Search Results (8,987)

This study investigated the effects of graded dietary barley replacement for corn on growth performance, rumen fermentation, carcass traits, meat quality, fatty acid profiles, and the rumen microbiome in Arbas White Cashmere goats, and further applied untargeted muscle metabolomics to elucidate the metabolic mechanisms underlying meat quality alterations. Growth performance was monitored for all goats (n = 10 per group); rumen fermentation, carcass traits, serum biochemistry and meat quality were determined in six randomly selected goats per group (n = 6); and rumen microbiota profiling and muscle metabolomics were performed on three selected goats per group (n = 3). Partial barley substitution (33% and 67% of starch from barley) significantly improved final body weight and average daily gain (p < 0.05), whereas total replacement (100% barley starch) did not confer additional growth advantages. Moderate barley inclusion increased ruminal propionate concentration (p = 0.001) and enriched the fiber-degrading bacterium Prevotella, while total barley replacement markedly reduced rumen microbial diversity (Sobs, Chao1, ACE, and Shannon indices; p < 0.01) and depleted multiple fibrolytic and hydrogenotrophic genera, including Christensenellaceae_R-7_group, NK4A214_group, and Methanobrevibacter. The 33% barley group exhibited elevated serum total bile acids, FGF-19, and CYP27A1 levels (p < 0.05), suggestive of modulated bile acid metabolism, along with decreased glucose-6-phosphatase (p < 0.05), pointing to suppressed hepatic gluconeogenesis. Muscle metabolomics revealed that moderate barley substitution predominantly affected glycerophospholipid metabolism, with upregulation of PE(P-18:1/20:4) and PC(16:0/16:0), whereas total replacement induced extensive metabolic reprogramming characterized by downregulation of glycine and glutathione-related metabolites, indicative of compromised antioxidant defense. The 33% barley group also exhibited the highest muscular C18:3n-3 content (p < 0.01), while the 67% and total replacement groups showed increased C22:6n-3 deposition (p < 0.05). Correlation analysis confirmed that PE(P-18:1/20:4) and PC(16:0/16:0) were significantly positively correlated with ruminal propionate and ADG (r = 0.841–0.986; p < 0.05), and Prevotella abundance showed a strong negative trend with G-6-Pase (r = −0.771). Collectively, these findings demonstrate that replacing one-third of dietary corn starch with barley starch optimally balances productivity, rumen health, and meat quality through enrichment of Prevotella, enhanced propionate production, and modulation of bile acid metabolism, while excessive barley inclusion destabilizes the rumen ecosystem and triggers muscle oxidative stress, providing a theoretical basis for precision grain formulation in intensive cashmere goat production. Full article

Free amino acids serve as a crucial hub connecting plant life activities with human nutrition. The fruits of Cucurbitaceae crops are natural nitrogen reservoirs, rich in free amino acids such as citrulline, arginine, and γ-aminobutyric acid (GABA). To investigate the spatial accumulation characteristics and differential distribution of free amino acids in cucurbit fruits, this study measured the contents of 19 free amino acids in the fruit stem, epicarp, endocarp, pulp, and seeds of 10 cucurbit species (12 varieties) at the mature stage. The results revealed that free amino acid accumulation exhibits significant tissue specificity and species specificity, forming diverse nitrogen metabolism hubs. Glutamine, aspartate, asparagine, arginine, and citrulline constitute a highly coordinated core module of nitrogen metabolism in Cucurbitaceae. Methionine, valine, and some other amino acids were consistently present at extremely low levels across tissues, indicating a competitive diversion of conserved metabolic pathways coexisting among species. Species classification based on free amino acids differed markedly from traditional taxonomy based on gene domestication, implying environmental selection pressures and non-linear gene-metabolite network relationships. Glutamine, GABA, citrulline, and arginine were identified as core hub metabolites supporting tissue type classification. This study revealed the allocation characteristics of free amino acids in fruits of various cucurbit crops and established a functional model integrating nitrogen assimilation, flavor/energy metabolism, stress tolerance/defense signaling, and storage across different tissue types of multiple cucurbit crops, providing new perspectives for precision breeding and domestication mechanism research in Cucurbitaceae. Full article

The continued digitization of open museum collections provides new opportunities for the intelligent organization and visual discovery of cultural heritage. However, morphological similarity between ceramic forms, intra-class variation, and changing photographic conditions remain challenges for automated classification, model interpretation, and similar-object retrieval. This study uses 3305 Chinese ceramic objects from the open collection of The Metropolitan Museum of Art (The Met) to develop an explainable and traceable workflow for ceramic-form classification and visual retrieval. Museum metadata were standardized into 13 form categories, from which 2755 objects were used to establish a seven-class primary classification task. Explicit morphological features, handcrafted visual features, ResNet50 representations, DINOv2 representations, and morphology–deep feature fusion were evaluated under a unified data split and evaluation protocol. Explainable artificial intelligence (XAI) methods were further used to examine spatial model responses and feature attributions of explicit morphological variables, while different representations were evaluated for content-based visual retrieval. The results show that deep visual representations effectively support ceramic-form classification, with DINOv2 demonstrating comparatively stable performance across multiple random seeds. Morphology–deep feature fusion did not provide a consistent classification advantage over DINOv2-only, but the fused representation showed clearer complementary value in visual retrieval, achieving the highest Precision@5 (0.819) and mean average precision at 10 (mAP@10; 0.773). XAI analyses further indicated that structurally meaningful spatial responses and explicit geometric descriptors contributed to form discrimination. By linking classification, interpretation, and retrieval outputs to Object IDs and original collection records, the proposed workflow provides a practical computational approach for ceramic-form organization, similar-object discovery, and traceable visual retrieval in digital museum collections. Full article

Precise data regarding the overall mass of the machinery and the gradient of the track are crucial for optimizing the control of monorail cranes and enhancing energy efficiency. Within the context of electric monorail cranes (EMCs), accurately estimating the total mass of the machinery and the track gradient poses a formidable challenge. This challenge arises from the strong coupling between the overall mass of the machine and the track gradient, the robustness of parameter estimation methods under varying operational conditions, and the generalizability of the algorithm to real-world operations and rail scenarios of EMCs. To address these challenges, this paper proposes a novel parameter estimation scheme that comprehensively considers the impact of parameter coupling relationships and multiple influencing factors in the transportation scenarios of EMCs under actual working conditions. First, to overcome measurement difficulties induced by strong coupling between the EMC mass and track gradient, a decoupling estimation method based on cascaded dual observers is proposed to jointly estimate the two states. Secondly, to mitigate track slope estimation errors under complex track types and diverse operating conditions, an enhanced immune optimization algorithm, integrating a Weibull function and Levy flight mechanism, in conjunction with an unscented Kalman filter (UKF), is developed. Furthermore, to achieve high-precision and stable parameter identification results, a Weibull dynamic forgetting factor is incorporated into the RLS algorithm, leading to the design of a WDFF-RLS estimator. Finally, real vehicle experiments were conducted on complex tracks at the test site to validate the accuracy and robustness of the proposed estimation method. Full article

Effective welfare monitoring in laying hens is increasingly challenged by growing flock sizes, declining farm numbers, and the practical limitations of assessor-based protocols under commercial conditions. Precision Livestock Farming (PLF) technologies offer opportunities to support large-scale welfare assessment, yet their application in laying hens remains predominantly limited to small-scale or prototype systems. This study addresses three complementary objectives aimed at informing future computer-vision-based PLF research and development in commercially housed laying hens. First, a systematic literature review was conducted to identify welfare-related categories monitored in laying hens between 2005 and 2025, the methods used to assess them, and the extent to which automated monitoring approaches have been applied. Second, outcomes of a TransRegional Expert Panel (TREP) within the OMELETTE project were synthesised to rank priority welfare challenges, evaluate the feasibility of different monitoring approaches, and compare expert perspectives with trends identified in the literature. Third, two pan-tilt-zoom (PTZ) camera setups implemented in semi-commercial aviary systems were described as proof-of-concept examples illustrating how multiple welfare challenges can be monitored using a single, multipurpose camera system. The literature review revealed a pronounced imbalance in monitoring frequency, with feather pecking dominating the literature while several other welfare challenges, including piling, disturbed sleep, and toe-pecking, remain comparatively underrepresented. TREP outcomes confirmed feather pecking as the highest-priority welfare challenge but also highlighted the importance of integrated monitoring approaches that combine time-intensive assessments, shorter checklists, and automated systems rather than relying on single indicators. Experts further considered the use of digital devices during routine barn inspections to be practically feasible. The PTZ proof-of-concept demonstrates how priority welfare challenges identified through both literature and expert input can be operationalised through automated, scheduled, and location-specific monitoring under commercial conditions. Together, these findings highlight the need for future PLF research to move beyond isolated measurements and small-scale trials towards integrated, cost-effective, and farm-specific welfare-monitoring systems that support adaptive, data-informed management strategies—for example, within a Plan–Do–Check–Act framework—and enable the development of digital standard operating procedures that generate actionable insights under real-world commercial constraints. Full article

Bacterial infection, biofilm formation, and the associated oxidative stress and persistent inflammation represent major obstacles to wound healing, tissue engineering, and implantable medical devices. Owing to their highly hydrated three-dimensional networks, favorable tissue compatibility, and versatile capacity for functional loading, hydrogels have been widely investigated for the treatment of infected wounds. This review systematically summarizes the major antibacterial mechanisms of hydrogels, including cationic contact-killing, chemical antibacterial activity mediated by metal ions and reactive halogen species, nanozyme-catalyzed reactions and bidirectional regulation of reactive oxygen species, as well as photothermal synergistic antibacterial therapy. Key design strategies are also discussed, including natural polymer-based matrices, multiple dynamic crosslinking, stimuli-responsive controlled release, three-dimensional printing, and spatial compartmentalization. In addition, recent advances in infection-microenvironment regulation, wet-interface adaptation, temporally coordinated tissue repair, and integrated diagnosis and therapy are highlighted. The field is currently shifting from single-mode bacterial eradication toward multistage tissue repair and intelligent theranostics. However, major challenges remain, including balancing antibacterial efficacy with biosafety, achieving reproducible manufacturing and sterilization-compatible formulations, maintaining functional stability during storage, and improving the clinical relevance and standardization of preclinical evaluation. In addition, most smart systems still lack quantitative coupling among pathological signals, therapeutic dosage, and treatment outcomes. Future studies should therefore integrate mechanistic design with manufacturing reproducibility, clinically relevant validation, and quantitative feedback regulation, thereby advancing antibacterial hydrogels from multifunctional proof-of-concept systems toward precise, controllable, and clinically translatable therapeutic platforms. Full article

Schizophrenia is a highly heterogeneous neuropsychiatric disorder characterized by positive symptoms, negative symptoms, and cognitive impairment, with substantial long-term functional consequences. Although dopaminergic dysfunction remains central to current disease models and treatment, dopamine-centered frameworks alone cannot fully explain cognitive deficits, treatment resistance, or marked variability in therapeutic response. Emerging evidence supports a broader view in which genetic susceptibility and environmental exposures converge on multiple interacting biological systems, including dopaminergic and glutamatergic neurotransmission, neuroimmune and glial dysfunction, kynurenine pathway metabolism, large-scale brain network dysconnectivity, and gut–brain communication. In this review, we integrate these mechanisms within a systems-level framework and discuss how their interactions may contribute to symptom heterogeneity and disease progression. We further examine the limitations of conventional dopamine D2-based antipsychotics, emerging non-dopaminergic pharmacological strategies, and adjunctive interventions targeting cognition and functional recovery. Finally, we highlight major translational barriers, including treatment resistance, medication non-adherence, adverse-effect burden, and the lack of clinically actionable biomarkers. An integrated understanding of converging neurobiological mechanisms may provide a stronger foundation for mechanism-informed patient stratification and precision treatment in schizophrenia. Full article

In multi-beam low-Earth-orbit (LEO) satellite communication networks, high-mobility aerial users, such as unmanned aerial vehicles (UAVs), high-speed aircraft, and near-space vehicles, may traverse multiple satellite beams within a short period of time. When the precise position of a target user is not continuously available to the network, the network needs to determine the set of beams in which the user is likely to be located when a paging request arrives. The corresponding communication satellites then transmit paging messages within these candidate beams to reach the target user. If the selected beam set does not cover the user’s actual position, the paging attempt fails; however, excessively enlarging the paging region or frequently updating the user’s location information introduces additional signaling and management overhead. Therefore, the key problem is to construct an accurate and adaptive paging region under the joint mobility of the user and LEO satellite beams. To address this problem, this paper proposes a network-side sensing- and prediction-aided dynamic location-area management method for high-mobility users. First, based on a three-stage motion model of high-mobility users, LEO satellite ephemeris information, and beam coverage parameters, the coverage performance during the whole flight process of high-mobility users is analyzed. Second, a high-mobility user state prediction mechanism integrating a three-stage motion model and square-root cubature Kalman filtering (TSM-SRCKF) is proposed. This mechanism can adaptively adjust the weights of different motion models according to the current motion state of the high-mobility user and suppress the influence of abnormal measurements during the measurement update process, thereby obtaining more reliable position prediction results and error covariance information. Finally, a TSM-SRCKF-aided dynamic location-area management method is proposed. Simulation results show that the root-mean-square error of the high-mobility user position under the proposed mechanism is only 23.3% of that of the comparison mechanism. Compared with the traditional velocity-based dynamic location area design method, the proposed method improves the paging success probability by about 60.1% and reduces the cumulative total management overhead by about 73%. Full article

Multi-modal synthetic aperture radar (SAR)–optical object detectors raise detection accuracy by fusing complementary physical responses, but require both modalities to be simultaneously available at inference. When the optical stream becomes unavailable—under heavy cloud cover, night-time conditions, or downlink disruption—the detector reverts to SAR-only operation and accuracy degrades sharply. A natural remedy is to distil a multi-modal teacher into a SAR-only student via privileged-information knowledge distillation. However, we observe that the leading channel-wise feature-level method (CWD) reduces the student’s accuracy below the non-distilled baseline, with its smallest-target AP collapsing to near zero, because SAR speckle and target high-frequency edges share the same band and the alignment loss is dominated by broadband speckle energy. We refer to this failure mode as the speckle-fitting trap, formalize it as a gradient-pollution effect, and validate it through spectral and feature-manifold diagnostics. To counter the trap, we propose M-FSAD-KD, a full-link distillation framework whose neck-stage Fourier-gated alignment transfers low-frequency structural content while preserving target-edge high-frequency content; a joint spatial–channel attention mask, a shallow backbone adapter, and a response-level knowledge distillation (KD) term complete the chain. With a MAIENet teacher on OGSOD-1.0, the advantage of M-FSAD-KD over the strongest response-level KD baseline scales with student capacity: it matches KD on a 2.39 M-parameter student (both ≈48% mean average precision at an intersection-over-union (IoU) threshold of 0.5 (${\mathrm{mAP}}_{50}$), averaged over multiple seeds) and exceeds it by 2.0 absolute points on a 19.98 M-parameter student ($+8$ over the non-distilled baseline), where it is the best of all distillation methods; at full convergence the 19.98 M-parameter student reaches 81.9% ${\mathrm{mAP}}_{50}$, within 8.9 absolute points of the multi-modal teacher. A frozen-feature transfer test to an out-of-domain SAR benchmark (SSDD ship detection) further shows that distilling from the multi-modal teacher yields substantially more transferable SAR features—about ten absolute points above the non-distilled backbone—with M-FSAD-KD transferring best. Cross-architecture validation with a dual-stream DEYOLO teacher yields 48.6% ${\mathrm{mAP}}_{50}$ at the student—1.1 absolute points below the MAIENet result—indicating that the framework transfers across the two representative teacher architectures tested (single-stream and dual-stream). Full article

Global climate change and human activities have jointly exacerbated habitat fragmentation and expanded ecotones, significantly increasing the frequency and intensity of cross-habitat litter decomposition. The phenomenon whereby litter decomposes faster in its habitat of origin (“home”) than in other habitats (“away”) is known as the home-field advantage (HFA) effect. In-depth exploration of its core mechanisms and key drivers is crucial for revealing the spatial heterogeneity of global carbon and nitrogen cycles and for refining the theory of substance cycling in forest ecosystems. This study integrated 1410 observations from 102 published studies and used meta-analysis to systematically evaluate global patterns, biogeographical differentiation, and key drivers of the HFA effect. The results show that the HFA exhibits significant biogeographical differentiation and is driven by multiple factors. Globally, we found a significant positive HFA (LnRR = 0.0576, p < 0.05), with decomposition-related metrics (e.g., mass loss, decomposition rate) being 5.9% higher at home than away. The strength of the HFA effect shows significant dependence on climate and vegetation types. Regarding vegetation type, significant home-field advantages were observed for coniferous forests (10.15%), deciduous broadleaved forests (8.31%), and evergreen broadleaved forests (6.57%), while no significant differences were detected for shrublands or grasslands. Regarding climate type, subtropical (6.46%), temperate (9.20%), and cold/highland climates (9.53%) exhibited significantly higher home-site effects, whereas tropical and arid/semi-arid climates showed no significant differences. All analyses demonstrated substantial heterogeneity (I² = 65.5–98.3%). This study also systematically analyzed the associations of three core factor categories (environmental factors such as elevation and precipitation, litter chemical composition, and soil chemical properties) with HFA effect. Among these factors, Litter C/P and Litter N/P were verified as significant positive regulatory factors governing the HFA effect. In conclusion, the driving mechanism of the HFA exhibits systemic characteristics that manifest as the interactive and synergistic effects of multiple factors rather than the independent control of a single factor. Specifically, climate and vegetation types jointly dominate the formation of its macro-geographical patterns, while local abiotic factors (e.g., altitude, precipitation) and litter properties (e.g., chemical composition) further and precisely regulate the strength of the HFA effect through complex interactions. Based on a systematic evaluation using meta-analysis, this study provides an important theoretical basis and data support for understanding the driving mechanisms of the HFA effect in cross-habitat litter decomposition across different ecosystems. Full article

Cancer immunotherapy has transformed the treatment of multiple malignancies; however, primary and acquired resistance remain major clinical challenges. Because effective immune recognition depends on the repertoire of peptides presented by major histocompatibility complex class I (MHC-I) molecules, increasing attention has focused on the antigen processing and presentation pathway as a therapeutic target to enhance tumor immunogenicity. Among its key regulators, the endoplasmic reticulum (ER) aminopeptidases ERAP1 and ERAP2 shape the MHC-I immunopeptidome by trimming peptide precursors before antigen presentation. Beyond this canonical function, accumulating evidence indicates that ERAP aminopeptidases are multifunctional proteins involved in inflammation, angiogenesis, ER stress responses, cell migration, and tumor-intrinsic signaling. These moonlighting activities suggest that ERAP enzymes influence cancer progression through both immune-dependent and immune-independent mechanisms. Recent advances in medicinal chemistry have enabled the development of selective ERAP1 inhibitors, leading to the first clinical evaluation of this therapeutic strategy and providing early clinical evidence that pharmacological modulation of antigen processing may complement existing immunotherapies. In this review, we summarize the multiple functions of ERAP aminopeptidases in cancer, discuss their role in regulating adaptive and innate immune responses, and highlight emerging therapeutic strategies and future challenges for exploiting ERAP-targeted interventions in precision immuno-oncology. Full article

Background: Multiple myeloma (MM) remains an incurable plasma cell malignancy characterized by marked clinical heterogeneity. Existing prognostic frameworks, including the International Staging System (ISS) and FISH-defined cytogenetic risk, are anchored at diagnosis and do not capture the evolutionary dynamics of disease or treatment response, leaving an unmet need for risk models that retain prognostic validity longitudinally. Methods: Using transcriptomic data from 762 CD138-selected MM plasma cells from newly diagnosed patient samples in the MMRF CoMMpass study (NCT01454297), we computed single-sample pathway activity scores for 469 curated cancer-relevant pathways (MSigDB Hallmark; Reactome) and learned a Bayesian causal network linking pathway activity to survival. The model was validated in five independent diagnostic cohorts (n = 1255) and in two independent treatment and relapsed/refractory cohorts (n = 319). Longitudinal risk tracking was additionally assessed in a 46-patient subset of the discovery cohort with serial pre- and post-treatment sampling. Results: The network identified five pathways associated with survival: unfolded protein response (UPR), FLT3 signaling through SRC family kinases, G2M DNA replication checkpoint, metabolism of selenium compound (SeMet), and nicotinate metabolism. The composite survival score stratified patients into high-risk (n = 76; 10%) and standard-risk groups with markedly divergent survival (median 1170 days vs. not reached; p < 0.0001). The score remained an independent prognostic factor after adjustment for age, sex, ISS stage, and KRAS, TP53, and UBR5 mutational status (HR 4.93; 95% CI 2.96–8.19; p < 0.001), and replicated across all five external diagnostic cohorts. Critically, the model retained prognostic discrimination in previously treated (GSE57317; p < 0.0001) and relapsed/refractory (GSE9782; p < 0.0001) settings, and patients transitioning from standard- to high-risk between serial samples exhibited significantly inferior survival compared to standard-risk patients. Conclusions: This pathway-based Bayesian network provides a reproducible, dynamically applicable risk model for MM that captures information complementary to ISS and FISH-defined cytogenetics. The framework supports longitudinal patient monitoring and may inform trial enrichment strategies and closer surveillance for high-risk subpopulations. Full article

The advent of large language models and Retrieval-Augmented Generation (RAG) models has greatly enhanced intelligent information systems. This has resulted in the development of context-aware and knowledge-grounded response generation. This has been highly beneficial in the context of religious advisory systems, which require precision, correctness, and knowledge grounding. For Islamic rituals like Hajj and Umrah, the user needs precise and accurate procedures to follow, which must adhere to specific sequences and knowledge grounding. However, the existing models have many limitations in this context, like hallucinations, a lack of procedural knowledge, bilingual inconsistencies, and an inability to incorporate safety constraints. This has made these models unsuitable for contexts in which incorrect responses can have serious implications. Therefore, in this context, this paper proposes a Deterministic Procedure-Aware bilingual Retrieval-Augmented Generation (DPAM-RAG) model, which can be highly beneficial in designing religious advisory systems. The proposed model can be highly beneficial in designing religious advisory systems. The proposed model integrates dataset modeling, procedure-aware chunking, bilingual alignment, and deterministic transformer-based response generation. Additionally, a confidence-based refusal strategy has been proposed to avoid the generation of responses that can be considered incorrect or out of context. The proposed model has been tested through an extensive experimental setup, which includes multiple transformer models like GPT, LLaMA-2, Mistral, MPT, and BLOOMZ. The experimental results have shown promising outcomes, which can be considered highly beneficial in designing trustworthy AI models. Full article

Designing Low Earth Orbit (LEO) constellations for applications like Positioning, Navigation, and Timing (PNT) is a challenging multi-objective optimization challenge. Conventional metaheuristics often suffer from premature convergence due to their reliance on static adaptive rules, limiting their effectiveness in complex search space. To address this limitation, we proposed a hybrid framework where a Double Deep Q-learning Network (DDQN) agent learns a policy to adaptively control the key parameters of a Particle Swarm Optimization (PSO) algorithm. The proposed framework formulates Walker constellation optimization as an sequential parameter control problem. Based on constellation performance feedback, the DDQN controller jointly selects the inertia weight and acceleration coefficients of PSO, guiding the PSO to more effectively balance exploitation and exploration. In a regional design case for China, our algorithm demonstrated superior performance. Compared to a 120 satellites benchmark constellation, the optimized constellation achieved a 27% reduction in Geometric Dilution of Precision (GDOP), a 27.6% enhancement in navigation accuracy, and a 5% increase in coverage multiplicity. This work establishes a robust methodology for the automated and intelligent design of LEO systems, validating the potential of deep reinforcement learning methods for complex aerospace optimization problems. Full article

Since its inception, Bitcoin has evolved from a decentralized peer-to-peer electronic cash system into a prominent digital asset, sparking an intense debate regarding its fundamental economic nature. This study investigates the financialization of Bitcoin—its transition toward increasing dependence on traditional macroeconomic forces—over the period 2019–2026. Employing a comprehensive suite of time–frequency methodologies, including Bivariate, Partial, and Multiple Wavelet Coherence (WTC, PWC, MWC), alongside a dynamic Sharpe ratio analysis, we examine the evolving interdependencies between Bitcoin and key systemic-risk indicators (VIX, OFR FSI), traditional safe-haven assets (Gold, U.S. T-Bills), and global equity benchmarks (Nasdaq, MSCI-IMI). The empirical evidence is inconsistent with the prevailing narrative of Bitcoin as a reliable safe-haven asset: it does not provide consistent downside protection during periods of high systemic stress, and its risk-adjusted performance declines synchronously across all holding horizons during macroeconomic contractions. Instead, once cross-market confounding factors are isolated, Bitcoin’s price dynamics co-move intensely with global equity cycles and systemic risk, particularly during crises. These results indicate a regime- and scale-dependent financialization: Bitcoin behaves as a highly integrated “risk-on” asset whose diversification properties are unstable precisely when downside protection is most needed. The study underscores the need for active portfolio management and coordinated regulatory frameworks to address Bitcoin’s deepening interconnectedness with traditional financial markets. Full article