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.

Abstract: The suspension system plays a significant role in ride comfort, car weight support, and road handling, which is crucial for the safety of the ride. This paper illustrates a derivation of a mathematical model and proportional-integral-derivative (PID) controller design for an active suspension system for a quarter car model of a passenger car. The performance of an active suspension system in terms of the vertical acceleration of the car body, suspension deflection, and tyre deflection is compared with that of a passive suspension system when subjected to road disturbance. The results show that the active suspension system with PID controller provides better performance compared to that of the passive suspension system.

Abstract: The proliferation of fake news across social media, headlines, and news articles poses major challenges for automated detection, particularly in multilingual and cross-media settings affected by data imbalance. We propose a fake news detection framework based on LLM-driven, feature-guided text augmentation. The method generates realistic synthetic samples across languages, media types, and text granularities while preserving factual structure and stylistic coherence. Experiments with classical and transformer-based models (Random Forest, Logistic Regression, BERT, XLM-R) across social media, headline, and multilingual news datasets show consistent improvements in performance. LLM-based augmentation improves overall accuracy by up to 1.6% over imbalanced baselines and increases minority-class F1-scores by up to 2.4% in low-resource languages such as Swahili. Hybrid fact- and style-based models achieve up to 93.8% accuracy with more balanced class-wise F1-scores and reduced language-related disparities, demonstrating improved robustness and cross-lingual generalization.

Abstract: Digital platforms produce a paradox: unprecedented connectivity alongside rising loneliness. Existing frameworks—built on assumptions of human senders and receivers—cannot explain this because the structure of communication has fundamentally changed.This paper introduces directionality as a formal variable tracing platform evolution from bidirectional social graphs, through unidirectional interest graphs, to Zero-Directionality: human–machine interaction in which the social other is entirely absent.We show that this zero-degree threshold enables two divergent trajectories. In the Inverted Loop (negative directionality), algorithms act preemptively, shifting the human from operator to operand. In the Triadic Mesh (triadic directionality), AI mediates between humans rather than replacing them, preserving human connection.Drawing on platform analysis, we examine how these trajectories reconfigure agency, citizenship, and social life, and identify degradation risks when mediation drifts into substitution. The framework extends platform studies to environments where the machine is communicative agent rather than intermediary.

Abstract: We present a parametrised charging infrastructure model developed to support the design of a hybrid-electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables as well as historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum.

Abstract: This article examines the impact of intermodal wagon technical specifica-tions and railway infrastructure parameters on electricity consumption in rail freight transport. To conduct this investigation, a three-stage analytical model was developed. The first stage establishes core assumptions, encompassing train lengths, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy-consumption model. The second stage identifies technical constraints of specific wagons, determines representative train compositions, and executes loading simulations. The third stage focuses on evaluating energy efficiency across diverse loading scenarios. The case study demonstrates that specific energy consumption varies significantly with wagon type, train mass, and route characteristics, challenging the use of static energy-consumption values prevalent in current literature. Results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite maximizing loading capacity. While 60-foot wagons consume less energy, they result in a high frequency of empty slots under a 20 t/axle limit. Conversely, 80-foot wagons emerge as the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists offer a balance of operational flexibility and competitive performance. Inter-estingly, extending train length from 600 m to 730 m increases volume but does not inherently reduce unit energy consumption. These findings underscore the necessity of aligning wagon fleet selection with infrastructure capabilities and cargo characteris-tics. Ultimately, this study provides actionable recommendations for planning ener-gy-efficient intermodal operations.