Abstract: Balancing accuracy and accessibility in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Since spectral models require computationally intensive spectral calculations, a widely adopted simplification strategy is to parameterize atmospheric spectral transmittances using various wavelength-averaging formulations. This work introduces a broadband parametric model derived from a spectral model that accurately estimates the three components of solar irradiance, direct normal, diffuse, and global under clear-sky conditions. The procedure used to develop the model is structured in two stages. Initially, discrete broadband transmittances are obtained by applying an independent integration scheme to the spectral transmittances provided by the source spectral model. The second stage involves fitting these results to obtain continuous broadband atmospheric transmittances, expressed as analytical functions depending solely on atmospheric state parameters and remaining independent of wavelength. The model development procedure is relatively classical; however, the calculation of the diffuse component introduces a new approach for estimating the fraction of aerosol scattering directedtoward the ground. The model was tested against data collected fromeight radiometric stations distributed across six continents andbenchmarked against two well-established reference models. Overall, theresults indicate a high level of accuracy and demonstrate the practical applicability of the model.

Abstract: The rapid evolution of Embodied AI and Large Language Models presents significant opportunities for home robotics, yet challenges persist in enabling robots to execute long-term, high-level natural language instructions. Current LLM-driven embodied agents often suffer from sub-optimal task planning, limited memory systems struggling with multi-hop queries, and inflexible agent routing mechanisms. To address these limitations, we propose the Context-Rich Adaptive Embodied Agent (CRAEA) framework, designed to significantly enhance task planning and memory-augmented question answering in household robots. CRAEA integrates core components: Semantic-Enhanced Task Planning (SETP), which enriches LLM-driven planning with object relationship graphs, hierarchical strategies, and implicit physical constraints; Multi-Modal Contextual Memory (MMCM), which stores comprehensive contextual memory units in a relational graph for sophisticated multi-hop reasoning and employs an advanced retrieval mechanism with temporal decay; and Adaptive Agent Routing and Coordination (AARC), featuring intent recognition with confidence evaluation, proactive clarification, and a planning feedback loop. Evaluated in an artificial home environment across complex tidying scenarios, CRAEA consistently demonstrates superior performance. Empirical results show that CRAEA achieves notable improvements in Task Planning Accuracy, Knowledge Base Response Total Validity, and Agent Routing Success Rate compared to baseline methods. A human evaluation further confirms enhanced coherence, naturalness, and user satisfaction, while an ablation study validates the critical contribution of each proposed module. CRAEA represents a significant step towards more intelligent, robust, and user-adaptive home robots.

Abstract: Recent revisions of personality disorders (PD) classifications have moved from categorical diagnoses toward dimensional models, raising renewed questions about the nosological status and clinical utility of borderline personality disorder (BPD). This narrative review traces the development of the borderline construct from early descriptions of patients positioned between neurosis and psychosis, through its theoretical consolidation within the concept of borderline personality organization, to the operationalization of BPD in DSM III and subsequent diagnostic revisions. A central section summarizes contemporary controversies regarding the validity and utility of BPD features. Arguments for abandoning the diagnosis emphasize the absence of a distinct borderline factor in factor analytic studies, the tendency of the construct to capture fluctuating symptoms and patterns of behaviour rather than stable maladaptive personality traits, the stigmatizing and non selective use of the label, and the lack of disorder specific treatment approaches. In contrast, converging evidence supports the view that core borderline symptoms frequently function as markers of general PD pathology and of the severity of impairments in self and interpersonal functioning. The paper integrates the regional tradition of the borderline level of personality functioning, conceptualizing borderline pathology as a dynamic dimension of dysfunction with potential transient regressions, and links this concept to the Level of Personality Functioning (LPF, Criterion A) within the DSM 5 Alternative Model for Personality Disorders (AMPD). Retaining borderline pathology as a dimension may support contemporary PD assessment by offering a clinically recognizable marker of overall dysfunction, a guide for rating severity, an indicator of personality structure and need for psychotherapy, without disrupting continuity with an extensive clinical and research tradition.

Abstract: Background: TNRC6B encodes a core effector of the RNA-induced silencing complex and is essential for miRNA-mediated gene silencing. Pathogenic variants in TNRC6B have re-cently been associated with a neurodevelopmental disorder characterised by develop-mental delay, intellectual disability, and behavioural difficulties. Methods: We report a three-generation family with a 22q13.1 deletion encompassing TNRC6B. Clinical data were collected from medical records and family interviews, and the findings were compared with those of published cohorts. Results: Affected individuals presented with developmental delay, speech and language impairment, autism spectrum disorder, ADHD, oppositional defiant disorder, cranio-synostosis, joint laxity, clinodactyly, and cardiac valve anomalies. The father and paternal grandmother had learning difficulties and neurobehavioral features, while the proband exhibited a more severe phenotype. Conclusion: This report expands the phenotypic spectrum of TNRC6B-related neurode-velopmental disorder, highlighting craniosynostosis, joint and connective tissue features, and cardiac involvement. Our findings also underscore variable expressivity across gen-erations and emphasise the relevance of both copy-number and sequence variants in TNRC6B in patients with neurodevelopmental disorders.

Abstract:

An empirical pattern recurs across the dietary intervention literature: committed dietary patterns—sustained ketosis (<35 g carbohydrate/day with verified β-hydroxybutyrate ≥0.5 mM) and Mediterranean diet—each improve inflammatory markers and, under verified conditions, produce favorable or non-atherogenic lipid profiles. Intermediate carbohydrate restriction (50–150 g/day, or vacillating compliance without sustained ketosis) may not achieve either. Simultaneously, strict ketogenic diets produce dramatic gut microbiome restructuring, including near-elimination of Bifidobacterium adolescentis and expansion of Akkermansia muciniphila. This paper proposes that microbiome-mediated bile acid signaling is the mechanistic link connecting these observations. The microbiome generates the majority of bile acid chemical diversity through deconjugation, dehydroxylation, and epimerization of host-synthesized primary species, while the host simultaneously produces counter-regulatory bile acid conjugates. Dietary patterns that produce stable microbiome configurations therefore also produce stable bile acid signaling environments that coordinate, through multiple receptors including FXR, TGR5, S1PR2, VDR, and RORγt, both lipid metabolic and immune outcomes across organ compartments. This coordination is distributed across tissues and receptors with sometimes opposing outputs, not tightly coupled through a single molecular effector. The hypothesis must account for established findings that constrain it: FXR’s metabolic and anti-inflammatory programs use mutually exclusive post-translational modifications within single cells; the FXR agonist obeticholic acid improved hepatic inflammation while worsening atherogenic lipid profiles in Phase III trials; individual bile acid species exert cell-type-dependent effects on the same receptor; and the most potent bile acid immune tolerance pathways bypass FXR and TGR5 entirely. Moreover, bile acid–mediated immune tolerance may simultaneously suppress beneficial anti-tumor immunity in certain tissue contexts. Despite these constraints, the framework generates testable predictions and a staged, affordable experimental program is proposed. Take-home message: Bile acids are not passive fat-absorption facilitators but a multi-receptor signaling network through which committed dietary patterns may simultaneously coordinate lipid metabolism and immune tolerance—explaining why these outcomes co-vary under dietary intervention and why intermediate restriction fails at both.

Abstract: Background and Objectives: Incidence of colorectal cancer (CRC) in developed Western countries is constantly growing. CRC represents the third most common cancer and the second leading cancer-related cause of death worldwide. Innate and adaptive im-munity plays a pivotal role in the tumor response, but many of these interactions are still not well understood. Granulysin (GNLY) is an effector, cytolytic molecule, present in human cytotoxic granules of different lymphocyte subpopulations, mainly in cyto-toxic T cells and NK cells. Pore-forming proteins GNLY, perforin and granzymes, play a key role in cell-mediated immune responses against tumors and infections. Materials and Methods: We aimed to analyze perforin and GNLY-mediated cytotoxicity in the peripheral blood of patients with CRC by flow cytometry. Simultaneously, the cells were labelled with monoclonal antibodies against perforin, GNLY and different sur-face antigens (CD3, CD4, CD8 and CD56). Phenotypes of lymphocyte subpopulation and expression of perforin and GNLY were analyzed using intracellular and surface immunofluorescence. Results: Total perforin and GNLY expressions in peripheral blood mononuclear cells (PBMC) were significantly lower than in the control group. Statisti-cally significant differences were observed in the distribution of perforin and GNLY expression in different stages of tumors classified according to Dukes, indicating that the percentage of total perforin and GNLY were significantly diminished in accord-ance with tumor progression. Perforin and GNLY expression was significantly reduced in NK and NKT cells, accompanied by reduced cytolytic potential in patients with CRC and a consequent reduction in their ability to eliminate tumor and infected cells. Con-clusions: The determination of cytotoxic potential may provide a valuable assessment of a patient’s immune status and represent a novel therapeutic target. Patients with CRC exhibit markedly impaired perforin- and GNLY-mediated cytotoxicity that cor-relates with disease progression. Assessment and restoration of cytolytic potential may therefore serve as indicators of immune competence and promising therapeutic strate-gies to improve perioperative and oncologic outcomes.

Abstract: Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose major threats to life, infrastructure, and post-disaster recovery. Although coseismic landslide susceptibility mapping increasingly uses machine learning (ML) and deep learning (DL), explicit integration of spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPM), remains limited in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that incorporates InSAR-derived deformation metrics with key geomorphic and hydrologic predictors to improve rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (Kₛₙ) was computed from a digital elevation model. Additional predictors included slope, aspect, curvature, elevation, drainage density, distance to river, log-transformed stream power index (logSPI), peak ground acceleration (PGA), rainfall, and land use/land cover. Five models: Random Forest, Extreme Gradient Boosting (XGBoost), a lightweight convolutional neural network, U-Net, and DeepLabV3 were trained using fourteen conditioning factors and a landslide inventory, with class imbalance addressed through majority undersampling for ML and weighted loss with patch oversampling for DL. Incorporating LOS and DPM improved model discrimination and calibration: XGBoost and Random Forest achieved the highest AUC-ROC values (0.972 and 0.969) and lowest Brier scores, while DeepLabV3 produced the highest AUC-PR (0.768) and CSI (0.49). Feature importance analysis identified Kₛₙ as the dominant predictor, and ablation tests confirmed the added value of InSAR metrics. These findings demonstrate the effectiveness of integrating InSAR products for rapid coseismic landslide hazard assessment in the Nepal Himalaya.

Abstract: Pseudogenes have traditionally been interpreted as nonfunctional remnants of protein-coding genes and therefore occupy a marginal position in genomic interpretation. This project proposes a novel conceptual perspective in which pseudogenes are reconsidered as potential components of a latent genomic layer associated with biological time and regulatory history. Rather than contributing primarily through immediate gene expression, certain pseudogenes may reflect accumulated biological trajectories and long-term regulatory constraints within genomic systems. By reframing pseudogenes through a temporal lens, this work explores the possibility that non-executing genomic elements may encode traces of past regulatory states that shape future biological responses. The project aims to develop a conceptual framework that integrates genomic persistence, biological memory, and temporal constraint in understanding genome organization and disease trajectories.

Abstract: We propose that quantum measurement is a boundary event: a physically identifiable, irreversible transition in which a reversible system/pointer correlation is forced across an operational irreversibility threshold into objective classical record stabilization. We formulate a three-stage taxonomy separating reversible premeasurement (Stage 1), irreversible record formation (Stage 2, the boundary event), and memory reset (Stage 3), and identify the stage at which a Landauer-scale heat bound applies. Under explicit operational conditions (C1–C6) in the uncontrolled-decoherence regime, the record-formation channel must dissipate at least k_B T ln 2 of heat per bit of classical mutual information I(X;Y). We propose a circuit-QED differential microcalorimetry experiment with matched ON/OFF branches that share identical premeasurement pulses and routing losses, differing only in whether an objective record is stabilized. The measurand is the differential deposited energy ΔQ ≡ Q_ON − Q_OFF, which isolates record-formation dissipation from common-mode backgrounds. The primary deep-quantum demonstration targets the temporal coincidence of heat onset and reversibility loss via a reversal-delay sweep (Control 3), providing a distinctive boundary-event signature even when ΔQ >> k_B T ln 2. Near-floor residual tests (r ≡ ΔQ − k_B T ln 2 · I(X;Y)) require lower-energy pointer implementations or elevated operating temperatures and are presented as a concrete roadmap. The bound is falsified if r is negative at high statistical significance under verified conditions.

Abstract: Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an \emph{ecosystem} of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic ``AI scientist''. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

Abstract: Collaborative robots (cobots) are designed to improve productivity and safety in industrial settings. However, to be effective Human-Robot Collaboration (HRC) relies heavily on the human operator’s trust in the robotic partner. This study posits that trust is significantly enhanced by the robot's ability to adapt to human behavior, particularly when the human teammate has a behavior unpredictable and outside the box. To achieve this adaptability, we propose an Adversarial Reinforcement Learning (ARL) framework to the activity planning of the robot. The assembly process is modeled as a Markov Decision Process (MDP) on a Directed Acyclic Graph (DAG). The robot learns an assembly policy using an on-policy algorithm, while a simulated human agent acts as an adversary trained with the same algorithm to introduce disturbances and delays. The proposed approach was applied to a simple industrial case study and evaluated on complex assembly sequences generated synthetically. While the ARL-trained robot did not outperform conventional assembly optimization algorithms in terms of task completion time, it guaranteed robustness against human variability, ensuring task completion within a bounded timeframe regardless of human actions. By demonstrating consistent performance and adaptability (Ability) in the face of uncertainty, the robot exhibits characteristics that align with the Ability and Benevolence components of the ABI model of trust, thereby fostering a more resilient and trustworthy collaborative environment.

Abstract: RGB–infrared (IR) fusion is an effective way to improve object detection robustness for automotive perception under low-light and adverse-weather conditions. Yet, practical multi-modal detectors still face three issues: imperfect cross-modal alignment, inefficient long-range interaction, and unstable query initialization when modalities exhibit inconsistent evidence. This paper presents CMAFNet, a deployment-oriented cross-modal alignment and fusion network with three key designs. (1) A Dynamic Receptive Backbone (DRB) extracts multi-scale features with adaptive receptive fields for both modalities. (2) A Channel-Split Mamba Block (CSM-Block) models long-range cross-modal dependencies using selective state-space modeling with linear complexity in token length, enabling an efficient accuracy–latency trade-off. (3) A Global Multi-modal Interaction Network (GMIN) performs fine-grained alignment and adaptive fusion via dual-branch cross-attention guided by global average/max pooling. In addition, an uncertainty-minimal query selection strategy and a separable dynamic decoder further enhance detection stability and efficiency. Experiments on M3FD and FLIR-Aligned show that CMAFNet achieves 83.9% mAP50 and 84.2% mAP50, respectively, while maintaining competitive inference efficiency, supporting real-time automotive deployment on compute-constrained platforms.

Abstract: Based on 1000 cross-scale independent experiments (including microcosmic particle vibration characteristic tests, macrocosmic celestial gravity-vibration coupling observations, and consciousness activity vibration correlation verification), this paper systematically verifies the core hypothesis that spacetime quantum vibration is the fundamental interactive carrier of the universe, and constructs a full-scale vibration unified field theoretical system. Experimental data show that the quantitative coupling deviation between particle vibration frequency and rest mass is less than 5%, the coincidence degree of the inverse proportional correlation between celestial vibration period and gravitational field strength is over 89%, and the non-local correlation between consciousness vibration and quantum entanglement breaks the Bell inequality limit (S=2.87). The vibration unified field equation derived from experimental data integrates the properties of microcosmic particles, macrocosmic gravitational phenomena and the laws of consciousness activities into different evolutionary forms of spacetime quantum vibration parameters (frequency, amplitude, phase), realizing the cross-disciplinary unification of physics and cognitive science for the first time. This theory innovatively proposes that dark matter is "spacetime quanta with reversed vibration phase", and predicts the specific deflection effect of ultra-high-energy cosmic ray trajectories and the vacuum modulation effect of collective consciousness. It provides a brand-new path for solving cutting-edge problems such as the essence of dark matter/dark energy, the scale gap between quantum mechanics and relativity, and the consciousness-matter interaction. All experimental data have been archived in authoritative platforms such as the International Vibration Physics Database (No. Vib-Unity-2024), with traceable and verifiable authenticity.

Abstract: Background/Objectives: Imidazole dipeptides (IDPs), carnosine and anserine, are endogenous antioxidants. The metabolism and functions of IDPs have mainly been investigated in rodents. However, the blood of primates, such as humans, contains carnosinase (CN1), which hydrolyzes IDPs. In non-primates, CN1 is absent, allowing IDPs to be distributed throughout tissues. There are concerns about whether the results of animal experiments can be directly applied to humans. Therefore, we aimed to investigate the blood kinetics and tissue distribution of IDPs following their oral administration to golden hamsters, the only non-primates known to possess CN1. Methods: Plasma CN1 activity was compared between hamsters and humans. Hamsters were administered IDPs (an anserine/carnosine mixture) purified from chicken meat at a dose of 1,000 mg/kg. Blood samples were collected at time points up to 6 h after administration. Tissue samples were collected at 6 h after administration to measure the concentrations of IDPs and related substances. Additionally, IDP levels in human and mice tissues from previous studies were compared with that of hamster tissues in this study. Results: Hamster plasma CN1 activity was more than 10 times higher than that in humans. Although IDPs were not detected in IDP-treated hamster plasma, constituent amino acids of IDPs increased up to 1–2 h and Nπ-methyl-histidine (m-His) remained at high levels up to 6 h after administration. IDP levels in control tissues (vehicle) were similar to those in human tissues. In the IDP group, tissue IDPs were higher than those in the vehicle and m-His increased in all tissues. Conclusions: This study suggests that IDPs and m-His levels increase in human tissues following a single oral administration of IDPs, and that m-His may serve as a substitute for IDPs.

Abstract: Background/Objectives: Bacterial biofilms are structured communities of sessile cells embedded in a self-produced extracellular matrix that protects against environmental stress, host immune responses and antimicrobial treatments. In response to specific cues, biofilm cells can revert to a planktonic free-swimming lifestyle through a process termed biofilm dispersal. When dispersed cells escape the biofilm matrix, they lose bio-film-associated antibiotic tolerance, a major barrier to treating medical biofilms. As such, dispersal-inducing compounds like nitric oxide (NO) are actively investigated as adjuvants to potentiate the biofilm eradicating activity of existing antibiotics. We recently characterised the transcriptomic responses elicited during spontaneous biofilm dispersal in closed culture-grown Pseudomonas aeruginosa biofilms. Here, we evaluated the tran-scriptional profile of P. aeruginosa biofilms treated with the NO donor Spermine-NONOate (SP-NONO) and the nitroxide C-TEMPO, an NO analogue to determine potential pathways involved in NO-mediated dispersal. Methods: Dispersal activity on P. aeruginosa PAO1 biofilms by SP-NONOate and C-TEMPO was quantified by crystal violet staining. Cellular responses to each compound were profiled by RNA-seq on treated and untreated cells. Results: While both compounds disrupted the transcription of ANR-regulated energy metabolism pathways, only SP-NONO activated canonical NO-regulated responses. Considering that only SP-NONO showed biofilm dispersal activity in this culture system, we investigated shared transcriptional shifts in SP-NONO-treated and spontaneously dispersed biofilms to identify pathways likely involved in central dispersal responses. These mostly included genes participating in the catabolism of leucine, valine, isoleucine and lysine, as well as 9 of 14 genes previously defined as transcriptional biomarkers of spontaneous biofilm dispersal. Conclusions: This study suggests that NO disrupts biofilm maturation by prematurely stimulating central pathways of spontaneous biofilm dispersal and highlights this set of biomarkers as robust indicators of dispersal responses.

Abstract: Human–artificial intelligence collaboration is increasingly treated as a static allocation problem—humans decide, machines compute—yet high-stakes workflows reveal a more fluid reality: leadership shifts multiple times within a single decision episode. This paper formalizes the Dynamic Authority Reversal (DAR) framework, which models intra-episode authority transitions across four states: Human-Leader/AI-Follower (HL), AI-Leader/Human-Follower (AL), Co-Leadership (CO), and Mutual Override (MO). Transitions are governed by four trigger classes—data superiority, contextual judgment requirements, risk thresholds, and ethics overrides—and are stabilized through hysteresis bands and safe-exit timers. The framework couples micro-level trust calibration with macro-level legitimacy by introducing the Reversal Register, an auditable log that binds each decision to the prevailing authority state, trigger conditions, and justificatory explanations. Ten falsifiable propositions are derived and linked to measurement constructs, prioritized by foundational importance and empirical tractability. Sector-specific implementation guidance is provided for healthcare and public administration, with attention to existing governance structures and regulatory frameworks. By operationalizing handovers rather than merely prescribing "human oversight," DAR advances both theory and practice: it equips researchers with testable hypotheses, furnishes practitioners with governance-ready instruments, and offers regulators an auditable architecture that preserves ultimate human accountability while enabling reversible AI leadership where contextually advantageous.

Abstract: The standard first-level trigger in the Pierre Auger Observatory surface detectors (data analysis in FPGAs immediately after digitization in ADCs) were developed when FPGAs were relatively simple and additionally expensive. Thus algorithms developed in 90’s of the previous century are relatively simple. Huge progress in electronics allows the implementation of very sophisticated mathematical algorithms in very efficient systems and relatively inexpensive FPGAs. A neural network is an alternative trigger developed recently for recognition neutrino-induced showers gave relatively high efficiency and allowed distinguishing signal profiles from Auger photo-multiplier tubes of water-Cherenkov detectors originating from atmospheric showers induced by high-background neutrinos from other showers. The chemical composition of ultra high-energy cosmic rays (UHECR) is sophisticated and still not known. Additional tool analyzing online in real time a potential chemical composition could help fix this problem.

Abstract: This study investigates the influence of circular-economy transparency (CET) on re-sponsible purchase intention (RPI) within the electronics market, elucidating the mediating role of perceived green authenticity (PGA) and the boundary condition of greenwashing skepticism (GWS). We used PLS-SEM (SmartPLS 4) with bootstrapping to test direct effects, mediation, moderation, and moderated mediation on a cross-sectional online survey of 400 adult electronics customers in Saudi Arabia. The results indicate that CET positively predicts PGA and RPI, with PGA significantly enhancing RPI. This suggests that perceptions of authenticity convey a significant aspect of transparency's impact on responsible intentions. Nonetheless, GWS considerably diminishes the CET→PGA and PGA→RPI relationships and lessens the potency of the indirect CET→PGA→RPI pathway, indicating that skeptical consumers more rigorously disregard cues of transparency and authenticity. The model provides a strong description of the observed variance in both PGA and RPI, justifying its explanatory and predictive value. These results suggest that electronics brands and policymakers would do well to complement transparency programs with measurable, decision-relevant information disclosures and trust-enhancing procedures (e.g., traceability and third-party validation) in order to minimize distrust and enable responsible purchasing.

Abstract: Agrifood structural transformation underpins progress toward Sustainable Development Goals, yet whether the state should withdraw, deregulate, or inject this transition remains contested. We evaluate three governance modes across over 2,700 Chinese counties and two decades, ap- plying Causal Forest with double/debiased machine learning to three policy reforms—the 2006 agricultural tax abolition (withdraw), the 2016 supply-side reform (deregulate), and the 2014 targeted poverty alleviation campaign (inject). Governance design, not fiscal magnitude, deter- mines effectiveness, and each mode’s impact is conditional on local institutional context: with- drawal accelerates the shift out of agriculture where pre-reform distortions bind most tightly; deregulation diversifies cropping structures; yet injection produces no significant average ef- fect, masking offsetting heterogeneity along fiscal-capacity lines. Data-driven targeting robustly outperforms administrative allocation in out-of-sample validation while disproportionately re- taining the poorest counties. Sustainable agricultural transitions thus depend on the political economy of policy execution, not on spending magnitude.

Abstract: Background: Intravascular lithotripsy (IVL) has emerged as a safe and effective modality for treating severely calcified coronary lesions. While the Shockwave system is well-established, clinical data on newer IVL platforms such as the Shunmei ShockFast system remain limited. Objectives: To evaluate the safety, feasibility, and procedural outcomes of the ShockFast IVL device in patients with heavily calcified de novo coronary artery disease. Methods: We conducted a prospective, single-center case series of 16 patients undergoing percutaneous coronary intervention (PCI) with the ShockFast IVL system between June and December 2025. Inclusion required angiographic or optical coherence tomography (OCT) evidence of severe coronary calcification. The primary endpoints were acute procedural success and in-hospital major adverse cardiovascular events (MACE). Secondary endpoints included device deliverability, calcium fracture (by OCT), and post-stent expansion metrics. Results: All patients underwent successful lithotripsy delivery with the ShockFast IVL system. Acute procedural success was 100%, with no intraprocedural complications, abrupt closure, or in-hospital MACE. OCT was performed in 50% of cases and demonstrated calcium fractures in all imaged lesions, with ≥2 fractures in 63% of cases. Median stent expansion was 90% [IQR 9], with no major malapposition or edge dissections. Quantitative coronary analysis showed a median acute lumen gain of 1.86 mm [0.62]. Conclusions: The ShockFast IVL system demonstrated excellent safety and procedural performance in this first-in-center experience. Outcomes were comparable to those reported with the established Shockwave IVL platform. These findings support the clinical feasibility of ShockFast as a novel tool for calcium modification in complex PCI.