Abstract: Limited fossil fuels have created a societal energy crisis necessitating the use of renewable energy. However, existing renewable energy sources are problematic and incur high costs. To solve these problems, we propose a new renewable energy source with a divergent current density and highly symmetric circuits. When starting the circuit, we calculated the large current to be harvested and the output electric power. During our experiments, a significantly large divergent current flowed into a huge resistance, boosting the output electric power to a level almost equal to that of a nuclear power station. In addition, the experimental results were consistent with the theoretical expectations.

Abstract: Early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challenges limit their effectiveness in clinical practice. One of the key issues is the lack of knowledge about the melanoma stage distribution in the training data, raising concerns about the ability of these models to detect early-stage melanoma accurately. Additionally, publicly available datasets that include detailed information on melanoma stage and tumor thickness remain scarce, restricting researchers from developing and benchmarking methods specifically tailored for early diagnosis. Another major limitation is the lack of cross-dataset evaluations. Most deep learning models are tested on the same dataset they were trained on, failing to assess their generalization ability when applied to unseen data. This reduces their reliability in real-world clinical settings. We introduce an early-stage melanoma benchmark dataset to address these issues, featuring images labeled according to T-category based on Breslow thickness. We evaluated several state-of-the-art deep learning models on this dataset and observed a significant drop in performance compared to their results on the ISIC Challenge datasets. This finding highlights the models’ limited capability in detecting early-stage melanoma. This work seeks to advance the development and clinical applicability of automated melanoma diagnostic systems by providing a resource for T-category-specific analysis and supporting cross-dataset evaluation.

Abstract:

Hawking’s cosmology logically leads to an observed multiverse. This article argues it is a superposition of at least three 3-dimensional universes in a 4-dimensional space, of which two dimensions overlap with our universe. Nothing that could disturb the superposition exists outside it. This explains why dark matter causes a linear decrease in gravity with distance to visible mass at large radii in galaxies. To support this, the visible matter distribution in the disks and bulges, calculated by the SPARC team, and the observed rotation velocities have been used. Lelli and Mistele showed that the common way to project dark matter halos around galaxies cannot be valid. Since General Relativity would need these halos too, it must be modified with additional terms, or an added wire-like mass must be modelled in galaxies with the Levi-Civita metric. Bekenstein and the paper in hand respectively do this. Using TeVeS, the decay of the contribution of dark matter to gravity with the expansion of space is confirmed. This explains the rapid development of large galaxies in the early universe as reported by Labbé. A new prediction method for rotation velocities, that works at all radii in galaxies, is 19 to 27 % more accurate than MOND and TeVeS. In galaxy clusters the improvement of the predicted velocity dispersions is 44 to 57 % over a huge range of cluster masses. It gives a logical explanation of the meaning of Milgrom's contant and the Tully-Fisher relationship does directly follow from the hypothesis.

Abstract: Background: Not all lung cancer patients respond well to immune checkpoint inhibitors targeting PD-L1/PD-1, and resistance to PD-L1/PD-1 inhibitors significantly limit their clinical application and therapeutic efficacy. Longchai Jiangxue Fang (LC) is a traditional Chinese medicine compound that has shown potential and effectiveness in the treatment of myeloproliferative neoplasms. However, the effects and potential underlying mechanisms of LC against lung cancer remain unclear. Methods: A co-culture model of T cells with lung cancer cells was established. Cancer cell growth was measured by colony formation. T cell activation was detected by ELISA of inflammatory cytokines and flow cytometry of CD8+GzmB+ T cells. PD-L1 expression was detected by western blot. In vivo growth was measured using a mouse xenograft tumor model. Immunohistochemistry analysis was performed to determine T cells activation in tumor tissues. The potential targets of LC in lung cancer were predicted by network pharmacology. The AKT signaling was detected using in T cell-cancer cell co-culture model.Results: LC treatment promoted immune activation of T cells and the toxicity to lung cancer cells in vitro and in vivo. Network pharmacology revealed the PI3K-AKT as the most abundant signaling pathway in lung cancer cells under LC treatment. Cellular experiments confirmed that the phosphorylation of PI3K and AKT was repressed by LC treatment in both A549 cells and H1299 cells. LC treatment could abolish the immune evasion of lung cancer cells induced by Akt activator. Conclusion: Our findings presented LC as a promising therapeutic agent for lung cancer and identified the involvement of AKT signaling in the function of LC.

Abstract: The recent losses and damages due to climate change have destabilized the insurance industry. As global warming is one of the most critical aspects of climate change, it is essential to investigate the extent to which greenhouse gas emissions affect the financial stability of insurers. Insurers do not typically emit substantial greenhouse gases directly, while their underwriting and investment activities play a substantial role in enabling companies that do. This article uses panel data regressions to analyze companies in all segments of insurance and from all geographic regions in the world from 2004 to 2023. The main finding is that insurers who increase their greenhouse gas emissions become financially unstable. This result is consistent across all three scopes (scope 1, scope 2, and scope 3) of emissions. Furthermore, the findings reveal that this impact is related to reserves and reinsurance. Specifically, reserves increase with greenhouse gas emissions, while premiums ceded to reinsurers decline. Thus, high-polluting insurers retain a significant share of carbon risk, eventually becoming financially weak. The results encourage several policy recommendations, highlighting the need for instruments that improve the assessment and disclosure of insurers’ carbon footprints. This is crucial to achieving environmental targets and enhancing the stability of both the insurance market and the economic system.

Abstract: Snail mucus is significant in promoting wound healing, however, its active components and their mechanisms are partially understood. The present study isolated and hydrolyzed snail mucus via trypsin to obtain snail mucus active peptides (SMAP). The SMAP was analyzed via liquid chromatography mass spectrometry and bioinformatics screening,an active peptide EK-12 (molecular weight, 1366.2 Da) comprising 12 amino acids was screened from the candidate peptides and synthesized through the solid-phase approach. In vitro functional verification showed that EK-12 significantly promoted the proliferation, migration, and tube formation ability of endothelial cells.In vivo experiment showed that EK-12 significantly accelerated the wound healing process on mice. Pathological examinations showed significantly upregulated expression of CD31 and vascular endothelial growth factor in wound tissues, suggesting this as the mechanism by which the active peptide promoted angiogenesis and wound healing. Thus, the active peptide screened from snail mucus showed a great potential in developing therapeutic agent for wound healing.

Abstract: This paper proposes a hierarchical cooperative tracking control method for multi-missile formations under dynamic event-triggered mechanisms, addressing parameter uncer-tainties and saturated overload constraints. The proposed hierarchical structure consists of a reference trajectory generator and a trajectory tracking controller. The reference tra-jectory generator considers communication and collaboration among multiple intercep-tors, imposes saturation constraints on virtual control inputs, and generates reference trajectories for each receptor, effectively suppressing aggressive motions caused by overload saturation. On this basis, a Radial Basis Function Neural Network (RBFNN) combined with a sliding mode disturbance observer is adopted to estimate unknown external disturbances and unmodeled dynamics, and the finite-time convergence of the disturbance observer is proved. A tracking controller is then designed to ensure precise tracking of the reference trajectory by missile. This approach not only reduces commu-nication and computational burdens but also effectively avoids Zeno behavior, enhancing the practical feasibility and robustness of the proposed method in engineering applica-tions. Simulation results verify the effectiveness and superiority of the proposed method.

Abstract: This paper proposes a theoretical framework for consciousness as an emergent manifestation of universal organizational principles in cosmic evolution. Extending relational ontology through thermodynamic and information-theoretic foundations, it argues that consciousness arises when systems achieve sufficient relational complexity to sustain self-referential organization. Mathematical formalization links neural structures to cosmic processes. The approach reinterprets the hard problem and explanatory gap from evolutionary-naturalistic perspectives, opening interdisciplinary research pathways across philosophy, cosmology, and complexity science.

Abstract: The fundamental divergence in foundation of modern physics lies in the incompatibility between quantum theory and relativity. This study created a relative continuum, found a mathematical tool for the cosmic continuum; proposed dark particle hypothesis, filled gap on minimum existence quantity particle; discovered new equivalence principle, bridged differences in foundation of physics; and established an ideal model, revealed deep essence of cosmic and material structure. In this model, any cosmic system is a continuum composed of existence continuum relative to the wavelength of bosons energy waves and its dimension continuum. This model provides new perspectives on the fundamental problems of physics and cosmology. Firstly, it elucidated the physical mechanism of bosons in fundamental interactions. Secondly, it reconstructed the understanding of the basic unit of the cosmic and material structure. Thirdly, it updated the inherent concepts about the existence form and existence dimension. Fourthly, it restored the causality truth of wave function collapse in quantum mechanics.

Abstract: The relentless progress of Artificial Intelligence (AI) has sparked a profound and enduring debate: which form of intelligence, human or artificial, is superior? This paper navigates this complex question, not by seeking a definitive victor, but by undertaking a comparative analysis of the distinct characteristics of human and artificial intelligence. It explores the foundational cognitive architectures that underpin both, delves into the enigmatic nature of consciousness, and examines the formidable open challenges confronting the pursuit of Artificial General Intelligence (AGI). Ultimately, this paper argues that the future lies not in a contest of supremacy, but in the synergistic potential of human-AI collaboration, a prospect that promises to redefine the boundaries of knowledge and innovation.

Abstract: Background: Paroxysmal atrial fibrillation (PAF) is a common arrhythmia often treated with catheter ablation, particularly pulmonary vein isolation (PVI). However, recurrence remains frequent and is often linked to unrecognized structural and functional remodeling of the left atrium. Methods: We introduce the Echocardiographic Atrial Strain and conduction Evaluation (EASE) Score as a theoretical, noninvasive model to stratify recurrence risk in patients undergoing catheter ablation for PAF. The score is based on the hypothesis that integrated echocardiographic parameters can reflect the extent of atrial remodeling relevant to ablation outcomes. Results: The EASE Score combines six echocardiographic metrics—left atrial reservoir strain (LASr), atrial conduction time (PA-TDI), left atrial volume index (LAVI), stiffness index (E/e'/LASr), E/e' ratio, and contractile strain (LASct)—each representing structural, electrical, or mechanical remodeling. The total score ranges from 0 to 12, stratifying patients into low, intermediate, and high-risk categories for arrhythmia recurrence. Preliminary retrospective data suggest a significant association between higher EASE Scores and increased recurrence rates following ablation. Conclusions: The EASE Score offers a biologically plausible, multidimensional framework for noninvasive risk prediction in PAF ablation. Prospective studies are warranted to validate its clinical utility and refine its structure.

Abstract: We introduce Pretopologically-Neighborhoood Modal Logic (PNML), a formal framework for reasoning about local knowledge and uncertainty based on pretopological neighborhood semantics. Unlike classical Kripke or general neighborhood models assuming global structural properties or arbitrary accessibility, PNML restricts neighborhood systems to satisfy the axioms of pretopological spaces, i.e., upward closure and self-inclusion, without requiring intersection stability or closure under arbitrary unions. This enables a finer-grained representation of agents’ information in contexts where only partial or locally available knowledge is relevant. We define the truth conditions for modal operators in terms of pointwise neighborhood filters, introduce a basic axiomatic system and prove its soundness/completeness with respect to the full class of pretopological frames, ensuring that the syntactic and semantic components of the logic are aligned. Then, we examine the expressivity of PNML in relation to both normal and non-normal modal logics, arguing that pretopological constraints introduce structural distinctions not captured by standard neighborhood models, particularly under minimal closure conditions. We present examples illustrating the framework’s utility in modeling observer-dependent scenarios, including epistemic uncertainty, context-sensitive reasoning and localized inference. Notably, PNML may accommodate settings in which traditional modal logic either overgeneralizes or underrepresents the dynamics of localized information. By grounding modal reasoning in pretopological structure, PNML may apply to distributed computing with local inputs, quantum mechanics involving contextual observation, cellular signaling and ecological systems in biology, modal update operations under information change, connections to fixpoint and coalgebraic semantic frameworks, proof systems for local inference and real-world modeling of belief revision and protocol verification.

Abstract: Energy storage systems (ESS) have recently emerged as a prevalent solution for mitigating the variability of intermittent renewable energy sources. One of the primary challenges associated with ESS is their cost. This paper aims to explore methods for reducing the size of ESS without compromising performance. Data was collected from a grid-tied 2MW PV unit in Malaysia over several days, as many variables exhibit significant fluctuations from hour to hour due to solar irradiation. Python code was developed to analyze the effects of varying ESS sizes on power grid smoothing. Both high-power density devices and high-energy density devices were tested, and the impact of changing output period durations was investigated. The output should remain stable for no less than five minutes, which is the minimum acceptable timeframe. SIMULINK was utilized to simulate the recommended ESS size, employing a vanadium redox battery (VRB) as the high-energy density device and supercapacitors (SC) as the high-power density device.

Abstract: Storytelling has long played a pivotal role in legal education and practice, evolving from classical oratory and apprenticeship systems to becoming an essential pedagogical tool in clinical legal education (CLE). This paper examines the multifaceted role of storytelling in legal education, highlighting its capacity to enhance cognitive skills, critical thinking, empathy, ethical awareness, and advocacy abilities among law students. Drawing from interdisciplinary perspectives across psychology, literary studies, communication, and technology, the paper demonstrates how narrative techniques facilitate a deeper understanding of legal concepts, professional identity formation, and client-centered advocacy. Additionally, it explores the integration of digital storytelling, artificial intelligence (AI), and virtual reality (VR) tools in legal pedagogy, offering innovative approaches to immersive and practice-oriented legal training. The global and cross-cultural dimensions of storytelling in legal education underscore its importance in promoting cultural sensitivity, inclusivity, and access to justice. The paper also discusses the development of structured evaluation rubrics for assessing students' narrative competence. Through a comprehensive analysis, the study advocates for a balanced, ethically mindful integration of storytelling into legal curricula to transform legal education and better prepare students for the complexities of modern legal practice.

Abstract: This study presents a bibliometric analysis of the literature on sustainability in higher education, supported by a bibliometric analysis of 270 publications indexed in the Web of Science and published between 2014 and 2024. This research used RStudio software, through the Bibliometrix package, for data processing and analysis, aiming to characterize trends, gaps, and main scientific contributions in this field of research and identify the most influential authors, themes, and publications. This approach facilitated the analysis of the main scientific journals, the productivity of authors over time, collaborations between institutions and countries, and the most relevant keywords within the scope of sustainable institutions. The analysis of the results made it possible to identify four main thematic areas, organized into two major axes: one focused on the educational and psychological dimensions of sustainability, and the other focused on the contextual and systemic factors that shape education for sustainability. The results inform a comprehensive picture of current trends, existing gaps, and future lines of research, contributing to the deepening of knowledge regarding integrating sustainability in higher education.

Abstract: We reformulate the Total Entropic Quantity (TEQ) framework using two axioms, extending the second to include spectral comparison via analytic continuation. This extension formalizes the treatment of renormalization, vacuum energy suppression, and spectral anomalies as structural consequences of entropy geometry. Using the extended Minimal Principle, we derive the exact Casimir energy, explain the chiral anomaly, and reinterpret zeta regularization as a physically grounded method for comparing entropy-curved spectra. Appendices confirm that core quantum corrections—including the Lamb shift and the running of the coupling constant α—remain derivable from the original two axioms. Crucially, these results are obtained without recourse to ad hoc regularization, arbitrary subtractions, or postulated operator structure; instead, regularization and anomaly arise as necessary features of entropy geometry and analytic continuation. These results reinforce TEQ's explanatory economy: a single resolution-based variational principle governs not only quantum dynamics but also spectral comparisons and anomalies. This work preserves axiomatic minimality while extending the empirical and structural reach of the TEQ framework.

Abstract: Background: The dietary habits of healthcare professionals, particularly dentists, are frequently compromised by demanding work schedules and occupational stress. These factors contribute to poor dietary choices, irregular eating patterns, and inadequate nutrient intake, negatively impacting both general and oral health. Methods: This comprehensive review analyzed literature on dietary habits, nutrition education, work environments, and oral health outcomes among healthcare professionals, with a particular focus on dentists. A systematic search of PubMed, Scopus, and the Cochrane Library was conducted using MeSH terms and keywords including "Dentists," "Dentistry," "Oral Health," "Diet," "Nutrition," "Nutrition Education," "Work Environ-ment," "Occupational Stress," "Physical Activity," and "Well-being." Studies published in English from 2010 to 2024 were included. Screening, quality assessment, and data extraction followed predefined criteria, and findings were narratively synthesized. Results: Of 617 articles screened, 27 met inclusion criteria. The findings demonstrate that occupational stress and poor workplace environments contribute to unhealthy dietary behaviors, which are linked to increased risks of oral disease, burnout, and poor overall well-being. Conversely, balanced nutrition, structured wellness routines, and interprofessional education were associated with improved oral and systemic health outcomes, including better oral hygiene, lower BMI, improved cardiometabolic markers, and enhanced mental health. Conclusions: Systemic integration of nutrition education into dental training, institutional policies promoting healthy workplace environments, and interprofessional collaboration are essential to support dentists’ well-being and clinical practice. Targeted interventions addressing the specific challenges of dental professionals, particularly those related to shift work and stress, are needed. Further longitudinal and interventional research is required to guide evidence-based strategies that improve both personal health and professional performance in dental practice.

Abstract: In physics, the two most successful theories, quantum mechanics and general relativity, appear to be incompatible with each other. Many theorists believe that the reason behind this, is that these theories treat space and time very differently, thus focus their attempts on finding a new way of modelling our universe and more specifically of modelling time [1]. In this paper we take a different approach to modelling the time dimension. We do not treat time as a fixed dimension which is experienced the same way for every phenomenon or interaction of any dimensionality. Instead, we model time to always be the plus one (+1) dimension relative to the dimensions through which a given phenomenon propagates and interacts. This means that time for one phenomenon can behave as space for a higher dimensional phenomenon whose time is a different +1 dimension. Through this dynamic modelling of time, we aim to integrate some of the mathematical tools of both quantum mechanics and general relativity such as Operators, Complex Functions (Wavefunctions), Probabilistic Behaviour, the Metric Tensor and the Einstein Energy Equation. Finally, we investigate the compatibility of our results with other theories and the possible testability of our framework.

Abstract: This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human-robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human-subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies.

Abstract: With the increasing frequency of marine activities, the significance of underwater target search and rescue has been highlighted, where precise and efficient path planning is critical for ensuring search effectiveness. This study proposes an underwater target search path planning method by incorporating the dynamic variations of marine acoustic environments driven by sea surface wind fields. First, wind-generated noise levels are calculated based on the sea surface wind field data of the mission area, and transmission loss is solved using an underwater acoustic propagation ray model. Then, a spatially variant search distance matrix is constructed by integrating the active sonar equation. Finally, a sixteen-azimuth path planning model is established, and a genetic algorithm (GA) is introduced to optimize the search path for maximum coverage. Numerical simulations in three typical sea areas (the South China Sea, Atlantic Ocean, and Pacific Ocean) demonstrate that the optimized search coverage of the proposed method increases by 54.32%–130.22% compared with the pre-optimization results, providing an efficient and feasible solution for underwater target search.