-FPN), designed to be integrated into the YOLO detection framework. CE-FPN introduces a contrast-guided multi-branch fusion module that enhances small-object representations by emphasizing texture boundaries and improving semantic consistency across feature levels. When incorporated into YOLO, the proposed CE-FPN significantly boosts detection accuracy on the HiXray dataset, achieving up to a +10.1% improvement in mAP@50 for the nonmetallic lighter class and an overall +1.6% gain, while maintaining low computational overhead. In addition, the model attains a mAP@50 of 84.0% under low-resolution settings and 87.1% under high-resolution settings, further demonstrating its robustness across different input qualities. These results demonstrate that CE-FPN effectively enhances YOLO’s capability in detecting small and concealed objects, making it a promising solution for real-world security inspection applications.

16 December 2025

In this paper, we introduce and investigate two generalized forms of classical contraction mappings, namely the p-Hardy–Rogers and p-Zamfirescu contractions. By incorporating the integer parameter p1, these new definitions extend the traditional Hardy–Rogers and Zamfirescu conditions to iterated mappings p. We establish fixed-point theorems, ensuring both existence and uniqueness of fixed points for continuous self-maps on complete metric spaces that satisfy these p-contractive conditions. The proofs are constructed via geometric estimates on the iterates and by transferring the fixed point from the p-th iterate p to the original mapping . Our results unify and broaden several well-known fixed-point theorems reported in previous studies, including those of Banach, Hardy–Rogers, and Zamfirescu as special cases.

16 December 2025

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Mathematics - ISSN 2227-7390