Abstract: Human-elephant conflict (HEC) has become a major conservation and socio-economic challenge across Asia, particularly in elephant range countries, due to rising human encroachment and habitat loss. In India, HEC escalation is linked to habitat degradation, agricultural expansion, and linear infrastructure development. In the West Singhbhum district of Jharkhand state, changes in LULC from deforestation, mining, and agricultural encroachment have severely altered elephant habitats. The loss of migratory routes has forced elephants to remain in disturbed areas, intensifying conflict. A three-year field study (2018–2020) across Porahat, Chaibasa, Kolhan, and Saranda Forest Divisions, combined with two decades (2000–2020) of LULC analysis, recorded 157 human deaths, 2837.90 acres of crop damage, 1925 house destructions, 3146 quintals of grain loss, and 35 elephant deaths, including nine poaching cases. Dense vegetation declined from 49.14% (2000) to 28.68% (2020), while sparse vegetation and agricultural land increased by 15.35% and 3.68%, respectively. Water bodies decreased by 0.33%, and barren land increased by 3.70%. Core forests (>500 acres) reduced by 16.86%, with forest perforation increasing by 15.69%. Only 6.7% of the district remains suitable for elephants, mostly in Saranda (44.2%), while NDVI shows 83.54% non-favourable change. Ensuring coexistence demands improved landscape connectivity and targeted conservation strategies, while exclusion zones need site-specific mitigation measures.

Abstract: Background: Population aging poses a growing public health challenge in low- and middle-income countries, including Indonesia. Functional independence is a key determinant of older adults’ quality of life, yet integrated community-based health promotion models addressing this issue remain limited. Objective: This study developed and empirically validated an Integrated 5I Health Promotion Model (Identify, Inspire, Initiate, Integrate, and Impact) to enhance independence and quality of life among pre-older adults and older adults in an urban Indonesian setting. Methods: A community-based cross-sectional survey was conducted among 240 pre-older adults and older adults in Surakarta, Indonesia, using proportional cluster sampling from community activity groups. The Integrated 5I Model was constructed based on the Health Belief Model, the Logic Model, and a pentahelix approach. Data were collected using a structured questionnaire and analyzed using path analysis to examine direct and indirect relationships among internal and external factors, perceptions, participation, independence, and quality of life. Results: The model demonstrated good structural fit and explained a substantial proportion of variance in independence and quality of life. Perception and participation played significant mediating roles between internal and external factors and independence. Increased independence was significantly associated with improved quality of life among older adults. Participation showed the strongest direct effect on physical independence (β = 3.018, p < 0.001), while independence significantly predicted quality of life (β = 0.599, p < 0.001). The model demonstrated excellent fit (CFI = 1.000; RMSEA = 0.000; SRMR = 0.012). Conclusions: The Integrated 5I Health Promotion Model provides a pragmatic and scalable framework for community-based interventions designed to promote independence and quality of life among aging populations in urban low- and middle-income settings. This model has important implications for public health programs and policies targeting healthy and active aging.

Abstract: Aiming to better understand how botanical products affect non-target organisms, the pre-sent work reviews current literature focusing on the toxicity of botanical pesticides to or-ganisms other than targeted pests, in order to trace a panorama on the future of sustaina-ble agricultural models worldwide, considering the importance of ecotoxicological studies in the development of new pesticides, including the botanical kinds, which are commonly recognized as essentially harmless. The article reviews published works gathered from digital databases and highlights modern tendencies in pest management research and the development of novel bioinputs, while discussing the Brazilian current legislature re-garding agricultural innovations and field obstacles. Nanotechnology techniques are dis-cussed as major innovations employed in the pest control field, and their employment in the improvement of botanical pesticides is addressed and explored. In this work we ana-lyze the factors involved in determining the success of botanical products and their im-portance to the implementation of a more sustainable way to manage crops. The results indicate a significant lack of studies focused on effects of botanical products on non-target organisms, and an increase in studies with nanoformulations.

Abstract: Background: Stroke remains a leading cause of mortality and long-term disability globally, necessitating effective primary prevention strategies. While machine learning (ML) models offer superior predictive capabilities compared to traditional linear risk scores, their application in clinical practice is often hindered by the "class imbalance" problem, where the rarity of stroke events leads to biased, low-sensitivity models. Furthermore, the literature currently lacks rigorous head-to-head benchmarking of modern boosting algorithms on moderate-sized clinical datasets. This study aimed to identify the optimal predictive model for stroke by systematically benchmarking seven ensemble algorithms and validating their clinical utility using Decision Curve Analysis (DCA).Methods: We analyzed a retrospective multi-center cohort of 5,110 patients, characterized by a severe class imbalance (4.9% stroke incidence). Feature engineering included the encoding of sociodemographic determinants and clinical biomarkers. We conducted a rigorous 10-fold stratified cross-validation tournament to compare seven classifiers: Linear Discriminant Analysis (LDA), Extra Trees, AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost. Performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC) and Brier Score for calibration. To address clinical safety, decision thresholds were optimized to maximize sensitivity. Clinical utility was assessed using Decision Curve Analysis to quantify net benefit across relevant risk thresholds.Results: The classical Gradient Boosting Classifier emerged as the top-performing model, achieving a mean AUC of 0.842 (95% CI: 0.82–0.86). It statistically outperformed both the linear baseline (LDA, AUC=0.833) and complex modern implementations such as XGBoost (AUC=0.787) and Extra Trees (AUC=0.748). By tuning the decision threshold to 0.01, the champion model achieved a screening Sensitivity of 86.0% and Specificity of 53.6%. SHAP (SHapley Additive exPlanations) analysis identified Age, Average Glucose Level, and BMI as the dominant non-linear predictors. Crucially, Decision Curve Analysis demonstrated that the Gradient Boosting model provided a higher net clinical benefit than "treat-all" or "treat-none" strategies across threshold probabilities of 1% to 40%.Conclusion: Contrary to current trends favoring deep learning or complex boosting implementations, classical Gradient Boosting architectures demonstrated superior generalization on imbalanced tabular clinical data. The developed model combines high discriminatory power with proven clinical utility, supporting its deployment as an automated, high-sensitivity screening tool in primary care settings.

Abstract: his study addresses the challenge of detecting polymorphic and camouflage-enabled cyberat tacks, which rapidly mutate, conceal structural traits, and blend into complex network environ ments. To overcome these , we introduces the Quantum AIO-ChameleonGAN, an advanced gen erative– discriminative framework nesting three complementary components: Quantum Variational Adver- sarial Learning (QVAL), Angle-of-Incidence Optimization (AIO), and Temporal Evolution Tracking (TET). QVAL enhances variational quantum circuits to expand the expressive capacity of feature embeddings, enabling the system to detect subtle spectral and subspace perturbations generated by evolving adversaries. AIO optimizes geometric orientation and incidence sensitivity, allowing the discriminator to capture fine-grained structural distortions common in camouflage driven intrusions. TET introduces temporal regularization to track evolutionary shifts across attack sequences, prevent- ing premature convergence and improving resilience against rapidly shifting threat landscapes.A comprehensive ablation analysis demonstrates that each component contributes uniquely to detec- tion fidelity. Removing QVAL, AIO, and TET results in statistically significant performance degra- dation (paired t-tests and Wilcoxon tests, p < 0.001; Cohen’s d = 0.89–2.10), confirming that quantum expressivity, geometric coherence, and temporal adaptivity independently strengthen ad- versarial robustness. The full QAC-GAN achieves an F1-score of 99.81%, precision of 99.79%, recall of 99.84%, and an anomaly-detection rate of 99.40%, consistently outperforming its classi- cal counterpart (CM-GAN). To further contextualize performance, QAC-GAN was benchmarked against state-of-the-art adversarial attack frameworks, including FGSM and PGD, which represent leading standards for evaluating robustness under perturbations. Whereas FGSM and PGD degrade classifier f idelity to 77–83% across key metrics and frequently induce discriminator collapse, QAC- GAN main tains > 99.7% accuracy, high geometric stability, and strong resilience to gradient-based distortions. This establishes QAC-GAN as a substantially more robust architecture for intrusion and anomaly detection in adversarially manipulated environments.

Abstract: Survival rates for metastatic Ewing sarcoma (EwS) have remained persistently low over recent decades, highlighting the need for more effective chemotherapeutic options. Potential targets may be found within the Neuropeptide Y (NPY) signalling pathway that has been implicated in EwS cell survival. However, confounding factors include hypoxia that modulates NPY signalling, dipeptidyl peptidase-4 (DPP4/CD26) that cleaves NPY and interactions via NPY signalling from infiltrating immune cells. We investigated these interactions in A673 and SK-ES-1 EwS cell lines and THP-1 monocytes to identify therapeutic targets suitable for drug repurposing. Both EwS cell lines secreted NPY into conditioned media and extracellular vesicles. Recombinant NPY enhanced viability of both A673 and SK-ES-1 cells, however the NPY1R antagonist BMS-193885 reduced viability in A673 cells only. Recombinant DPP4 widely promoted EwS viability and, under hypoxic conditions, it increased cell metabolism. The DPP4 inhibitor linagliptin, which is used clinically, consistently suppressed EwS viability with elevated sensitivity under hypoxia, where there was increased cell death of SK-ES-1 cells. Conversely, in THP-1 monocytes NPY suppressed metabolism, BMS-193885 increased live-cell staining and DPP4 induced cell death. These findings suggest that NPY and DPP4 enhance EwS survival through autocrine/paracrine signalling while reducing monocyte viability. Thus, targeting NPY/DPP4 signalling may provide therapeutic benefit by directly suppressing EwS growth and enhancing efficacy of immunotherapy.

Abstract: Background: Collagen XIα1, encoded by the COL11A1 gene, is a minor fibrillar collagen that is overexpressed in various human cancers, in which its presence correlates with tumor aggressiveness and progression. Methods: In this study, we developed two novel mouse monoclonal antibodies (mAbs), Anti-colXIα1 clone 3 and Anti-colXIα1 clone 9, that target the putative C-telopeptide of human collagen XIα1. The antibodies were raised to the RRHTEGMQA sequence, a unique nine-amino acid stretch within the putative C-telopeptide of human collagen XIα1. Results: Corresponding to nearly identical V(D)J gene segments and complementarity-determining regions (CDRs), the antibodies specifically bound the RRHTEGMQA epitope in ELISAs but did not react with the C-propeptide. This specificity was further confirmed with the purified Anti-colXIα1 clone 9 mAb, which demonstrated strong reactivity to recombinant proteins containing the RRHTEGMQA sequence in both ELISAs and Western blot assays. This sequence seems to behave as a linear B-cell neoepitope, in which the RRHT motif is crucial for epitope recognition. Otherwise, no immunodetections were observed either in cultures and lysates from the COL11A1-highly expressing A204 human cell line or on tissue sections from specimens of human pancreatic ductal adenocarcinoma (PDAC), with strong desmoplastic reactions, Conclusions: Lacking a precise knowledge of the characteristics of the putative C-telopeptide of human collagen XIα1, these antibodies could enhance our understanding of the processing of human procollagen XIα1 and contribute to a better characterization of the tumor microenvironment of COL11A1-expressing cancers.

Abstract: This paper extends Julian Barbour’s relational formulation of General Relativity—wherein gravity arises from evolving 3-dimensional conformal geometries—by identifying the "hitherto unrecognized fundamental symmetry principles" of the York degrees of freedom with the aperiodic order of the Einstein Monotile (Ξ). We propose that the growth of Shape Complexity from the Janus Point is not merely a gravitational phenomenon but a fundamental aperiodic topological tiling transition. By mapping the configuration space of N-body systems onto Combinatorial Complexes, we demonstrate that the "Rigid Gauge" provided by aperiodic fixity ensures the global nilpotency of the BRST operator (Q²=0), thereby resolving the Gribov ambiguities inherent in periodic manifolds. We further show that the "Creative Core" of gravity acts as a topological low-pass filter, "lifting" the central charge of the vacuum from a dissipative early state (c≈-0.1) to a stable state (c=1) via the constructive gain of arithmetic murmurations. This provides an algebraic origin for inertial mass as the work required to shift monotile boundaries against the vacuum’s topological tension, ultimately deriving the "Arrow of Time" from the intrinsic optimization of arithmetic coherence.By mapping holographic "bit threads" onto this discrete structure, we demonstrate that the Markov gap identified by Hayden (2021) is minimized when the causal set joint terms —specifically the cothθ contributions Dowker (2025)—align with the topological zero-modes of the bulk TQFT. In this framework, the Standard Model Lagrangian emerges as the effective action of gapless excitations localized at the hinges and corners of the aperiodic vacuum, providing a purely geometric origin for mass and gauge symmetry.

Abstract: The proliferation of AI in collaborative environments underscores the need for systems that intuitively adapt to human personalities and cognitive processes, all while upholding stringent privacy and security standards against emerging quantum threats. This paper proposes an innovative framework that synergizes neuro-symbolic federated learning with quantum-safe cognitive twins to realize personality-aware human-AI collaboration. At its core, neuro-symbolic architectures merge the inductive power of deep neural networks excelling in multimodal feature extraction from text, speech, and biometrics with symbolic reasoning engines that enforce interpretable rules for personality traits, such as the Big Five model (openness, conscientiousness, extraversion, agreeableness, neuroticism).Federated learning facilitates decentralized training across heterogeneous edge devices, aggregating local updates without raw data exchange, thus mitigating privacy risks inherent in centralized paradigms. A key innovation is our weighted aggregation scheme tailored to personality divergence where captures client-specific cognitive profiles. Complementing this, quantum-safe cognitive twin’s virtual replicas of user mental states leverage lattice-based post-quantum cryptography (Kyber-1024) for secure bidirectional synchronization, enabling predictive simulations resilient to harvest-now-decrypt-later attacks.Rigorous evaluations on a diverse dataset comprising 500 participants' interaction logs, personality assessments, and cognitive benchmarks reveal superior performance: 24.7% improvement in interaction success rate, 18% reduction in edge latency, and zero information leakage under quantum simulations versus baselines (FedAvg, non-symbolic twins). Ablation analyses validate each component's contribution, while scalability tests on Raspberry Pi clusters affirm deployability. This framework paves the way for empathetic, secure AI in domains like swarm robotics, predictive maintenance, and federated cyber-physical systems, bridging the human-AI cognitive divide.

Abstract: Extreme solar eruptions convert stored magnetic energy at the solar surface and in the 2 solar atmosphere into fast electromagnetic transients, particle acceleration, and coronal 3 mass ejections (CMEs) capable of coupling into planetary magnetospheres. Using the 4 1859 Carrington Event as a historically documented benchmark, this article traces the 5 energy pathway from magnetic breakdown and reconnection in a high-conductivity plasma 6 environment, through CME propagation in interplanetary space, to interaction with Earth’s 7 magnetic field and the generation of large-scale geomagnetically induced electric fields. 8 These fields drive quasi-DC currents in long conductors, including power-transmission 9 lines and communication wiring, where voltage stress, insulation failure, arcing, and 10 fire hazards can arise. The analysis integrates established space-weather and power- 11 engineering literature with an electromagnetic compatibility (EMC) framework to clarify 12 how conductor geometry, grounding topology, and network scale govern vulnerability, and 13 why modern protections mitigate but do not eliminate risk. Within Photony Theory, solar 14 eruptions are interpreted as magnetic-chain breakdown events in a conductive plasma, 15 while terrestrial damage is framed as electric-chain (voltage) breakdown favored in low- 16 conductivity materials, providing a unified physical interpretation of the magnetic origins 17 and electrical failure mechanisms underlying both historical and modern Carrington-class 18 events.

Abstract: Alena Tensor is a recently discovered class of energy-momentum tensors that proposes a general equivalence of the curved path and geodesic for analyzed spacetimes which allows the analysis of physical systems in curvilinear (GR), classical and quantum descriptions. This paper demonstrates that extending the existing dust description to a form that provides a full matter energy-momentum tensor in GR, naturally leads to the development of a halo effect for continuum media. This result provides a good approximation of the galaxy rotation curve for approximately 100 analyzed objects from the SPARC catalog and allows for further adjustments dependent on anisotropy and energy flux. The same equations in flat spacetime allow for the inclusion of rotation-related effects in the quantum description, model quantum vortices and reproduce Mashhoon effect. This provides a physical interpretation of mass generation as an emergent property of the phase-spin equilibrium and enables an effective reconstruction of the Yukawa and Higgs-like mechanisms as consequence of the stability conditions of quantum vortices.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: 2D elastostatic displacement solutions for the Yoffe Mode I and Rice Mode II crack models are reviewed. These solutions are used to introduce the elastostatic displacement solution for a 2D Mode I/II multi-crack configuration in terms of meromorphic differential forms on a hyperelliptic curve. The complex dimension of the vector space of mermorphic forms is demonstrated to be g+1, where g is the genus of the hyperelliptic curve, using the Riemann-Roch theorem. Limitations of the 2D multi-crack model to modeling 3D fracture networks are identified, and a mathematical description of 3D fracture network dynamics preceding an earthquake based on singular spectrum analysis of crack phase fields is conjectured.

Abstract: Paradoxical insomnia is characterized by a discrepancy between subjective sleep com-plaints and objectively preserved sleep, yet its autonomic mechanisms remain poorly un-derstood. This study examined stage-specific autonomic characteristics of paradoxical insomnia using heart rate variability (HRV)–based statistical, multivariate, and machine learning analyses in a large population-based cohort. HRV features were extracted from non-overlapping five-minute windows across non–rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wake after sleep onset (WASO), and compared among paradoxical insomnia, objective insomnia, and normal sleep groups. Whole-night and consolidated sleep–stage HRV features showed substantial overlap among groups. In contrast, consistent stage-dependent differences emerged selectively during WASO, dur-ing which paradoxical insomnia exhibited distinct autonomic patterns relative to both comparison groups. Multivariate analysis showed the greatest group displacement dur-ing WASO, with UMAP centroid distances exceeding those observed during NREM and REM sleep. Supervised models trained on WASO-specific features achieved the highest classification performance, yielding an accuracy of 0.61 and an F1-score of 0.69 for para-doxical insomnia versus normal sleep, although overall discriminability remained mod-est. These findings indicate that autonomic alterations in paradoxical insomnia are pref-erentially expressed during post–sleep-onset wakefulness. Stage-resolved analysis identi-fies WASO as a physiologically informative window for objective phenotyping and for characterizing heterogeneity in insomnia subtypes.

Abstract: This study examines how firms restore moral legitimacy after ethical disruption within interdependent and competitive networks. Existing research on trust repair emphasizes competence and reliability, yet the behavioral processes that rebuild ethical integrity remain underexplored. Conceptual analysis and semiconductor evidence support a multi-level framework that defines ethical trust repair as moral resilience. The model identifies three behavioral mechanisms: relational repair through moral dialogue and empathy, institutional reinforcement through accountability and transparent governance, and systemic renewal through shared moral norms and collective learning. Together, these mechanisms illustrate how organizations transform moral failure into behavioral adaptation and sustained cooperation. Ethical resilience emerges as a proactive capability that integrates moral reasoning with organizational learning and decision processes. By linking moral cognition with responsible innovation, this research extends behavioral ethics theory and offers a foundation for examining how moral recovery sustains long-term organizational legitimacy and ecosystem stability.

Abstract: To ensure economic stability, accurately forecasting the effects of domestic and external factors has become increasingly critical. This study aims to develop a novel model to predict Mongolia’s macroeconomic dynamics by integrating theoretical economic relationships with deep learning methods. Quarterly macroeconomic data from 2015 to 2024 are employed, focusing on key indicators such as inflation, unemployment, GDP growth, and the policy interest rate. The interdependence among these variables is dynamically estimated using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. For comparison, traditional ARIMA and VAR models are also applied to assess the predictive performance of deep learning approaches. The results reveal that deep learning models achieve higher accuracy in short- and medium-term forecasts (MAPE ranging from 3.7% to 5.2%) and exhibit greater sensitivity to business cycle fluctuations and policy shifts. Moreover, by incorporating a theory-guided deep learning framework, the model’s interpretability is enhanced, enabling a more realistic representation of the dynamic trade-off between inflation and unemployment. The primary contribution of this research is the development of a theoretically consistent deep learning state-space forecasting model that bridges economic theory and artificial intelligence. The proposed framework provides practical insights for macroeconomic policy analysis, fiscal planning, and monetary decision-making in Mongolia.

Abstract: To support real-time anti-money-laundering (AML) surveillance, this study introduces a communication-efficient federated learning (FL) protocol combining parameter sparsification, quantization, and adaptive client participation. The evaluation uses a dataset representing 28.4 million daily transactions from five commercial institutions. Under a 5-second alert-latency constraint, the proposed method reduced communication volume by 61.1% and update latency by 47.3% compared with standard FL. Detection performance remained stable, with AUC values decreasing only from 0.90 to 0.89 and false-positive rates increasing by 2.0 percentage points at 80% recall. When network congestion occurred, the adaptive mechanism prioritized banks with higher model drift and prevented performance degradation. The system demonstrates the feasibility of deploying FL-based AML models under strict real-time requirements.

Abstract: Gamified language teaching has expanded rapidly, yet evidence remains uneven because studies often report outcomes without specifying the psychological processes and boundary conditions that generate observable learning behaviors. This 30-year bibliometric review maps research published from 1995 to 2025 using records from the Web of Science Core Collection. CiteSpace was used to examine collaboration pattern, co-occurrence trajectory, and document co-citation clustering with timeline views. Results show a technology-led and highly interdisciplinary field with a persistent core to periphery collaboration structure and thematic fragmentation across education, psychology, linguistics, and computer science. Keyword and co-citation analyses indicate a shift from feasibility-oriented applications toward mechanism focused work that links game design conditions to motivation, engagement, and skill specific language outcomes, with increasing attention to mobile platforms, learning analytics, and immersive modalities. While self-determination theory (SDT) remains the most visible explanatory lens, the intellectual base also recurrently invokes social comparison, achievement goals, self-efficacy and expectancy value beliefs, reinforcement and feedback, habit formation, and achievement emotions. Building on these signals, we synthesize a three-module model that connects technology-mediated design conditions, psychological mechanisms, and learning outcomes, and we provide a mechanism comparison map that translates common game elements into competing testable propositions and behavioral signatures under explicit boundary conditions. The review offers a behaviorally grounded agenda for cumulative theory development and evidence-informed design in language education.

Abstract: The skeletal muscle tissue of dairy cows plays a crucial role in energy metabolism and is the site of β-hydroxybutyrate (BHBA) metabolism. However, the regulatory mechanism of BHBA in muscle differentiation remains unclear. This study used bovine skeletal muscle satellite cells (BMSCs) to investigate the effects of BHBA on the differentiation of bone marrow mesenchymal stem cells and to explore the regulatory roles of LNC297, novel-miR-145, and GAS7 in this process. The results of dual-luciferase reporter gene assays and miRNA pull-down experiments verified the targeting relationship between LNC297, novel-miR-145, and GAS7. BHBA inhibits the differentiation of BMSCs in a dose-dependent manner. Both LNC297 and GAS7 promote the differentiation of BMSCs and attenuate the inhibitory effect of BHBA. In contrast, novel-miR-145 inhibits BMSCs differentiation and enhances the inhibitory effect of BHBA. Mechanistically, LNC297 acts as a competing endogenous RNA (ceRNA) or molecular sponge for novel-miR-145, thereby upregulating GAS7 expression and promoting its differentiation function. Impaired muscle development in ketosis dairy cows is associated with the accumulation of high concentrations of BHBA in muscle tissue. LNC297 may promote muscle differentiation through the novel-miR-145/GAS7 axis, thereby alleviating muscle damage in ketosis cows. These findings provide new insights into the mechanisms of muscle development disorders in ketosis dairy cows.

Abstract: This paper explores innovation and accountability in spiritual and pastoral care for frontline personnel facing chronic stress, trauma, and moral injury. Police and emergency service psychologists and chaplains operate within stressful and morally charged environments where trauma, psychosocial safety and recovery are constant challenges. Amid such pressures, there is a vital need for credible, evidence-informed, yet deeply human psycho-spiritual frameworks that protect confidentiality while promoting care and wellbeing. Using a Critical Interpretive Synthesis enriched by heuristic and bricolage perspectives, this study integrates recent research across psycho-spirituality, positive psychology, and occupational health. It demonstrates how pastoral carers—particularly chaplains—co-lead moral repair, meaning-making, and value realignment within a biopsychosocial-spiritual (BPSS) framework. From this synthesis emerges a new psycho-spiritual self-care model anchored in humility, self-compassion, and meaningful detachment as virtues that buffer burnout, reduce harsh self-talk, and foster relational safety. Key innovations include early, embedded pastoral interventions; clear referral pathways with clinical partners; and virtue-based micro-skills that complement psychology and medicine while maintaining the integrity of spiritual presence, ritual, and trust. The paper also addresses the enduring tension between institutional demands for measurable outcomes and the ineffable nature of pastoral impact. It proposes blended evaluation indicators such as moral-injury scales (MIOS/MISS-M), spiritual wellbeing tools (FACIT-Sp-12), alliance markers, and organizational climate measures, interpreted heuristically to safeguard authenticity and confidentiality.By reframing pastoral care and chaplaincy as both evidence-informed and spiritually grounded, this paper offers a transformative model for psycho-spiritual care that renews moral resilience and meaning in high-risk professions. Finally, future research possibilities and limitations are also discussed.