Search Results (563)

This paper addresses an attraction–repulsion chemotaxis system governed by Neumann boundary conditions within a bounded domain $\Omega \subset {\mathbb{R}}^3$ that has a smooth boundary. The primary focus of the study is the chemotactic response of a species (cell population) to two competing signals. We establish the existence and uniqueness of a weak solution to the system by analyzing the solvability of an approximate problem and utilizing the Leray–Schauder fixed-point theorem. By deriving appropriate a priori estimates, we demonstrate that the solution of the approximate problem converges to a weak solution of the original system. Additionally, we conduct computational studies of the model using the finite element method. The accuracy of our numerical implementation is evaluated through error analysis and numerical convergence, followed by various numerical simulations in a two-dimensional domain to illustrate the dynamics of the system and validate the theoretical findings. Full article

The present study addresses the European option pricing problem based on the Black–Scholes (B-S) model using a hybrid analytical approach known as the Sawi homotopy perturbation transform scheme (SHPTS). We formulate fractional derivatives in the Caputo sense to effectively capture the memory effects inherent in financial models. The competency and reliability of the SHPTS are demonstrated through two illustrative examples. This method produces a closed-form series solution that converges to the precise solution. We perform convergence and visual analyses to demonstrate the competency and reliability of the proposed scheme. The numerical findings further reveal that the strategy is straightforward to apply and very successful in resolving the fractional form of the B-S problem. Full article

Ports in the Baltic Sea region play an integral role in advancing sustainable maritime practices in the area, due to their geographic interconnectedness, economic importance, and sensitivity to environmental challenges. While numerous port sustainability assessment methods exist, most of which are grounded in the Triple Bottom Line (TBL) metric, many tend to emphasise whether specific targets have been met, rather than evaluating port sustainability on a scalar basis. This study explores the sustainability strategies of seven selected ports in five Baltic Sea countries using an innovative qualitative evaluation framework developed by the Swedish Maritime Competence Centre (SMCC). The SMCC model integrates the three core pillars of sustainability-environmental, social, and economic dimensions, while incorporating energy efficiency and digitalisation as critical enablers of modern port operations. The findings reveal significant variation in sustainability performance among the selected ports, shaped by regional contexts, operational profiles, and prior engagement with sustainability initiatives. Also, the results bring into light the most common sustainable practices used in the ports, e.g., LED lightning, onshore power supply, and port information systems. Full article

In this paper, an exponential delay feedback method is proposed to improve the performance of the digital chaotic maps against their dynamical degradation. In this paper, the performance of the scheme is verified using one-dimensional linear, exponential, and nonlinear exponential, Logistic, and Chebyshev maps, and numerical analyses show that the period during which the chaotic sequence enters the cycle is considerably prolonged, and the correlation performance is improved. At the same time, in order to verify the practicality of the method, an image encryption algorithm is designed, and its security analysis results show that the algorithm has a high level of security and can compete with other encryption schemes. Therefore, the exponential delay feedback method can effectively improve the dynamics degradation of a digital chaotic map. Full article

Background/Objectives: Mobile healthcare units (MHUs) comprise flexible, ambulatory healthcare teams that deliver community care services, particularly in underserved or remote areas. In Greece, MHUs were pivotal in epidemiological surveillance during the COVID-19 pandemic and are now evolving into a sustainable and integrated service for much-needed community-based healthcare. To support this expanded role, targeted, competency-based training is essential; however, this can pose challenges, especially in coordinating synchronous learning across geographically dispersed teams and in ensuring engagement using an online format. Methods: A nationwide, online training program was developed to improve the knowledge of the personnel members of the Hellenic National Public Health Organization’s MHUs. This program was structured focusing on four core themes: (i) prevention–health promotion; (ii) provision of care; (iii) social welfare and solidarity initiatives; and (iv) digital health skill enhancement. The program was implemented by the University of Crete’s Center for Training and Lifelong Learning from 16 January to 24 February 2025. A multidisciplinary team of 64 experts delivered 250 h of live and on-demand educational content, including health screenings, vaccination protocols, biomarker monitoring, chronic disease management, treatment adherence, organ donation awareness, counseling on social violence, and eHealth applications. Knowledge acquisition was assessed through a pre- and post-training multiple-choice test related to the core themes. Trainees’ and trainers’ qualitative feedback was evaluated using a 0–10 numerical rating scale (Likert-type). Results: A total of 873 MHU members participated in the study, including both healthcare professionals and administrative staff. The attendance rate was consistently above 90% on a daily basis. The average assessment score increased from 52.8% (pre-training) to 69.8% (post-training), indicating 17% knowledge acquisition. The paired t-test analysis demonstrated that this improvement was statistically significant (t = −8.52, p < 0.001), confirming the program’s effectiveness in enhancing knowledge. As part of the evaluation of qualitative feedback, the program was positively evaluated, with 75–80% of trainees rating key components such as content, structure, and trainer effectiveness as “Very Good” or “Excellent.” In addition, using a 0–10 scale, trainers rated the program relative to organization (9.4/10), content (8.8), and trainee engagement (8.9), confirming the program’s strength and scalability in primary care education. Conclusions: This initiative highlights the effectiveness of a structured, online training program in enhancing MHU knowledge, ensuring standardized, high-quality education that supports current primary healthcare needs. Future studies evaluating whether the increase in knowledge acquisition may also result in an improvement in the personnel’s competencies, and clinical practice will further contribute to assessing whether additional training programs may be helpful. Full article

Amidst escalating concerns about climate change, manufacturers are increasingly pressured to adopt a low-carbon supply chain (LCSC). Financial constraints deter numerous companies from embracing low-carbon initiatives in a competitive landscape. Inclusive internal financing (IIF) provides operational funds from capital-abundant members to capital-constrained members, resolving funding shortages internally within the system. However, when dominant members cannot support all such enterprises, selective internal financing (SIF) or hybrid financing (HF) becomes necessary. This paper studies the operation and financing strategies of a competitive LCSC. Within the framework of an LCSC where two capital-constrained retailers compete, using Stackelberg game theory and the backward induction method, three game-theoretical models are developed under IIF, SIF, and HF. The results indicate that increased competition intensity reduces product sales price, the manufacturer’s carbon emission reduction level, and profit. When competition intensity is high, SIF more effectively enhances carbon emission reduction level, product sales quantity, and profit acquisition. HF reduces profits for the allied retailer and diminishes its competitiveness, yet enhances the competitive strength of the rival retailer. Numerical analysis demonstrates that when equity financing in HF exceeds 0.546, the allied retailer becomes unprofitable and is driven out of the market. This study complements LCSC finance research and provides references for supply chain operations and financing strategy formulation. Full article

This paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or orientation. Traditional methods, such as analytical and numerical approaches, have limitations, especially for redundant manipulators, or involve high computational costs. Recent advances in machine learning, particularly with DNNs, have shown promising results and seem fit for addressing these challenges. This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. The performance of these models is compared, with a focus on curriculum learning to optimize training. The results demonstrate the potential of DNNs to efficiently solve the IK problem while avoiding issues such as singularities, competing with traditional methods in precision and speed. Full article

Programmed Death-Ligand 1 (PD-L1) is a major target for immunotherapy using checkpoint inhibitors (CPIs), particularly in lung cancer treatment. Tumoral PD-L1 expression has been recognized as a natural predictor of CPI response. This predictive relationship is primarily due to its upregulation by interferon-gamma, which is released by immune cells (mainly T lymphocytes and natural killer cells) in proximity to tumor cells, driving an immune resistance mechanism. However, PD-L1 expression is modulated at multiple levels, including oncogenic signaling pathways, and transcriptional and post-transcriptional regulations, potentially leading to false positive predictions. Conversely, variable glycosylation of PD-L1 may compromise the accuracy of immunohistochemical measurements, resulting in false negative predictive data. In addition, PD-L1 expression demonstrates relative instability throughout treatment courses (e.g., chemotherapy and tyrosine kinase inhibitors), further limiting its clinical utility. In this review, we focused on the molecular mechanisms governing PD-L1 expression with a special emphasis on lung cancer. We also discussed biomarker strategies for optimizing patient selection for checkpoint inhibitor therapy where multimodal/multi-omics meta-biomarker approaches are emerging. Such comprehensive PD-L1-enriched biomarker strategies require evaluation through large-scale prospective studies, particularly in lung cancer, where numerous competing predictive candidates exist for CPI response. Full article

In complex reliability applications, it is common for the failure of an individual or an item to be attributed to multiple causes known as competing risks. This paper explores the estimation of the Hjorth competing risks model based on an adaptive progressive Type II censoring scheme via a binomial removal mechanism. For parameter and reliability metric estimation, both frequentist and Bayesian methodologies are developed. Maximum likelihood estimates for the Hjorth parameters are computed numerically due to their intricate form, while the binomial removal parameter is derived explicitly. Confidence intervals are constructed using asymptotic approximations. Within the Bayesian paradigm, gamma priors are assigned to the Hjorth parameters and a beta prior for the binomial parameter, facilitating posterior analysis. Markov Chain Monte Carlo techniques yield Bayesian estimates and credible intervals for parameters and reliability measures. The performance of the proposed methods is compared using Monte Carlo simulations. Finally, to illustrate the practical applicability of the proposed methodology, two real-world competing risk data sets are analyzed: one representing the breaking strength of jute fibers and the other representing the failure modes of electrical appliances. Full article

McComas et al. (2025) introduced a numerical experiment, where ordinary uncorrelated collisions between collision pairs are followed by other, controlled (correlated) collisions, shedding light on the emergence of kappa distributions through particle correlations in space plasmas. We extend this experiment by introducing correlations indicating that (i) when long-range correlations are interwoven with collision pairs, the resulting thermodynamic kappa are described as that corresponding to an ‘interatomic’ potential interaction among particles; (ii) searching for a closer description of heliospheric plasmas, we found that pairwise short-range correlations are sufficient to lead to appropriate values of thermodynamic kappa, especially when forming correlated clusters; (iii) multi-particle correlations do not lead to physical stationary states; finally, (iv) an optimal model arises when combining all previous findings. In an excellent match with space plasmas observations, the thermodynamic kappa that describes the stationary state at which the system is stabilized behaves as follows: (a) When correlations are turned off, kappa is turning toward infinity, indicating the state of classical thermal equilibrium (Maxwell-Boltzmann distribution), (b) When collisions are turned off, kappa is turning toward the anti-equilibrium state, the furthest state from the classical thermal equilibrium (−5 power-law phase-space distribution), and (c) the finite kappa values are generally determined by the competing factor of collisions and correlations. Full article

Numerous studies have shown a positive relationship between intelligence and health, with higher intelligence quotient (IQ) linked to better health outcomes, longer life expectancy, and lower rates of non-communicable diseases. Better health behaviour in the more intelligent (either due to better health knowledge or more advantageous social-financial opportunities) and system integrity theory (overlaps in the background causes of intelligence and health, such as genetic factors) are competing explanations for this link. This study aimed to examine the dietary habits of high-IQ individuals compared to a control group. An online questionnaire was completed by Mensa members (IQ ≥ 130) and control group participants from three countries, assessing various lifestyle factors, especially dietary habits. Key findings include lower smoking rates among Mensa members, special diets primarily for personal rather than medical reasons, and more frequent consumption of some national staples. There was no clear trend for healthier nutritional habits among Mensa members, suggesting that this aspect of health behavior does not account for better health in the more intelligent and supporting system integrity theory instead. Full article

A sentiment analysis is a Natural Language Processing (NLP) task that identifies the opinion or emotional tone of documents such as customer reviews, either at the general or detailed level. Improving domain-specific models is important, as it provides smaller and better-suited models that can be implemented by entities that own textual data. This paper presents a deep learning model trained on Portuguese restaurant reviews using recurrent and self-attention mechanisms, which have consistently delivered strong results in prior research studies. Designing an effective model involves numerous hyperparameters and architectural choices. To address this complexity, a discrete genetic algorithm was used to find an optimal configuration, selecting the layer types, placement of self-attention, dropout rate, and model dimensions and shape. A key outcome of this study was that the optimization process produced a model that is competitive with a Bidirectional Encoder Representation from Transformers (BERT) model retrained for Portuguese, which was used as the baseline. The proposed model achieved an area under the curve of 92.1% and F1-score of 75.4%, demonstrating that a small, optimized model can compete and even outperform larger state-of-the-art models. Moreover, this work helps address the scarcity of NLP resources for Portuguese, and highlights the potential of customized architectures over generic solutions. Full article

Detecting heavy metal ions is essential for maintaining environmental safety, ensuring industrial quality control, and protecting public health. In this study, we have synthesized a novel Rhodamine B-based fluorescent probe, RhB-DCT, which is functionalized with 2,4-dichloro-1,3,5-triazine (DCT) to enhance selectivity and sensitivity for metal ions detection. The probe functions through a “turn-on” fluorescence mechanism activated by the opening of the spiro-lactam ring induced by Fe³⁺ ions, resulting in a distinct color change from colorless to deep pink. The RhB-DCT probe demonstrated a rapid and robust fluorescence response within seconds, exhibited a broad pH stability from 4 to 13, showed excellent reversibility, and possessed a low detection limit of 0.0521 μM, surpassing numerous existing fluorescent probes. The RhB-DCT probe exhibited significant selectivity for Fe³⁺ than other competing metal ions. The integration of high sensitivity, rapid response, and strong stability positions RhB-DCT as a viable option for real-time detection of Fe³⁺ ions in aqueous settings. This study demonstrates the efficacy of the RhB-DCT probe in environmental monitoring, water quality assessment, and analytical sensing platforms, serving as an effective and dependable tool for detecting heavy metal ions. Full article

This study introduces a comprehensive educational intervention designed to strengthen numerical thinking in third-grade mathematics education through problem-solving. Anchored in Pólya’s problem-solving framework, this research employs a qualitative, descriptive methodology to design and assess a didactic unit. The intervention comprises six structured activities aimed at evaluating and enhancing students’ competencies in numerical thinking and problem-solving. This study’s findings reveal a marked improvement in students’ mathematical abilities, particularly in applying problem-solving techniques to various mathematical contexts. This progress highlights the intervention’s effectiveness in fostering critical thinking and boosting educational outcomes in mathematics. The proposed model demonstrates versatility, offering a scalable approach for enhancing mathematical competencies in diverse educational environments. By integrating Pólya’s systematic steps into instructional practices, this research contributes significantly to pedagogical strategies in mathematics. The innovative use of problem-solving as a learning and evaluation tool not only bridges theoretical and practical knowledge, but also provides a replicable framework for advancing numerical thinking. The methodological approach, which combines qualitative analysis with tangible classroom applications, underscores the potential for transforming mathematics education and improving students’ engagement and proficiency in the subject. Full article

At the end of the 18th century, due to the neighbouring states of Austria, Prussia, and Russia, Poland lost its independence. Despite not being represented on the political maps of Europe, its citizens embarked on numerous initiatives that fostered their sense of belonging to the Polish national community. This was achieved, among other things, through various organisations that promoted Polish culture, language, customs, and faith. Additionally, a space was created for organic efforts aimed at individual and social development, ultimately intended to lead to the regaining of independence. One of the organisations involved in these activities was the Catechists’ Association, which sought to develop the professional qualifications of its members through various initiatives. This, in turn, was expected to enhance the level of education and religious formation within Polish society, particularly concerning children and youth. The article aims to define and characterise the activities of the Catechists’ Association in Galicia that were aimed at advancing professional competencies in the first decade of the 20th century in the territories annexed by the Habsburg Monarchy. Full article