Abstract: Entanglement swapping can connect segmented quantum channels to form long-distance quantum channels, which is a key mechanism for realizing large-scale quantum networks and remote quantum communication. In this paper, we show the basic principles of entanglement swapping and propose a new calculation method to derive entanglement swapping results, which is demonstrated through the entanglement swapping between two bipartite entangled states. In addition, we propose an infinitely scalable star-shaped quantum network, which is composed of quantum computers connected through quantum channels. These computers interact with an observable universe: obtaining observational information from it and automatically providing feedback after processing the information.

Abstract: Dirty paper coding (DPC) is applied to linear multi-input multi-output (MIMO) broadcast channels with additive white Gaussian noise and one message per receiver. The method decomposes each receiver channel into parallel scalar channels with known interference, then applies modulo operators, amplitude-shift keying (ASK), and probabilistic shaping. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals. Simulations with short polar codes show significant rate gains from DPC compared to conventional linear precoding, while maintaining similar encoder and decoder complexities.

Abstract: Generative artificial intelligence is increasingly embedded in recursive informational ecosystems in which outputs are produced, published, retrieved, summarized, copied and pasted into new human or machine production. This paper proposes a preliminary predictive framework for Dissipative Semantic Homogenization (DSH), a possible re-gime in which recursive generative AI ecosystems dissipate physical energy while corpus-level semantic diversity contracts and saturates. The framework does not iden-tify thermodynamic entropy with semantic entropy. Instead, it treats them as opera-tionally coupled variables: semantic distributions are transformed by physically im-plemented computation, while energy dissipation provides a macroscopic cost proxy. We model semantic diversity as Shannon entropy over a corpus-level partition of se-mantic states and introduce modal amplification, independent novelty injection, and AI assimilation of nominally human production as control variables. The model yields empirically testable implications: semantic contraction should occur only when effec-tive independent novelty falls below a stability threshold; contraction should be scale-dependent; and cumulative semantic loss should saturate even while physical entropy production continues. The framework is not presented as an empirical law, but as a testable theoretical model for future longitudinal and controlled studies.

Abstract: Flash drought, as a rapidly developing form of drought, has become an increasing threat to agricultural production, ecosystem stability, and regional carbon cycling, par-ticularly in croplands within monsoon regions. Existing studies have mainly focused on point-scale identification or conventional vegetation indices, while a systematic understanding of the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remains limited. Using mul-ti-source remote sensing data from the North China Plain and the Middle-Lower Yangtze Plain during 2001–2024, this study integrated triple collocation error assess-ment, root-zone soil moisture percentile-based identification, connected component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their impacts on vegetation. The results showed that the southern Middle-Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the cen-tral North China Plain was characterized by relatively low frequency but higher inten-sity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, while rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days ear-lier than gross primary productivity (GPP), and all cropping systems exhibited a “rapid physiological response–lagged carbon assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors controlling photosynthetic recovery. These findings extend the flash drought monitor-ing framework from the perspectives of regional connectivity and crop recovery mechanisms, and provide a remote sensing-based scientific basis for agricultural early warning, drought mitigation, and food security management.

Abstract: Objective: Thyroid cartilage calcification and glucose metabolism may vary with age and gender. This study aimed to investigate the role of [18F]-sodium fluoride ([18F]NaF) and 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT for the evaluation of physiological molecular calcification and glucose metabolism of thyroid cartilage with age. Methods: This retrospective study analyzed [18F]NaF and [¹⁸F]FDG PET/CT images from the CAMONA study (NCT01724749). Regions of interest were placed around the thyroid cartilage on CT images using OsiriX software. The mean standardized uptake value (SUVmean) was measured for [¹⁸F]NaF and [¹⁸F]FDG PET/CT images. Pearson correlation coefficients were calculated to evaluate the effects of aging on the uptake of [¹⁸F]NaF and ^[18F]FDG in the thyroid cartilage. Results: A total of 127 healthy subjects (65 females and 62 males) with a mean age of 48.46±14.13 (range 21–75) years for [¹⁸F]NaF PET/CT and a total of 114 healthy subjects (52 females and 63 males) with a mean age of 49.05±14.29 (range 21–75) years for [¹⁸F]FDG PET/CT were included. A significant positive correlation was observed between age and SUVmean of [¹⁸F]NaF in the thyroid cartilage (r=0.18, p=0.04). This result indicates that molecular calcification of this cartilage increases with aging. However, the correlation between age and SUVmean of [¹⁸F]FDG in the thyroid cartilage was not statistically significant (r=-0.09 p=0.31). Conclusion: This study presents a novel methodology for the determination of molecular calcification and glucose metabolism of laryngeal cartilage using [¹⁸F]NaF and [¹⁸F]FDG PET/CT. Molecular calcification of thyroid cartilage was found to increase with age, whereas glucose metabolism did not show a statistically significant correlation with age.

Abstract: AI beats humans in chess, exceeds humans in language translation, drives more safely than most of us -- we better believe that it can brute-force analyze ciphertexts and extract from them the secret plaintext -- no matter how much math complexity is packed into the exposed data. The noted cryptographer Adi Shamir, predicted years ago: "Encryption," he said, "would not be cracked, it would be circumvented." Which is exactly what AI does to cryptography. No matter how hidden the pattern, AI will discern it. Post quantum cryptography may or may not be effective against quantum computers, but it would surely be ineffective against the brute force analysis of neural networks and other AI means. The only way to survive a charging Grizzly bear is to confuse it by arriving at a junction that spreads to many roads, letting the bear roam ahead in the wrong direction. The only way to escape the AI onslaught is to meet AI with waves of entropy -- content-devoid bits, to sidetrack the AI bear off and away. Fortunately, academia is ready with a new class of tools: Pattern Devoid Cryptography. Explained here. No time to wait, the AI bear is shaking the ground under our feet.

Abstract: Coastal lagoons are key wintering habitats for waders, yet long-term changes in their community structure remain poorly understood in Mediterranean systems. We analysed a 30-year dataset (1993–2022, excluding 2021) of wintering waders in the Venice Lagoon to assess trends in abundance, community structure, thermal composition and spatial patterns. Total abundance increased significantly (+3.5% yr⁻¹), while species richness ranged between 12–21 species per winter. Community structure changed markedly, with increasing dominance of a few species, particularly Dunlin, leading to reduced evenness. Species-level analyses showed a prevalence of increasing trends: nine of the 19 species analysed increased significantly, one declined, one was stable and eight showed uncer-tain trends. The Community Temperature Index (CTI) increased significantly (p = 0.001), suggesting a shift towards species with higher thermal affinities, but this pattern was not robust to the exclusion of Dunlin C. alpina, indicating dominance-driven dynamics. Spatial analyses revealed a strong increase in the open lagoon (p < 0.001) and a decline in fish farms (p = 0.008), indicating a shift towards natural tidal habitats. Overall, the assemblage is increasing but structurally simplified, highlighting the need to integrate species- and community-level approaches when interpreting ecological indicators.

Abstract: Friction stir welding (FSW) began as a fairly specialized joining method, but over the past three decades it has evolved into something considerably more versatile, a manufacturing platform that now handles complex multi-material assemblies and solid-state additive processes with reasonable reliability. This review follows this evolution, paying particular attention to friction stir additive manufacturing (FSAM) and the persistent difficulties that arise when joining dissimilar systems: aluminum to steel or metals to polymers, where the fate of the joint is largely decided by how well the intermetallic compounds are kept under control. Machine learning, artificial intelligence, and high-fidelity numerical models are reducing the reliance on trial-and-error that once dominated parameter selection and defect prediction, bringing FSW closer to the operating principles of Industry 4.0. Hybrid variants, including ultrasonically assisted and underwater FSW, are also receiving attention here, as they offer researchers finer control over heat generation and plastic flow than the standard process allows. Throughout the study, microstructural observations are directly connected to mechanical results, with the aim of analyzing the current state of solid-state manufacturing and identifying the questions that most urgently need answering.

Abstract: Within modern culinary education, education for sustainable development is essential for vocational students. Using the example of sous vide, its suitability for addressing sustainability in culinary education was investigated as well as to which extent it is currently implemented in Germany. Therefore, literature on potential environmental, social, economic and health impacts of sous vide cooking was reviewed and its current implementation in German culinary educational materials was analyzed. The analysis revealed a number of sustainability aspects of sous vide. Despite being covered in textbooks, it is not brought into a sustainability context. Moreover, existing sustaina-bility concepts for the gastronomy sector neither identify environmental conditions as a basic requirement for any socio-economic activity nor illustrate interdependencies and trade-offs between different sustainability dimensions. Hence, currently in Ger-many available sustainability concepts and culinary teaching and training materials do not support the development of a systemic understanding and multi-dimensional engagement when training future chefs.

Abstract: Accurate and timely detection of brain tumors on magnetic resonance imaging (MRI) is a critical prerequisite for effective neuro-oncological management. While convolutional neural networks (CNNs) have been the dominant paradigm for medical image classification over the past decade, the recent emergence of vision-capable large language models (LLMs) offers a complementary, training-free pathway to image-based decision support. This study presents a controlled, head-to-head comparison between 17 ImageNet-pretrained CNN architectures and 8 state-of-the-art multimodal LLMs on the publicly available Brain MRI Images for Brain Tumor Detection dataset (n = 253; 155 tumor, 98 non-tumor). Following an 80/20 train–test partition (n = 202 / n = 51), CNN models were fine-tuned via transfer learning, whereas LLMs were evaluated in a zero-shot configuration using a standardized prompt. Test-set performance was assessed using accuracy, precision, recall, specificity, F1-score, Cohen's kappa, and the area under the receiver operating characteristic curve (AUC). Among CNNs, six architectures (DenseNet169, DenseNet201, InceptionV3, ResNet101V2, VGG16, Xception) tied at 94.12% test accuracy, while ResNet50 and NASNetMobile exhibited pronounced overfitting (45.10% and 49.02%, respectively). Among LLMs, ChatGPT 5.4 Thinking achieved perfect classification (100% on all metrics), with ChatGPT 5.5 Thinking and Gemini 3.1 Thinking attaining 98.04% and 94.12% accuracy. These findings indicate that modern multimodal foundation models can match or exceed bespoke CNNs in low-data medical imaging tasks and support their further investigation as components of clinical decision-support pipelines.

Abstract: The purpose of this study is to explore how Zimbabwean firms use Environmental Management Accounting (EMA) and climate risk disclosure in times of policy uncertainty and how these relate to sustainable growth and macroeconomic stability. The study was couched in the interpretivist research philosophy and adopted the inductive research approach. A case study research design, which aligns with a qualitative research design, was chosen for the study. The study employed in-depth interviews with management accountants, finance executives, and industry leaders across firms in Harare. The study adopted the cross-sectional time horizon and analysed data using thematic analysis to develop insights into the role of EMA and climate risk disclosure in times of policy uncertainty. The study's findings show that climate policy uncertainty compels business leaders to reconfigure management accounting systems to integrate environmental performance measures and scenario-based capital planning. The findings indicate that strategic EMA is essential because it enhances cost visibility, which, in turn, supports proactive risk management and stabilises investment decision-making within an enterprise. Firms that have integrated climate disclosure frameworks were found to demonstrate stronger stakeholder confidence and had high adaptability capacity. In an uncertain policy environment, firm-level adjustments support macroeconomic resilience and sustainable growth by lowering regulatory shock sensitivity and reducing the costs they impose. The study contributes to the literature by connecting the discussions of macroeconomic stability with micro-level accounting procedures and providing a process-based approach and understanding of how strategic EMA disclosure serves as a transmission mechanism between economic resilience and climate policy uncertainty. The study contributes to the emerging discourse on climate risk accounting within the fragile macroeconomic context of developing countries. It is therefore recommended that the regulatory institutional pillar be strengthened to reduce uncertainty and enhance the EMA's strategic adaptation.

Abstract: This article examines cross-Strait variation in Chinese baseball terminology through a document-based comparison of two primary sources: the terminology appendix contained in the Chinese Taipei Baseball Association’s baseball rules and the China Baseball Association’s Basic Terminology of Baseball standard. Based on Supplementary Dataset S1, a cleaned 363-entry English-Chinese comparison dataset, the study investigates how baseball terms differ across the Strait in documentary coverage, lexical designation, expression style, and communicative relevance. The analysis identifies 214 directly comparable entries with renderings on both sides. Of these, 101 are classified as convergent or near-convergent, while 113 show lexical divergence. A further 149 entries do not enter the directly comparable subset. The findings show that cross-Strait baseball terminology is shaped by more than isolated word-level difference. Taiwan-side terms often preserve compact and conventionalized forms used in baseball practice, whereas Mainland standardized forms frequently display a more explicit and institutionally codified style. The article argues that such variation should not be treated simply as inconsistency, but as specialist-language variation shaped by different historical, institutional, and communicative conditions. On this basis, the article suggests a graded, communication-oriented approach that tolerates low-sensitivity variants, cross-references moderate-sensitivity terms, and coordinates high-sensitivity rule terms for umpiring, commentary, translation, and instruction.

Abstract: We study positive–negative guarded systems of language equations over a fixed finite alphabet. The ambient space is the complete ultrametric space of all formal languages equipped with a length-based distance, where two languages are close whenever they agree on all words up to a sufficiently large length. The systems considered here contain both positive recursive dependencies and negative dependencies expressed through language complements. To handle this mixed structure, we introduce a suitable product order on pairs of languages and prove that the associated system operator has the weak monotone property. We show that complement is an isometry for the length-based ultrametric and establish a signed wrapping estimate for guarded positive and negative language terms. These estimates lead to an ordered contraction principle for comparable pairs. As a consequence, the canonical lower and upper Picard iterations converge to the same limit, which is the unique fixed pair of the system. We also derive an explicit convergence rate and a finite-depth certification result: after a prescribed number of iterations, the approximants agree with the fixed-point semantics on all words below a given length. Additional symmetry assumptions are shown to force the unique fixed pair to be diagonal, reducing the system to a single language equation. Finally, we discuss an application to trace-based policies for tool-using AI agents. In this interpretation, finite executions of an agent are represented as words over an alphabet of observable tool-events, and the two components of the fixed point provide a stable semantics for policy-defined admissible and risky trace classes. The resulting framework gives a mathematically certified method for finite-depth analysis of recursive trace-based policies based on ultrametric fixed-point techniques.

Abstract: It is shown that the Clausius entropy and the internal energy can be taken to be additive for systems existing next to each other, contemporaneously, and without relative speed. Also, it is shown that any thermodynamic state quantity is extensive if it is additive and if its volume density is finite. It is argued that for many state quantities the assumption of extensivity can be replaced by the assumption of finite density. The point of view is the macroscopic and non-statistical one of classical thermodynamics.

Abstract: Bridging the renewable energy funding gap in African countries remains a major challenge, as domestic resources are often insufficient to support capital-intensive investments. In this context, donor financing, particularly grants and concessional loans, is vital for supporting the energy transition. This paper examines the effect of public debt on donor financing for renewable energy in twenty-eight African countries over the period 2000–2023. Using Driscoll–Kraay standard errors, Panel-Corrected Standard Errors (PCSE), and Feasible Generalized Least Squares (FGLS) techniques, we identify a significant nonlinear relationship, indicating an inverted U-shaped effect of public debt on donor financing. The results also show a negative effect of total debt service on donor financing support, while the role of institutional quality appears to be moderately important. These findings underline the importance of maintaining sustainable debt levels and effective debt management to attract donor financing and support the energy transition.

Abstract: Old murine bone marrow-derived monocytes and macrophages (BMMs) display enhanced CD38 protein, a nicotinamide adenine dinucleotide (NAD⁺) glycohydrolase, and reduced NAD⁺ level after infection with oral pathogens compared with young controls. We aimed to determine whether treatment with a CD38-specific inhibitor (78c) in mice with experimental periodontitis could alleviate alveolar bone loss and enhance NAD⁺ levels in tissues compared with vehicle treatment. Twenty young (2-month-old) and twenty old (18-month-old) C57BL/6J mice with experimental periodontitis were treated with either vehicle or 78c twice daily via intraperitoneal injection for 4 weeks. The liver, spleen, and right maxillary tissues were harvested to analyze NAD⁺ levels. The left maxillary tissues were scanned by micro-CT, processed for tissue sectioning, and stained with hematoxylin and eosin (H&E) and tartrate-resistant acid phosphatase (TRAP). Treatment with 78c significantly enhanced NAD⁺ levels in the liver and spleen of both young and old mice, and significantly increased NAD⁺ in the right maxilla of old mice compared with vehicle treatment. Additionally, treatment with 78c alleviated alveolar bone loss in both young and old mice. Our results support the notion that 78c is a promising therapeutic strategy for treating periodontal disease associated with aging.

Abstract: To reveal the solute sources, migration and enrichment mechanisms of water bodies in the endorheic lake region of the Qiangtang Plateau, Tibetan Plateau, and to clarify the hydrogeochemical cycling patterns in alpine arid zones, this study took typical en-dorheic lake areas in the region as the research object, conducted a systematic hydro-geological survey, collected 28 groups of water samples of various types (including springs, rivers, thermal springs, freshwater lakes, salt lake brines, atmospheric precip-itation and glacial meltwater), tested their major ions, trace elements and physical properties, and comprehensively investigated the hydrogeochemical characteristics, evolution laws and solute sources of water bodies, quantified the dominant control-ling factors and established a conceptual hydrogeochemical model by combining methods such as PHREEQC modeling, principal component analysis (PCA) and Pear-son correlation analysis; the results show that water bodies in the study area exhibit a distinct evolutionary gradient, from the low-salinity HCO₃-Ca recharge end-member, through transitional HCO₃·SO₄-Ca(Mg) water, to highly mineralized Cl-Na(SO₄·Cl-Na) salt lake brine, with synchronous enrichment of Li, B, As and other elements; solute sources are controlled by a ternary coupling mechanism of evaporative concentration, rock weathering and leaching, and deep geothermal fluid input, while cation ex-change and mineral dissolution-precipitation further regulate ionic ratios; As, Li, B and Cl⁻ display conservative migration in non-hydrothermal waters, whereas thermal springs show unique geochemical signatures due to the input of deep-seated fluids; PCA reveals that evaporative concentration (contribution rate 55.39%) is the dominant controlling factor, rock weathering (17.09%) provides the basic solute load, and the coupled process of deep fluid mixing and carbonate precipitation (14.21%) regulates elemental fractionation, and this study constructs a conceptual model of "multi-source recharge–water–rock interaction–evaporative concentration", which clarifies the evo-lutionary laws of regional water bodies and provides a scientific basis for water cycle research and green exploration of strategic mineral resources in salt lakes of the en-dorheic regions on the plateau.

Abstract: Convolutional Neural Networks (CNNs) constitute the foundation of modern computer vision systems, yet their empirical performance is frequently governed as much by training configuration choices as by architectural design. Despite this, critical hyperparameters—including optimizer selection, batch size, learning rate, training duration, dropout regularization, and batch normalization—are often selected heuristically or reported incompletely. Based on two benchmark datasets (CIFAR-10 and MNIST) and two classical CNNs (CifarNet-11 and LeNet-5), this paper presents a systematic, configuration-aware empirical study that quantifies how these variables influence predictive accuracy, convergence stability, generalization behavior, and computational efficiency of CNN models. Using a controlled experimental methodology in which individual configuration variables are isolated and evaluated under identical conditions, we demonstrate that configuration choices can induce performance variations comparable to those achieved through substantial architectural modifications. The results reveal explicit accuracy–efficiency trade-offs, identify regime-dependent optimal configurations, and highlight the central role of early stopping and normalization in stabilizing learning dynamics. These findings elevate configuration-aware optimization to a first-order design principle for reproducible research and reliable deployment of CNN-based artificial intelligence systems.

Abstract: Physical activity is a safe and effective intervention for chronic musculoskeletal pain. However, a literature search revealed a lack of synthesis of evidence on the extent to which physical therapists in Saudi Arabia incorporate physical activity as part of health promotion in the management of such pain. This review aims to identify, map and report literature related to physical activity for health promotion in people with chronic musculoskeletal pain presenting to physical therapy settings in Saudi Arabia. A six-step approach will be followed to conduct the scoping review. Step 1, the primary research question is: What is the scope and nature of the existing literature in this research area? The secondary research question is: What insights does the existing literature reveal regarding physical therapy clinical practice? Step 2, a comprehensive search will be conducted for relevant literature using the following electronic databases: Scopus, Medline, PubMed, Cochrane library, CINAHL, ScienceDirect and PEDro. In addition, supplementary search methods will be conducted for identifying additional relevant literature via screening process of the reference lists of all included literature and screening process of the included studies of retrieved systematic reviews through electronic databases. A parallel search including the main keywords will also be undertaken on the website of the Saudi Ministry of Health, the website of Saudi Physical Therapy Association, Google and Google Scholar for grey‑literature searching. Step 3, records will be screened by two independent reviewers and managed using Rayan software. Step 4, the nature of included literature, including study characteristics and outcomes where appropriate, will be documented using a data extraction. Step 5, the characteristics and outcomes of included records will be collected, summarised and reported. Step 6, Stakeholders will be consulted to interpret the scoping review findings from their perspective and assess the findings’ relevance and applicability.

Abstract: This paper examines the empirical evidence on the use of generative artificial intelligence (GenAI) in scientific writing. A search was conducted in Google Scholar and PubMed, followed by an analysis of the included studies, which was performed according to the academic field, AI tool, writing task, study design, and main findings. Following the PRISMA guide, this scoping review included 18 studies published between 1st January 2023 and 1st January 2026, representing the disciplines of medicine, education, dentistry, radiology, humanities, library, information science and cognitive science. The evidence base was dominated by studies on ChatGPT, making it the most empirically researched GenAI tool in this field. According to the studies reviewed, GenAI performed well on an array of measures (readability, fluency, and organization) and efficiency (the latter especially in terms of manuscript drafting, abstract writing, proposal development, and literature reviewing). However, the findings also disclosed several limitations, including incorrect or falsified references, inaccurate bibliographical metadata, shallow analysis, lack of originality, and insufficient methodological depth. Based on comparative evidence, newer model versions show improved coherence and reasoning and although improved with the newer GenAI versions, reference reliability still appears to be a recurring problem. Overall, GenAI can be a useful assistive tool for scientific writing; however, its usefulness is dependent upon human supervision and the task at hand, especially with regard to the accuracy of facts and their sources.