Advancing Open Science

Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or outliers. The traditional extended Kalman filter (EKF), based on the minimum mean square error (MMSE) criterion, assumes Gaussian noise distributions and exhibits degraded performance under non-Gaussian conditions. To overcome this limitation, the minimum error entropy (MEE) criterion was proposed to reduce random uncertainty in estimation error distributions; however, due to its translation invariance property, MEE may inadvertently increase bias when errors contain systematic offsets, leading to poor convergence. In contrast, the maximum correntropy criterion (MCC) concentrates the error probability density function (PDF) around zero, enabling effective entropy adjustment even in the presence of bias and achieving superior error convergence. This paper presents the centered error entropy (CEE) extended Kalman filter (CEE-EKF) that integrates the complementary merits of both MEE and MCC approaches to overcome their individual limitations. Experimental validation in complex nonlinear GPS environments with non-Gaussian noise demonstrates that the CEE-EKF significantly outperforms individual algorithms in noise suppression, particularly exhibiting enhanced robustness and accuracy when handling outliers. These results offer an effective approach to enhancing the reliability of GPS navigation in challenging real-world environments, and the algorithm can be readily extended to other GNSS applications.

Background: Breast cancers are heterogeneous in nature, including many molecular subtypes, each displaying varying characteristics in clinical outcomes as well as in responses to treatments. Subtyping requires absolute precision for the application of precision medicine; however, this is not an easy task, given the dimensionality as well as noise in miRNA expression profiles. Even though miRNAs display potential as a biological marker for subtyping breast cancers, feature selection and optimizing learning algorithms would help harness their potential as a diagnostic tool. Methods: We propose the Adaptive Hill Climbing Artificial Lemming Algorithm (AHALA), a hybrid optimization framework that integrates the global search capability of the Artificial Lemming Algorithm with an adaptive hill-climbing local search strategy. Low-variance filtering and differential gene expression analysis were first applied to reduce dimensionality and enhance biological relevance. AHALA was then used to optimize deep neural network hyperparameters for miRNA-based multi-class breast cancer subtype classification. The method was validated using TCGA breast cancer miRNA expression data and benchmarked against state-of-the-art optimization algorithms using the CEC2021 test suite. Results: AHALA had a high classification performance measure for each type of breast cancer with a mean accuracy of 95.74%, precision of 95.98%, recall of 95.74%, F1 measure of 95.74%, and AUC value of 0.9682. The new algorithm had superior convergence and significance compared with other optimization algorithms. Feature selection revealed miRNAs that belong to each subtype, such as hsa-miR-190b, hsa-miR-429, hsa-miR-505-3p, hsa-miR-3614-5p, and hsa-miR-935. Conclusions: The AHALA framework offers a potent and efficient method of performing miRNA-based subtyping of breast cancer that integrates global exploration and local search to its advantage. Its high level of classification, stability, and ability to identify biologically important biomarkers mark this method as promising.

Metastatic breast cancer (BC) remains a major clinical challenge, and identifying molecular mechanisms driving tumor cell migration and invasion is critical to develop effective therapeutic strategies. Clusterin (CLU), a secreted chaperone-like protein, is upregulated in BC and metastatic tissue; however, its functional contribution to tumor aggressiveness remains unclear. Here, we silenced CLU by siRNA in two BC cell lines with distinct aggressiveness and examined its impact on migration, invasion, and associated signaling pathways. Following CLU silencing, cell migration and invasion were assessed using transwell assays. Cytoskeletal organization was evaluated by F-actin staining, while downstream signaling pathways were analyzed by RT-PCR, Western blotting, and Rho GTPase pull-down. A comparative proteomic analysis was performed in CLU-expressing and CLU-silenced MDA-MB-231 cells. CLU knockdown significantly reduced migration and invasion in MDA-MB-231, concomitantly with loss of F-actin-rich membrane protrusions, reduced expression of MMP9, COL1A1, and COL4A1, and decreased activation of Akt, NF-κB, and RhoA. Proteomic profiling revealed extensive remodeling of pathways involved in cell adhesion, cytoskeletal dynamics, and extracellular matrix interactions. Differently, no or very mild effects were observed in CLU-silenced MCF-7 cells. These findings identify CLU as an upstream regulator of a pro-metastatic adhesion–cytoskeleton signaling in BC, selectively operative in EMT-engaged, basal-like cells, highlighting the importance of patient stratification for CLU-targeted therapeutic strategies.

Evolutionary changes can significantly impact interactions among populations and disrupt ecosystems by driving extinctions or collapsing population oscillations, posing substantial challenges to biodiversity conservation. This study addresses the ecological rescue of a predator population threatened by a mutant prey population using the optimal control method. To study this, we study a model that incorporates a genotypically structured prey population comprising wild-type, heterozygous, and mutant prey types, as well as the predator population. We prove that this model has both local and global existence and uniqueness of solutions, ensuring the model’s robustness. Then, we applied the optimal control method, incorporating Pontryagin’s Maximum Principle, to introduce a control input into the model and minimize the mutant population, thereby stabilizing the ecosystem. We utilize a reproduction number and a control efficacy measure to numerically demonstrate that the undesired dynamics of the model can be controlled, leading to the suppression of the mutant and the restoration of the oscillatory dynamics of the system. These findings demonstrate the applicability of optimal control strategies and provide a mathematical framework for managing such ecological disruptions.

Owing to population expansion, widespread diseases and pandemics, climate alterations, and evolving consumer preferences, the optimization of production processes and technological advancements in food processing have become imperative. The integration of nanotechnology with food processing technology, characterized by numerous advantages, holds the promise to establish a secure, efficient, and sustainable food supply system. Nanoparticles can mitigate the risk of microbial contamination through the generation of reactive oxygen species and by leveraging their electrical charge properties to exert antibacterial effects or detoxify; they can serve as an energy transfer medium to enhance food quality; or utilize its high catalytic efficiency for the recycling and decomposition of food waste. When integrated with food processing technologies, they demonstrate a synergistic or additive effect. This paper reviews representative instances of the convergence between nanotechnology and food processing technologies, elucidates the practical application effects and underlying mechanisms, aims to inform the development of more advantageous application strategies for nanotechnology in the realm of food processing.

Background/Objectives: Workplace violence (WPV) is a major occupational concern in psychiatric settings, where mental health nurses (MHNs) are consistently identified as a high-risk professional group. Within this context, sexual violence (SV) remains understudied as a distinct phenomenon and is often embedded within aggregated measures of WPV. This systematic review aimed to synthesize the available evidence on SV against MHNs working in inpatient settings by: (1) describing its prevalence, forms, and characteristics; (2) examining psychological, occupational, and physical outcomes; and (3) identifying associated risk factors. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251103606). A literature search was performed across PubMed, CINAHL, Scopus, Web of Science, and PsycInfo, supplemented by reference list checking and citation tracking. Peer-reviewed quantitative and qualitative studies published in English or Italian were eligible if they involved MHNs working in inpatient settings and addressed SV. Study selection, data extraction, and risk-of-bias assessment were conducted independently by two reviewers. A narrative synthesis following SWiM guidance was undertaken, and the certainty of evidence for statistically significant outcomes was assessed using the GRADE approach. Results: Twenty-five studies published between 2003 and 2025 were included. Definitions of SV varied substantially. Reported prevalence ranged from 0% to 68%, with verbal sexual harassment ranging from 19.5% to 53.4%, physical sexual harassment ranging from 14% to 42.9%, and sexual assault up to 18.6%. Evidence indicated associations between SV exposure and poorer quality of life, burnout, and days lost from work. The main risk factors included gender, age, education, work experience, employment type, acute psychiatric settings, night shifts, normalization of violence, and history of physical and sexual violence. Conclusions: SV against MHNs represents a relevant issue in psychiatric settings. Findings suggest significant psychological and occupational consequences. Standardized definitions and measurement, longitudinal research, and intervention studies are needed to inform effective prevention strategies and organizational responses.

In the context of intensifying industrial emissions and rapid urban sprawl, balancing environmental sustainability and economic expansion has grown more pronounced in Anyang. Drawing on land-use data spanning the years 2000, 2010, and 2020, this research employed the InVEST model to examine the spatiotemporal dynamics of habitat quality across the region. To uncover key influencing factors, this study integrated the GeoDetector method, while future habitat trends were projected to 2030 using a coupled intPLUS-InVEST simulation framework. The analysis revealed that over the past two decades, habitat quality remained consistently low, displaying a geographic gradient associated with elevated levels in western mountainous zones, along with lower levels across eastern plains. The evolution of habitat conditions appears to result from the intricate interdependencies between environmental variables, anthropogenic pressures, and industrial expansion. Projections in various development scenarios point to stark contrasts in future habitat outcomes: notably, the scenario combining industrial transformation with ecological rehabilitation fosters moderate gains and stabilizes declining trends in habitat quality. These insights emphasize the urgency of implementing robust policy mechanisms and spatially nuanced land-use strategies to facilitate Anyang’s ecological transition and ensure long-term regional sustainability.

This study examines the associations between participation in recycling-based STEM activities and secondary school students’ STEAM attitudes, recycling-related behaviours, and design thinking skills. A nested mixed-methods design was employed. The quantitative part used a one-group pre-test–post-test experimental design with 32 students, while the qualitative part included semi-structured interviews with 7 students selected through criterion sampling. Data were collected using a STEAM attitude scale, an attitude towards recycling scale, a design thinking scale and an interview form. Paired-samples t-tests were conducted for quantitative analyses, and the interview data were examined using content analysis. Statistically significant increases were observed from pre-test to post-test in students’ STEAM attitudes, recycling-related behaviours, and design thinking skills following participation in the recycling-based STEM activities. Qualitative findings indicated that students described coping with challenges in the design process by using problem-solving strategies and collaborating with peers. They also reported perceived increases in self-efficacy, creativity, and understanding of interdisciplinary (STEM) approaches. In addition, students reported greater awareness and described changes in recycling-related behaviours. Overall, the findings suggest that integrating recycling into STEM education may be associated with sustainability-oriented behaviours and higher-order thinking skills among secondary school students.