Abstract: Background: Colorectal cancer (CRC) caused over 1.9 million new cases and 930,000 deaths globally in 2020. There is an urgent need for novel biomarkers capable of predicting disease progression and therapeutic response. The gut microbiome has emerged as a promising source of diagnostic and prognostic indicators. Objective: This narrative review summarizes current evidences on gut microbiota and their metabolites as potential biomarkers for CRC diagnosis and prognosis. Main Content: Gut microbiomes influence CRC development through metabolism, immune modulation, inflammation, proliferation/apoptosis regulation, genotoxicity, and mucosal barrier disruption. Pathogenic species including Fusobacterium nucleatum and enterotoxigenic Bacteroides fragilis promote tumorigenesis via FadA-mediated signaling and Th17/IL-17 responses. Beneficial bacteria such as Faecalibacterium prausnitzii and Akkermansia muciniphila exert protective effects through short-chain fatty acid production. Macrophages phenotype physiological equilibrium is interrupted or maintained by different floras and inflammatory status fluctuates under the former. Metabolically, hydrogen sulfide damages mitochondrial DNA and secondary bile acids stimulate proliferation. Common detection methods include 16S rRNA sequencing and shotgun metagenomics, while organoids and gene arrays as innovate carriers are in exploratory stage so far. Clinical studies show F. nucleatum abundance correlates with advanced tumor stage, with combined F. nucleatum and colibactin-producing E. coli detection achieving 84.6% sensitivity for early CRC. A. muciniphila levels also predict response to PD-1 blockade immunotherapy. These microbiomes or metabolites support predictions in diagnosis, prognosis, therapeutic efficacy and even locations in earlier stages. Conclusion: Microbiome-based biomarkers represent a promising frontier in CRC management. Future research should focus on standardizing detection protocols, validating multi-marker panels, and exploring metabolite-based approaches to enhance clinical translation.

Abstract: We study Wilf’s conjecture for a numerical semigroup S using only the first Kunz layer S ∩ (m, 2m). Let η = |S ∩ (m, 2m)| and write the conductor as c = qm − ρ with 0 ≤ ρ < m. We prove the staircase bound |L| ≥ q + (q − 2)η + ηρ, where L = S ∩ [0, c) and ηρ = |S ∩ (m, 2m − ρ)|. This yields a lower bound for Wilf’s number and the criterion e(η + 2) ≥ 2m, hence also (η + 1)(η + 2) ≥ 2m, implying Wilf’s conjecture. When m | c, we obtain the stronger condition e(3η + 4) ≥ 4m, and in particular 3η² + 7η + 4 ≥ 4m. We also derive an exact cumulative-layer formula for |L| and apply it to interval-generated semigroups, where the second cumulative layer gives a strictly stronger infinite family.

Abstract: We prove that the set \( T=\Bigl\{n\in\mathbb{N}: \exists p,q\in\mathbb{N}\;\Bigl((2n=(p+q)(p+q+1)+2q)\;\wedge\ \) \( \forall (x_0,\ldots,x_p)\in\mathbb{N}^{p+1}\;\exists (y_0,\ldots,y_p)\in\{0,\ldots,q\}^{p+1}\ \) \( \bigl((\forall k\in\{0,\ldots,p\}\;(1=x_k \Rightarrow 1=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i+x_j=x_k \Rightarrow y_i+y_j=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i\cdot x_j=x_k \Rightarrow y_i\cdot y_j=y_k))\bigr)\Bigr)\Bigr\}\ \) is not recursively enumerable. By using Gödel's \( \beta \) function, we prove that the formula that defines the set T can be easily translated into a first-order formula which uses only + and \( \cdot \). The same properties has the set \( \Bigl\{n\in\mathbb{N} : \exists p,q\in\mathbb{N}\;\Bigl((2n=(p+q)(p+q+1)+2q)\;\wedge\ \) \( \forall (x_0,\ldots,x_p)\in\mathbb{N}^{p+1}\;\exists (y_0,\ldots,y_p)\in\{0,\ldots,q\}^{p+1}\ \) \( \bigl((\forall j,k\in\{0,\ldots,p\}\;(x_j+1=x_k \Rightarrow y_j+1=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i\cdot x_j=x_k \Rightarrow y_i\cdot y_j=y_k))\bigr)\Bigr)\Bigr\}\ \).

Abstract: Background: Non-communicable diseases (NCDs) account for the majority of global mortality, yet healthcare systems remain largely oriented toward the treatment of acute conditions. This study examines the structural mismatch between contemporary disease patterns and healthcare system organization. Methods: A narrative analytical review was conducted using secondary data from the Global Burden of Disease (GBD) study and World Health Organization (WHO) reports, supplemented by literature from PubMed, Scopus, and Google Scholar (2000–2026). Findings were interpreted using epidemiological transition theory, health systems analysis, and political economy frameworks. Results: The analysis identifies multiple structural drivers of treatment-oriented healthcare systems, including economic incentives favoring curative services, short-term political decision-making cycles, and the historical dominance of the biomedical model. These factors contribute to systematic underinvestment in prevention, rising healthcare expenditures, and persistent global inequalities in access to medical technologies, as demonstrated during the COVID-19 pandemic. The current model is associated with increasing economic burden and projected losses in global productivity by 2030–2050. Conclusions: The findings indicate that the current healthcare model is structurally misaligned with population health needs. Improving health outcomes and system sustainability requires a reorientation toward prevention, long-term health metrics, and the evidence-based integration of complementary approaches within healthcare systems.

Abstract: This position paper argues that remote-capable knowledge work should default to AI-enabled flexibility because the workflow-integrated foundation-model stack changes the coordination economics that once favored daily co-presence. By foundation-model stack, we mean systems that combine natural-language interaction, multimodal capture, long context, retrieval, transcription, translation, and increasingly bounded tool use inside everyday workflows. Their organizational significance is not generic automation but the accumulation of artifact capital: durable, queryable, reusable traces such as transcripts, summaries, decisions, tickets, code comments, and retrieval layers. The argument rests primarily on capabilities that are already widely deployed---transcription, summarization, retrieval, translation, drafting, and code assistance---with bounded agents treated as an amplifying but not necessary extension. Rather than eliminating the office, this shift supports selective co-presence, reserving in-person time for tasks with high tacitness, high coupling, or high relational stakes, including apprenticeship, conflict repair, trust formation, and early-stage synthesis. Because the same systems can also intensify surveillance, skill atrophy, and compute-related emissions, we outline a machine-learning research agenda centered on team-level evaluation, privacy-preserving memory layers, scaffolded AI for learning, carbon-aware routing, and pro-agency workflow design.

Abstract: Artificial Intelligence (AI) and its subset, Machine Learning (ML), play transformative roles in the manufacturing sector, forming the foundation of the “Industry 4.0 and 5.0” frameworks. This research contributes to that evolution by developing AI-based advisory systems that utilize advanced data models to optimize casting processes. These systems exemplify the principles of smart manufacturing, where machines and processes are interconnected, adaptive, and driven by data. They support key objectives such as automation, seamless connectivity, real-time data exchange, human-centric innovation, operational resilience, and sustainability. The models developed in this work enable manufacturers to fine-tune product quality, minimize waste, and accelerate time-to-market through predictive analytics and dynamic process control. By integrating AI-based advisory systems, hybrid modeling, and reduced-order modeling techniques, the systems facilitate real-time decision-making and continuous improvement—essential for achieving flexible, efficient, and customized production environments. A real-world case study further demonstrates the effectiveness of these AI-based advisory systems in casting applications, detailing the steps involved in database construction, data training, and predictive modeling.

Abstract: The reaction of aluminum with water is a promising method for producing hydrogen on-demand for autonomous energy systems. However, its practical implementation faces the challenge of process control due to high exothermicity, leading to particle sintering and thermal instability, especially when using highly reactive nanopowders. The goal of this study is to implement an integrated approach to controlling this reaction, aimed at minimizing these risks. The approach is based on the principle of spatial and temporal distribution of reactants to ensure uniform heat release. Two process management methods were investigated: electrostatic application of aluminum powder to the reactor walls with its gradual release and pre-treatment of a nanopowder-ice mixture. Using a macrokinetic mathematical model, calculations of the conversion kinetics and heat release were performed and compared with experimental data. The results showed that both methods prevent slurry self-heating and achieve uniform hydrogen generation at a constant rate. In particular, the use of a pre-frozen mixture ensured stable hydrogen production over a long period of time without additional heating or stirring. The proposed approaches can be used in the design of safe and efficient hydrogen generators for autonomous power plants.

Abstract: The numerical simulation of incompressible viscous flows remains a central pillar of modern computational fluid dynamics (CFD). Over the past decades, a wide spectrum of numerical methodologies has been developed, reflecting fundamentally different mathematical formulations and discretization philosophies. Among these, domain-based approaches—such as finite difference, finite element, finite volume, and meshfree methods—have emerged as versatile and general-purpose frameworks, while boundary element methods provide efficient alternatives for problems governed by linear physics, particularly in unbounded domains. This review presents a comprehensive examination of the historical development, mathematical foundations, and computational characteristics of these approaches for Newtonian incompressible flows. Emphasis is placed on the conceptual distinctions between boundary-integral and domain-based formulations, their applicability to internal and external flow regimes, and their compatibility with turbulence modeling strategies, including Reynolds-averaged Navier–Stokes (RANS), large-eddy simulation (LES), and direct numerical simulation (DNS). The intention is to provide a unified perspective that clarifies the strengths and limitations of the principal CFD methodologies and offers guidance on their suitability for different classes of flow problems.

Abstract: This study examines and compares the macroeconomic volatility impacts of overall financial globalization with those of de facto and de jure financial globalization in 39 Sub-Saharan African (SSA) countries from 2000 to 2023, using the PCSE and 2SGMM. The empirical results show that macroeconomic volatility responds differently to overall, de facto, and de jure measures of financial globalization. Additionally, the study demonstrates that fiscal balance, central government debt, population growth rate, leading export commodity price changes, and institutional quality can influence macroeconomic volatility. Resultantly, the study recommends global financial integration should be optimized to achieve macroeconomic stability in SSA countries.

Abstract: To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open cathode air cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan speed regulation. A three dimensional single cell multiphysics model is developed and validated using a 300 W experimental platform. The effects of atomization frequency and water temperature on stack performance and internal hydrothermal distribution are systematically investigated. Results show that ultrasonic atomization provides inlet precooling, latent heat absorption, and active region humidification, thereby improving hydrothermal uniformity within the stack. Under the optimal condition of 100 kHz and 55 °C, the peak stack power increases by 21.0% to 319.00 W, while voltage consistency and surface temperature uniformity are also improved. Analysis based on the Stokes number and Dalton’s law of partial pressures indicates that the optimum results from a balance between suppressing droplet agglomeration and inertial deposition, and limiting oxygen dilution caused by excessive water vapor. The proposed strategy provides a compact and practical approach for improving the stability, uniformity, and efficiency of air cooled PEMFCs.

Abstract: The fishing processing industry in Chimbote, Peru, reflects structural vulnerabilities common in extractive sectors of the Global South, including labour informality, weak occupational safety, and limited Internal Corporate Social Responsibility (ICSR). These conditions hinder progress toward Sustainable Development Goal 8 (SDG 8). While prior studies link ICSR to positive employee outcomes, the mechanisms through which its effects translate across organisational levels remain theoretically underdeveloped, par-ticularly in high-informality contexts. A quantitative, explanatory, cross-sectional design was employed using data from 384 workers in fishing processing firms. Data were col-lected through a 26-item Likert-scale instrument. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to test a sequential mediation model, where ICSR in-fluences organisational-level labour management through individual and group-level processes. Reliability and validity were confirmed using Cronbach’s alpha, Composite Reliability, AVE, Fornell–Larcker, and HTMT. Structural relationships were assessed via bootstrapping (5,000 subsamples), and predictive relevance was evaluated using Q² and PLS Predict. The measurement model showed adequate reliability and validity. The direct effect of ICSR on organisational-level labour management was non-significant (β = 0.029, p = 0.567). However, all mediated paths were significant: ICSR → Individual (β = 0.608), Individual → Group (β = 0.526), and Group → Organisational (β = 0.396), all p < 0.001. Sequential mediation was confirmed (β_indirect = 0.127; 95% CI [0.090, 0.164]). Model fit (SRMR = 0.045) and predictive relevance (Q² = 0.150–0.361) were satisfactory. ICSR does not directly influence organisational outcomes; instead, its impact operates through a bottom-up multilevel mechanism, reinforcing individual, group, and organ-isational dynamics. These findings contribute to sustainable labour governance and multilevel organisational theory.

Abstract: World models aim to enable agents to perceive states, predict future outcomes, and reason for decision-making by simulating real-world environments, and are widely regarded as a crucial pathway toward artificial general intelligence (AGI). Video, as one of the most accessible and intuitively representative media of dynamic environments, naturally contains rich implicit representations of the physical world. Consequently, learning world models from videos has become a prominent research direction. However, a significant gap remains between video data and the real physical world: videos capture only superficial visual phenomena and lack explicit representations of three-dimensional structure, physical properties, and causal mechanisms. This limitation severely constrains the physical consistency and practical applicability of world models. Motivated by this, the present work provides a prospective study of recent research in this domain, encompassing: (1) key challenges arising from the video–physical world gap and representative solutions; (2) three major construction paradigms of physical world models; (3) a thorough summary of existing evaluation benchmarks; and (4) future research directions and discussions. It is noteworthy that this study is the first to systematically examine video-driven world model research from the perspective of physical world. In contrast to prior study that primarily focus on generative modeling or provide broad overviews, this work emphasizes world models with tangible physical grounding, explicitly excluding generative tasks such as video synthesis or 3D/4D modeling that diverge conceptually from the goal of modeling the physical world. Adopting a problem-oriented perspective, this study aims to provide subsequent researchers with a systematic framework and decision-making guidance for understanding existing work, designing innovative methods, and facilitating the deployment of world models in real-world applications.

Abstract: The development of biocompatible functional nanostructures has emerged as a key driver in advancing nanomedicine, environmental remediation, and sustainable energy technologies. However, conventional synthesis methods often rely on toxic reagents, hazardous solvents, and energy-intensive processes, raising significant concerns regarding environmental impact and biological safety. In this context, green synthesis has gained increasing attention as a sustainable alternative, utilizing biological systems, renewable resources, and environmentally benign solvents to produce functional nanomaterials. This mini-review provides a comprehensive overview of recent advances in the green synthesis of organic, inorganic, and hybrid nanostructures, highlighting their physicochemical properties and functional performance. Particular emphasis is placed on their applications in nanomedicine, including drug delivery, bioimaging, antimicrobial and anticancer therapies, and theranostic platforms. Additionally, their roles in environmental applications, such as pollutant degradation and water treatment, and in energy-related systems, including catalysis, solar energy conversion, and energy storage, are critically discussed. Despite significant progress, key challenges remain, including limited mechanistic understanding, reproducibility issues, scalability constraints, and uncertainties related to long-term toxicity and environmental impact. Addressing these limitations will be essential for the safe and large-scale implementation of green nanotechnology. Overall, the integration of green chemistry principles with advanced nanomaterial design offers a promising pathway toward the development of multifunctional, sustainable, and high-performance nanostructures capable of addressing global health, environmental, and energy challenges.

Abstract: Polypharmacy presents a major challenge when caring for older adults and increasingly causes preventable health problems in both inpatient and outpatient settings. While it is often defined as taking five or more medications, it is more accurately seen as a clinical condition where medication burden surpasses physiological capacity, is unnecessary, or is incongruent with patient-centered goals. Age-related changes in drug absorption, distribution, metabolism, and excretion, along with multiple chronic conditions, fragmented care, and frequent transfers between healthcare environments, create a perfect storm for medication-related adverse effects. Falls, syncope, confusion, fatigue, low blood pressure, slow heart rate, or functional decline can be mistaken for evidence of underlying illness rather than side effects of medication. Medication reconciliation can therefore serve as a powerful diagnostic, therapeutic, and safety measure to avoid the harmful effects of polypharmacy. This review offers a practical, detailed, step-by-step approach to managing polypharmacy for internists, with a particular focus on medication reconciliation in older adults.

Abstract: Quantum key distribution (QKD) networks require relaying when distant key management entities share no direct quantum link. Most relay strategies, however, rely on centralized control or globally maintained routing state. This paper asks whether useful security and efficiency can still be obtained with topology-oblivious stochastic forwarding. It studies the security-overhead trade-off in a model in which fragmented key material is relayed via random-walk variants and reconstructed under privacy amplification. Under a restricted model with at most one compromised relay, the analysis asks whether strictly local forwarding can retain useful information-theoretic security. Evaluation on the GÉANT topology, representing a European academic backbone network, shows clear differences between random-walk variants. The proposed highest-score-neighbor local path-diversification heuristic reduces the risk that relayed key material passes through a compromised node. The evaluation also shows that a preliminary loop-erasure step significantly shortens sampled routes and improves throughput in the model. These findings position topology-oblivious stochastic forwarding as a decentralized alternative to global-state maintenance or centralized orchestration in QKD networks.

Abstract: Background/Objectives: African swine fever (ASF) is a highly lethal disease of domestic pigs and wild suids that continues to cause substantial economic losses worldwide. Despite recent progress in live-attenuated ASF vaccine development, evidence supporting durable protection under repeated exposure conditions representative of endemic settings remains limited. Here, we assessed the long-term safety and protective efficacy of a live-attenuated ASFV-G-ΔI177L/ΔLVR vaccine using a repeated-challenge experimental design intended to model re-exposure in ASF-endemic regions. Methods: Vaccinated pigs were subjected to homologous virulent ASF virus challenges at multiple intervals, including repeated challenges (three sequential inoculations) and single challenges administered at 8 and 12 weeks post-vaccination. Results: Across all challenge regimens, vaccinated animals survived and remained clinically healthy, including those receiving three challenges, supporting sustained protection under repeated exposure pressure. Animals challenged at 8 or 12 weeks post-vaccination likewise exhibited complete survival, indicating maintained efficacy through at least 12 weeks. No vaccine-associated adverse clinical outcomes were detected over the study period, and post-challenge viral shedding was minimal. Conclusions: Overall, these data demonstrate that the candidate live-attenuated ASF vaccine provides excellent protective efficacy and confers sustained protection against homologous ASF virus infection. This result is expected to be equally applicable under repeated exposure conditions in regions with unstable ASF biosecurity, making it a sufficiently promising model experiment for field application in ASF epidemic areas.

Abstract: Recent offshore hydrocarbon discoveries along the Senegalese–Mauritanian margin increase the need to quantify oil-spill risk under the highly dynamic circulation of the southern Canary Current upwelling system. We investigate seasonal pollutant dispersion along the Senegalese Grande Côte using Lagrangian particle-tracking experiments forced by CROCO ocean model outputs. The analysis focuses on the role of wind-driven circulation, Ekman transport, and upwelling variability in controlling cross-shore and alongshore transport pathways. Results show a strong seasonal contrast. During the cold season (January–May), intensified northerly winds drive coastal upwelling and offshore Ekman transport, enhancing surface divergence and promoting the export of particles away from the coast. This regime limits nearshore accumulation but favors broader offshore dispersion over the continental shelf. In contrast, during the warm season (June–September), weakened upwelling-favorable winds and the establishment of anticyclonic circulation north of the Cape induce onshore transport and coastal retention. Particle-release experiments reveal enhanced trapping and accumulation along the Grande Côte during this period. The Kayar region and the Cape Verde Peninsula exhibit relatively higher exposure during the cold season, whereas the inner shelf along the Grande Côte becomes particularly vulnerable during the warm season. These findings demonstrate that seasonal wind forcing and associated Ekman dynamics exert first-order control on oil-spill pathways. Incorporating this variability into contingency planning is essential, as the inner continental shelf of the Senegalese Grande Côte is a dynamically sensitive, high-risk zone under the warm-season circulation regime.

Abstract: The rapid translocation of pesticide and metal residues in the environment poses a health risk to honeybees and provides a potential route for consumer exposure to these pollutants through the consumption of honeybee products. Given the high global consumption of honey, especially by immunocompromised individuals, children, and the elderly, quality control highlights the need for ongoing monitoring and risk assessment. In total, 38 comb honey and 22 extracted honey samples were collected in northern Croatia in June 2023 and analysed for 197 pesticides (using LC-MS/MS and GC-MS/MS) and 17 trace metal(loid)s (using ICP-MS) to assess contamination levels. Comb honey generally contained higher concentrations of active substances than extracted honey, with the highest detection frequencies of fipronil-sulfone, trifloxystrobin, and coumaphos in comb honey, and DMF and DMPF in honey. Glyphosate was the only pesticide to exceed the EU MRL of 0.05 mg/kg in three honey samples. Elemental analysis quantified the levels of most target metals, with Al, Cu, Fe, Mn, Ni and Zn being the most abundant, while Ag, As, and Se were the only ones not detected in this study. None of the samples showed Pb content above the regulatory limit for honey established in the EU (0.1 mg/kg).

Abstract: Optimising production layouts in manufacturing plants is a time-consuming and often manual process that typically only considers individual performance indicators. This paper presents an end-to-end pipeline that uses variational autoencoders to generate and optimise layouts. The method simultaneously considers multiple KPIs such as throughput time, energy consumption, space utilisation, machine density and material flow complexity. Different scenarios like standard, bottleneck, energy focus are supported. Results show that the proposed method generates valid layouts that outperform existing layouts in terms of efficiency, energy consumption and material flow. The pipeline enables fast, reproducible layout generation and can be directly integrated into production control systems to achieve measurable technical improvements.

Abstract: Abstract Low-salinity stress poses a critical constraint on the commercial aquaculture and survival of the sea cucumber (Apostichopus japonicus). This study investigated the regulatory network involving lncRNA011760, miR-novel-91, and their target gene NIPA2 in response to salinity fluctuations. Using integrated in vivo and in vitro functional assays, we demonstrate that lncRNA011760 acts as a competitive endogenous RNA (ceRNA) to sponge miR-novel-91, thereby alleviating the post-transcriptional repression of NIPA2. Based on these molecular dynamics, we propose a novel inhibition-adaptation-survival three-stage model. Initially (0–3h), acute NIPA2 upregulation enhances Mg²⁺ transport efficiency to mitigate osmotic shock. During the mid-stage (3–24 h), miR-novel-91-mediated NIPA2 suppression creates a transient biosynthetic window, facilitating a shift from passive tolerance to active metabolic adaptation. Ultimately (24–48 h), lncRNA-driven NIPA2 restoration sustains Mg²⁺ homeostasis, allowing the organism to enter a low-metabolism survival mode. These stage-specific shifts reflect the inherent physiological strategies of sea cucumbers as osmoconformers. Our findings elucidate the complex epigenetic orchestration of osmotic stress tolerance and highlight the lncRNA011760/miR-novel-91/NIPA2 axis as a promising molecular target for the marker-assisted breeding of salt-tolerant strains.