Search Results (971)

This study explores the γ-diagonal dominance and product γ-diagonal dominance properties of extended Perron complements. We prove that nonnegative irreducible matrices that are (strictly) γ-diagonally dominant maintain their nonnegativity, irreducibility, and (strictly) γ-diagonal dominance after applying the extended Perron complement operation. Similarly, (strictly) product γ-diagonally dominant nonnegative irreducible matrices also retain these properties under the same operation. Two numerical examples illustrate and validate these theoretical results. Full article

We introduce a novel technique for computing the periods of $(d,k)$-IETs based on Rauzy induction $\mathcal{R}$. Specifically, we establish a connection between the set of periods of an interval exchange transformation (IET) T and those of the IET $T^{\prime }$ obtained either by applying the Rauzy operator $\mathcal{R}$ to T or by considering the Poincaré first return map. Rauzy matrices play a central role in this correspondence whenever T lies in the domain of $\mathcal{R}$ (Theorem 4). Furthermore, Theorem 6 addresses the case when T is not in the domain of $\mathcal{R}$, while Theorem 5 deals with IETs having associated reducible permutations. As an application, we characterize the set of periods of oriented 3-IETs (Theorem 8), and we also propose a general framework for studying the periods of $(d,k)$-IETs. Our approach provides a systematic method for determining the periods of non-transitive IETs. In general, given an IET with d discontinuities, if Rauzy induction allows us to descend to another IET whose periodic components are already known, then the main theorems of this paper can be applied to recover the set of periods of the original IET. This method has been also applied to obtain the set of periods of all $(2,k)$-IETs and some $(3,k)$-IETs, $k\ge 1$. Several open problems are presented at the end of the paper. Full article

This study examined muscle coordination during cutting movements in athletes with a history of groin pain. A total of 15 athletes who had experienced groin pain in the past two years (GP) and 14 healthy controls (CON) participated. Electromyography (EMG) and ground reaction force (GRF) data were collected, and EMG was analyzed using non-negative matrix factorization to extract muscle synergies. Three synergies were identified in both groups: Synergy 1 (landing), Synergy 2 (deceleration), and Synergy 3 (acceleration). No group differences were observed in GRF. However, compared with the CON, the GP demonstrated a 58.1% greater contribution of the latissimus dorsi and a 31.5% greater contribution of the erector spinae (SES) in Synergy 1, suggesting excessive trunk involvement during landing. In Synergy 2, SES contribution was 97.0% lower in the GP. In Synergy 3, the external oblique contribution decreased by 118.4%, while rectus abdominis contribution increased by 54.3%. These muscles are critical for pelvic stability, and their altered contributions indicate disrupted neuromuscular coordination in athletes with GP. Full article

Prosopis cineraria (L.) Druce is a keystone tree species in the arid and semi-arid regions of West and South Asia, with critical ecological, cultural, and conservation significance. In the United Arab Emirates (UAE) and other regions of the Arabian Peninsula, this beneficial tree is called Ghaf. Despite its importance, genomic resources and population-level diversity data for the tree remain limited. Here, we present the first comprehensive population genomics study of Ghaf based on whole-genome resequencing of 204 individual trees collected across the UAE. Following Single-Nucleotide Polymorphism (SNP) discovery and stringent filtering, we analyzed 57,183 high-quality LD-pruned SNPs to assess population structure, diversity, and gene flow. Principal component analysis (PCA), sparse non-negative matrix factorization (sNMF), and discriminant analysis of principal components (DAPC) revealed four well-defined genetic clusters, broadly corresponding to geographic origins. The genetic diversity varied significantly among the groups, with observed heterozygosity (Ho), inbreeding coefficients (F), and nucleotide diversity ($\pi$) showing strong population-specific trends. Genome-wide fixation index F_ST scans identified multiple highly differentiated genomic regions, enriched for genes involved in stress response, transport, and signaling. Functional enrichment using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam annotations indicated overrepresentation of protein kinase activity, ATP binding, and hormone signaling pathways. TreeMix analysis revealed gene flow into one of the genetic clusters from both others, suggesting historical admixture and geographic connectivity. This work provides foundational insights into the population genomic profile of P. cineraria, supporting conservation planning, restoration strategies, and long-term genetic monitoring in arid ecosystems. Full article

Against the background of increasingly scarce water resources and intensifying water use conflicts, achieving the scientific and optimized allocation of water resources has become crucial to ensuring regional sustainable development. Based on the traditional water resource optimization models that consider social, economic, and ecological objectives, this study introduces a spatial equilibrium level as a fourth optimization objective, constructing a multi-objective water resource allocation optimization model. The model simultaneously incorporates constraints on water supply, water demand, and decision variable non-negativity, as well as coupling coordination constraints among the water resources, socio-economic, and ecological subsystems within each water use unit. The NSGA-III algorithm is employed to obtain the Pareto front solution set for the four objectives, followed by a comprehensive ranking of the Pareto solutions using an entropy-weighted TOPSIS method. The solution exhibiting the best trade-off among the four objectives is selected as the decision basis for the water allocation scheme. Taking Jiuquan, Jiayuguan, and Zhangye cities in western Gansu Province as the study area, the results indicate that the optimal allocation scheme can guide the cities to shift from “water-deficit usage” toward “water-saving usage,” achieving a reasonable balance between meeting water demand and water conservation requirements. This promotes coordinated development among the water resource, socio-economic, and ecological subsystems within each city as well as among the cities themselves, thereby facilitating sustainable utilization of water resources and sustainable development of socio-economics and the ecological environment. The findings can serve as a reference for water resource allocation strategies in the study region. Full article

X-linked adrenoleukodystrophy (X-ALD) is the most common peroxisomal disorder, caused by mutations in the ABCD1 gene. Early diagnosis is critical to manage adrenal insufficiency and cerebral forms of the disease. Since 2021, a pilot newborn screening (NBS) program for X-ALD has been launched in Lombardy, Italy. From September 2021 to June 2025, 138,116 newborns (≥37 weeks’ gestational age) were screened for elevated C26:0-lysophosphatidylcholine (C26:0-LPC) levels using a two-tier algorithm. Genetic testing was performed in non-negative cases. Males found to be ABCD1 variant carriers were enrolled in multidisciplinary follow-up, including neurological, endocrinological, and nutritional assessments. Eleven individuals (six males, five females) carried pathogenic or likely pathogenic ABCD1 variants. Three males were diagnosed with adrenal insufficiency and started hydrocortisone therapy between 1 and 2 years of age. Growth parameters were within normal range overall, but two children showed signs of stunting associated with poor dietary compliance. Additionally, three patients were diagnosed with Zellweger spectrum disorders (ZSDs). No patients affected with Aicardi-Goutières Syndrome were identified. Newborn screening for X-ALD in Italy is feasible and enables early detection and intervention. Biochemical markers and genetic analysis are reliable tools for identifying affected males and female carriers. Multidisciplinary management is essential to address medical and psychosocial challenges during follow-up. Full article

This study extends the Theory of Functional Connections, previously applied to constraints specified at discrete points, to encompass continuous integral constraints of the form ${\displaystyle {\int }_{x_0}^{x_f}f(x,t)\mathrm{d}x=\mathcal{I}\left(t\right)}$, where $\mathcal{I}\left(t\right)$ can be a constant, a prescribed function, or an unknown function to be estimated through optimization. The framework of continuous integral constraints is developed within the context of initial value problems (IVP) and boundary value problems (BVP). To demonstrate the effectiveness of this analytical approach, examples validate the method and highlight distinctions between satisfying continuous integral constraints via simple interpolation versus functional interpolation. A limitation of the proposed approach is the inability to inherently enforce inequality constraints, such as the positivity constraint $f(x,t)\ge 0$, for modeling probability density functions in classical mechanics. Despite this, numerical experiments on boundary-value problems rarely result in negative values, indicating that the issue occurs infrequently. However, a mitigation strategy based on non-negative least-squares methods combined with Bernstein polynomials is proposed to address these rare cases. This approach is validated through an additional numerical test, demonstrating its efficacy in ensuring nonnegativity when required. Full article

A two-dimensional shallow flow model is developed by integrating a positivity-preserving, well-balanced central-upwind scheme with a block-structured quadtree AMR grid implemented in the Afivo open-source framework. A non-negative water depth reconstruction ensures second-order spatial accuracy and the robust treatment of wetting and drying, while coarse-grid fluxes at refinement boundaries are obtained by summing the corresponding fine-grid fluxes, thereby guaranteeing strict mass conservation between refinement levels. Mesh refinement is driven by gradients in water surface elevation, which focus resolution on regions of rapid flow variation, thereby improving both accuracy and computational efficiency. Model validation through benchmark problems and the Malpasset dam-break event show close agreement with analytical solutions, laboratory measurements, and previous numerical simulations, while achieving substantial reductions in computational cost. Full article

Multimodal transportation systems, particularly those combining metro and bikesharing, have become central to addressing the first- and last-mile connectivity challenges in urban environments. This study presents a comprehensive data-driven framework to analyze the spatiotemporal interplay between metro and dockless bikesharing usage using real-world data from Tianjin, China. Two primary methods are employed: K-means clustering is used to categorize metro stations and bike usage zones based on temporal demand features, and non-negative Tucker decomposition is applied to a three-way tensor (day, hour, station) to extract latent mobility modes. These modes capture recurrent commuting and leisure behaviors, and their alignment across modes is assessed using Jaccard similarity indices. Our findings reveal distinct usage typologies, including mismatched (misalignment of jobs and residences), employment-oriented, and comprehensive zones, and highlight strong temporal coordination between metro and bikesharing during peak hours, contrasted by spatial divergence during off-peak periods. The analysis also uncovers asymmetries in peripheral stations, suggesting differentiated planning needs. This framework offers a scalable and interpretable approach to mining multimodal travel patterns and provides practical implications for station-area design, dynamic bike rebalancing, and integrated mobility governance. The methodology and insights contribute to the broader effort of data-driven smart city planning, especially in rapidly urbanizing contexts. Full article

Fibrinolysis plays a crucial role in maintaining coagulation homeostasis, but its functions in hepatocellular carcinoma (HCC) remain poorly understood. This study aimed to develop a fibrinolysis-based molecular classification and prognostic signature for HCC and to identify a key regulatory gene. Using non-negative matrix factorization (NMF), we identified distinct fibrinolysis-related HCC subtypes with specific clinical outcomes and tumor microenvironment characteristics. A six-gene prognostic signature comprising ACAT1, GRHPR, HPX, PCK2, IYD, and PON1 was established through weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression, which effectively stratified patients into different risk groups across multiple cohorts. Hemopexin (HPX) was identified as the top candidate and functionally validated: HPX overexpression suppressed clonogenicity and migration, promoted apoptosis, and inhibited xenograft tumor growth. RNA sequencing analysis suggested associations between HPX and apoptosis as well as TNF-α signaling pathways, which were confirmed through flow cytometry apoptosis assays, mitochondrial membrane potential measurements, and TUNEL staining. Western blot and immunohistochemical analyses further demonstrated that HPX upregulates the Bax/Bcl-2 ratio via the TNF-α signaling pathway. This study defines novel molecular subtypes of HCC and reveals that HPX exerts anti-tumor effects through TNF-α-mediated mitochondrial apoptosis, characterized by an increased Bax/Bcl-2 ratio. Full article

This study provides new equivalent descriptions of the Bounded Lower Oscillation ($\mathrm{BLO}$) space through Luxemburg-type $L^{\phi }$ integrability conditions, where $\phi$ is a nonnegative function with either convexity or concavity. The framework accommodates various representative forms of $\phi$, such as the power function $\phi \left(t\right)=t^p$, exponential-type functions $\phi \left(t\right)=e^{pt}-1$, and logarithmic functions $\phi \left(t\right)={log}_{+}^{k}t$, with parameters $p\in (0,\infty )$ and $k\in \mathbb{N}$. These results unify and extend existing characterizations of $\mathrm{BLO}$ by encompassing a broad class of generating functions. Full article

Cervical cancer (CC), which continues to be a major public health concern that causes cancer deaths among women worldwide, is mostly caused by persistent human papillomavirus (HPV) infection. This study suggests a dual-delay model of HPV-C infection dynamics that takes into account both cancerous delay and the immune response delay. We identify disease-free and diseased equilibria, investigate their local asymptotic stability, and show that the system is non-negative and bounded. We prove the global asymptotic stability of the equilibria by building Lyapunov functions and using the basic reproduction number $R_0$, and look into the existence of Hopf bifurcations. Additionally, we use forward sensitivity analysis to determine important control parameters. Lastly, the theoretical results were confirmed by numerical simulations. The study demonstrates that time delays play a crucial role in viral transmission and carcinogenesis. The process from HPV infection to the formation of cervical cancer is more correctly simulated by this model, which offers a theoretical mathematical basis for researching the pathophysiology of cervical cancer and developing clinical prevention and control measures. Full article

Clustering techniques significantly enhance recommender systems by improving predictive accuracy and interpretability, particularly in sparse, high-dimensional datasets. This research presents a comprehensive comparative analysis of traditional clustering methods such as K-means and Fuzzy C-Means (FCM) against advanced probabilistic clustering methodologies based on Non-negative Matrix Factorization (NMF), focusing specifically on Bayesian NMF. Experiments conducted using the widely recognized MovieLens 1M dataset reveal Bayesian NMF’s superior performance in terms of predictive accuracy, intra-cluster cohesion, and interpretability compared to classical methods. The study systematically evaluates the influence of key parameters such as overlap ($\alpha$) and evidence threshold ($\beta$) in Bayesian NMF, demonstrating that careful parameter tuning substantially improves recommendation quality. The results highlight the inherent trade-off between cluster cohesion and predictive accuracy, emphasizing the flexibility and robustness of probabilistic approaches in accurately modeling user preferences and behaviors. The paper concludes by proposing future directions, including the exploration of hybrid clustering methods, dynamic adaptation to evolving user preferences, and integration of contextual information, thereby fostering continued advances in personalized-recommendation research. Full article

In this work, we analyze a scalar field model which gives rise to stable bound states in field space characterized by nonzero motion that breaks the underlying time translation symmetry of its Hamiltonian, known as time crystals. We demonstrate that an ideal fluid made up of these time crystals behaves as phantom dark energy characterized by an equation of state $w<-1$, speed of sound squared $c_s^2\ge 0$, and nonnegative energy density $\rho \ge 0$. Full article

The problem of geolocating Reddit users without access to the author information API is tackled in this study. Using subreddit data, we analyzed and identified user location based on their interactions within location-specific subreddits. Using unsupervised learning methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, we examined conversations about COVID-19 and immunization across the U.S., focusing on COVID-19 vaccination. Our topic modeling identifies four themes: humor and sarcasm (e.g., jokes about microchips), conspiracy theories (e.g., tracking devices and microchips in the COVID-19 vaccine), public skepticism (e.g., debates over vaccine safety and freedom), and vaccine brand concerns (e.g., Pfizer, Moderna, and booster shots). Our geolocation analysis shows that regions with lower vaccination rates often exhibit a higher prevalence of misinformation-labeled comments. For example, counties such as Ada County (Idaho), Newton County (Missouri), and Flathead County (Montana) showed both a low vaccine uptake and a high rate of false information. This study provides useful information on the many different examples of misinformation that are disseminated online. It gives us a better understanding of how people in different parts of the U.S. think about getting a COVID-19 vaccine. Full article