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.

Abstract: This paper presents OSI4IOT, an open-source software platform designed to support the integration of sensor-driven Internet of Things (IoT), Asset Information Modelling (AIM), Geographical Information Systems (GIS), and data-driven analysis within a Digital Twin (DT) framework. The platform provides a modular architecture for connecting heterogeneous data sources and enabling the coupling between physical assets and numerical models. In particular, it supports the integration of Finite Element Method (FEM)-based structural models for simulation and comparison with monitored responses. A case study involving a structural frame is used to demonstrate the platform workflow, including data acquisition, model execution, and result visualisation. The results are used to assess the consistency between analytical, numerical, and monitored responses under varying loading conditions. The paper focuses on the system architecture and the coupling strategy between data acquisition and simulation components within an open-source environment.

Abstract: The landscapes of most tropical regions have been shaped by the indigenous peoples’ and their livelihood practices. The utility of plants within these landscapes for traditional purposes has been facing intense competition from commercial logging. To gain insights into this conflict, this paper examined how landscape conditions may influence the presence and spatial distribution of indigenous subsistence and commercial logging ecosystem services relative to one another. Data on the ecosystem services and landscape conditions in the form of physical environment variables were obtained for twelve indigenous villages in the Rupununi, Southern Guyana. For each village, the relative log risk ratios of subsistence values to logging values were computed and regressed against six physical environment variables – village presence, distance to village, distance to road, distance to waterways, elevation, and slope – to examine if and how landscape conditions may favor the presence of one service over the other. The estimates were then used to map the relative differences in the spatial distributions of subsistence and commercial logging services in each village. It was found that mean relative log risk ratios for the villages were generally positive, indicating an inclination towards the presence of subsistence services. However, the maps revealed that while some areas within a given village were indeed more favorable for the presence of subsistence services, other areas within the same village were inclined towards the presence of logging services. Similar spatial analyses can be explored to guide policy-makers in developing land-use strategies that allocate forest lands between competing users by identifying areas that are best suited for indigenous peoples’ subsistence activities and for commercial logging operations.

Abstract: Biofilms (BFs) bacteria are dramatically intensifying tolerance to conventional antibiotics, no longer effective. Therefore, the research for new antibiofilm (ABF) compounds are noticeably increasing the studies proliferation rate. In this regard, intriguing questions should raise to be debated. To this end, the problematics of BF, mainly in medical setting, have been afforded here in an original way, examining the tension “between efficacy and understanding”. Questions include: are BF mechanistic studies indispensable and strictly required especially at academic levels with poor economic support? When may a purely phenotypic approach still hold scientific value? Could be demonstrate empirical efficacy alone, sufficient for scientific relevance of the study? Do high costs, long times mechanistic insights, also associated to environmental issues, represent the necessary key to defeating BFs and the benchmark that determines the robustness and impact of ABF research? The state of the art of global challenge against BF, responsible for difficult-to-treat and even lethal chronic infection, has been provided. The available armamentarium of best functioning antibiotics/combinations has been discussed, while the correct way to investigate ABF mechanisms has been clarified. Among 102 studies on the ABF activity, considered, distributed in Tables and discussed, mechanistic investigations carried out correctly have been found in only 34 ones. Only efficacy screens, stopping at phenotypic descriptions, as reported in 68 out of 102 papers, are considered essential for discovering efficacious ABF compounds and are welcome by Editors and scientific community. Such approach represents the main trend of most recent literature and is strongly desirable for publication.

Abstract: Background/Objectives: Treatment decisions for elderly patients with diffuse large B-cell lymphoma (DLBCL) often rely on subjective clinical impression rather than systematic frailty assessment. We evaluated whether a modified simplified frailty score (mSFS)—a binary adaptation of the Isaksen score—predicts treatment selection, toxicity, and survival. Methods: In this retrospective study of 117 patients aged ≥65 years with DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or dose-attenuated R-CHOP (R-mini-CHOP) at MedStar Health community hospitals (2000–2025), the mSFS assigned one point each for age ≥80, Eastern Cooperative Oncology Group (ECOG) performance status ≥2, and ≥5 comorbidities; score ≥2 defined frailty. Results: Among 86 R-CHOP recipients, 17 (19.8%) were mSFS-frail; among 31 R-mini-CHOP recipients, 15 (48.4%) were mSFS-fit. In R-CHOP recipients, frailty independently predicted worse overall survival (adjusted hazard ratio [aHR] 7.67, 95% confidence interval [CI] 2.36–24.97), progression-free survival (aHR 2.90, 95% CI 1.18–7.13), grade ≥3 adverse events (adjusted odds ratio [aOR] 3.90, p = 0.035), and early discontinuation (aOR 4.41, p = 0.034). Frail R-CHOP patients had lower complete response rates (aOR 0.24, p = 0.038). Fit R-mini-CHOP patients had 88% lower odds of complete response versus fit R-CHOP patients (aOR 0.12, p = 0.003). Among R-mini-CHOP recipients, frailty was not significantly associated with outcomes. Conclusions: The mSFS revealed bidirectional discordance with oncologist-assessed frailty and independently predicted survival, toxicity, and response, supporting its integration into community oncology practice.

Abstract: Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic multifactorial metabolic disorder requiring multi-target therapeutic strategies. This study aimed to clarify the potential material basis, key targets and molecular mechanisms by which PuRenDan (PRD) acts against T2DM through an integrated network pharmacology and molecular simulation approach. Methods: Active compounds of PRD were screened from TCMSP, HERB 2.0 and the literature, and compound-related targets were predicted using TCMSP, SwissTargetPrediction and PharmMapper. T2DM-associated targets were collected from OMIM, DrugBank, DisGeNET, HPO, ClinPGx and GeneCards to obtain drug-disease intersection targets. Cytoscape was used to construct herb-compound-target and protein-protein interaction (PPI) networks, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was performed using AutoDock Vina, and representative ligand-receptor complexes were further assessed by 100 ns molecular dynamics (MD) simulations and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) binding free-energy analysis. Results: A total of 163 active compounds, 597 PRD-related targets, 9138 T2DM-associated targets and 483 intersection targets were identified. β-sitosterol, emodin, quercetin, kaempferol and formononetin were predicted as major active compounds, whereas AKT1, TP53, SRC, IL6, TNF, EGFR and ESR1 were identified as core targets. KEGG enrichment highlighted the PI3K-Akt, MAPK, HIF-1, FoxO, mTOR, AGE-RAGE and TNF signalling pathways. Docking suggested strong multi-target binding potential for β-sitosterol. MD and MM/PBSA analyses further indicated favourable dynamic stability for β-sitosterol-TNF, β-sitosterol-AKT1, β-sitosterol-SRC and emodin-EGFR complexes, with β-sitosterol-TNF showing the lowest binding free energy. Conclusions: PRD may exert therapeutic effects against T2DM through coordinated multi-compound, multi-target and multi-pathway regulation involving inflammation, insulin signalling, oxidative stress and metabolic pathways. β-sitosterol may represent an important candidate material basis of PRD, with TNF, AKT1, SRC and EGFR as potential key targets.