Abstract: Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Traditional patrol strategies suffer from low efficiency due to insufficient utilization of multi-source surveillance data. This study proposes a maritime patrol framework integrating AIS fishing effort, Sentinel-1 SAR dark vessel detections, and vessel encounter records. An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware construction ensures high-priority coverage within 72 hours while minimizing total distance. Experiments on real satellite datasets demonstrate that APB-ACO achieves 7% shorter routes with 46× lower variance than conventional methods, with 100% high-priority task coverage. The framework provides an effective decision-support tool for maritime law enforcement. This framework can serve as a practical decision-support tool for maritime law enforcement and marine resource management.

Abstract: Sustainable corporate social responsibility (CSR) has increasingly been recognized as a strategic tool for strengthening consumer-brand relationship. However, its impact on consumer behavior depends on how consumers interpret and respond to these initiatives. Based on Social Exchange Theory, Attribution Theory and Social Identity Theory, this study examines how sustainable CSR influences brand citizenship behavior by considering the roles of brand reliability and CSR skepticism. Data were collected from 385 consumers through an online survey and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that sustainable CSR positively affects both brand reliability and brand citizenship behavior and that brand reliability also has a positive effect on brand citizenship behavior. In addition, brand reliability partially mediates the relationship between sustainable CSR and brand citizenship behavior. The findings further indicate that CSR skepticism weakens the positive effect of sustainable CSR on both reliability and brand citizenship behavior as well as its indirect effect through brand reliability. Overall, the results suggest that sustainable CSR is more effective in fostering brand citizenship behavior when consumers perceive CSR initiatives as credible. This study provides an integrated perspective on how sustainable CSR shapes consumer responses and offers insights into the conditions under which these effects become stronger.

Abstract: Background: Obesity arises from complex interactions beyond energy imbalance, with the gut microbiome increasingly recognised as a key modulator of metabolic function in obesity. This narrative review examines microbiome-targeted interventions for obesity prevention and treatment. Objective: To synthesise evidence on diet, exercise, biotics (pre/pro/post/synbiotics) and faecal microbiota transplantation (FMT) as modulators of gut microbiota composition and function to improve body composition and metabolic health. Methods: A structured literature search of PubMed, Scopus and Web of Science (2015–2026) informed this narrative review, focusing on studies evaluating the role of the gut microbiome in obesity and the impact of microbiome-targeted interventions. Randomised controlled trials, systematic reviews, meta-analyses and key observational and preclinical studies were prioritised. Evidence was synthesised narratively. Results: Microbiome-targeted interventions including dietary modification, physical activity and biotic therapies demonstrate modest and variable effects on adiposity but may improve metabolic outcomes through mechanisms involving short-chain fatty acids (SCFA), inflammation and gut barrier function. High-fibre diets (e.g., resistant starch, Mediterranean) consistently enhance SCFA-producing taxa and reduce fat mass. Exercise induces modest microbiome shifts favouring beneficial bacteria such as Akkermansia / Bifidobacterium. Biotics (Lactobacillus / Bifidobacterium strains) yield small-moderate reductions in BMI / fat mass with next-generation strains (A. muciniphila, F. prausnitzii) showing promise in preclinical / human pilots. Evidence for FMT in obesity remains limited and inconsistent in humans. Mechanisms converge on energy harvesting, barrier integrity, endotoxemia reduction and GLP-1 / bile signalling. Conclusions: Microbiome modulation appears to complement lifestyle and therapeutic interventions but translation into clinical practice requires strain-specific, well-designed randomised controlled trials and longitudinal data. Personalised multiomics approaches offer future potential.

Abstract: Across the Global South, heightened contestation over rural land is placing land administration at the centre of policy attention, as persistent mismatches between official title records and lived realities of occupancy generate legal challenges, political conflicts, and limited access to state programs. Existing systems often alienate landholders who lack valid documentation, limiting their access to welfare and compensation. Digitization of land records is frequently advanced as a solution; however, when implemented without meaningful community inclusion, it risks excluding local voices and producing inequalities in rigid and legally entrenched forms. This article critically examines whether contemporary digitization initiatives adequately address the structural challenges embedded within land administration systems, while also proposing a governance framework that addresses the institutional disconnect between policy design and implementation through decentralization, and co-governance. Drawing on qualitative research from two sites in Western India – Talasari and Chiplun – the study combines Focus Group Discussions (FGDs), field-based Key Informant Interviews (KIIs), and institutional process-mapping conducted between December 2024 and October 2025. The findings show that digitization without community-engaged implementation processes often produces inaccuracies and governance gaps, intensifying fragmentation rather than resolving it, and underscore the need for decentralized, hybrid frameworks that integrate statutory and customary systems through co-governance and community participation.

Abstract:

Objective: To compare the efficacy, safety, weight reduction and treatment adherence of oral versus subcutaneous semaglutide in adults with uncontrolled T2DM and obesity. Methods: A multicenter retrospective cohort study was conducted between January 2023 and January 2024. Adult patients (≥18 years) with T2DM (HbA1c ≥ 7%) and obesity (BMI ≥ 30) who received either oral or subcutaneous semaglutide were included. Demographic, clinical, and biochemical variables including body weight, BMI, HbA1c, side effects, and adherence were extracted from electronic medical records. Adverse effects were categorized by severity. Comparative analyses between groups used Chi-square and Mann Whitney U tests, with p<0.05 considered statistically significant. Results: A total of 208 patients were included: 89 on oral semaglutide and 119 on subcutaneous semaglutide. Baseline demographics, including gender, age, and physical activity, were comparable between groups (all p>0.05). The severity of adverse effects predominantly gastrointestinal symptoms such as nausea, vomiting, constipation, and diarrhea did not differ significantly between groups (p=0.994). However, dizziness was significantly more frequent in the subcutaneous group (p = 0.04). Adherence was markedly higher with oral semaglutide (p<0.05), with cost identified as the primary barrier among oral users, while subcutaneous users more frequently cited side effects, forgetfulness, and limited weight loss. Weight reduction was comparable at 3 months (p=0.23), but significantly greater with oral semaglutide at 6, 9, and 12 months (all p<0.01). Conversely, HbA1c reduction favored subcutaneous semaglutide at 3 and 6 months (p=0.03 and 0.02), although baseline glycemic control was similar. Conclusions: This study demonstrates that while subcutaneous semaglutide may provide a faster early HbA1c decline, oral semaglutide offers superior long-term weight reduction and significantly better adherence, likely attributable to easier administration. Both formulations exhibited comparable safety profiles.

Abstract: This paper argues that evaluating AI–IoT climate adaptation in water systems cannot rely solely on performance metrics; it requires legitimacy stress-testing grounded in contextual validity and incident-based assessment. While artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies are transforming water management—enhancing forecasting, monitoring, and decision-making for floods, droughts, and agricultural use—current evaluations remain largely model-centric, prioritising predictive accuracy over real-world viability. As a result, even technically robust systems can fail in practice, manifesting as missed events, false-alarm fatigue, delayed escalation, exclusion of vulnerable groups, and weak accountability—especially under climate variability and institutional constraints. The paper introduces a Legitimacy Stress-Test as a structured protocol for evaluating AI–IoT water systems as socio-technical infrastructures. Anchored in the Contextual Research Validity Index (CRVI), the framework comprises eight dimensions: data reliability, sensor performance, institutional readiness, governance of decision rights, equity, contestability, redress, and auditability. It links weaknesses across these dimensions to specific incident pathways, enabling proactive identification of governance risks and mitigation priorities. An illustrative flood early-warning case shows how strong predictive performance can fail to deliver resilience when contextual and governance conditions are misaligned. The proposed stress-test complements, rather than replaces, hydrological validation by clarifying when and why model performance breaks down. It offers a practical evaluation tool for agencies, donors, and regulators scaling AI–IoT climate adaptation systems.

Abstract: The trajectories of complex biological systems are commonly inferred from long-term observations of recovery or deviation after perturbation. We suggest that early-time state-space geometry could contain information enough to anticipate system trajectories before recovery. This hypothesis is informed by extensions of the quantum adiabatic theorem suggesting that under fast, nonadiabatic perturbations, a system prepared in its ground state within the same phase retains the largest overlap with the post-perturbation ground state. Translating to biological systems, we consider cellular functional identity as a stable attractor in a high-dimensional state space where abrupt perturbations like brief inflammatory pulses do not induce regime transitions. Our simulations suggest that post-perturbation states distribution is biased toward the original attractor, reflecting persistence of structural alignment rather than uniform exploration of accessible configurations. Early-time overlap with the baseline attractor, attractor dominance and state-space entropy could stand for operational metrics for inferring system fate. Higher initial overlap should correspond to increased return probability and reduced dispersion, whereas reduced overlap may indicate proximity to regime boundaries. We predict that system fate can be inferred from initial post-perturbation configurations without requiring long-term observation. Potential applications of our framework include fast assessment of cellular resilience, early identification of instability preceding disease transitions and optimization of intervention strategies based on early system responses.

Abstract: Background/Objectives: To evaluate whether no suction management to chest drains after pulmonary resection shortens the duration of chest tube placement and improves postoperative outcomes compared to routine application of negative-pressure suction to chest drains. Methods: A single-center randomized controlled study was conducted in patients undergoing lung resection. Patients received a single 28Fr chest tube attached to a digital drainage system. Patients were randomized to Control (chest tube on continuous suction) or No Suction. Results: From December 2022 to April 2025, 309 patients were enrolled; 23 patients were excluded for protocol deviations. 286 patients were analyzed (149 Control, 137 No Suction). Chest tube duration was shorter in the No Suction (mean 40.2 ± 43.8 hours) than the Control group (53.6 ± 67.8 hours, p=0.002). Hospital length of stay and the incidence of prolonged air leak did not differ between No Suction and Control. In multivariable regression, suction was associated with a 13-hour longer time to drain removal (95% confidence interval 0.03 to 27 hours; p=0.050), without a significant effect on length of stay or odds of prolonged air leak. Patients with underlying lung disease or undergoing anatomical resection had overall longer chest tube durations. Conclusions: Application of suction to chest tubes after lung resection prolongs the duration of chest drainage without improving clinical outcomes. Managing chest tubes with physiologic intrapleural pressure and no suction may allow for earlier removal of drains and should be considered as the approach in uncomplicated lung resections.

Abstract: We describe the evolution of DEMOCRITUS, a system for inferring causality from language. Extracting causal claims from natural language is unstable under paraphrase granularity shifts, and context drift. A document collection may express the same causal statement in many surface forms, while neighboring studies may agree locally yet fail to glue globally because relation families or polarities change across regimes. This paper studies that problem through successive versions of DEMOCRITUS, an implemented system for compiling documents into local causal models, causal databases, and interactive diagnostic artifacts. Our central claim is that categorical homotopy offers a useful computational language for finding equivalence classes of paraphrastic causal statements while avoiding collapsing genuinely distinct claims. We formalize weak equivalence between causal mentions via a normalization functor, motivate localization into homotopy classes of extracted claims, and connect missing higher-order coherence to failures of causal gluing. We then describe how these ideas are realized in the current DEMOCRITUS that uses an AGI chatbot named CLIFF (Consciousness Layer Interface to Functor Flow) pipeline through homotopy-localized claim classes, regime-gluing diagnostics, topic partitions, archived experimental artifacts, topos-style study collation via soft pullbacks and pushout merges, and an underlying categorical learning stack based on Diagrammatic Backpropagation, Geometric Transformers, and Kan Extension Transformers. Finally, we report focused case studies, including Mediterranean diet, red-wine cardiovascular studies, and rising-ocean-temperature corpora, showing that homotopy localization reduces paraphrase inflation while preserving diagnostically important regime-sensitive and obstructed claims.

Abstract: Nursery producers and tree giveaway hosts must do their best to anticipate demand for the wide range of species and traits available. When trying to gauge customer response to various product design choices, companies often employ conjoint analysis to determine what features garner the most customer interest. For this study, we used the method to assess various tree attributes ranging from mature size to hurricane resistance. Our findings indicate that large nursery trees significantly deter consumer interest, though it remains unclear whether this is due to their cost or physical bulk. Similarly, consumers preferred trees that grew to small- and medium-stature at maturity over large-stature trees. Trees labeled as Florida-Friendly, native, or hurricane-resistant had a strong positive effect on purchasing interest among Florida residents.

Abstract: The Automatic Identification System (AIS) generates massive volumes of real-world ship trajectory data, providing a critical foundation for maritime ship type classification. However, existing methods often struggle to simultaneously capture long-range temporal dependencies, maintain computational efficiency, and ensure model interpretability, which makes accurate multi-class classification challenging in real-world maritime environments. To address these limitations, this study proposes a robust and efficient hybrid framework. The proposed architecture integrates a Feature Transformer module for deep temporal feature extraction with a LightGBM model for efficient ensemble classification. Specifically, the multi-head self-attention mechanism within the Feature Transformer captures long-range dependencies in preprocessed AIS sequences to generate compact trajectory fingerprints. These deep temporal representations are then concatenated with carefully designed statistical and kinematic tabular features and fed into the LightGBM classifier for final ship type identification. To validate the proposed framework, we construct a comprehensive real-world AIS dataset consisting of 2,196 trajectories collected between 2019 and 2023, encompassing diverse ship types that reflect authentic maritime scenarios. Experimental results show that the proposed method achieves 82.42% overall accuracy and 77.35% Macro-F1, significantly outperforming comparative baseline models, including LSTM (64.85% accuracy), GRU (64.85%), vanilla Transformer (61.21%), and standalone LightGBM (59.09%). Furthermore, the hybrid model offers ultra-fast inference (1.58 ms per batch) and enhanced interpretability through SHAP-based analysis, making it highly suitable for near real-time maritime traffic monitoring and decision-support applications.

Abstract: This study addresses the challenge of transdermal delivery of cyanocobalamin (vitamin B12), a hydrophilic macromolecule with low permeability, by developing biodegradable polymeric microneedle (MN) patches. Conventional methods often suffer from poor bioavailability, but microneedle technology can bypass the stratum corneum barrier, thereby improving drug delivery efficiency. We fabricated MN patches using hydroxypropyl methylcellulose (HPMC K4M), polyvinylpyrrolidone (PVP K30), and polyethylene glycol (PG 4000) through a mold-casting technique, followed by characterization of drug content, release kinetics, and mechanical properties. The optimized formulation (M18) demonstrated high drug content (95.2%) and sustained release (96.4% at 24 hours), while FTIR confirmed no drug-polymer interactions, ensuring stability. Moreover, SEM revealed uniform needle dimensions (867.25 ± 7.35 µm in height), and texture analyzer tests validated robust mechanical integrity. The patches exhibited low moisture content (3.42%) and high folding durability (&gt;200 folds), indicating suitability for storage and application. These results highlight the potential of polymeric MN patches as a non-invasive, efficient alternative for transdermal delivery of hydrophilic macromolecules. The study contributes to the field by providing a scalable, stable, and high-performance delivery system, which could significantly impact treatments for vitamin B12 deficiency and similar therapeutic needs.

Abstract: Load testing is a critical component of performance engineering, but traditional script-based methodologies often fail to accurately represent the dynamic, stochastic behavior of real users in modern distributed systems. As web applications grow in complexity, linear testing sequences leave critical execution paths untested, obscuring concurrency bottlenecks. This paper proposes a hybrid conceptual framework that integrates probabilistic navigation graphs with Markov transition models to simulate realistic, chaotic user behavior. The proposed model represents application workflows as directed graphs, employing Markov chains to dictate virtual user navigation across system states based on probabilistic weights. By shifting from deterministic scripting to stochastic workload generation, the framework theoretically increases state space coverage and path diversity while providing a more flexible representation of user navigation behavior. We detail the multi-layered system architecture, formalize the mathematical foundation of the traversal engine, and introduce rigorous analytical metrics including transition entropy and state coverage probability. Ultimately, this framework introduces a probabilistic graph traversal approach that enables the stochastic exploration of application state spaces and emergent concurrency behavior.

Abstract: The Dirac equation predicts a gyromagnetic ratio g_D = 2 for charged _spin-1/2 particles and g_D = 0 for neutral ones. The neutron — electrically neutral yet possessing a large magnetic moment with g_N =2−5.82608552(90) —this presents a fundamental challenge to any unified g-factor theory. The standard explanation invokes the neutron’s internal quark structure; in the present framework, which seeks a description in terms of the modified Dirac equation of Papers (I) and (II), an alternative must be found. We extend the framework of Papers (I) and (II) to electrically neutral particles by introducing an effective charge q^eff_N = κ_Hd^N_EDM associated with the neutron’s internal electric dipole moment. This allows the neutron to couple to the ambient magnetic vector potential in analogy with charged particles. Wethen revisit Rutherford’s historical proton–electron composite model of the neutron, resolving its fatal spin objection by extending to a three-body system. We propose that the neutron may very well be aquantumsuperposition of three states: an excited electron (the tauon τ⁻), a de-excited proton (p⁺₁), and an associated neutrino (ν̅ ). Solving the normalization, mass, and magnetic anomaly equations yields probability coefficients P1 ≃ 0.53, P2 ≃ 0, P3 ≃ 0.47, implying that the proton contributes negligibly to the neutron’s bulk properties while the tauon and neutrino dominate nearly equally. Westress that this model is highly speculative and rests on several unverified assumptions, detailed in §(8). Most critically, the system of equations is underdetermined: the neutrino’s effective magnetic anomaly ∆g₃ ≃ −12.37 is not predicted but fitted to reproduce the observed neutron moment. This value exceeds Standard Model expectations by approximately ten orders of magnitude and requires a physical explanation that the present framework does not yet provide. The model should therefore be understood as an exploratory proposal motivating future theoretical and experimental work, not as an established result.

Abstract: Autonomous AI agents operating in high-stakes domains—financial trading, medical diagnostics, autonomous code execution—lack formal safety guarantees for their core operational loops, including memory management, tool invocations, and human interactions. Current verification approaches either fail to scale to neural components or ignore the structured control flow of agentic systems entirely. We introduce AgentVerify (Compositional Formal Verification of AI Agent Safety Properties via LTL Model Checking), a model checking framework that specifies and verifies safety properties for agent architectures using temporal logic. AgentVerify defines compositional specifications for memory integrity, tool call pro tocols, MCP/skill invocations, and human-in-the-loop boundaries, enabling rigorous runtime monitoring and post-hoc behavioral analysis. In an empirical evaluation across 15 diverse agent scenarios (low- and high-difficulty), our post-hoc behavioral analysis component achieved a verification accuracy of 86.67% (mean over 3 seeds, σ=0.00), outperforming a monolithic contract verification baseline (80.00%) and a runtime monitoring baseline without temporal logic (46.67%). A monolithic neural verifier, which attempts to verify the LLM outputs directly, performed poorly at 13.33%, confirming that end-to-end neural verification is currently intractable for production-scale agents. These results demonstrate that formal methods applied to the agent’s observable control flow provide a tractable and effective path to safety assurance, complementing rather than replacing neural-centric efforts to align large language models.

Abstract: The relevance of this study stems from the need for a scientifically sound assessment of the environmental risks associated with launch vehicle launches and for ensuring the environmental safety of areas potentially impacted by space activities. Comprehensive environmental monitoring in the impact areas of rocket parts and adjacent populated areas is particularly important, taking into account natural and climatic factors and the spatial heterogeneity of pollution. This study assessed the environmental impacts of the “Soyuz-2.1a” launch with the ““Progress MS-29”” cargo spacecraft in Kazakhstan based on integrated field research and geoinformation analysis. The study covered the launch area, adjacent populated areas, and the impact zone. A before-after control impact (BACI) design with distance stratification and wind pattern considerations was used to identify post-launch changes. Data containing values below the detection and quantification limits were processed using censored observation analysis methods (ROS Regression on Order Statistics and Kaplan-Meier). A spatial analysis of pollutant distribution was conducted, with thermal field and contour maps generated, revealing the anisotropy of the risk field and localized areas of increased environmental stress. An integrated environmental risk (HQ) metric was used to compare the state of atmospheric air, water, and soil, providing a unified approach to interpreting the results. It was established that the post-launch impact is localized and time-limited, with the greatest sensitivity observed in the soil component in the first period after launch. Measures are recommended to temporarily restrict access to areas of increased stress, conduct primary reclamation, and organize staged environmental monitoring using WebGIS technologies to support management decisions. The scientific novelty of the work lies in the development of an anisotropic model for assessing environmental risk taking into account wind rose and in the integration of methods for analyzing censored data into a unified system for monitoring environmental components.

Abstract: Consensus sequences at sites such as exon-intron boundaries or branch points are succinctly displayed with sequence logos. Implicit in this representation is a presumption of independence of nucleic acids at distinct sites; consequently sequence logos fail to elicit features such as correlations between neighboring sites or sub-sequences which may be crucial for hypothesis testing, especially in searching for principles underlying the nature of consensus sequencing. We introduce a graphical approach to display such secondary features. Probability distribution functions (PDFs) on these point-sets are used to highlight correlations at exon-intron boundaries and at branch points. Differences in PDFs at normal exon-exon boundaries and cancer fusion junctions as well as differential correlations at cancer junctions are evaluated and shown to have similar characteristics. The formation of cancer junctions appears to pass through a more restrictive selection than the creation of normal exon-exon junctions.

Abstract: Angina or objective myocardial ischaemia in the absence of obstructive coronary artery disease, referred to as ANOCA/INOCA, represents a prevalent and clinically significant condition associated with persistent symptoms, impaired quality of life, and increased healthcare utilisation. Contemporary evidence has reframed these syndromes as manifestations of coronary vascular dysfunction, encompassing structural and functional coronary microvascular dysfunction, epicardial vasospasm, microvascular spasm, and mixed phenotypes. In this context, multimodality imaging should not be conceptualised as sequential test accumulation, but rather as a structured, mechanism-based diagnostic strategy aimed at defining the underlying coronary endotype. The 2024 ESC Guidelines for chronic coronary syndromes endorse dedicated diagnostic pathways beyond a stenosis-centred paradigm and support the use of invasive coronary function testing (ICFT) in selected patients with persistent symptoms or inconclusive non-invasive findings. An integrated approach combining anatomical assessment (coronary computed tomography angiography or invasive angiography ± pressure-based indices), quantitative perfusion imaging (positron emission tomography or stress cardiovascular magnetic resonance), and ICFT (including coronary flow reserve, microvascular resistance indices, and acetylcholine provocation testing) enables comprehensive characterisation of coronary physiology and vasomotor function. This review proposes a pragmatic framework linking diagnostic findings to targeted therapy through a test-to-endotype-to-therapy paradigm. We summarise the strengths and limitations of each modality, discuss implementation challenges, and highlight the clinical relevance of endotype-driven management. By shifting from a stenosis-centred to a physiology- and mechanism-based approach, this strategy has the potential to close the longstanding gap between diagnosis and treatment in patients with ischaemia beyond obstructive coronary disease.

Abstract: Traffic sign detection in autonomous driving faces challenges including multi-scale objects, complex backgrounds, and limited edge-computing power. To address insufficient multi-scale feature representation and high false negatives for small traffic signs in YOLOv8n, this study proposes an improved algorithm integrating the VoVGSCSP module with a Multi-scale Contextual Attention (MCA) mechanism. The original C2f module is replaced with VoVGSCSP, enhancing feature representation through parallel residual branches and cross-stage connections. A lightweight neck, SlimNeck, is designed and combined with MCA, employing multi-branch pooling and dynamic weight fusion to capture geometric features and color semantics. The PAN-FPN path is optimized with cross-level connections and learnable weights for adaptive multi-scale fusion. Experiments on the GTSRB dataset show that the improved model reduces parameters to 2.66 M (an 11.6% decrease) and computational complexity to 7.49 GFLOPs, while mAP@0.5 increases from 94.7% to 96.3% and FPS improves from 82.3 to 90.6. The proposed algorithm achieves comprehensive gains in lightweighting, accuracy, and speed, demonstrating its effectiveness and practical applicability.

Abstract: Prostate-specific membrane antigen (PSMA) targeting radiopharmaceuticals have been successfully used for the diagnosis and therapy of prostate cancer. Most of the PSMA molecules for the diagnosis and treatment are based on the peptidomimetic glutamate-urea-lysine (Glu-CO-Lys) pharmacophore connected to various linker groups. Optimization of the available agents is desirable to improve tumor uptake and reduce uptake in non-target organs. This can be achieved, for instance, via linker modifications and/or multivalent approaches. In this study, we synthesized several new Glu-CO-Lys-based PSMA ligands, each connected to different linkers to explore the role of these linkers on cell binding and tumor targeting potential. Additionally, a bivalent (bis) PSMA ligand, containing two PSMA targeting motifs (Glu-CO-Lys) in the same structure, was synthesized by conventional Fmoc-based solid-phase synthe-sis. DOTA- or Aoa-coupled PSMA conjugates showed high radiolabeling efficiency (≥ 90%) with [68Ga] and [18F] and resulted in the formation of one major radiolabeled product. Also, a high stability of the PSMA conjugates was found in human plasma. The [68Ga/18F]-labeled PSMA ligands exhibited the nanomolar affinity (<95 nM) specific to the PSMA-positive LNCaP tumor cell line. In the PSMA-positive tumor xenograft model, the radiolabeled PSMA ligands exhibited rapid clearance from the blood and excretion primarily via the renal system. Biodistribution and imaging studies revealed high accumulation of bis-PSMA ligand in LNCaP tumor xenografts. These render that bis-PSMA may be a promising ligand for diagnostic imaging of PSMA-positive pros-tate cancer.