Abstract: Metachromatic leukodystrophy (MLD) results from arylsulfatase A (ARSA) deficiency and progressive demyelination. This study evaluates the safety and therapeutic potential of intravenously administered allogeneic mesenchymal stem cells transduced with AAV9 encoding human ARSA in a porcine in vivo study. While ARSA activity in plasma and cerebrospinal fluid did not significantly change, CNS tissues showed a marked increase in ARSA activity, indicating successful CNS targeting and local enzyme expression. Biochemical parameters and cytokine profiles remained within physiological ranges, demonstrating good tolerability and absence of systemic inflammation. These findings suggest that MSC-based delivery of AAV9-ARSA is a safe approach capable of enhancing ARSA activity in the CNS and may represent a promising therapeutic strategy for MLD.

Abstract: his study investigates the application of causal representation learning in financial auditing risk identification, aiming to address problems in traditional methods such as spurious correlations, limited interpretability, and unstable recognition. The proposed framework is built around causal-driven latent representations, where nonlinear mapping is used to obtain deep feature representations of financial data, and structural equation models are employed to establish causal dependencies, thereby removing the interference of non-causal features in risk modeling. On this basis, causal regularization constraints are introduced, and the joint optimization of the objective function enhances the consistency and robustness of representations, improving the reliability and interpretability of the model in complex scenarios. Furthermore, in the risk scoring stage, causal representation is combined with intervention effect calculation, which enables risk identification to provide not only outcome judgments but also insights into the underlying driving mechanisms, thereby improving traceability of risk sources. To verify effectiveness, a dataset closely related to financial auditing tasks was constructed, and comparative experiments under an alignment robustness benchmark were conducted. The results show that the proposed method outperforms existing models in ACC, Precision, Recall, and F1-Score, with notable advantages in robustness and interpretability. In addition, hyperparameter sensitivity experiments analyzed the impact of the causal regularization coefficient on model performance, and the results indicate that appropriate causal constraints can significantly improve stability while maintaining predictive accuracy. Overall, the proposed causal representation learning framework enables more precise and reliable risk identification in financial auditing and provides strong support for building intelligent and data-driven auditing systems.

Abstract: In this paper, we investigate the lexicographic and colexicographic orderings of m-ary vectors of length n, as well as the mirror (left-recursive) reflected Gray code, complementing the classical m-ary reflected Gray code. We present efficient algorithms for generating vectors in each of these orders, each achieving constant amortized time per vector. Additionally, we propose algorithms implementing the four fundamental functions in generating combinatorial objects—successor, predecessor, rank, and unrank—each with time complexity Θ(n). The properties and the relationships between these orderings and the set of integers {0,1,…,mn−1} are examined in detail. We define explicit transformations between the different orders and illustrate them as a digraph very close to the complete symmetric digraph. In this way, we provide a unified framework for understanding ranking, unranking, and order conversion. Our approach, based on emulating the execution of nested loops, proves to be powerful and flexible, leading to elegant and efficient algorithms that can be extended to other combinatorial generation problems. The mirror m-ary Gray code introduced here has potential applications in coding theory and related areas. By providing an alternative perspective on m-ary Gray codes, we aim to inspire further research and applications in combinatorial generation and coding.

Abstract: Type 2 diabetes mellitus (T2DM) is strongly associated with cardiovascular mortality, with coronary artery disease (CAD) being the main manifestation. The pathophysiology of this condition is exacerbated by the accumulation of advanced glycation end-products (AGEs), specifically carboxymethyllysine (CML), which intensifies inflammation, vascular dysfunction, and the progression of atherosclerosis. Considering that diet is the primary exogenous source of these compounds and a modifiable risk factor, this study aimed to evaluate the effect of a low-CML diet on reducing serum levels in patients with T2DM and CAD. This was a randomized clinical trial involving 36 overweight elderly patients, divided into an intervention group (n=19, assigned to a low-CML diet) and a control group (n=17), over a period of 15 days. The intervention reduced CML intake by approximately 56% (p<0.001), resulting in a 30% decrease in serum CML (from 2.90 to 2.03 µg/g; p=0.015). The proposed diet also increased fiber intake and significantly reduced the consumption of trans fatty acids, polyunsaturated fatty acids, and cholesterol. A positive correlation was observed between serum CML and lipid peroxidation (r=0.33; p=0.045), body water (r=0.35; p=0.03), and dietary AGEs (r=0.52; p<0.01), indicating a relationship with oxidative stress and osmolarity. We conclude that reducing CML consumption for 15 days, through temperature control in food preparation, proved to be an effective nutritional strategy. The intervention promoted vascular and metabolic protection, suggesting potential for ameliorating diabetes complications. Future studies with a longer duration and the development of Brazilian food composition tables are recommended to expand upon these findings.

Abstract: We develop a framework in which lightcones, $(p,q)$ strings, and M-theory compactifications are equipped with an explicit layer of modal structure. Starting from Minkowski spacetime $\mathbb{M}^4$ with its usual null cones, we consider a background $\mathbb{M}^{4\times 7} = \mathbb{M}^4 \times G_2$ and attach to each spacetime point $\mathfrak{x}$ an internal $G_2$-source fiber. Local field configurations are encoded by propositions in a fuzzy Heyting--Kripke semantics, and a localized action $S(\mathfrak{x})$ defines normalized truth-values $\sigma(\mathfrak{x},\varphi) \in [0,1]$ which we interpret as a ``modal radius'' on a refined lightcone. Nilpotent extensions of the truth-value space capture unphysical ``eternal photon'' modes as purely internal, non-observable directions in the $G_2$-source. On this background we introduce \emph{interstices} $\varcurlywedge = G_2 \cap \mathbb{M}^4$ at $(p,q)$ string junctions, regarded as higher-dimensional analogues of Chan--Paton defects. Each interstice supports a pair of S-dual field-families (FFs) $(\Phi,\Psi)$ with a coprime arithmetic grading that organizes primary, descendant, and auxiliary modes, and we show how a $G_2$-inspired flux superpotential can be reinterpreted as a ``modal'' functional controlling the exponents in $\sigma(\mathfrak{x},\varphi) = n^{-z_i}$. We work out explicit examples: a three-leg $(p,q)$ junction and a minimal two-junction web with an internal $(1,1)$ segment, including concrete numerical assignments for $\sigma(\mathfrak{x},\varphi)$, an S-duality reshuffling of the FFs, and a simple propagation rule for modal data along the internal edge. The resulting picture can be phrased in terms of a small Kripke frame whose accessibility relation is determined by the (p,q) web geometry. Taken together, these constructions suggest that junctions, defects, and compactification data in string/M-theory admit a natural description in terms of modal structure on lightcones, with logical semantics providing a controlled way to track which field configurations are realized, suppressed, or confined to nilpotent internal sectors.

Abstract: The Extended, Generalized, and Grand Riemann Hypotheses are proved under a unified framework, which is based on the divisibility of entire functions contained in the symmetric functional equation, where the uniqueness of zero multiplicities (although unknown) of a given entire function plays a critical role. Consequently, the existence of Landau-Siegel zeros is excluded, thereby confirming the Landau-Siegel zeros conjecture.

Abstract: Contemporary artificial intelligence research and deployment have primarily emphasized task optimization, efficiency, and information processing. While these objectives remain im- portant, they fail to capture an increasingly dominant mode of human-AI interaction: the shaping of human experience. This paper introduces AI-as-an-Experience (AIaaE) as a high- level conceptual paradigm in which artificial intelligence systems are explicitly designed to generate, guide, and sustain human experiences, particularly emotional, psychological, and narrative experiences. The paper formalizes the core concept of AIaaE, situates it within established theories from psychology and human behavior, explains why such a paradigm is becoming inevitable, and examines its long-term societal, ethical, and technological impli- cations. The central claim is that artificial intelligence can be used not merely to perform tasks for humans, but to enable humans to experience structured, meaningful, and evolving states of being.

Abstract: Cloud-native systems based on microservices, containers, and server- less architectures present unprecedented challenges for observabil- ity and incident management. Traditional rule-based monitoring and manual root cause analysis are increasingly inadequate for han- dling the complexity and scale of modern distributed systems. This paper presents a novel framework that leverages large language models (LLMs) to enhance cloud-native observability, enabling automated root cause analysis and self-healing capabilities. Our system integrates OpenTelemetry-based telemetry collection with a domain-adapted LLM capable of performing multimodal analysis over metrics, logs, and traces. Through fine-tuning on operational data and chain-of-thought reasoning, the LLM generates explain- able root cause hypotheses and actionable remediation plans. Exper- imental evaluation on public microservice datasets demonstrates that our approach reduces mean time to resolution (MTTR) by 84.2% compared to rule-based methods, achieving 95% F1-score in anomaly detection while maintaining low computational overhead. The system successfully automated 91% of common incidents with- out human intervention, significantly improving service reliability and reducing operational burden.

Abstract: The rapid adoption of cloud-native architectures has created an urgent demand for automated development tools that can translate natural language requirements into deployable cloud-native microservices. While recent advances in large language models (LLMs) have enabled AI-assisted code generation, existing approaches predominantly focus on isolated code completion tasks rather than end-to-end software delivery. This paper presents CloudMAS, a multi-agent coding assistant framework that orchestrates specialized agents to transform user requirements into deployable cloud-native applications. Our system comprises six specialized agents: an Architect Agent for service decomposition and API design, three parallel Coder Agents specialized in backend, frontend, and infrastructure-as- code (IaC) generation respectively, a Tester Agent for automated test synthesis and execution, and an Ops Agent for container configuration and Kubernetes manifest generation. These agents are coordinated by a dedicated Orchestrator Agent that manages workflow execution and conflict resolution. We introduce a novel conflict resolution mechanism that enables agents to iteratively refine outputs through structured feedback loops. To address the lack of systematic benchmarks for end-to-end cloud-native development, we construct CloudDevBench, a publicly available evaluation dataset containing 50 real-world development tasks with associated test suites and deployment validation criteria. Experimental results demonstrate that CloudMAS achieves 92% compilation success, 81% test pass rate, and 84% deployment success rate, substantially outperforming single-LLM and single- agent baselines across all metrics.

Abstract: We investigate the oscillatory behavior of a first-order difference equation with several advanced arguments. New sufficient conditions for oscillation are established, and we show, through carefully constructed counterexamples, that many well-known criteria for equations with a single advanced argument fail to generalize to the several-argument setting, even when each advanced argument is increasing. Several illustrative examples are also provided to demonstrate the sharpness and practical effectiveness of the obtained con- ditions and to highlight their clear improvements over all existing results in the literature

Abstract: The continuous increase in the number of records collected and the amount of traits available for beef cattle genetic evaluations poses statistical and computational challenges when estimating the genetic and environmental covariance matrices needed to predict breeding values. Structural equation models (SEM) using either factor analysis (FA) or recursive models (REC) can be used to structure genetic and environmental covariance matrices and to obtain more parsimonious and efficient parameterizations. In this article, we use SEM to estimate parameters for growth and ultrasound carcass traits in beef cattle. Data consisted of 2,942 animals, and six traits were analyzed using standard multiple-trait mixed models with either unstructured covariance matrices (SMTM) or structured covariance matrices (SEM). For the latter, we considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Comparing with SMTM, all heritability estimates from 2-factor SEM for the additive genetic matrix (FA2G) and the model with six recursive effects zeroed out at the residual covariance matrix (REC1) were within one standard error of those obtained by SMTM. The correlations between estimated breeding values (EBV) for all traits and models ranged from 0.94 to 1.00. The most parsimonious model in terms of number of estimated parameters (p_D) was FA2G, with 431.2 p_D, and 25.3 p_D fewer than the traditional model SMTM. The REC1 model showed as a good alternative for this kind of dataset, as it had a smaller p_D (443.6) than the SMTM model (456.5) and a better deviance information criterion than all other models tested (e.g., 37,868.6 for REC1 and 37,874.7 for SMTM). Results from this study indicate that mixed-effects multi-trait models in beef cattle can be successfully analyzed with FA or REC models. These models offer a parsimonious representation of the underlying covariance patterns and offer an interesting option for breeding value prediction.

Abstract: Addressing contemporary sustainability challenges requires higher education institutions to adopt integrative approaches that align education, research, and societal engagement. While service learning is widely recognized as an experiential pedagogical approach, its potential role in systematically integrating education and research for sustainable development remains underexplored. This study adopts conceptual research design and synthesizes prior literature on Education for Sustainable Development (ESD), service learning, and sustainability-oriented research. Based on this synthesis, the paper proposes an integrative conceptual framework that positions service learning as a central mechanism linking educational practices, research activities, and community engagement. The framework explains how service-learning fosters sustainability competencies, enhances civic engagement, and improves research relevance, thereby contributing to sustainability outcomes aligned with the United Nations Sustainable Development Goals (SDGs). By clarifying key mechanisms and pathways, this study advances theoretical understanding of sustainability-oriented service learning and offers practical insights for educators, researchers, institutions, and policymakers. The paper further outlines directions for future empirical research to test and refine the proposed framework.

Abstract:

Adolescents enrolled in drama classes face unique emotional and social demands that may challenge their self-regulation. This study investigated factors associated with impulsivity among drama students, examining the roles of lifestyle, family dynamics, academic stress, and vocational activities. A mixed-methods approach was employed: two focus groups with 28 upper-grade students (grades 11–12) identified key themes, including emotional overload, academic stress, and strained communication with parents. Based on these insights, a 77-item anthropological questionnaire was developed and applied to 90 ninth-grade students. Impulsivity was measured using the Barratt Impulsiveness Scale (BIS), and multiple linear regression analysis identified three significant predictors of higher impulsivity scores: perceived stress during school days (β = 0.370, p < 0.001), conflictual discussions with parents (β = 0.273, p = 0.013), and discomfort during academic-related conversations at home (β = 0.331, p < 0.001). The model demonstrated high explanatory power (adjusted R² = 0.874). These findings indicate that impulsivity in drama students is influenced by neurodevelopmental factors and environmental stressors, particularly family and school-related pressures. The results underscore the importance of targeted interventions, including stress management strategies and family communication support, to enhance self-control and emotional resilience in performing arts education contexts.

Abstract: Anterior cervical discectomy and fusion (ACDF) and cervical vertebrectomy with interbody fusion (CVIF) are commonly performed spine procedures that rely on anterior plate fixation for stability. This biomechanical study evaluated the effects of screw length, plate length, and posterior fixation augmentation on the stability of anterior cervical fixation constructs under worst-case conditions. Using osteopenic-grade polyurethane foam blocks, four construct configurations representing discectomy and corpectomy models were tested with varying anterior plate lengths (22 mm, 34 mm), screw lengths (14 mm, 16 mm), and the addition of posterior fixation. Static and fatigue testing were per-formed using methods adapted from ASTM F1717, with fatigue run-out defined at 1.2 million cycles to simulate the fusion period. Static testing demonstrated the lowest yield load in constructs using a short plate with shorter screws. Fatigue testing showed that increasing screw length from 14 to 16 mm increased maximum fatigue load by 1.6-fold, while the addition of posterior fixation increased fatigue capacity by 3.6-fold. Increasing plate length resulted in a modest reduction in fatigue performance. These findings indicate that posterior fixation provides the greatest improvement in construct fatigue resistance, while increased screw length offers a less invasive means of enhancing stability, informing surgical strategies for high-risk ACDF and CVIF cases.

Abstract:

Accurately distinguishing true hardware failures from false alarms is a critical requirement in large-scale optical networks, where unnecessary Return Material Authorizations (RMAs) result in significant operational and financial overhead. This paper presents a novel AI-driven predictive framework that integrates multi-domain telemetry fusion, Transformer-based temporal modeling, and a domain-aware hybrid ensemble to deliver carrier-grade hardware failure detection in optical embedded systems. Unlike prior works that rely on single-sensor or threshold-based diagnostics, the proposed approach jointly analyzes optical power fluctuations, laser bias-current drift, TEC thermal instability, voltage dynamics, and DSP-layer soft metrics, enabling the model to capture degradation signatures that emerge only through cross-sensor interactions. A customized ensemble combining Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and TimeSeriesBERT is introduced to fuse complementary pattern-recognition capabilities--including long-term drift modeling, high-frequency anomaly detection, and global multi-sensor attention--resulting in superior robustness and generalization. Evaluation of real-time telemetry from optical devices demonstrates the effectiveness of the proposed system, achieving high accuracy with a high F1-score and significantly reducing unnecessary RMAs. These results highlight the novelty and practical value of the presented framework, establishing it as the first comprehensive AI solution tailored for reliable hardware-failure prediction in optical embedded systems.

Abstract: Hydraulic redistribution (HR) in plants facilitates bidirectional water transport through the vascular system in response to soil water potential gradients, enabling water movement from deeper to shallower soil layers or vice versa, with implications for ecological facilitation. This study aimed to evaluate the efficacy of high-precision weighing lysimeters in detecting HR in olive (Olea europaea L.) using a split-root experimental setup with potted trees. Sixteen pots, each containing half of a plant's root system, were independently monitored for mass changes to quantify water transfer between irrigated and water-stressed compartments. The purposedly built precision lysimeter array effectively isolated weights despite mechanical connections between pot pairs. Results demonstrated measurable water redistribution via roots from irrigated to dry pots and highlighted the potential of the lysimeters for precise quantification of plant-mediated water dynamics. It was observed that water transfer intensity peaked shortly after irrigation and diminished over time, with pronounced effects observed during nocturnal periods or cloudy humid daily conditions. These findings confirm previous data observed with reverse flow sap flow sensors and advance understanding of HR in olive agricultural systems.

Abstract: In the Earth–Moon–Sun system, the Newtonian gravitational force exerted by the Sun on the Moon exceeds the force exerted by the Earth. A naive force-magnitude interpretation might therefore suggest that the Moon should be classified as a planet orbiting the Sun rather than as a satellite of the Earth. Newtonian mechanics resolves this situation through relative motion and stability analysis; however, it does not introduce a primitive scalar criterion that determines binding dominance in multi-body systems. This paper presents Desmos theory as an axiomatic framework that embeds Newtonian gravity as a strict special case, connects consistently with General Relativity through a metric-based transformation, and admits a formal correspondence with energy quantization. Desmos is interpreted as a causal and explanatory layer that classifies structural binding prior to dynamics, geometry, or quantization.

Abstract: Around 1637, Pierre de Fermat famously wrote in the margin of a book that he had a proof for the equation aⁿ + bⁿ = cⁿ having no positive integer solutions for exponents n > 2. While Andrew Wiles provided a complete proof in 1994 using advanced 20th-century machinery, the question of whether a simpler proof exists remains a subject of intense mathematical interest. In this work, we focus on a significant restricted case of the theorem: the situation in which the exponent n possesses a prime divisor p that does not divide the quantity abc. Under this natural arithmetic condition, we develop an elementary argument—based on Barlow’s Relations and p-adic valuations—that leads to a contradiction. These methods lie closer to the classical number-theoretic framework that Fermat himself might have envisioned, and they illuminate structural features of the Fermat equation that persist across related Diophantine problems.

Abstract: In Louisiana’s marsh creation projects designed to mitigate wetland loss, riverine sediments are hydraulically dredged and transported through pipelines. These dredged materials are extremely soft, with moisture contents well above 100%, resulting in significant consolidation settlements even under minimal self-weight loads. Conventional one-dimensional (1-D) oedometer consolidation tests are commonly used to assess consolidation behavior; however, they are limited to soils with much lower moisture contents. At higher moisture levels, the soft slurry tends to overflow due to the weight of the standard stainless-steel dial cap and porous stone, which together apply a seating pressure of 1.07 kPa (0.01 TSF). This study presents a modified oedometer setup utilizing 3D-printed dial caps made from lightweight materials such as polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS), reducing the seating pressure to 0.21 kPa (0.002 TSF). This modification enables testing of dredged soils with moisture contents up to 100% without overflow. Settling column tests were also integrated with the modified oedometer tests, allowing for the development of void ratio–effective stress relationships spanning from 0.02 kPa (0.0002 TSF) to 107.25 kPa (1 TSF). The results demonstrate that combining settling column and modified oedometer tests provides an effective approach for evaluating the consolidation behavior of high-moisture slurry soils.

Abstract: Novel colourimetric sensors are readily devised by combining multifunctional (nano)materials with miniature optoelectronic components. The demand to detect and monitor metal ions has resulted in the invention of new colourimetric sensing schemes, especially for use at the Point-of-Need (PoN). Nonetheless, the design of fully reversible optical materials for continuous real-time ion monitoring remains a bottleneck in the practical realisation of sensors. Magnesium ion is vital to physiological and environmental processes, but monitoring can be challenging, particularly in the presence of Ca2+ as a cross-sensitive interferent in real samples. In this work, a chromophore molecule Hyphan I (1-(2-hydroxy-5-ß-hydroxyethylsulfonyl-phenyl-azo)-2-naphthol) has been grafted onto a cellulose matrix with a simple one-pot vinylsulfonyl process, to form a transparent, biocompatible and highly flexible thin-film colourimetric magnesium ion sensing material (Cellulose Film with Hyphan - CFH). The CFH film has a pH response time of &lt; 60s over the pH range 4 to 9, with a pKa = 5.8. The LOD and LOQ for Mg2+ at pH 8 are 0.089 mM and 0.318 mM, respectively, with an RSD = 0.93%. The CFH film exhibits negligible interference from alkaline and alkaline earth metals, but irreversibly binds certain transition metals (Fe3+, Cu2+ and Zn2+). The CFH material has a fast and fully reversible colourimetric response to pH and Mg2+ over physiologically relevant ranges without interference by Ca2+, demonstrating good potential for integration into microfluidic systems and wearable sensors for biofluid monitoring.