Abstract: The Nicolas criterion gives an equivalent formulation of the Riemann Hypothesis as an inequality involving the Euler totient function evaluated at primorial numbers. A natural strategy for establishing this inequality is to prove that a suitable subsequence of the associated ratio sequence is eventually strictly decreasing under the assumption that the Riemann Hypothesis is false. The present work shows that such a subsequence exists. When this monotonicity property is combined with the known limiting behavior of the ratio sequence and the Nicolas equivalence, a contradiction emerges: assuming the Riemann Hypothesis is false forces the subsequence to converge to a limit that is simultaneously equal to $e^{\gamma}$ (by a subsequence argument) and strictly less than $e^{\gamma}$ (by strict monotonicity). The Riemann Hypothesis therefore follows as a direct consequence.

Abstract: A specific key point in liver cirrhosis is the decreased metabolic capacity for drugs. So medicines which are metabolized by oxidative biotransformation play a great role in liver pathology. Responsible for the drug metabolism that takes place in liver during illnes, are three cytochrome P450 (P450 or CYP) gene families in liver microsomes (CYP 1, CYP2 and CYP3). In attention of various studies, is cytochrome P450. Folowing the currently aim,we try to assess the effect of liver disease on multiple CYP enzymes by use of a validated cocktail composed of medicins. Liver diseases are associated with metabolic activity changes It is important to know and to tell a little bit about different directions in cirrhosis diagnostic, including laboratory tests or management ideas, including historical key points.

Abstract: Despite transformative therapeutic advances, multiple myeloma (MM) remains an incurable malignancy characterized by sequential relapses and progressive treatment resistance. Patients with heavily pretreated relapsed or refractory MM continue to face limited therapeutic options and poor outcomes. Melflufen (melphalan flufenamide) is a peptide–drug conjugate that enhances intracellular delivery of alkylating agents via aminopeptidase-mediated activation. Early clinical studies demonstrated encouraging activity in advanced MM, leading to accelerated approval by the U.S. Food and Drug Administration in 2021. However, results from the phase III OCEAN trial raised concerns re-garding overall survival, ultimately resulting in withdrawal of the drug from the U.S. market. In this review, we examine the biological rationale, clinical development, and regulatory trajectory of melflufen, and critically reassess its role within the evolving therapeutic landscape of MM. The negative survival signal observed in OCEAN challenges the clinical viability of melflufen, yet also provides key insights into patient selection and mechanism-based drug delivery strategies. These observations support a selective, mechanism-based positioning of melflufen and argue for a broader shift from drug-centered evaluation to biology-driven therapeutic strategies.

Abstract: One hundred and sixteen million equines are in contact with humans today and contribute to the flourishing or demise of our planet. The implications of the mental attributes attached to sentience are reviewed in the light of equine welfare and their environmental effects. A detailed assessment of sentient equines mental aptitudes points out that several mental aptitudes that have previously been considered unique to humans must also be present in equines. Several of these are then discussed including social contracts, moral agency, collective intentionality, and desire independent reason with examples from human equine interactions. Out mutual moral obligations as a consequence are discussed As a result of this increased understanding of equine ontology ( their world view), we can delineate positive welfare for equines in their husbandry,

Abstract: Companion dogs are increasingly recognized as translational models for studying human physiology and disease. Unlike conventional or genetically engineered laboratory models, dogs are outbred, immunocompetent animals that spontaneously develop complex diseases whose pathogenesis and environmental exposures commonly overlap with those of humans. These distinctive features create opportunities to study mechanisms of disease, progression, and therapeutic responses under conditions that more closely resemble clinical reality. This review highlights evidence for the translational relevance of canine models across multiple therapeutic areas. We further discuss how advances in genomics, transcriptomics, spatial biology, in vitro, and in silico model systems are expanding the translational utility of canine models for applications in human medicine. Although important species differences must be carefully weighed, dogs represent a uniquely valuable comparative model for elucidating disease mechanisms, informing drug development, and accelerating the translation of scientific discoveries to human medicine.

Abstract: Higher education institutions increasingly face expectations to respond to the climate crisis through instruction that strengthens students’ capacity to analyze sustainability problems, design feasible interventions, and implement solutions with accountability. Yet many sustainability courses remain knowledge-heavy and leave the pathway from learning to action implicit, which can constrain action readiness and complicate assessment of applied competence. This paper presents a competence-to-action instructional framework for environmental sustainability education in higher education. The framework is grounded in an integrative conceptual review and synthesis across Education for Sustainable Development, sustainability competency scholarship, experiential and transformative learning traditions, whole-institution approaches, campus living lab research, and Universal Design for Learning (UDL). The analysis applies iterative thematic synthesis to identify recurring instructional mechanisms, institutional enablers, and assessment implications, and then translates those themes into testable propositions and design and assessment tools. The synthesis yields six propositions specifying instructional and institutional conditions that support sustainability competency development and action readiness. Across the included literatures, the propositions emphasize authentic, place-based problems; sustained engagement with stakeholders; structured reflection that links values, trade-offs, and decisions; opportunities to test, revise, and communicate proposed interventions; and enabling infrastructures that connect curriculum to campus operations and community partnerships. Building on these propositions, the paper articulates six design commitments and provides two implementation tools: a competency-to-activity-to-evidence map and a performance-based assessment rubric aligned to widely used competency categories (systems thinking; anticipatory, normative, strategic, and interpersonal competence). As a conceptual framework paper rather than a systematic review or empirical validation study, it offers practical guidance for faculty, program leaders, and sustainability offices seeking to align curriculum, campus operations, and external partnerships while generating valid, transparent evidence of student learning and action preparedness. It treats UDL as a validity and equity safeguard that maintains rigorous expectations while reducing construct-irrelevant barriers through multiple means of engagement, representation, and action and expression. The paper concludes with implications for course redesign and institutional scaling through living lab infrastructure and whole-institution coherence, and it identifies priorities for future research, including cross-disciplinary pilots, refinement of assessment guidance through shared scoring practices, and longitudinal study of whether competence-to-action indicators relate to sustained civic or professional sustainability action.

Abstract: Friedreich ataxia (FRDA) is a rare, autosomal recessive, progressive neurodegenerative disorder characterized by multisystem involvement, including gait and limb ataxia, cardiomyopathy, skeletal deformities, and metabolic dysfunction. Most patients harbor biallelic GAA trinucleotide repeat expansions in intron 1 of FXN, whereas others are compound heterozygotes with a GAA expansion on one allele and a pathogenic FXN variant on the other. FXN encodes frataxin, a nuclear-encoded mitochondrial protein essential for iron-sulfur (Fe-S) cluster biogenesis and mitochondrial energy production. Frataxin deficiency disrupts mitochondrial metabolism, promotes iron dysregulation and oxidative stress, and leads to progressive cellular injury, particularly in high-energy tissues such as the nervous system and myocardium. Despite substantial advances in understanding FRDA pathogenesis, no curative therapy is currently available. In 2023, omaveloxolone (Skyclarys) became the first approved treatment for FRDA, marking a significant therapeutic milestone. Concurrently, disease-directed strategies have expanded rapidly, including small-molecule modulators, adeno-associated virus (AAV)-mediated gene replacement, and transcriptional or epigenetic approaches aimed at restoring endogenous FXN expression. In addition, antisense oligonucleotide-based therapies and emerging CRISPR-mediated gene editing platforms are advancing through preclinical and early clinical development. This review provides a comprehensive overview of the evolving therapeutic landscape in FRDA, highlighting mechanistic rationales, preclinical progress, clinical trial outcomes, and the key translational challenges that must be addressed to achieve durable disease modification.

Abstract: The increased production and utilization of nanoparticles (NPs) in consumer products have raised concerns about their potential impact on the environment at national and international levels as they move through different trophic levels. Previous research has shown that they possess the ability to infiltrate cellular and subcellular structures, potentially interfering with important physiological processes and leading to toxicity. Studies have also indicated that nanoparticle toxicity varies significantly with changes in physicochemical properties, even among nanoparticles with identical chemical compositions. Because of this variable toxicity potential, it has become imperative to study the toxicity of these materials on vital physiological systems on a case-by-case basis, particularly before their widespread utilization in consumer products. This study evaluated the cytotoxic effects of selected nanoparticles (i.e. AgNPs, TiO₂NPs, CD NPs, CD-Amine), with a particular emphasis on their toxicity to macrophages. The study involved exposing the immune-representative RAW 264.7 cell line to various concentrations of NPs, both with and without the presence of lipopolysaccharide (LPS). Results showed that silver nanoparticles (AgNPs) and titanium dioxide nanoparticles (TiO₂NPs) had an adverse impact on cell viability under both conditions. The unmodified carbon dot nanoparticles only had a moderate impact on viability. However, toxicity increased significantly when carbon dot NPs were modified with amine groups, surpassing that of metal-based NPs (i.e., AgNPs and TiO₂NPs), highlighting the critical role of surface charge in influencing cytotoxicity. Concurrently, the study also comprehensively assessed the potential neurotoxicity of these nanoparticles by measuring their modulatory potential on acetylcholinesterase (AChE) activity using Ellman's reagent. Findings indicated that both AgNPs and amine-modified carbon dots (CD-Amine) significantly inhibited AChE activity, while TiO₂NPs and CDNPs had no impact on AChE activity. Interestingly, this inhibition was not dependent on whether the nanoparticles were metal- or carbon-based, or the size of the nanoparticles, suggesting that the interaction between nanoparticles and enzymes is likely influenced by the chemistry of the enzyme and the nanoparticles themselves. This study seeks to contribute valuable insights into the diverse biological interactions of nanoparticles, informing risk assessments and the development of safer nanomaterials for various applications.

Abstract: Background.Staple line leak after sleeve gastrectomy remains one of the least predictable complications in bariatric surgery. Despite numerous proposed explanations, no consensus pathogenetic model exists.Objective.To develop a deterministic biomechanical hypothesis accounting for the mechanism of staple line failure after sleeve gastrectomy.Hypothesis.The present work proposes the formula: Leakage = Obstruction & "Dog Ear". Leak is posited to be the predictable consequence of two co-occurring conditions: (1) mechanical or functional obstruction generating excess intraluminal pressure in the proximal gastric sleeve, and (2) a "dog ear" — a residual triangular pouch at the angle of His acting as a gas-and-fluid trap that prevents pressure decompression into the esophagus. Neither factor alone is sufficient: isolated obstruction results in stenosis; an isolated “dog ear”, in the absence of elevated pressure, remains clinically inconsequential.Conclusion.The formula Leakage = Obstruction & "Dog Ear" offers a reproducible biomechanical framework for understanding and preventing staple line failure after sleeve gastrectomy. Prospective experimental investigation is required.

Abstract: Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. Conventional neural network models trained on specific geometric configurations typically fail to generalize to unseen preform designs, requiring costly retraining for each new geometry. This work proposes a unified geometry-aware deep learning framework that predicts spatial temperature distributions across multiple preform designs using a single neural network model. The approach reformulates temperature prediction as a coordinate-level regression task conditioned on spatial location, geometric descriptors, process parameters, and structural region labels. A domain-bounded training strategy based on extreme feasible preform geometries is introduced, ensuring that predictions for intermediate designs remain within the interpolation regime of the network. The framework is evaluated on six distinct preform geometries, demonstrating that a single model can generalize reliably to new, unseen preform designs when their geometric parameters fall within the bounds of the training data. This is achieved through a domain-bounded training strategy that constructs datasets from the extreme feasible geometries, thereby converting the prediction of any intermediate design into an interpolation task. Since neural networks are inherently limited in their ability to extrapolate beyond the training domain, this formulation is essential for ensuring stable and accurate predictions across the full range of industrially relevant preform configurations. The proposed methodology provides a foundation for geometry-informed surrogate modeling in thermal process control and can be extended to other manufacturing systems characterized by strong geometric variability.

Abstract: Digital platforms increasingly mediate economic coordination, labor allocation, and decision-making. As artificial intelligence becomes embedded within these platform ecosystems, automation no longer targets only manual labor. Instead, algorithmic systems are displacing routine tasks across both low-wage entry-level work and middle-management functions. This paper argues that the emerging phase of platform-mediated automation risks hollowing out labor structures from both directions, from below through the erosion of repetitive, junior roles, and from above through the automation of supervisory coordination functions. Drawing on institutional economics, platform governance literature, and recent research on AI-enhanced learning and workforce development, the paper examines how this dual displacement creates structural vulnerability. Entry-level roles have historically functioned as apprenticeships in which workers acquire tacit knowledge and critical judgment. At the same time, experienced workers are aging out of the workforce. If platforms curtail formative occupational layers, organizations may face a shortage of workers capable of exercising contextual reasoning required to manage complex systems. The paper situates these developments within broader debates about technological unemployment, platform labor, and the political economy of capitalism. It argues that the challenge is not merely job quantity, but institutional continuity, how societies reproduce practical competence when platforms optimize for efficiency rather than formation. This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience.

Abstract: A Fe3O4/HNTs composite was synthesized, characterized by XRD, FTIR, SEM, and N2 adsorption-desorption, and was used for an ibuprofen adsorption and oxidation study. The response surface methodology (RSM) and Box-Behnken experimental designs were used. The effect of pH, contact time, IBU concentration, and Fe3O4/HNTs dosage on ibuprofen adsorption were evaluated. Additionally, adsorption isotherms and a kinetic study were performed. The effect of pH, H2O2 concentration, and Fe3O4/HNTs dosage for IBU oxidation were also studied. The results of ibuprofen adsorption on Fe3O4/HNTs indicate that adsorption was favored at acidic pH. The adsorption followed pseudo-second-order kinetics and a Freundlich isotherm. A 99.99% IBU oxidation and 99% mineralization were achieved at pH 7, Fe3O4/HNTs dosage of 1.5 g L-1, and 0.5 M H2O2. The Fe3O4/HNTs catalyst prepared in this study was efficient in removing aqueous ibuprofen through heterogeneous Fenton-like reaction.

Abstract: The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The methodology integrates Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to resolve temporal variability in sea surface temperature, cyclone frequency, and intensity, complemented by two-dimensional kernel density estimation (KDE) and non-stationarity analysis. Using NOAA and National Hurricane Center datasets, results identify dominant periodicities at annual and ENSO (2–7 year) scales, a post-1995 spectral energy shift associated with the positive AMO phase, and a thermodynamically consistent energy corridor along 12-16°N. A statistically significant change point in 1987 (Pettitt test, p < 0.05) is detected, although spatial displacement is not significant. An integrated Wind Risk Index highlights the central-western Caribbean as a high-exposure zone overlapping offshore wind development areas. Exceedance analysis shows that 39.8% of observations surpass 25 m/s, 6.0% exceed 50 m/s, and 1.3% approach 70 m/s, indicating relevant design considerations. These findings support the need for non-stationary, multi-scale approaches in offshore wind risk assessment under tropical cyclone influence.

Abstract: Let $ \xi(z) $ be the Riemann xi function. In a previous paper we prove the boundedness of coefficients of the power series expansion of $ \xi'(1/z)/\xi(1/z) $ and thus give a proof of the Riemann hypothesis. In this paper we generalize the method there to the study of the extended Riemann hypothesis for general number fields.

Abstract: Marine renewable energy systems, including offshore wind, tidal, and wave technologies, are central to global net-zero strategies but remain constrained by reliability-driven costs and uncertainty in structural performance. In harsh offshore environments, interacting degradation mechanisms (such as corrosion–fatigue, hydrogen embrittlement, variable-amplitude loading, wear, and manufacturing-induced variability) govern failure, yet are not adequately captured by existing empirical design frameworks. This review presents a comprehensive, mechanism-based perspective on structural integrity in marine renewable energy systems, explicitly linking microstructure-sensitive deformation and damage processes to engineering-scale performance and reliability. The materials landscape, including structural steels, titanium alloys, fibre-reinforced composites, and additively manufactured materials, is critically examined with emphasis on process–structure–property–performance relationships. Multiscale modelling approaches are synthesised, spanning crystal plasticity finite element modelling, mesoscale damage formulations, fracture mechanics, structural reliability methods, and emerging digital twin and data-driven frameworks. A key contribution of this work is the integration of microstructure-resolved modelling with system-level reliability and qualification, addressing a critical gap between materials physics and engineering design standards. The review identifies critical limitations in current practices, including the lack of explicit treatment of coupled degradation mechanisms, insufficient representation of manufacturing variability, and the absence of consistent uncertainty propagation across scales. Building on these insights, an integrated, mechanism-resolved framework is proposed that combines multiscale modelling, manufacturing-aware qualification, inspection-informed updating, and hybrid physics–data approaches. This framework supports a transition from static, empirical design towards predictive, lifecycle-based structural integrity assessment, enabling improved reliability, reduced uncertainty, and more cost-effective deployment of next-generation marine renewable energy systems.

Abstract: Aging is associated with chronic, low-grade inflammation (“inflammaging”), which contributes to neuropsychiatric and neurodegenerative disorders such as depression, Alzheimer’s disease, and Parkinson’s disease. Conventional pharmacotherapies often provide limited benefit in older adults and are further complicated by polypharmacy and drug-drug interactions. Psilocybin, a serotonergic psychedelic acting primarily as a 5-HT2A receptor agonist and currently undergoing accelerated clinical development, has emerged as a potential multimodal therapeutic agent addressing these challenges. Acting via its active metabolite psilocin, 5-HT2A-mediated signaling biases cortical glutamatergic transmission, enhances TrkB/BDNF pathways, and modulates neuro-immune cascades (including NF-κB), with convergent systems-level effects such as re-organization of the default mode network. Human studies report acute reductions in TNF-α with variable effects on IL-6 and CRP, consistent with an immunomodulatory profile. Pharmacokinetically, psilocybin shows properties advantageous in geriatric care: rapid onset, short half-life, and predominant phase-II glucuronidation, reducing interaction risk. Controlled studies demonstrate rapid antidepressant and anxiolytic effects in major depressive disorder, treatment-resistant depression, and existential distress, with emerging feasibility signals in neurodegeneration. Together, these find-ings support the hypothesis that a time-limited, mechanism-based intervention may improve mood and cognition while attenuating inflammation. This review integrates current evidence on psilocybin’s neuroimmune and pharmacokinetic mechanisms rel-evant to aging, outlining its potential role in inflammation-related disorders and high-lighting the need for targeted studies in older adults, who remain underrepresented in psychedelic research.

Abstract: Food fermentation is a widely used processing technique that enhances sensory properties, shelf life, and nutritional value, partly through the activity of beneficial microorganisms. This study investigated the microbial communities associated with traditional pearl millet fermentation and their potential nutritional contributions. Pearl millet (TiftLHB open-pollinated variety) was obtained from USDA-ARS and subjected to spontaneous fermentation in sterilized water at 28 ± 2 °C for 72 hours, followed by wet milling and an additional 72-hour fermentation. Microbial DNA was extracted, and 16S rRNA amplicon sequencing was performed after PCR amplification and quality control. Sequence data were analyzed using DADA2 and PICRUSt2 pipelines for taxonomic and functional prediction. The dominant bacterial genera identified were Weissella and Lactobacillus, both commonly associated with cereal fermentations. Weissella is known for reducing antinutrients and contributing to folate production, while the overall microbial profile was consistent with reports from other regions, including the presence of lactic acid bacteria such as Leuconostoc. These findings suggest that spontaneous fermentation of pearl millet supports microbial communities with potential nutritional and functional benefits. Metagenomic approaches may provide an effective strategy for identifying and optimizing beneficial microorganisms to enhance the nutritional quality and health-promoting properties of fermented cereal-based foods.

Abstract: Network access control and identity legitimacy verification have been implemented by establishing a secure foundation for the trusted establishment of communication entities. However, successful identity authentication alone does not guarantee secure communication. In open-network environments, it remains essential to establish a secure session key via a robust key agreement mechanism—one that prevents explicit disclosure of identity information while ensuring post-quantum security. To address these requirements, we propose a lattice-based key agreement protocol. The protocol integrates identity binding, implicit authentication, and session key establishment into a single ciphertext exchange. Furthermore, it supports secure key evolution and revocation verification through a version-control mechanism and a blockchain-maintained revocation list—thus realizing a comprehensive, post-quantum-secure key agreement scheme under reasonable computational and communication overhead.

Abstract: Artificial intelligence offers tremendous potential for landscape-scale biodiversity conservation, yet the significant energy consumption of large-scale AI models creates a fundamental paradox: the computing resources required to train and deploy these systems add to the very environmental degradation they seek to prevent. This paper proposes a multi-level, energy-aware AI architecture for constructing ecosystem digital twins that enables prescriptive, rather than merely descriptive or predictive, conservation management. The proposed framework classifies conservation tasks across three levels: classic machine learning for continuous environmental monitoring and species distribution prediction; deep learning for perception-oriented tasks such as computer vision and bioacoustics analysis; and foundation models for cross-domain synthesis and stakeholder interaction, where their capabilities are irreplaceable. We apply this architecture to a conceptual digital twin of the Greater Yellowstone Ecosystem, demonstrating how multi-tiered AI integration can model ecological systems spanning wolves, elk, vegetation, beavers, and hydrology to generate actionable, prescriptive insights concerning conservation. A comparative energy footprint analysis estimates that the tiered approach decreases computational energy consumption by approximately 62–74% relative to a foundation-model-centric baseline, while sustaining or improving conservation decision quality. This work addresses a key gap in the literature by providing the first integrated architectural framework that explicitly optimizes the trade-off between AI capability and environmental cost for landscape-scale conservation applications, supplying a replicable blueprint for resource-constrained conservation organizations worldwide.

Abstract: The classical binary heap sink operation based on swap has a significant write overhead. We examine two intuitive improvements: swapping siblings (verified via bounded SMT search) and adding a local hint called pref (the hint-assisted variant). In our bounded SMT checks and implementation comparisons, we did not find evidence that these variants provide consistent benefits; PerfView measurements show the hint-assisted variant was slower in most configurations. Our results suggest that reverting to the straightforward hole-based sink is the practical choice for write-efficient implementations