Abstract: Underground mining environments are complex and hazardous operations where emergencies continue to happen. Post-incident investigations consistently identify training gaps in human related factors such as situational awareness and decision-making under stress. Conventional mine emergency training largely relies on instruction-based approaches which provide insufficient exposure to the cognitive and behavioral demands of real underground emergency situations. There has been an identified need to train miners for knowledge, skills, abilities, and other characteristics (KSAOs). This study proposes an adaptive immersive training framework (AITF) for miner self-escape readiness integrating immersive technology, situational awareness theory, KSAOs, and cognitive task analysis (CTA). The AITF aligns NIOSH-identified self-escape competencies with immersive training scenarios designed to assess and develop cognitive readiness and decision-making. CTA of historical mine accidents is introduced as a foundational design method for translating accident investigation findings into simulation scenarios and performance metrics. CTA of 2006 Darby Mine No. 1 explosion is presented as a proof of concept. The proposed framework supports individualized assessment, iterative scenario refinement, and data-driven feedback. The AITF advances miner training toward cognitive preparedness during mine emergencies and provides a foundation for future training systems that leverage digital tools, digital twins, and artificial intelligence for the mines of the future.

Abstract: Intimate relationships among contemporary emerging adults frequently manifest as situationships, characterized by emotional closeness in the absence of explicit commitment. Shaped by digital culture and evolving social norms, these relationships reflect heightened uncertainty and psychological tension within modern intimacy. The present study conceptualizes situationship as a multidimensional psychological construct, including commitment ambiguity, avoidance of emotional investment, and anxiety related to relationship uncertainty. Associations with attachment anxiety, trust, and subjective well-being are also investigated.To examine these dynamics, an integrated scale development and validation methodology was employed. The results indicated a stable three-factor structure. Structural equation modeling demonstrated that experiences of situationships were positively associated with attachment anxiety and psychological distress, and negatively associated with trust and well-being. Importantly, attachment anxiety partially mediated the relationship between relational ambiguity and relationship-related well-being.These findings establish relational ambiguity as a measurable psychological construct. The study contributes to positive psychology by enhancing understanding of relationship health and emotional regulation within contemporary intimate contexts. The results suggest that interventions promoting commitment clarity and emotional openness may enhance psychological well-being in emerging forms of intimate relationships.

Abstract: This paper presents a systematic comparison of neural architectures for English-to-Spanish machine translation. We implement and evaluate five model configurations ranging from vanilla LSTM encoder-decoders to Transformer models with pretrained embeddings. Using the OPUS-100 corpus (1M training pairs) and FLORES+ benchmark (2,009 test pairs), we evaluate translation quality using BLEU, chrF, and COMET metrics. Our best Transformer model achieves a BLEU score of 20.26, closing approximately 65% of the performance gap between our strongest LSTM baseline (BLEU 10.66) and the state-of-the-art Helsinki-NLP model (BLEU 26.60). We analyze the impact of architectural choices, data scale, and pretrained embeddings on translation quality, providing insights into the trade-offs between model complexity and performance.

Abstract:

This article examines how technological asymmetries—understood as differences in access to advanced digital tools, AI capabilities and IT infrastructure—shape the financial stability and market performance of enterprises of various sizes. The study integrates comparative analyses of 100 industrial joint-stock companies from multiple countries, including technologically advanced large corporations and innovative SMEs, to assess how disparities in digitization and AI implementation influence financial resilience. Using multivariate regression models and index-based financial metrics such as MC, EV, P/E, PEG, P/S, P/B, EV/R and EV/EBITDA, the research identifies relationships between technological advancement, operational efficiency and risk exposure. The findings indicate that companies with higher levels of digitization and AI adoption demonstrate stronger resistance to market disruptions, more effective risk management and more favorable capital structures than SMEs with limited technological resources. However, restricted access to detailed operational data for smaller firms may affect the precision of comparative assessments. The study concludes that investments in digital competences and international cooperation enhance financial stability and support strategic decision-making, while SMEs play an important complementary role by providing outsourcing services that facilitate AI implementation in larger corporations.

Abstract: Rural defensive heritage sites are highly vulnerable assets that require decision-making under conditions of limited data and high uncertainty, particularly in the context of large-scale infrastructure projects and accelerating environmental processes. This study proposes a modular decision-support model for defining conservation priorities in a transparent, traceable, and data-sensitive manner, based on four selected fortress sites in the Yusufeli district of Artvin, Türkiye. The model employs a risk-based approach to quantify anthropogenic risks (AR) through the combined assessment of impact (I) and probability (P). Topographic and contextual vulnerability (TC) is structured through sub-indicators including visual dominance disruption, access discontinuities, landscape fragmentation, and microclimatic exposure, while material and intervention compatibility (MS) is evaluated as a distinct compatibility–risk component. These three modules are integrated through normalization and weighted aggregation into a single Priority Index (PI). In addition, the study introduces a Data Completeness Index (DCI) to explicitly address heterogeneity and gaps in field data, allowing prioritization outcomes to be interpreted with an associated confidence level. Laser-scanning-based documentation, deterioration mapping, and photographic records support the evidence-based construction of indicators. The proposed framework offers a transferable approach for generating intervention and monitoring priorities for rural defensive heritage under rapid landscape transformation, while explicitly managing data uncertainty rather than obscuring it.

Abstract: Background: There is a growing interest in the effects of coffee consumption on the human health. This study was conducted to identify a causal relationship between the coffee consumption and the risk of metabolic syndrome (MetS). Methods: We analyzed the data of the 5th Korea National Health and Nutrition Examination Survey in 2010 for the current study. Results: The risk of MetS, high triglyceride (TG) and low high-density lipid-cholesterol (HDL-C), was significantly lower in the female subjects with a daily amount of coffee consumption of ≥ 3 cups as compared with those with a daily amount of coffee consumption of < 1 cup. There was a significant dose-response inverse correlation between the amount of coffee consumption and the risk factors of MetS (high TG and low HDL-C) after the adjustment of multiple confounding factors (P=0.015 and 0.011, respectively). There was also a modest dose-response relationship between the amount of coffee consumption and MetS (P=0.056). There was no significant correlation between the amount of coffee consumption and MetS in the male subjects. Conclusions: The coffee consumption might have a beneficial effect in lowering the risk of MetS. The current results suggest that it would be mandatory to consider individuals’ recognition of health impacts of coffee consumption.

Abstract: We study the local infrared content of four-dimensional Palatini gravity in the projective equivalence class, with the observable sector defined by a scalar PT projection. Restricting to a strictly local, curvature-linear two-derivative truncation, we (i) give an explicit and complete basis for all PT-even, projectively admissible bulk scalars in the trace/scalar channel and (ii) define the admissible equivalence relations that preserve the posture, including IR closure of the matter sector and tensorsector locking diagnostics used as auditable admissibility tests. A key structural consequence of full one-form projective invariance, \( \Gamma^\lambda{}_{\mu\nu}\to \Gamma^\lambda{}_{\mu\nu}+\delta^\lambda_{\mu}\xi_\nu \), is the appearance of a projectively invariant residue one-form \( \mathcal{T}_\mu \equiv T_\mu-A_\mu \), where \( A_\mu \) is a compensator transforming as \( A_\mu\to A_\mu+3\xi_\mu \). We then prove a conditional local no-go: within the closed two-derivative operator class and modulo admissible equivalences, there exists no reformulation that removes \( \mathcal{T}_\mu \) from the bulk dynamics in the observable trace/scalar channel while simultaneously (a) preserving IR closure of the minimal matter-coupling posture and (b) preserving the tensor-sector locking diagnostics (in particular luminality). Any attempted bulk removal is necessarily exhausted by a small set of controlled failure modes, including collapse to a trivial residue-free branch, departure from the admissible operator class / IR non-closure, or locking failure. On admissible domains one may restrict to longitudinal representatives, where a scalar \( \epsilon \) parameterizes the physical longitudinal content of \( \mathcal{T}_\mu \); \( \epsilon \) is not a compensator and cannot be eliminated as a pure gauge artifact. We summarize the exclusion logic in a compact diagnostic table and provide a minimal counterexample on standard backgrounds, thereby making the local IR residual (“IR island”) operationally auditable.

Abstract: Wedevelop a fully gauge-invariant analysis of gravitational-wave polarizations in metric f(R) gravity with a particular focus on the modified Starobinsky model f(R) = R +αR² −2Λ, whose constant curvature solution R_d = 4Λ provides a natural de Sitter background for both early- and late-time cosmology. Linearizing the field equations around this background, we derive the Klein–Gordon equation for the curvature perturbation δR and show that the scalar propagating mode acquires a mass m_ψ² = 1/(6α), highlighting how the same scalar degree of freedom governs inflationary dynamics at high curvature and the ropagation of gravitational waves in the current accelerating Universe. Using the scalar–vector–tensor decomposition and a decomposition of the perturbed Ricci tensor, we obtain a set of fully gauge-invariant propagation equations that isolate the contributions of the scalar, vector, and tensor modes in the presence of matter. We find that the tensor sector retains the two transverse–traceless polarizations of General Relativity, while the scalar sector supports a massive breathing/longitudinal mode determined by the massive scalar propagating mode. Through the geodesic deviation equation—computed both in a local Minkowski patch and in fully covariant de Sitter form—we independently recover the same polarization content and identify its tidal signatures. The resulting framework connects the extra scalar polarization to cosmological observables, providing a unified, gauge-invariant link between gravitational-wave phenomenology and the cosmological implications of metric f(R) gravity.

Abstract: The computational analysis of therapeutic communication presents fundamental challenges in multi-label classification, severe class imbalance, and heterogeneous multimodal data integration. We introduce a comprehensive bidirectional framework that addresses patient emotion recognition and provider behavior analysis through advanced data mining techniques. For patient-side emotion recognition, we employ ClinicalBERT fine-tuned on human-annotated CounselChat comprising 1,482 counseling interactions across 25 emotion categories exhibiting class imbalance ratios reaching 60:1. Through frequency-stratified class weighting combined with dynamic per-class threshold optimization, we achieve macro-F1 of 0.74, representing a six-fold improvement over baseline multi-label approaches. Recognizing that patient emotion detection alone provides insufficient analytic utility, we extend our framework to provider-side behavior recognition using real-world psychotherapy sessions. We process 330 YouTube therapy sessions through an automated pipeline incorporating speaker diarization, automatic speech recognition, and temporal segmentation, yielding 14,086 annotated 10-second communication segments. Our provider-side architecture combines DeBERTa-v3-base for contextual text encoding with WavLM-base-plus for self-supervised audio representation learning, integrated through cross-modal attention mechanisms that learn content-dependent prosodic associations. On controlled human-annotated HOPE data comprising 178 sessions with approximately 12,500 utterances, the provider model achieves macro-F1 of 0.91 with Cohen's kappa of 0.87, comparable to inter-rater reliability reported among trained human annotators in psychotherapy process research, outperforming simple concatenation-based fusion by 12 percentage points. On automatically annotated YouTube data, the model achieves macro-F1 of 0.71, demonstrating feasibility of analyzing naturalistic clinical communication at scale while highlighting the performance gap between controlled and real-world scenarios.

Abstract: Background: Boron Neutron Capture Therapy (BNCT) represents a highly selective therapeutic modality for recalcitrant cancers, leveraging the nuclear reaction initiated by thermal neutron capture in Boron-10 (10B) to deliver high-linear energy transfer radiation (α-particles and 7Li ions) directly within tumor cell boundaries. However, the widespread clinical adoption of BNCT is critically hampered by the pharmacological challenge of achieving sufficiently high, tumor-selective intracellular 10B concentrations (20-50 μg of 10B /g tissue). Conventional small-molecule boron carriers often exhibit dose-limiting non-specificity, rapid systemic clearance, and poor cellular uptake kinetics. Methods: To overcome these delivery barriers, we synthesized and characterized a novel dual-modality nanoplatform based on highly biocompatible, functionalized graphene oxide (GO). This platform was structurally optimized through covalent conjugation with high-boron content carborane clusters (dodecacarborane derivatives) to enhance BNCT efficacy. Crucially, the nanocarrier was further decorated with plasmonic gold nanostructures (AuNPs), thereby endowing the system with intrinsic surface-enhanced Raman scattering (SERS) properties, which enabled real-time, high-resolution intracellular tracking and quantification. Results: We evaluated the synthesized GO@Carborane@Au nanoplatforms for their stability, cytotoxicity, and internalization characteristics. Cytotoxicity assays demonstrated excellent biocompatibility against the non-malignant human keratinocyte line (HaCaT), while showing selective toxicity (upon irradiation, if tested) and high cellular uptake efficiency in the aggressive human glioblastoma tumor cell line (T98G). The integrated plasmonic component allowed for the successful, non-destructive monitoring of nanoplatform delivery and accumulation within both HaCaT and T98G cells using SERS microscopy, confirming the potential for pharmacokinetic and biodistribution studies in vivo. Conclusion: This work details the successful synthesis and preliminary in vitro validation of a unique Graphene Oxide-based dual-modality nanoplatform designed to address the critical delivery and monitoring challenges of BNCT. By combining highly efficient carborane delivery with an integrated photonic trace marker, this system establishes a robust paradigm for next-generation theranostic agents, significantly advancing the potential for precision, image-guided BNCT for difficult-to-treat cancers like glioblastoma.

Abstract: This work presents the development, modelling, integration, and validation of a flight control system (FCS) designed to convert a piloted ultra-light aircraft (ULM) into a fully autonomous vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), while maintaining Optionally Piloted Vehicle (OPV) capability. Unlike conventional ULM autopilots focused mainly on stabilization or pilot assistance, the proposed architecture enables full mission-phase autonomy, including take-off, hover, transition, cruise, approach, and landing, and ensures safe coexistence between autonomous and manual control pathways. A high-fidelity simulation framework was developed in MATLAB/Simulink and Simscape, integrating aerodynamic models derived from XFLR5 and VSPAERO, structural and inertia modelling, propulsion and energy-storage dynamics, and the complete cascaded control structure. Hardware-in-the-Loop (HIL) experiments were conducted using a modular test bench featuring a six-degree-of-freedom force–moment balance and an internal-combustion propulsion unit, allowing the injection of realistic vibration signatures into the control loop. Results demonstrate robust tracking of attitude and angular-rate commands under significant perturbations and AHRS measurement noise, indicating the system’s readiness for initial VTOL flight tests and subsequent transition-mode refinement. Overall, the paper details the control architecture, modelling methodology, simulation environment, and preliminary ground-testing efforts supporting advancement toward Technology Readiness Level 6.

Abstract: Background: Occupational use of pyrethroid insecticides indoors remains widespread due to their effectiveness and perceived safety compared with older pesticide classes. However, enclosed workplaces with central heating, ventilation, and air-conditioning (HVAC) systems—especially those with wall-to-wall carpeting and dust-accumulating surfaces—can sustain residue persistence and repeated low-dose exposure far beyond the spray event. No evidence-based or standardized guidelines currently define safe spray-ing frequency, residue decay intervals, or ventilation requirements for such environ-ments, representing a major regulatory and research gap; Objective: This review intro-duces the concept of the occupational indoor pyrethroid exposome—the cumulative exposure environment created by recurrent spraying, residue persistence, and resuspension—and identifies mechanistic links to thyroid, neurological, cardiovascular, hepatic, and immune toxicity; Methods: A structured literature review was conducted across biomedical and environmental databases. Included studies addressed (i) indoor pyrethroid use and resi-due persistence in dust or surfaces; (ii) resuspension and HVAC-mediated redistribution; (iii) human biomonitoring and health outcomes; and (iv) mechanistic data related to en-docrine, hepatic, neurological, cardiovascular, and immune effects. Only peer-reviewed studies were analyzed; Results: Evidence shows that pyrethroid residues persist in indoor dust and textile reservoirs, leading to chronic, low-dose exposure through resuspension and contact transfer. Mechanistic findings indicate that voltage-gated sodium channel modulation, mitochondrial dysfunction, oxidative stress, and inflammatory signaling act as convergent pathways linking chronic exposure to neuroexcitation, endocrine disrup-tion, hepatic enzyme induction, vascular dysfunction, and immune dysregulation. Limited occupational data highlight a consistent association between long-term indoor spraying and elevated biomarkers of hepatic and thyroid stress; Conclusions: The absence of clear occupational safety standards for indoor pyrethroid use constitutes a major gap in pesti-cide regulation and workplace health protection. Integrating residue monitoring, ventila-tion assessment, and mechanistic biomarkers into occupational risk frameworks is essen-tial to guide safe spraying intervals, improve exposure surveillance, and prevent chronic multisystem toxicity among workers in enclosed environments. This framework not only supports the development of evidence-based occupational pesticide safety standards and improved ventilation policies in enclosed workplaces, but also highlights a critical regu-latory gap — the current absence of any evidence-based guidelines defining safe frequen-cy or interval for indoor pyrethroid spraying.

Abstract:

The G14R mutation in α-synuclein is associated with aggressive, early-onset Parkinson’s disease, yet its impact on the protein’s N-terminal regulatory domain remains poorly understood. As an intrinsically disordered protein, α-synuclein’s conformational landscape is highly sensitive to sequence perturbations and ligand interactions. This study investigates a hypothesized "allosteric tug-of-war" between pro-aggregatory zinc ions and inhibitory dopamine at the N-terminus. Using a Python-based physicochemical structural proxy model, we assessed residue-level charge, volume, and interaction heuristics for the first 20 residues of the G14R variant. Our results demonstrate that the substitution of glycine with arginine at residue 14 creates a localized "rigidity hotspot" characterized by enhanced electrostatic coordination with Zn²⁺ ions. Crucially, we found that dopamine competitively attenuates this stabilization at overlapping residues, suggesting a displacement-based mechanism. This modeling framework provides a mechanistic basis for the G14R phenotype, suggesting that dopamine depletion may permit persistent zinc-mediated structural stabilization, thereby promoting aggregation. These findings highlight the N-terminus as a critical switch for modulating α-synuclein pathology through small-molecule competition.

Abstract: Background: Transcatheter aortic valve implantation (TAVI) has transitioned from a therapy for inoperable or extreme-risk patients to a standard option across broader risk categories. How this evolution has reshaped patient selection, procedural practice, and early outcomes within a mature program over extended time remains incompletely described. Methods: We conducted a single-center retrospective observational cohort study including all consecutive patients undergoing TAVI for severe symptomatic aortic stenosis between 2012 and 2024. Patients were stratified into three temporal eras (2012–2015, 2016–2020, 2021–2024). Baseline clinical, imaging, and procedural variables were prospectively recorded. The primary endpoint was 1-month major adverse events (MAE), defined as a composite of all-cause death, stroke, myocardial infarction, major vascular complications, or major bleeding. Comparisons across eras used ANOVA and chi-square or Fisher exact tests, as appropriate; multivariable logistic regression was applied to identify independent predictors of MAE. Results: A total of 1,946 patients were included (n=230, 396, and 1,320 across the three eras). Mean age (~81 years) and sex distribution (~60% women) remained stable, whereas cardiovascular risk factors became more prevalent over time (dyslipidemia 46.4% to 89.9%, hypertension 90.9% to 97.7%, smoking 1.1% to 11.2%; all p&lt;0.001). Functional status improved (NYHA III–IV 78.2% to 18.0%; p&lt;0.001), and EuroSCORE II decreased (5.7±5.1 to 3.2±3.6; p&lt;0.001). Angiographically significant coronary artery disease and bicuspid valves were more frequently treated in the most recent era. Transfemoral access under local anesthesia remained predominant, while fluoroscopy time, contrast volume, and procedural duration significantly decreased (all p≤0.003). Residual aortic regurgitation ≥moderate became rare, with none/trace regurgitation increasing from 57.8% to 91.3% (p&lt;0.001). Hospital stay shortened (7.3±2.8 to 6.1±3.1 days; p&lt;0.001). MAE declined from 17.4% to 7.2% (p&lt;0.001), driven by marked reductions in major bleeding (10.0% to 0.7%; p&lt;0.001) and stroke (2.6% to 0.3%; p=0.002), while 1-month mortality remained low (2.6% to 1.0%; p=0.087). The need for new permanent pacemaker implantation was frequent but stable (~17–19%; p=0.645). Conclusions: Over 12 years, this high-volume TAVI program has progressively shifted towards patients with more conventional cardiovascular risk profiles, lower surgical risk, and more complex coronary and valvular anatomy, while achieving shorter, more efficient procedures and improved early safety. These findings support the robustness of a structured Heart Team approach and underscore the importance of continuous optimization of TAVI pathways in an evolving and increasingly heterogeneous patient population.

Abstract: Information on the breeding of the Pinto’s Spinetail Synallaxis infuscata, an endangered Brazilian Furnariid, is scarce. This study aims to complement it, by looking at nest and clutch parameters, breeding period, and nest success. Once nests were found, they were closely monitored from a hide. Between 1986 and 2018, 33 nests were found in the Pedra Talhada Forest near Quebrangulo. Nests were found year-round, except in middle of the dry season. Nests were of the closed/retort type, weighing 552.1 g, and measuring 37.6 x 28.8 cm, with a side arm of 30.6 x 4.9 cm., on average. The clutch size averaged 2.10 eggs, which measured 22.3 x 17.2 mm and weighed 3.2 g. They were white and had an oval to pointed-oval shape. Mean incubation period was 21.5 days and mean nestling period 14.71 days. The apparent nest success was 27.3%, while Mayfield’s was 21.8%. Predation was the main cause of nest failure, accounting for 81% of cases. The breeding data we collected on S. infuscata falls within the range of observations of a comprehensive analysis on other Synallaxis species. This breeding information is important for conservation, as rates of nest loss are a key factor in evaluating population viability.

Abstract: Indonesia’s long-term climate strategy targets net-zero emissions by 2060; in this context, this paper develops a simulation for the Java–Madura–Bali (Jamali) grid to quantify the joint impact of electric-vehicle (EV) uptake and rooftop photovoltaic (PV) “negative load” on system performance over 2025–2060. Historical statistics and national planning projections are used to calibrate annual capacity, peak load, and energy trajectories, which are then downscaled to a 168-hour representative week and a 365-day year. EV charging demand is generated from an hourly initial-charging-time distribution with state-of-charge–dependent AC/DC profiles, while rooftop PV is modeled using hourly irradiance and performance parameters. A 5×5 policy matrix (EV: BAU, Subsidy, Regulation, Charge-Time Management, Combination; PV: BAU, Subsidy, Regulation, Smart-Home, Combination) is evaluated using a min–max composite index (40% weekly supply-demand balance, 40% annual production–consumption balance, 20% policy cost), where higher values are preferable. Results show that EV3_PV1 (regulated EV growth with BAU PV) achieves the highest average composite score, EV4_PV1 provides the best adequacy, and EV3_PV3 yields the poorest adequacy, while EV3_PV1 and EV1_PV3 define the best and worst long-term production–consumption balance, respectively. EV1_PV1 is the least costly pathway and EV5_PV5 the most expensive, indicating that moderate, regulation-driven and charge-time–oriented strategies outperform highly interventionist EV–PV packages when adequacy, balance, and cost are jointly considered.

Abstract: Against the backdrop of artificial intelligence (AI) and cyber intelligence (CyINT) becoming increasingly embedded within intelligence systems, the core challenge facing intelligence organisations is no longer ‘whether to adopt new technologies’, but rather ‘how to transform technological disruption into governable, measurable, and trainable institutional capabilities’. This paper examines the proceedings of the Intelligence Studies Summit 2025, published by the National Intelligence University (NIU), to propose the Institutional Absorption Discourse Model (IADM). (Institutional Absorption Discourse Model, IADM). Through computational content analysis, semantic embedding, and longitudinal discourse drift detection, it conducts computable modelling on this academic-practical hybrid corpus—a ‘non-news stream, non-policy text’—comprising conference proceedings. Findings reveal: textual discourse follows a distinct phased progression—‘technological disruption → threat framing → governance and accountability → measurability → education and disciplinary institutionalisation’; governance and accountability discourse significantly lags behind technological topics in sequence yet erupts concentratedly as institutional modules; education and effectiveness measurement constitute stabilisers for institutional absorption. This paper's theoretical contribution lies in translating intelligence discourse into a testable chain of institutional mechanisms. Its methodological contribution proposes a quasi-longitudinal modelling paradigm for conference proceedings, providing an operational pathway for auditing AI governance and intelligence research.

Abstract:

Sesame (Sesamum indicum L.) is a nutrient-rich oilseed valued for its high-quality oil and protein-rich seeds. Sesame breeding can be accelerated by unlocking the untapped genetic variation present in African landraces. This study integrated a global meta-quantitative trait loci (QTL) analysis with genome‑wide association study (GWAS) of Ethiopian germplasm to identify molecular markers for two key agronomic traits: plant height and seed coat color. To address inconsistencies among published studies, we explicitly documented the genetic maps, marker systems, mapping populations, linkage mapping and GWAS analysis methods used in each source study before conducting the meta-analysis. Only QTL whose markers could be reliably anchored to the sesame reference genome v3.0 were retained. Meta-analysis of eight published studies identified six conserved QTL hotspots on chromosomes 3, 4, 6, 8, 9, and 11. Field evaluation of 200 Ethiopian accessions over two seasons revealed wide phenotypic variation and high heritability (H² > 0.85). Using 3,633 genome-wide SNPs, GWAS detected 36 significant marker-trait associations, including multiple novel loci on chromosomes 12 and 13 not reported in Asian germplasm-focused studies. Key SNPs explained up to 14.2% (plant height) and 9.2% (seed coat color) of phenotypic variance. Candidate genes linked to significant SNPs included brassinosteroid-related CYP90B1 and ethylene-responsive AP2/ERF for plant height, and transcription factors WRKY23, DOF3.1, and SBP-like for seed coat color. Population structure showed two distinct groups (K = 2), and linkage disequilibrium decayed rapidly (~204 kb), enabling fine‑mapping. The study provides validated meta‑QTL intervals, trait-associated SNPs, and candidate genes that form a molecular foundation for marker-assisted selection in sesame improvement programs.

Abstract: Human intelligence is strongly heritable, but the genes that fine-tune the brain's wiring are still being mapped. We re-examined the large IQ genome-wide association study by Savage et al. (2018; N = 269 867) with three complementary tools—partitioned SNP-heritability, MAGMA gene tests, and a transcriptome-wide association study (TWAS)—to ask whether synaptic pruning makes an independent contribution beyond classic glutamatergic signalling. Seven pre-registered gene sets were contrasted: two glutamate lists, two pruning lists, two negative-control lists (monoamine and housekeeping genes), and a "pruning-only" set that removed every glutamatergic gene.Heritability was significantly enriched in both glutamate and pruning sets, but the pruning-only panel still carried a clear signal (1.10-fold, P ≈ 5 × 10⁻¹⁵), showing that pruning effects are not simply spill-over from glutamate pathways. MAGMA supported this pattern, highlighting SEMA3F, RHOA, MAP1LC3B and TCF4 after Bonferroni correction. TWAS added tissue context: pruning genes showed the strongest over-representation (core set 1.38-fold, P ≈ 2 × 10⁻⁵), with RHOA down-regulated in caudate and SEMA3F up-regulated in anterior cingulate.Together, the results inspire a "Calibrated Pruning Framework." We propose that common variants adjust the timing of a multistep pruning cascade—TCF4 keeps critical periods open a little longer, SEMA3F–RHOA steers branch retraction, MAP1LC3B clears debris, and HLA tags mark synapses for removal—thereby fine-tuning network efficiency and, ultimately, cognitive ability. Limits of the work include the European bias of the base GWAS and reliance on adult-brain expression panels; future longitudinal imaging and multi-ancestry studies will be needed to test the model's predictions.

Abstract: This paper conducts a rigorous comparative analysis of U.S. and Chinese strategic frameworks for AI literacy and adoption, with specialized focus on agentic AI systems capable of autonomous reasoning and execution. We systematically examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent strategic paradigms: the U.S. favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; China pursues centralized, state-led strategies with comprehensive content labeling and rapid systemic integration. We propose a hybrid governance architecture that synthesizes strengths from both models, supported by algorithmic implementations and sensitivity analyses. Drawing from recent publications (2021-2025), we identify critical trends, challenges, and strategic implications. The paper concludes with evidence-based recommendations for policymakers, educators, and industry stakeholders navigating the complex landscape of global AI competition. The paper concludes with actionable recommendations for policymakers, educators, and industry leaders engaged in the global AI race.