Abstract: Protected areas are often treated as internally homogeneous conservation units, yet their communities may be structured either as discrete modules or as continuous gradients shaped by environmental heterogeneity and human disturbance. Using camera-trap data from Liziping Nature Reserve, China, we examined the spatial organization of mammal and galliform bird communities and tested whether species-level environmental responses help explain community structure. From 148 camera-trap sites surveyed between July 2018 and June 2019, we obtained 4,065 independent detections and retained 15 species for analysis. We combined β-diversity decomposition, clustering, NMDS ordination, single-species occupancy models, clustering of environmental response coefficients, and Mantel tests. Community variation was dominated by turnover rather than nestedness, and clustering based on co-occurrence and relative activity patterns did not reveal well-separated discrete modules. Instead, NMDS indicated continuous variation along environmental gradients, with elevation and vegetation productivity as the strongest correlates. Occupancy models showed marked species-specific environmental responses, especially to elevation, habitat structure, and human disturbance, and β-based clustering identified two distinct environmental response groups. These results indicate that communities in Liziping are better characterized as continuous gradient structures than as discrete modules, and suggest that conservation should emphasize the maintenance of environmental heterogeneity, habitat continuity, and connectivity within mountain protected areas.

Abstract: This article provides a comprehensive critical analysis of Benjamin F. Jones's influential work on age and great invention, which documents a significant secular trend toward older ages at which inventors make breakthrough contributions. Drawing on data from Nobel Prize winners and great inventors across the twentieth century, Jones finds that the mean age at great invention increased by approximately six years over this period, attributing this shift to the expanding "burden of knowledge." This article examines Jones's theoretical framework, empirical methodology, and the broader implications of his findings for innovation policy and economic growth. While acknowledging the paper's substantial contributions, this analysis identifies important limitations—including concerns about measurement validity, alternative causal interpretations, and the generalizability of findings—and engages with contradictory evidence that complicates the burden of knowledge narrative. The article situates Jones's work within broader literatures spanning economics, psychology, and the sociology of science, ultimately arguing that while the burden of knowledge hypothesis offers a compelling partial explanation for observed trends, the phenomenon is likely more complex and contingent than the original framework suggests.

Abstract: The accurate prediction of feedback from user comments is essential yet challenging, often limited by the nuanced semantics that traditional Natural Language Processing and existing Large Language Model prompts struggle to capture. We propose the Hierarchical Feedback Reasoning Prompting (HFR-Prompt) framework to address this. HFR-Prompt guides Large Language Models through a multi-stage, logically progressive analysis comprising Initial Tendency Assessment, Fine-grained Feedback Type Identification, and Result Integration and Explanation Generation. Each successive stage builds upon the contextual understanding established by the previous one. Extensive experiments on a substantial dataset demonstrate that HFR-Prompt significantly outperforms strong LLM baselines and standard prompting techniques in terms of accuracy, Macro-F1 score, and crucial explanation consistency. While introducing a computational overhead, HFR-Prompt sets a new standard for interpretable and accurate comment feedback prediction, validating the efficacy of structured, hierarchical reasoning in complex LLM applications.

Abstract: Background: Goals of care discussions are essential communication skills in medical training that bridge patient values with clinical decision-making. Integrating palliative care principles into these conversations enables holistic, patient-centered care, yet medical trainees often lack structured preparation for these critical interactions. Objective: This narrative review examines how medical training can effectively integrate palliative care approaches into goals of care discussions through structured communication frameworks, interdisciplinary collaboration, and emerging innovations to promote patient-centered outcomes. Methods: Literature on evidence-based communication frameworks (SPIKES, REMAP, SUPER, Serious Illness Conversation Guide) was reviewed to identify training approaches. Clinical outcomes including patient satisfaction, hospice utilization, ICU transfers, and intervention intensity were examined. Educational barriers and facilitators—including communication training curricula, cultural competency, language considerations, and multidisciplinary team involvement—were evaluated. Emerging technologies supporting clinician education and practice were also assessed. Results: Training in structured communication frameworks improves patient-physician relationships, reduces patient anxiety, and increases family satisfaction. Early palliative care integration through effective discussions leads to increased hospice awareness and utilization while reducing burdensome interventions. Key educational facilitators include dedicated communication skills training, multidisciplinary team participation (including chaplains and palliative care specialists), and AI-assisted documentation tools that support learning while preserving humanistic clinician-patient interactions. Conclusions: Integrating palliative care principles into medical training for goals of care discussions is essential for developing patient-centered clinicians. Combining structured communication frameworks, interprofessional education, targeted skills training, and technological support creates a comprehensive educational approach that prepares trainees to elicit patient goals, create individualized care plans, and deliver holistic care that honors patient values.

Abstract: Microplastics have been known to kill fish and other microorganisms that feed on them in water bodies. The microplastics are also harmful to human beings when consumed directly or indirectly. This paper focuses on extracting features that can be used to build a model for identifying microplastics in images taken from open sewers that lead to the Indian Ocean. One thousand (1000) pictures were taken from selected points in Kilifi, Mombasa and Kwale counties in Kenya using a still picture camera. The pictures were then subjected to auto-cropping using a code written in python programming language. TensorFlow tool with openCV was used to capture the shape of the microplastics and annotate them by drawing bounding boxes. This was followed by application of Scale-Invariant Feature Transform (SIFT) algorithm to extract features from the images. The output of the process was a dataset of features for model building to identify microplastics in images. Further research can be conducted to extract more features using different algorithms and build models for identifying microplastics in images.

Abstract: After decades of in vivo isotope tracing, human solid tumors have not been shown to derive the majority of their carbon from circulating glucose. Despite this, glucose uptake by tumors continues to be widely interpreted as evidence of glucose dependence for growth. In contrast, mounting clinical and metabolic evidence indicates that glucose and glutamine are consumed primarily as regulatory and competitive substrates rather than as dominant carbon sources, with tumor biomass supplied largely by lactate, glutamine, and host-derived amino acids and lipids.Cachexia is commonly described as a secondary complication of advanced cancer, yet this metabolic behavior suggests it functions instead as a tumor-maintained systemic state that favors malignant survival at the expense of host tissues. By consuming glucose and glutamine at high rates, tumors restructure host metabolism, suppress immune function through substrate deprivation, and induce a catabolic shift that mobilizes host tissues as the tumor’s true nutrient reservoir. Dietary deprivation strategies therefore fail in solid tumors not because tumors adapt to starvation, but because restriction accelerates host metabolic collapse rather than depriving the tumor.Central to this argument is a newly proposed construct: homeostatic deception via dissociated catabolic ketosis, a tumor-orchestrated state in which physiological ketogenesis is genuinely present but decoupled from its normal protein-sparing function. Circulating ketones satisfy central energy-sensing mechanisms, silencing counter-regulatory alarms while unrestrained muscle proteolysis and lipolysis proceed. The resulting catabolic loop supplies tumors with substrates released from host tissues while the host’s regulatory systems interpret the state as normal adaptive fasting. Cachexia persists as long as the tumor driver remains active and reverses primarily when tumor burden and inflammatory signaling are controlled. A case of metastatic NSCLC, with photographic documentation, serves as the observational origin of this framework (Johnson CL, 2026, https://doi.org/10.5281/zenodo.18988466). This manuscript integrates metabolic tracing, immunometabolism, and clinical observation to propose a mechanistic hypothesis reframing cachexia as a tumor-maintained state. The framework identifies multiple targets for companion therapeutic intervention and explains the failure of diet-based strategies.

Abstract: This paper represents a further academic deepening and upgrading of the authors' 2019 publication A Hypothesis on the Spatial Motion Mode of Photons. It should be explicitly stated that this paper falls within the category of natural philosophical thought experiments—its core value lies in constructing a unified physical image of the nature of light through rigorous logical deduction, and proposing verifiable theoretical hypotheses and experimental schemes; the validity of all conclusions must ultimately be verified by rigorous and extensive scientific experiments before being incorporated into the theoretical system of physics. As a foundational concept of quantum mechanics, the wave-particle duality of light has been accompanied by profound philosophical perplexities and theoretical tensions since its proposal, becoming a core bottleneck in the integration of classical and quantum physics. This paper systematically sorts out the logical incompleteness in the current quantum interpretation system—including the self-negation of the complementarity concept, the problem of photon localization, the fundamental opposition between the statistical and non-statistical interpretations of the wave function, and the philosophical controversy over the Heisenberg Uncertainty Principle, revealing the inherent contradictions of the traditional wave-particle duality framework. On this basis, adopting classical physical images and the logic of reduction to absurdity, and based on six axioms and six preparatory propositions, this paper puts forward a natural philosophical hypothesis on the essence of photons: a photon is an energetic mass point with a diameter smaller than the Planck length, moving in a uniform spiral linear motion in space. The paper deduces the core characteristics such as velocity, frequency, and wavelength of the photon's uniform spiral linear motion, and designs three operable, repeatable, and quantifiable physical experimental schemes to provide specific paths for the empirical verification of the hypothesis. The research deduces that the angular momentum of photon spatial motion (excluding photon spin motion) is always the reduced Planck constant ℏ, the energy E=mc² is naturally unified with E=hν (the standard formula for wave energy), and the standard expression of the Heisenberg Uncertainty Principle ΔxΔpₓ≥ℏ/2 can be given a classical physical interpretation from the perspective of superposition of measurement deviations. This paper systematically responds to potential questions regarding the origin of photon particle nature, wave nature, and compatibility with relativity, arguing that the hypothesis provides a logically consistent and clearly visualized path for understanding the nature of light, builds a new natural philosophical framework for the integration of quantum and classical theories of light, and also offers a new thinking perspective for the paradigm shift in the study of the nature of light.

Abstract: Detectability of auditory signals in built environments is a critical issue in architectural acoustics, particularly in public spaces where notification sounds must be perceived reliably under background noise. This study investigated reaction times (RTs) to amplitude-modulated pure tones under silent, white noise, and bandpass-noise conditions. Twenty young and twenty elderly participants responded to 1- and 2-kHz tones with flat, gentle, and steep onset envelopes. To describe perceptual detection in physically interpretable terms, a time-integrated sound-exposure level model, LAE(t), was applied. RT was defined as the moment when cumulative acoustic energy exceeded a criterion value relative to the hearing threshold. In silent conditions, RTs were accurately predicted by LAE(t), with onset-envelope shape influencing early energy accumulation. In noise conditions, RTs increased systematically with spectral proximity between target and masker, consistent with auditory filter theory. When spectral separation exceeded approximately four ERB numbers, masking effects were minimal and RT approached silent-condition values. These findings demonstrate that perceptual detection timing is governed by cumulative acoustic energy and spectral masking rather than instantaneous sound pressure level. The LAE(t) model provides a detection-oriented metric that complements conventional room-acoustic parameters and may support evidence-based design of perceptually robust auditory signals in architectural environments.

Abstract: Textile dyeing industry is a significant contributor of complicated and extremely polluting wastewater. This wastewater has intermittent loads of chemical oxygen demand (COD), stains and other pollutants which puts dangerous effects on the sustainability of the environment and human beings in general. The traditional operation of wastewater treatment plants is reactive and rule-based to a large extent. These methods are ineffective in dealing with the non-linear dynamic character of the effluent of the textile business, resulting in low efficacy and recurring regulatory breach. To overcome these shortcomings, this paper will suggest a new hybrid architecture SAGE-GBTCN (Shock-Aware Gated Ensemble with Gradient Boosting and Temporal Correction Network) to be used in the effective prediction of wastewater pollution. This model combines a gradient boosting ensemble to produce baseline predictions and a parallel temporal network with a residual correction. A shock-sensitive gating system is used to dynamically modify the correction process to consider any sudden, non-stationary changes in the nature of the effluents. This design makes the model very useful in capturing the long-term trends as well as abrupt disruptions within textile wastewater. The suggested SAGE-GBTCN model was tested with the help of data on a full-scale wastewater treatment facility. The findings are shown to be more accurate in prediction and better resistant to abnormal operating condition. The model also demonstrates high possibilities to facilitate active and energy saving management of textile wastewater treatment processes, which will result in an R2 predictive value of 0.942 and a RMSE of 30.30 of COD. Although validated on full-scale industrial WWTP data, the proposed framework targets operational characteristics typical of textile effluent treatment plants, including batch-wise COD loading, abrupt shock events, and chemically driven variability.

Abstract: The intensification of ESG disclosure requirements under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) has increased the demand for artificial intelligence (AI) and data analytics to support large-scale sustainability reporting and verification. However, the existing academic literature remains fragmented across disciplinary domains, including natural language processing, machine learning, auditing, and regulatory compliance. This study addresses this gap through a PRISMA 2020-compliant systematic literature review of 45 peer-reviewed articles published between 2020 and 2025 and indexed in the Scopus database. The analysis combines bibliometric techniques using VOSviewer with qualitative thematic content analysis. The results reveal a rapidly expanding research field with a compound annual growth rate of 91.9%. Four major thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning applications for ESG scoring and corporate performance; (iii) AI-enabled ESG assurance, auditing, and governance; and (iv) regulatory frameworks and the digital transformation of sustainability reporting. The findings indicate that AI technologies are progressively transforming ESG disclosure from a predominantly narrative and self-reported practice into a data-driven and verifiable transparency system. These developments have important implications for regulators, corporate practitioners, assurance providers, and investors seeking to enhance the reliability and comparability of sustainability disclosures.

Abstract: The proliferation of 4D point cloud videos highlights their potential, but the high cost of obtaining large-scale annotated data severely limits supervised methods. Consequently, self-supervised learning (SSL) is vital for learning generalizable representations from unlabeled 4D data. While existing SSL frameworks, such as Uni4D, have made progress, they often struggle with fine-grained motion understanding in extremely dynamic scenes, maintaining robustness under severe occlusion, and developing explicit predictive capabilities. To address these, we propose Dynamic4D, a novel and robust self-supervised framework tailored for dynamic 4D point cloud understanding. Dynamic4D introduces an Adaptive Causal Temporal Attention (ACTA) mechanism in the encoder for explicit causal temporal modeling and dynamic region-focused learning. Its decoder employs Motion Prediction Tokens (MPT) to directly infer motion vectors for masked regions. A novel adaptive motion-sensitive masking strategy further enhances robustness by intelligently prioritizing high-dynamic zones. Our multi-objective pre-training strategy integrates a new Dynamic Perception Loss alongside geometric reconstruction and latent-space alignment. Extensive experiments on diverse challenging benchmarks demonstrate that Dynamic4D consistently achieves state-of-the-art performance. It substantially outperforms prior methods, validating its superior capacity to learn highly robust, generalizable, and motion-aware representations for complex dynamic 4D point cloud scenes.

Abstract: SARS-CoV-2 is a zoonotic virus with a proven ability to infect various animal species, including domestic cats. In the post-pandemic period of COVID 19, there are still limited data on the clinical course, shedding of infectious virus and diagnostic features in cats. The aim of this study was to investigate the spread of SARS-CoV-2 in cats in 2023, the clinical manifestations of the infection, the diagnostic algorithm including molecular detection of viral components, differential diagnosis of co-infection with FHV, FCV, Mycoplasma spp. and Chlamydia felis, serology and isolation of infectious SARS-CoV-2. The immunomodulatory therapy in animals with a standalone SARS-CoV-2 infection was applied. The study included oropharyngeal, conjunctival and nasal swab samples from 102 domestic cats with clinical signs. Of them, 20.6% (21/102) were positive for SARS-CoV-2, with 16.67% (17/102) of the cats showing various variants of co-infection with FHV, FCV, Mycoplasma spp. and Chlamydia felis. Four of the cats had a standalone SARS-CoV-2 with mild clinical manifestations that included serous discharges from the eyes, without change in the general condition. The virus was isolated from these samples. These four cats and their owners were positive for antibodies to the virus, and the owners were PCR-negative. The treatment of SARS-CoV-2 infection included the preparations Viusid, RX immunosuport, Vetomun and Lisymun. This is one of the first post-pandemic study covering FHV, FCV, Mycoplasma spp. and Chlamydia felis in domestic cats with SARS-CoV-2 infection and further expands on the essential main idea including the specified pathogens of interest.

Abstract: Intraoperative hypotension (IOH) is a critical complication during surgical procedures that can lead to severe adverse outcomes including myocardial injury, acute kidney injury, and increased mortality. Early prediction of hypotensive events remains a significant challenge in perioperative medicine. This study leverages the Medical Informatics Operating Room Vitals and Events Repository (MOVER) dataset, a comprehensive collection of intraoperative physiological signals and clinical events, to develop and evaluate machine learning models for predicting hypotensive events 5, 10, and 15 minutes before onset.The MOVER dataset contains high-frequency vital sign measurements including heart rate, blood pressure, oxygen saturation, and respiratory metrics from over 5,000 surgical procedures. Extensive preprocessing and feature engineering were performed to extract statistical, temporal, and interaction features across multiple time windows. Multiple machine learning algorithms were implemented and compared including XGBoost, Random Forest, Histogram-based Gradient Boosting (HGB), Support Vector Machines (SVM) with RBF kernel, Long Short-Term Memory (LSTM) networks, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN).Experimental results demonstrate that XGBoost achieves the highest predictive performance with an accuracy of 94.2%, precision of 93.8%, recall of 94.5%, and AUC-ROC of 0.973 for 5-minute prediction windows. Performance remained strong for 10-minute (AUC-ROC = 0.942) and 15-minute (AUC-ROC = 0.908) predictions. Feature importance analysis revealed that mean arterial pressure (MAP) trends, heart rate variability, shock index, and time since last vasopressor administration were the most significant predictors. Error analysis identified borderline MAP values and rapid hemodynamic changes as primary sources of misclassification.The proposed models demonstrate strong potential for real-time clinical decision support systems to alert anesthesiologists of impending hypotensive events, enabling proactive interventions and improved patient outcomes. This research represents the first comprehensive comparison of multiple machine learning algorithms on the MOVER dataset for hypotension prediction, providing a foundation for future clinical implementation and prospective validation studies.

Abstract: The retention of Zillennial employees (born 1990–2005) presents significant challenges for contemporary organizations navigating competitive labor markets. This study investigates the relationships among perceived organizational support (POS), employee well-being (EWB), career development (CD), employee engagement (EE), and turnover intention (TI) within this workforce segment in Indonesia. Grounded in social exchange theory and complemented by conservation of resources theory, this research employed a quantitative cross-sectional survey design, collecting data from 360 Zillennial employees across multiple industries. Partial least squares structural equation modeling (PLS-SEM) tested the hypothesized relationships. Results indicate that POS (β = -0.285, p < 0.001) and CD (β = -0.198, p < 0.01) demonstrate significant negative direct effects on turnover intention, while EWB shows no significant direct relationship (β = -0.082, p > 0.05). All three antecedent variables significantly predicted employee engagement, which exhibited a strong negative relationship with turnover intention (β = -0.387, p < 0.001). Mediation analyses confirmed that employee engagement fully mediates the well-being–turnover relationship and partially mediates the effects of POS and CD. The model explained 64.8% of variance in turnover intention. These findings suggest that organizations seeking to retain Zillennial talent in Indonesia should prioritize organizational support systems, career development opportunities, and engagement-fostering initiatives. This study contributes to the literature by empirically examining these integrated relationships within an understudied demographic and cultural context, while acknowledging limitations inherent in cross-sectional, self-report designs.

Abstract: What psychological and behavioral factors distinguish those who produce exceptional, original contributions from those who achieve competence without breakthrough impact? This article synthesizes research from cognitive psychology, motivation science, expertise studies, and the sociology of knowledge to propose an integrative framework for understanding exceptional achievement. Drawing on both empirical research and theoretical analysis, the paper identifies four sequential phases through which great work emerges—domain selection, frontier attainment, gap identification, and persistent exploration—and examines three enabling conditions that sustain the process: deep curiosity, earnest engagement, and resilient morale. The framework reconciles deliberate practice models with creativity research, addresses the role of social and institutional factors, and offers implications for education, mentorship, and self-directed development. The analysis suggests that exceptional achievement, while rare, follows discernible patterns that can inform both individual practice and institutional design.

Abstract: Theories guide scientific inquiry by describing, explaining, and predicting human behavior and development across the lifespan. However, the social sciences have been largely shaped by theories rooted in Western philosophy, with Indigenous theories notably underrepresented. This scoping review identified Indigenous theories of human development and examined how they conceptualize development across the lifespan. Searches across four databases yielded 18 articles and 21 theories. Across theories, three developmental domains were prioritized (identity, relationships, and spirituality) embedded within four life stages: prenatal/childhood, youthhood, adulthood, and elderhood. Indigenous theories overwhelmingly centered community wellbeing and interconnectedness at each life stage. Last, rather than a linear, age-related progression, Indigenous theories reflected relational, cyclical, and narrative developmental trajectories- each with shared expectations for how development unfolds across the lifespan. These findings elevate Indigenous frameworks within developmental science and offer a foundation for theoretical and empirical innovation.

Abstract: The article describes the results of research on the power supply quality of selected fluorescent lamps and solid-state light sources powered by voltage with different waveforms and supply voltage values. The power factor, total harmonic distortion (THD) factor and values of individual harmonics were measured and their compliance with international standards was assessed. The measurement set-up used and the measurement results obtained with it are described. The results of the experimental research showed that the light sources under consideration did not meet the criteria specified in international standards for the THD factor and the values of individual harmonics, regardless of the shape of the supply voltage waveform. However, it was shown that supplying some light sources with a triangular voltage waveform can increase the illuminance value. On the other hand, the use of a rectangular voltage waveform leads to an increase in the power factor and a decrease in reactive power.

Abstract: Background/Objectives: Macrophage phenotype and function are highly sensitive to environmental cues; however, most in vitro studies rely on 2D culture systems that lack physiologically relevant structural context. The spatial dimensionality can influence immune cell signaling, yet the roles of these cells in regulating macrophage behavior remain incompletely understood. This study aimed to investigate how cultural dimensionality affects the phenotype, signaling, and functional activity of monocyte-derived macrophages. Methods: GFP-expressing THP-1 monocytes were differentiated into M0, M1, and M2 macrophages and cultured either on planar substrates or within 3D matrices composed of Matrigel or type I collagen. Macrophage morphology and viability were monitored. Membrane receptor expression and secreted cytokines were examined and quantified. Functional activity was further assessed through coculture experiments with RFP-expressing MDA-MB-231 breast cancer cells. Results: Compared with 2D culture, 3D environments induced distinct morphological and viability changes in macrophages. Collagen matrices supported sustained growth, subtype-specific morphologies, and enhanced functional activity, whereas Matrigel promoted aggregation and reduced viability. Core lineage markers remained stable across conditions, but activation-associated receptors and cytokine profiles were strongly influenced by dimensionality. 3D culture enhanced TNF-α expression and altered serglycin glycosylation patterns. In coculture assays, macrophage effects on tumor cell growth depended on polarization state and were more pronounced in 3D systems. Conclusions: These findings demonstrate that culture dimensionality and ECM composition are key regulators of macrophage phenotype and function. Collagen-based 3D systems better reproduce physiologically relevant macrophage behaviors than conventional 2D platforms, highlighting the value of structurally biomimetic models for immunological studies and therapeutic screening.

Abstract: Telecommunication networks operate as highly distributed, multi-vendor, and mis-sion-critical infrastructures, making them prime targets for sophisticated cyber threats. As networks evolve toward cloud-native, virtualized, and software-defined architec-tures, traditional perimeter-based security models have become insufficient. Zero-Trust Architecture (ZTA) has therefore emerged as a key security paradigm in telecommu-nications, enabling continuous verification, fine-grained access control, and improved protection of network and information assets. While ZTA strengthens technical security and operational resilience, its large-scale deployment introduces significant so-cio-technical and governance challenges that extend beyond network engineering. This study examines the implementation of ZTA in a multinational telecommunications in-frastructure organization using a four-wave longitudinal design (2020 - 2023). Drawing on an extended Technology Acceptance Model incorporating Perceived Trust, we ana-lyze employee perceptions of productivity, ease of use, usefulness, and trust before and after ZTA deployment, and following a structured governance intervention. Results reveal a substantial decline in the composite TAM index following ZTA enforcement (−25%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A Communication Campaign emphasizing transparency and stakeholder engagement produced a partial but incomplete recovery (d ~ 0.52), indicating that trust erosion under Zero-Trust conditions is measurable and contingent upon governance design rather than technological determinism. The findings demonstrate that ZTA functions not merely as a technical safeguard but as a socio-technical governance mechanism that restructures organizational trust. The study advances a Proactive Trust Management framework tailored to telecommunications environments, integrating security en-forcement with transparency, participatory oversight, and ethical calibration to sustain operational resilience in cloud-native infrastructures.

Abstract: Zone 2 thoracic endovascular aortic repair (TEVAR) frequently requires left subclavian artery (LSA) preservation to maintain vertebrobasilar and upper-extremity perfusion while obtaining a durable proximal seal. Dedicated single-branch endografts were de-veloped to standardize this step and to convert a traditionally hybrid scenario into a reproducible fully endovascular strategy. Two different concepts currently dominate this field: integrated unibody branch platforms, represented by Castor and the sec-ond-generation Cratos, and modular retrograde-branch systems, represented by the Gore TAG Thoracic Branch Endoprosthesis (TBE). The Castor/Cratos evidence base is broader, older, and much more heavily weighted toward type B aortic dissection, including long-term prospective multicenter data and several large real-world cohorts with fa-vorable branch patency and aortic remodeling. By contrast, TBE evidence is expanding rapidly and is supported by prospective midterm data in arch aneurysms as well as by increasingly large post-commercial series and comparative analyses across zones 0–2. Beyond outcomes, the two platforms differ substantially in branch directionality, con-tribution to proximal fixation, modularity, branch diameter range, proximal landing requirements, access profile, and regulatory/off-the-shelf availability, all of which have direct consequences for anatomical suitability in dissection, aneurysm disease, and trauma. This narrative review synthesizes current evidence and proposes an anato-my-first, pathology-aware framework for selecting between Castor/Cratos and TBE in totally endovascular zone 2 TEVAR with LSA revascularization.