Abstract: Long-duration human spaceflight exposes very healthy astronauts to complex risks including neuroocular changes, musculoskeletal and cardiovascular deconditioning, radiation injury, immunologic disturbances, and surgical emergencies. An integrated, autonomy-focused medical architecture for missions of 30 days to over 2 years is needed, emphasizing in-situ diagnosis, therapy, and monitoring under severe resource constraints. The clinical framework maps conditions to mission phase and outlines space-adapted diagnostic strategies centered on AI-guided point-of-care ultrasound, wearable biosensors, and microfluidic lab-on-chip assays. Preventative countermeasures are specified including structured exercise, lower-body negative pressure, bone-protective pharmacotherapy, radiation shielding, and AI-assisted psychological support. Evaluating the clinical need for monitoring, diagnosing, and even for some possible invasive therapeutical interventions led to the definition of a compact modular system combining miniaturized surgical robotics, on-demand 3D printing, and AR/AI guidance to even enable minimally invasive procedures by a non-expert crew. The ressources that are required to build such a system for a very limited application and benefitting just very few people are very high. They might provide an ideal base with dual-use potential for low- and middle-income countries however, where similar design drivers—ease of use, automation and autonomous operation, small footprint, and local service, repair and parts fabrication—address the current critical gaps in under-resourced health systems. Of course low cost of manufacturing and operation is likely the most important feature for that application. Co-designed "space–global health" technologies could simultaneously enable safer deep-space exploration, for which development ressources are available, and expand access to high-quality diagnostics and interventions on Earth providing very high impact to the population, which unfortunately does not attract sufficient development funds despite a huge need.

Abstract: North America´s largest semi-arid lands form the Chihuahuan Desert Biome, which had fluctuated between Interglacial and Glacial conditions for eight million years. Cacti prob-ably came from South America after substantial distancing from Africa, and pollen fossils reveal their arrival in Mexico some 51.6 Ma. We have examined distributions of 119 strict-ly endemics (36.17 % of overall 329 species) and model 75 species represented in well de-fined and relatively large disjunct area groups. We modeled Species Distribution Models (SDMs) using MAXENT algorithms for present and past climates for the region, following our detailed models on climate after Sánchez-Santillán and García detailed numerical methods and Co-Kriging tools. Scotese, Van Devender, Betancourt, and Roy-Priyadarsi were utilized for modelling the glacial part. A total of 4030 registers were sampled from the Central America and North America Cacti Database (UNAM), a comprehensive set of hard information from 68 herbaria and containing over 62,000 vouchers. Registers com-prised 3719 modelable species´ specimens. Track and node analyses were applied using PANBIOTRACKS. We identified the colonization patterns and general evolutionary trends for the species. We modeled detailed combined layers of idoneity and overlap them to tracks and nodes in order to detect biogeographic trends and patterns.

Abstract: Background: Patients with chronic cardio-respiratory diseases face substantially elevated perioperative complication risks. High-flow nasal oxygen (HFNO) therapy has emerged as a promising non-invasive respiratory support modality, yet evidence specific to this high-risk population has not been comprehensively synthesized. Objectives: To systematically evaluate HFNO effectiveness across the perioperative continuum—including pre-oxygenation, apneic oxygenation, and post-extubation support—in patients with chronic obstructive pulmonary disease, heart failure, interstitial lung disease, obesity, and related conditions. We aimed to compare HFNO with alternative modalities and provide evidence-based implementation guidance. Methods: We conducted a comprehensive narrative review with systematic search of major databases including PubMed, Embase, and the Cochrane Library for randomized controlled trials, systematic reviews, and observational studies involving adult patients with chronic cardio-respiratory diseases undergoing surgery with HFNO intervention at any perioperative phase. Quality assessment using established tools was performed with structured narrative synthesis organized by perioperative phase and disease population. Results: The review synthesizes evidence across multiple perioperative applications, comparing HFNO effectiveness with conventional oxygen therapy and non-invasive ventilation. Disease-specific considerations for chronic obstructive pulmonary disease, heart failure, obesity and obstructive sleep apnea, interstitial lung disease, and thoracic surgery populations are delineated. Evidence-based clinical algorithms for patient selection, protocol optimization, and escalation strategies are provided. Cost-effectiveness, implementation barriers, training requirements, and integration into Enhanced Recovery pathways are addressed. Conclusions: HFNO represents a valuable non-pharmacological intervention for perioperative respiratory optimization in chronic cardio-respiratory disease patients. This comprehensive synthesis provides clinicians with evidence-based guidance for implementation while identifying critical research gaps. Proper patient selection and protocol optimization can reduce postoperative pulmonary complications, prevent reintubation, and improve outcomes in this high-risk population. Future research should focus on personalized approaches, long-term outcome assessment, and implementation science. Keywords: High-flow nasal oxygen; HFNO; perioperative care; chronic obstructive pulmonary disease; heart failure; respiratory failure; non-invasive respiratory support; surgical complications; perioxygenation.

Abstract: Background: Muscle function determines overall health and is often impaired in metabolic syndrome, largely due to oxidative stress and inflammation. Olive mill wastewater (OMWW) is rich in bioactive polyphenols (e.g., hydroxytyrosol, oleuperine and verbascoside) that may hinder these pro-sarcopenic mechanisms, representing a potential nutraceutical to maintain muscle health. Objective: To evaluate the effects of short-term supplementation with an OMWW-derived polyphenol extract (Oliphenolia^®, OMWW-OL) on muscle-related parameters and antioxidant biomarkers in adults at metabolic risk, while maintaining dietary habits. Methods: This exploratory, hypothesis-driven secondary analysis was based on a single-arm longitudinal pilot study assessing patients at baseline (T0), after 30 days of supplementation (T1), and 30 days post-discontinuation (T2). Anthropometry, bioelectrical impedance, and biochemical assessments were performed. Results: Supplementation was associated with modest increases in skeletal muscle mass, muscle mass percentage, and wrist, arm, and calf circumferences. Fat mass decreased progressively, while total body water percentage and hydration status improved. Ferritin levels rose at T2, alongside increases in protein thiols (PSH) and Trolox equivalent antioxidant capacity (TEAC), suggesting improved iron status and reduced oxidative stress. Body weight and BMI decreased, as expected in a dietary intervention for metabolic syndrome, while muscle health showed a tendency to improve. Conclusions: Although findings require cautious interpretation, short-term Oliphenolia^® supplementation was associated with modest but consistent directional changes across muscle-related and metabolic indicators in adults at metabolic risk. The results support hypothesis generation and the need for larger studies aimed at investigating the potential preventive role of OMWW-OL in the context of cancer-associated sarcopenia.

Abstract: Background: Higher education institutions are expanding online delivery and integrating generative artificial intelligence (GenAI), yet faculty readiness remains uneven, raising concerns about assessment validity, academic integrity, institutional legitimacy, and the quality of scalable online provision. Objective: This study develops the EPIQ-AI Readiness Framework to explain how institutions can align faculty capacity, governance, and quality assurance for AI-supported teaching and online program delivery. Methods: Using an integrative secondary evidence synthesis, the study triangulates recent official statistics, large-scale faculty and institutional surveys, peer-reviewed studies, and policy frameworks published between 2020 and 2025. The analysis is organized across four readiness domains: epistemic, pedagogical, institutional, and quality-and-compliance readiness. Results: The evidence converges on four main findings. First, faculty adoption of AI is increasingly widespread, but confidence, pedagogical clarity, and depth of use remain limited. Second, institutional ambitions for online scale and AI integration are advancing faster than policy maturity, professional development, and support capacity. Third, assessment has become the central pressure point, with growing evidence that detection-centered academic integrity regimes are unreliable, potentially biased, and insufficient for high-stakes decisions. Fourth, faculty readiness is best understood not as an individual skills deficit but as a sociotechnical alignment problem shaped by governance, incentives, workload, literacy, course design support, and equity-sensitive implementation. Conclusions: The EPIQ-AI framework reframes readiness as a multidimensional condition for credible AI-enabled and online higher education. It offers a theoretically grounded and operationally actionable model for institutions seeking to strengthen AI literacy, redesign assessment, improve governance, and sustain epistemic integrity while advancing scalable, policy-compliant online delivery.

Abstract: A comprehensive petrophysical analysis of 232 core samples from the J-III productive horizon (wells 182, 1136, and 8096), supported by routine laboratory analyses and X-ray diffraction (XRD) data from 70 samples, was carried out. Integration of reservoir-property parameters and mineralogical characteristics made it possible to establish genetic relationships between material composition, post-sedimentary transformations, and the formation of reservoir properties. The rock-forming framework is dominated by quartz, albite, and chlorite, while calcite—primarily of secondary origin—is confined to pore spaces, having precipitated during the diagenetic and catagenetic stages. Minor phases, including nacrite, kaolinite, chalcopyrite, molybdenite, and graphite, record superimposed hydrothermal events, with graphite indicating episodic exposure to elevated temperatures during the petrogenetic evolution of the rocks. Mineralogical heterogeneity is pronounced: quartz is ubiquitous and albite widely distributed, yet the abundances of calcite and chlorite show considerable variability. Statistical analysis reveals modal populations of albite and calcite, alongside a near-lognormal distribution of chlorite. Examination of paired mineral associations distinguishes clay-rich from clay-poor varieties and confirms the genetic independence of albitization, chloritization, and calcitization, as well as the secondary nature of carbonate mineralization. The J-III productive horizon is characterized by extremely poor reservoir properties: modal porosity is approximately 1%, more than 95% of the values are below 2%, and permeability is predominantly below 0.01 mD. These rocks therefore belong to the class of tight, low-porosity, and low-permeability reservoirs. Local storage anomalies are largely controlled by the development of microfractures. The lack of a consistent correlation between porosity and the extent of carbonation or dolomitization suggests that these processes exert only a subordinate effect on reservoir properties. Dolomitization is frequently accompanied by additional mineralization and compaction. However, when pore-space volume is preserved, it can lead to an increase in void ratio, as dolomite is denser than calcite while the total pore volume remains nearly unchanged. The reconstructed petrogenetic model involves the deposition of sandy-clayey material containing plagioclase and organic matter, followed by diagenetic and catagenetic transformations—particularly albitization and calcitization—that resulted in a dense, secondarily mineralized rock mass. Late tectono-hydrothermal reactivation led to the development of a fracture system, which governs present-day reservoir properties and serves as the main conduit for hydrocarbon migration and accumulation. Mineralization along these fractures confirms their fluid-conducting role. Experimental acid treatment demonstrated that permeability can increase by up to four orders of magnitude, revealing the presence of hidden storage capacity and mobilizable micropore systems. The J-III horizon is thus interpreted as a fractured reservoir, with a development strategy focused on identifying and mapping fracture zones and enhancing their connectivity through horizontal drilling and stimulation techniques.

Abstract: A nonlinear backstepping control framework is developed for autonomous landing of a quadrotor on a wave-excited marine platform. The study addresses the underactuated nature of the aerial vehicle and the strong coupling between translational and rotational dynamics, ensuring stable trajectory tracking under sea-induced disturbances. Reference trajectories are generated through physically grounded Pierson-Moskowitz (PM) and Modified Pierson-Moskowitz (MPM) wave spectra, enabling realistic modeling of vertical heave motion, while horizontal position and yaw are defined through harmonic components adapted to the sea-state regime. The controller is designed through a seven-step recursive backstepping procedure, with Lyapunov functions guaranteeing asymptotic stability of the tracking errors for the regulated outputs. A modular MATLAB simulation platform is implemented, integrating the full 6-DOF quadrotor dynamics, the control algorithm, and spectral reference generation. Numerical simulations demonstrate that the Lyapunov function derivatives remain negative over the entire simulation horizon, confirming asymptotic convergence. Comparative results with a tuned PID (Proportional-Integral-Derivative) controller indicate superior tracking performance, damping and reduced amplitude and phase errors for the backstep-ping approach, especially under MPM-based trajectories representing rough sea states. The proposed framework establishes a reliable basis for adaptive extensions and future Hardware-in-the-Loop validation of autonomous landing on moving marine platforms.

Abstract: Hexokinase 2 (HK2) catalyzes the first committed step of glucose metabolism - the conversion of glucose to glucose-6-phosphate – directing carbon flux into an array of metabolic pathways such as glycolysis, pentose phosphate pathway, amino acid biosynthesis and others. Given its prominent role in glucose metabolism, it is critical we understand the regulation of HK2 to appreciate its role in normal physiological function as well as disease states like cancers. Herein we establish a fundamental link between cell survival mechanism and metabolic regulation by demonstrating myeloid cell leukemia 1 (MCL1) directly binds and enhances HK2 enzymatic activity through interactions with reverse Bcl-2 homology motifs (rBH3) on HK2. Consequently, we observe significant reductions in glucose-derived metabolites and impaired cellular metabolic plasticity with disruption of the HK2-MCL1 interaction. These findings establish a novel mechanism by which anti-apoptotic proteins can directly regulate glucose metabolism.

Abstract: Role-playing games (RPGs) rely on character systems as the primary interface between narrative, player agency, and gameplay mechanics. Unlike static visual assets, RPG characters must preserve identity across customization, animation, equipment variation, and long-term progression. This paper presents a comprehensive review of character design and animation pipelines in RPG-based games, integrating perspectives from visual identity construction, motion synthesis, and AI-assisted generation workflows. The review synthesizes foundational studies on shape language, avatar representation, and expressive animation with recent advancements in generative artificial intelligence, including diffusion models, generative adversarial networks (GANs), dialogue-based editing systems, and speech-driven facial animation. The analysis highlights a transition from manual, artist-driven workflows toward hybrid pipelines where AI supports ideation, parameter editing, sprite generation, and animation synthesis. However, significant challenges remain, including weak semantic controllability, lack of identity preservation across transformations, inconsistencies between stylized and realistic character pipelines, and the absence of unified frameworks integrating design, rigging, and animation. This paper identifies these gaps and proposes future directions toward unified, human-in-the-loop character pipelines that ensure scalability, controllability, and consistency in next-generation RPG systems.

Abstract: A locally parametric framework is proposed for Monte Carlo simulation of electricity prices that jointly reproduces the key stylized facts of power markets: mean-reversion, fat tails, asymmetry, and volatility clustering. Following a two-stage pipeline in which mean-reversion is estimated separately from the innovation distribution, the paper focuses on the second stage: simulating the residual innovations via topological conditioning on Natural Visibility Graphs (NVG) built on the observed innovation sequence. At each simulation step, the local structure of the graph is used to identify historically similar market states and to draw the next innovation from a locally fitted distribution. The key methodological contribution is that this topological conditioning mechanism simultaneously determines the local scale, skewness, and tail weight of the innovation distribution — three properties that parametric models such as GARCH must address through separate equations — without any assumption on regime dynamics or transitions. The framework is locally parametric: the number of model parameters grows with the sample size rather than being fixed in advance, and the specific distributional family used as a local working model can be replaced without altering the conditioning mechanism. Applied to two power markets with contrasting distributional characteristics — the Italian Power Exchange (PUN) and PJM West Hub (US) — the framework achieves simultaneous coverage of three distributional statistics (\( \hat\sigma \), \( \hat\gamma, \hat\kappa \)) and the first-order autocorrelation of squared innovations \( \hat\rho_1(\varepsilon_t^2) \) for both markets, with a single neighbourhood size k=10 and no market-specific re-calibration; more generally, k serves as the natural adaptation parameter for markets with substantially different distributional characteristics.

Abstract: Delirium is a common and serious condition among neurological patients, and the overlap between delirium symptoms and neurological disorders complicates both diagnosis and management. Despite its clinical impact, guidance for delirium management in neurological settings remains limited. This qualitative study aimed to investigate healthcare professionals’ perceptions of delirium management in a Danish neurological hospital setting. Focus group interviews were conducted with five multidisciplinary healthcare professional groups. Maximum variation sampling was used to capture diverse perspectives, and 24 healthcare professionals from the same neurological department participated. Data were analyzed using reflexive thematic analysis. Three themes were identified: (1) delirium care practices in an acute neurological setting; (2) multidisciplinary collaboration in delirium care; and (3) responsibility for delirium care. The findings highlight challenges related to prioritization, mono-professional practices, and organizational structures that shape how responsibility for delirium management is understood and enacted. Overall, the study illustrates the complexity of delirium management within multidisciplinary neurological teams and suggests the need for context-sensitive approaches that support collaboration and clarify responsibilities in clinical practice.

Abstract: Structural Execution Sequence (The Deductive Itinerary): To prevent algorithmic prior contamination, this header excludes all final hardware parameters. This manuscript does not propose a physical theory; it executes a formal mathematical theorem. It serves solely as a methodological map, outlining the deductive sequence that isolates the minimal necessary architectural class capable of shadowing empirical reality. The manuscript executes as a strict algorithmic hunt, unrolling across four sequential proofs:1. The Methodological Foundation (Chapter 1): Axiomatizing the absolute boundary of empirical science. By enforcing the Principle of Empirical Primacy and the Axiom of the Embedded Observer, we establish that scientific prediction evaluates strictly as finite, localized physical computation within the Computable Domain (M_TTG).2. The Objective Function (Chapter 2): Deriving the Universal Cost Ledger. We formalize the thermodynamic penalty for continuous mathematical bloat. Foundational scientific progress evaluates not as syntactic elegance, but strictly as the algorithmic minimization of static memory allocation and dynamic execution trace.3. The Architectural Class (Chapter 3): The execution of the deductive trap. We run continuous assumptions through the universal cost compiler, proving they trigger unresolvable hardware halts and phase-space divergence. By algorithmically excluding the uncomputable, we deduce the exact minimal necessary architectural class necessitated to compile the macroscopic Evidence Vector: a local, discrete, Base-72 symplectic state-machine. Note: These lemmas constrain the generative class to a narrow finite-discrete family with zero continuous degrees of freedom. The concrete forcing term f₀ within this class remains to be calibrated against raw observational data D, per Axiom I.4. The Checksum Protocol (Chapter 4): Dynamically unrolling the architectural class. We verify that the macroscopic phenomenological invariants of the universe—emergent Lorentz invariance, quantum measurement bounds, fractal self-similarity, objective causality, and thermodynamic irreversibility—are structural invariants guaranteed by the architectural class itself. We conclude by isolating the exact, falsifiable dispersion limit where continuous spacetime mathematically fails.Structural validation proceeds sequentially. The reader must verify the inescapable thermodynamic bounds before the collapse of continuous modeling. Execution trace commences in Chapter 1.

Abstract: This paper addresses complex decision-making scenarios characterized by high uncertainty and high-cost errors, researching a risk-sensitive decision-oriented reinforcement learning mining method. It focuses on resolving the reliability issues arising from tail instability in the reward distribution and out-of-distribution actions under offline data conditions. Methodologically, the decision-making process is modeled using a Markov framework, with the reward distribution as the learning object to retain value information under adverse conditions. Based on this, a conditional risk-value metric is introduced to explicitly characterize and suppress tail risk, ensuring that policy optimization no longer relies solely on expected returns. To mitigate estimation bias and over-extrapolation in offline learning, conservative constraints based on behavioral distribution are further incorporated. By limiting the deviation between the policy and the implicit behavioral distribution in the data, out-of-distribution action expansion is suppressed, and the controllability of policy updates is improved. The overall framework unifies risk measurement and conservative learning into a single optimization form, forming a policy learning mechanism that balances returns and safety. Comparative experimental results show that this method exhibits superior overall performance in terms of average returns, tail reward robustness, and safety-related indicators, validating the effectiveness of the co-modeling of risk-sensitive objectives and conservative constraints, and providing an auditable and adjustable risk control approach for highly reliable intelligent decision-making systems.

Abstract: Patients with inflammatory bowel disease (IBD) and primary sclerosing cholangitis (PSC) share a characteristic depletion of bile acid-transforming bacteria, undermining intestinal mucosal immune tolerance. These organisms convert lithocholic acid into immunomodulatory secondary bile acid species: 3-oxolithocholic acid, isoallolithocholic acid, and isolithocholic acid, which suppress Th17 differentiation and expand Foxp3⁺ regulatory T cells through RORγt binding and mitochondrial signaling. This paper proposes that committed dietary patterns at either metabolic pole, verified nutritional ketosis or traditional Mediterranean diet, restore coherent signaling across multiple interdependent receptor and metabolic sensing networks (FXR, TGR5, S1PR2, RORγt, and an intersecting oxysterol-LXR axis) and, through that coherence, support mucosal immune tolerance. Bile acid signaling is the best-characterized candidate mechanism for one arm of this network and the primary testable intermediate the experimental program interrogates. Under committed ketosis, lipoprotein remodeling is most directly attributable to malonyl-CoA depletion and CPT-1 disinhibition; bile acid pool restructuring is a parallel candidate for immune effects. Mediterranean diet co-directional lipid and immune improvements are supported by IBD-specific randomized controlled trials. The equivalent prediction for committed ketosis, that both domains improve simultaneously in the same subjects under BHB-verified conditions, has not been demonstrated; IBD-specific clinical evidence for the ketogenic pole currently rests on a ten-patient case series, making it the primary hypothesis under experimental test rather than an established parallel. Intermediate carbohydrate restriction, defined by the absence of verified ketosis, is hypothesized to produce oscillating rather than coherent receptor engagement, producing neither lipid nor immune improvement reliably. No study has simultaneously characterized dietary metabolic state, the bile acid metabolome, and Th17/Treg balance in the same IBD patients. A staged experimental program is proposed; a cross-sectional design in quiescent IBD patients constitutes the immediate test. The framework is directly falsified if committed and intermediate dietary groups do not differ in species-level immunomodulatory bile acid concentrations despite differing in lipid and immune outcomes; such a result would redirect Stage 3 design toward BHB-direct NLRP3 inhibition and Kbhb-mediated mTOR signaling as the primary mechanistic candidates. The Host-Microbe Counter-Regulation Index (HMCRI) — a candidate index requiring empirical validation, with no currently established reference ranges — is proposed as a systems-level index of signaling coherence whose reference ranges and dietary responsiveness constitute primary Stage 1 analytical objectives.

Abstract: Journalism plays a structurally indispensable yet under-theorized role in democratic governance. This article situates journalism within a recalibrated constitutional architecture in which the Quarta Politica—conceptualized as an Ombudsman Council—constitutes a fourth power alongside the legislative, executive, and judicial branches, endowed with distinct coercive authority oriented toward systemic correction and participatory accountability. Within this framework, journalism is not a separate power but a constitutionally protected function that secures the informational conditions upon which all four powers depend. By producing, verifying, and disseminating public information, journalism sustains accountability, enables informed participation, and facilitates collective judgment. The article reconceptualizes democracy as dependent on three interrelated dimensions: the distribution of coercive authority, the institutionalization of participatory oversight, and the integrity of the informational environment. It demonstrates that journalism performs a non-substitutable role as an early-warning and accountability-generating mechanism. Under conditions of digital transformation, platform dominance, and media fragility, these informational foundations are increasingly at risk. The article therefore advances a calibrated framework for the constitutional protection of journalism—grounded in independence, sustainability, and accountability—embedded within the Quarta Politica. A comparative perspective, including Global South contexts, underscores the generalizability of this approach across diverse democratic settings.

Abstract: Smooth window functions that restrict field actions to finite spacetime domains appear throughout quantum field theory, quantum optics, and open quantum systems, wherever interactions are switched on and off, detectors couple for finite durations, or systems decohere within bounded regions. When such a window function ⋄(x) is introduced into the matter action of a covariant field theory, two structural consequences are unavoidable: the windowed Ward identities acquire boundary-layer corrections confined to the decoherence transition region, and the contracted Bianchi identity requires a compensating stress-energy contribution at the window boundary. Both consequences follow from the product rule of covariant differentiation and are independent of any specific physical motivation for the window. The present paper develops these consequences systematically for each sector of the Standard Model in curved spacetime. The windowed action prescription is applied to Dirac fermions, complex scalar fields, Maxwell theory, and the complete SU(3)c×SU(2)L×U(1)Y gauge Lagrangian. Each sector is shown to recover standard curved-spacetime quantum field theory exactly within the localization window, with all deviations confined to a boundary layer of thickness set by the decoherence timescale. A Noether analysis yields windowed Ward identities of the form ∇μ(⋄Jμ)=0: gauge invariance and Lorentz symmetry are preserved exactly within the window, and apparent non-conservation is a kinematic boundary effect mathematically identical to open-system flux terms from decoherence theory , . The non-local boundary term Tμνnl required by the Bianchi identity decomposes as Tμνnl=Tμνcomp+TμνRem, where Tμνcomp is the boundary-layer compensator and TμνRem is its macroscopic coarse-grained remnant in the high-localization-density regime. A formal Lemma establishes that for any regular quantum field, Tμνcomp vanishes upon coarse-graining, so standard field evolution leaves no macroscopic stress-energy remnant. The sharp-window limit recovers the Israel junction conditions exactly, and the smooth-window generalization is structurally identical to the Ashtekar–Krishnan dynamical horizon flux balance laws.

Abstract: This study investigates a method that integrates retrieval-augmented mechanisms into large language model agents for scientific literature review generation. The approach addresses the limitations of traditional review models that rely on parametric knowledge with insufficient timeliness and limited coverage. Incorporating external document retrieval and dynamic information fusion into the generation process enhances the accuracy and completeness of the output. The overall framework consists of query encoding, semantic retrieval, document filtering, knowledge fusion, language modeling, task planning, memory storage, and reinforcement optimization, forming a closed loop of retrieval, understanding, and generation. Relevant document fragments are first retrieved through semantic vector search to ensure comprehensive and reliable information sources. These external representations are then integrated with the internal embeddings of the language model through weighted fusion, which preserves fluency while maintaining factual grounding. The task planning module constrains logical flow and text structure, and reinforcement learning optimization further improves relevance and consistency. Comparative experiments on large-scale scientific literature datasets demonstrate that the method outperforms existing approaches on ROUGE, BLEU, METEOR, and diversity metrics, validating its effectiveness and practicality. The findings show that combining retrieval augmentation with agent architectures can significantly improve coverage, accuracy, and language quality in review generation, providing a feasible solution for knowledge organization in complex literature environments.