Abstract: This study proposes a geometric procedure for robust controller tuning under parametric uncertainty, based on root-contour analysis of the closed-loop control system. For a fixed candidate controller tuning, the set of possible pole locations induced by the admissible variations of the control plant parameters is constructed. Robust admissibility is formulated as a geometric set-inclusion problem, requiring this set to remain inside a prescribed dynamic performance region in the complex s-plane. A distinction is introduced between nominal admissibility, robust stability, and robust admissibility, showing that stability over the entire uncertainty set is not sufficient to guarantee the desired dynamic performance. To quantify the root contours, several indices are defined, including the dispersion along the real and imaginary axes, the maximum pole displacement with respect to the nominal pole locations, and the geometric margin to the boundary of the performance region. The procedure is applied to the selection and verification of PI controller tunings for an uncertain single-input single-output (SISO) control system and is further validated through examples with different structures of parametric uncertainty, including a system with a single uncertain parameter and a PID-controlled system with several uncertain control plant parameters. The results show that root-contour analysis can distinguish tunings that are only robustly stable from tunings that preserve the prescribed dynamic performance over the entire uncertainty set. Thus, the method can be used as a practical tool for the diagnosis, comparison, and selection of controller tunings under parametric uncertainty.

Abstract: This paper provides the first systematic, cross-country empirical comparison of the Recovery and Resilience Facility (RRF) and Cohesion Policy funds (CPF) in the domain of renewable energy deployment. Covering 14 EU Member States, the analysis combines quantitative cross-country evidence on financing volumes, technology mixes, implementation speed, and reported capacity achievements. The findings show that the RRF represents a major amplification of EU renewable energy financing, with planned allocations exceeding Cohesion Policy expenditure by a factor of five to ten. At the same time, claims of superior performance-based delivery require qualification: green transition financial progress lags the general RRF disbursement rate, milestone fulfilment for renewable energy falls short of planned indicative rates in most countries, and reported operational capacity figures raise plausibility concerns. The analysis reveals no meaningful correlation between milestone and target fulfilment and progress with renewable energy Country-Specific Recommendations, suggesting that administrative compliance with milestones does not immediately translate into structural reform outcomes. These findings carry direct implications for the design of the post-2027 EU financial framework, particularly regarding the stabilisation of performance indicators, the introduction of attribution protocols for reform-linked achievements, and the preservation of complementarity between performance-based and non-performance-based approaches.

Abstract: Maritime operations rely on the Automatic Identification System (AIS), an open broadcast protocol whose unauthenticated, self-reported Messages are easily abused. This survey takes an AIS-first, security-focused view, grounded in a comprehensive review of prior AIS-security research. We (i) explain how AIS works and use that to expose fundamental weaknesses; (ii) synthesize from the literature the main threats and their technical and operational impacts; (iii) categorize, from the surveyed works and operational practice, mitigations by the layers they target and, for each mitigation, indicate whether it primarily prevents, detects, responds, or supports recovery; and (iv) provide practical recommendations. Bringing together cybersecurity, maritime operations, and data-science perspectives, we consolidate recommendations for securing AIS-based systems and assess their current use in practice, thus highlighting the gaps that standards and implementations still need to address.

Abstract: This study examines the evolution of research on mergers and acquisitions (M&A) in the banking sector through a bibliometric analysis aimed at identifying the main con-tributors, dominant research themes, and emerging gaps in the literature. The study situates banking M&A research within broader discussions of efficiency, market structure, integration performance, and financial stability, while highlighting the need to better understand how scholarly influence and thematic development shape the field over time. Using bibliometric techniques, the analysis evaluates publication trends, journal con-centration, authorship patterns, international collaboration networks, citation struc-tures, keyword co-occurrence, and thematic mapping. The study synthesizes relation-ships among influential publications, institutions, and countries to assess how ideas circulate and which research themes remain central or underexplored within the liter-ature. The findings reveal a concentrated body of scholarship dominated by a limited number of journals, recurrent author networks, and strong transatlantic collaboration patterns, with relatively limited representation from the Global South. Research themes related to efficiency, firm performance, and market structure occupy the core of the literature, whereas technology integration, sustainability considerations, consumer outcomes, and conduct risk remain comparatively peripheral. Citation patterns are highly une-ven, reflecting a small group of highly influential studies alongside a broader set of context-specific contributions. The analysis also identifies a persistent gap between pre-merger strategic narratives and post-merger integration realities, particularly in relation to operational outcomes and systemic risk considerations. The study concludes that future research would benefit from integrating traditional finance approaches with transaction-level integration measures, governance and op-erational performance indicators, and more robust identification strategies around regulatory and policy shocks. The findings further suggest that banking practitioners should place greater emphasis on integration feasibility, risk-control capabilities, digi-tal transformation readiness, and sustainability considerations when evaluating and implementing merger strategies.

Abstract: Infrared object detection from unmanned aerial vehicles (UAVs) is critically challenged by multi-type composite degradation—including noise, blur, and low contrast—which severely undermines feature discriminability and multi-scale target perception. This study proposes SGW-DETR (Spectral-Guided Graph-structured Wavelet Detection Transformer), a novel framework built upon RT-DETR, incorporating three synergistic modules across the backbone, neck, and encoder. FDSANet (Frequency Domain Spectral Awareness Network) replaces the conventional ResNet backbone, integrating the Multi-Scale Frequency Perception Module (MSFPM), Selective Channel Frequency Decomposition (SCFD), and Dynamic Kernel Spectral Modulation (DKSM) to achieve instance-level adaptive spectral feature extraction without degradation-type supervision. The Graph-Structured Fusion Network (GSFN) combines the Adaptive Semantic Fusion Module (ASFM) with the Graph Structure Perception Module (GSPM), employing Gaussian kernel soft membership and two-stage message passing to explicitly model spatial topological dependencies among object components. The Wavelet-guided Contrast Feature Aggregation module (WCFA) restructures the Attention-based Intra-scale Feature Interaction (AIFI) encoder via a Haar-based Frequency Decomposition Unit (HFDU), decomposing features into foreground-edge and background-thermal components and achieving hierarchical foreground–background decoupling through nested dual-path causal contrastive attention. A UAV infrared degradation dataset comprising 4,686 images spanning six degradation types with component-level annotations was constructed for evaluation. SGW-DETR achieves 75.2% mAP50, outperforming RT-DETR by 3.5%, while simultaneously reducing GFLOPs and parameter count by 16.8% and 9.9% at an inference speed of 85.5 FPS. Sustained performance gains on M3FD and IndraEye benchmarks further demonstrate the framework’s cross-domain generalization capability, offering practical value for UAV-based surveillance, search-and-rescue, and border monitoring under adverse imaging conditions.

Abstract: The Gibbs Paradox (concerning the entropy of mixing and entropic extensivity) was explored in depth by Edwin Jaynes (1992). We take up Jaynes’ treatment, considering the special cases for which entropy is (approximately) extensive, and the general case in which it is not. We also explore the Holographic Principle which (strictly speaking) excludes the extensivity of entropy. The formalism of Quantitative Geometrical Thermodynamics shows that, being isomorphic to energy, it is entropy production (not entropy) that is extensive. As a corollary, Shannon information is also not extensive, although information production is extensive.

Abstract: Supernumerary robotic limbs (SRLs) represent an emerging class of wearable robotic systems designed to augment, rather than replace, human motor capabilities. Unlike prostheses or exoskeletons, SRLs operate as independent kinematic agents that enable users to perform multi-limb tasks, reduce physical workload, and enhance operational efficiency in complex environments. This study presents a systematic review of SRL technologies, focusing on mechanical design configurations, sensing modalities, and control strategies, and their influence on key performance metrics such as payload capacity, positioning accuracy, and human–robot interaction efficiency. A structured literature review methodology was adopted following PRISMA guidelines, covering publications from 2010 to 2025 across major scientific databases. The analysis reveals fundamental trade-offs between degrees of freedom, weight, and payload capacity, where high-dexterity systems often impose increased ergonomic burden. Control strategies have evolved from direct teleoperation toward hybrid and shared-autonomy frameworks integrating vision, bio-signals, and machine learning, although challenges remain in achieving intuitive and low-latency interaction. Application domains span industrial manufacturing, construction, rehabilitation, and assistive daily activities, with growing interest in precision-constrained environments such as healthcare. Despite significant progress, limitations persist in actuator back-drivability, long-term wearability, and robust intention recognition under real-world conditions. This review synthesizes current advancements, identifies critical research gaps, and outlines future directions toward scalable, human-centric SRL systems capable of seamless integration into industrial and clinical workflows.

Abstract: Monitoring dynamic post-catastrophic landscapes necessitates unsupervised classification approaches capable of incorporating newly emerging landscape-cover states without relying on predefined classes. Within this framework, the temporal matching of independently derived spectral clusters presents a critical methodological challenge. This study compared alternative temporal matching approaches for multi-temporal Sentinel-2 imagery of the post-catastrophic floodplain landscape of Khortytsia Island (Ukraine) from 2021 to 2026. In addition to conventional methods based on centroid distance, Mahalanobis distance, Linear Discriminant Analysis, and Random Forest, geometrically oriented approaches employing the elongation and principal-axis orientation of spectral point clouds were evaluated. A series of tests assessed matching accuracy, robustness to seasonal and interannual drift, graph connectivity, and consensus structure among alternative matching solutions. The results demonstrated that geometrically oriented approaches preserved temporal correspondence among landscape-cover states with high stability despite phenological and interannual variability. In particular, axis-based matching more effectively maintained separation between corresponding and competing clusters amid progressive temporal divergence. Consensus analysis revealed that disagreement among methods was concentrated in ecotonal and actively transforming zones, indicating areas of increased landscape instability. This study shows that the geometry of spectral trajectories contains valuable information for temporal matching and provides a promising foundation for monitoring dynamic post-catastrophic landscape systems.

Abstract: Accurate information on atmospheric temperature profiles is crucial for improving numerical weather forecasting and short-term numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which fails to meet the high spatial resolution required for NWP. In this study, we present a method to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) using data from the polar-orbiting satellite FY-3C. The aim is to provide high-resolution atmospheric structure for NWP in the TP. The temperature profile in VASS exhibits temporal and spatial heterogeneity due to the significant impact of clouds on the radiative transfer mode (RTM). To address this, we employ a combination of variation and artificial neural network (Var-ANN) methods to calibrate the satellite product and improve its compatibility with the model. To confirm the feasibility of our method, we compare the calibrated results with the observed data from 121 radiosonde soundings and 2400 meteorological stations in China, both of which represent conditions closest to the real atmospheric states. The calibrated temperature shows improvements over the original temperature, with a root mean square error, bias, and agreement with radiosonde soundings of 2.11, -0.72, and 0.998, respectively. We also select two classical cases involving the eastward movement of the plateau vortex (PV) and the formation of precipitation to verify the applicability of the calibration in NWP. The results demonstrate that the performance of NWP improves after assimilating the calibrated data, with the Var-ANN data assimilation scheme achieving the highest threat score of 66.9 and 66.7 for case 1 and case 2, respectively. These findings suggest that the Var-ANN method is suitable for calibrating satellite temperature profiles, and the calibrated data holds potential for precipitation forecasting. Furthermore, the novel method can also be applied in global temperature profile correction and satellite cross-calibration.

Abstract: The cross-border Lima River Basin, shared between Portugal and Spain, is prone to recurrent meteorological droughts, which are projected to intensify under climate change. This trend underscores the need for robust early-warning systems to support proactive water management. Under the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. The 12-month Standardized Precipitation Index (SPI12) is employed as a long-term drought indicator, computed from hybrid 12-month accumulations that combine observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). These data are integrated into four hybrid configurations (1 to 6 months lead time) to maximise forecast skill while preserving observed drought memory: 11 months of observations plus 1 month of forecast (11 obs + 1 fcst), 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed over the period October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using a suite of regression-based metrics (e.g., Pearson correlation, root mean square error (RMSE), and skill scores) and contingency-table-based metrics (e.g., false alarm rate (FAR) and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; defined as the Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of r=0.97, an RMSE of approximately 0.17, and near-perfect categorical performance (probability of detection (POD) = 1.00; FAR = 0.00). Conversely, longer lead-time configurations (9 obs + 3 fcsts and 6 obs + 6 fcsts) exhibit degraded performance, with the 6 + 6 configuration providing limited added value relative to climatology. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of up to six months in the Lima River Basin when integrated within the SPI12 framework. The proposed blending methodology therefore provides a robust technical basis for the operational early-warning system being developed under the RISC_PLUS project to support transboundary drought risk management in the Minho–Lima region.

Abstract: Continuous gauge symmetries are usually introduced through Lie groups acting on quantum fields. In this paper we show that the algebraic structure associated with non-abelian gauge symmetry already arises naturally inside the complex group algebra of a finite non-abelian group. The dihedral group D₄, the symmetry group of the square, is used as an explicit example. The complex group algebra C[D₄] decomposes into irreducible matrix blocks under the Artin–Wedderburn theorem. While the character table describes only the subspace of class functions, the full group algebra contains additional intra-class directions invisible to the character table. For D₄ these directions form a three-dimensional subspace which, after elementary normalization, satisfies the Pauli algebra and generates continuous SU(2) transformations inside the two-dimensional irreducible block. The construction is carried out explicitly using only the multiplication table of D₄. The continuity of the complex coefficients allows continuous rotations to arise through exponentials of finite group algebra elements, without requiring the underlying symmetry group itself to be continuous. The mechanism generalizes to any finite group possessing higher-dimensional irreducible representations, where the associated matrix blocks naturally support the corresponding su(N) Lie-algebra structures.

Abstract: Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k=5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Abstract: This study proposes a novel measurement system repeatability and reproducibility (R&R) framework for zero-inflated correlated defect-count data in semiconductor wafer automated optical inspection (AOI). In advanced semiconductor manufacturing environments, AOI systems are extensively used to detect wafer defects such as particles, scratches, and structural abnormalities. However, conventional Gauge R&R methods are primarily developed for continuous Gaussian-type measurements and are therefore not fully appropriate for high-yield semiconductor inspection data characterized by discrete defect counts, excessive zero observations, and correlated defect categories. To address these limitations, this study develops a zero-inflated bivariate Poisson (ZIBP) measurement system model capable of simultaneously capturing correlated defect-generation mechanisms and structural zero-defect states. A latent-variable representation is introduced to model shared and category-specific defect sources, while a zero-inflation mechanism accounts for defect-free wafer observations commonly encountered in precision manufacturing. An expectation-maximization (EM) algorithm is further developed for parameter estimation, including latent common defect counts and structural-zero probabilities. Based on the fitted model, repeatability variance, reproducibility variance, total measurement variation, and Percent R&R are estimated under the proposed probabilistic framework. In addition, bootstrap resampling is employed to construct confidence intervals for the proposed R&R measures. Theoretical properties of the proposed framework, including covariance structure, identifiability, EM monotonicity, estimator consistency, and asymptotic behavior of the Percent R&R estimator, are analytically established. The proposed framework extends traditional Gauge R&R analysis from continuous Gaussian measurements to zero-inflated correlated count-type defect inspection data and provides a statistically rigorous methodology for evaluating AOI measurement system reliability in semiconductor wafer manufacturing environments.

Abstract: As IoT and wireless sensor networks (WSNs) increasingly rely on federated intrusion detection, the ability to remove a client’s contribution from a trained model without full retraining has become an important requirement. However, existing federated unlearning methods are not well suited to transformer-based intrusion detection systems, particularly when the unlearning trajectory may be manipulated and multiple removal requests must be processed under severe class imbalance. We present ARFU-IDS, a transformer-oriented and adversary-aware federated unlearning framework. ARFU-IDS combines attention- head attribution, dual-path layer criticality probing, trajectory verification, and conflict- aware scheduling. Specifically, the proposed Attention-Head Attribution Graph localizes removal-sensitive heads in transformer layers, Dual-Path Layer Criticality Probing sepa- rates task-critical layers from adversary-influenced layers, Manipulation-Resistant Iterative Verification with Audit validates whether the unlearning trajectory follows the expected optimization path, and a conflict-graph scheduler supports concurrent client removal while preserving rare-category performance. Experiments on UNSW-NB15, CICIoT2023, and IoTID20 show that ARFU-IDS achieves 87.1% Macro-F1 and 77.6% rare-category recall on UNSW-NB15, reduces the attack success rate to 8.2% at f = 0.1 and 9.7% at f = 0.2, and shortens concurrent unlearning latency by 43.4% compared with sequential FU-IDS. These findings suggest that ARFU-IDS offers a practical framework to robust federated unlearning in transformer-based IDSs for IoT and sensor-network environments.

Abstract: Breast cancer screening patterns exhibit geographic variation across Zip Code Tabulation Areas (ZCTAs) in Florida, yet most spatial analyses rely on frequentist point estimation without formally characterizing uncertainty. This study applied a three-stage analytical framework to ZCTA-level breast cancer screening data in Hillsborough County, Florida (n = 55 ZCTAs): frequentist Poisson regression with stepwise multicollinearity diagnostics, global spatial autocorrelation analysis using Moran’s I with inverse-distance weighting, and Bayesian Poisson and Bayesian negative binomial regression with Jeffreys non-informative priors estimated via the No-U-Turn Sampler in R (brms/Stan). Spatial analysis was conducted in ArcGIS Pro. Racial and ethnic female population counts for White, Black or African American, and Hispanic or Latino groups were the strongest and most consistent predictors of screening counts. Median household income, insurance status, and age-stratified variables showed no independent association at the ZCTA level. Global Moran’s I was near zero and non-significant (I = 0.003, z = 0.326, p = 0.745). The Bayesian Poisson model showed superior fit compared with the Bayesian negative binomial model (Bayesian R² = 0.91, DIC = 367.2, RMSE = 5.40, MBE = 0.02). These findings associate screening concentration with the geographic distribution of demographic groups and demonstrate the value of a Bayesian uncertainty-oriented framework for small-area public health analysis.

Abstract: We report the use of our group’s hierarchical Bayesian implementation of the Multi-dimensional Nominal Categories Model followed by standard factor rotations of the principal dimensions to obtain 29 curated sparse dimensions from a set of 203,564 (104,998 pre and 98,566 post) administrations of a multiple-choice concept test in mechanics. We emphasize our careful attention to issues common to fitting such multi-parameter models to large data sets: a novel set of filters to remove administrations from non-conscientious testees, use of Bayesian methods to avoid overfitting, selecting the best transformations to find easily identifiable sparse dimensions, and verification and pruning of these using bootstrap samples. We demonstrate that most dimensions are invariant across different demographically different samples of students as well as between pre-instruction vs post-instruction samples. Most sparse dimensions correspond to well-known misconceptions in mechanics.

Abstract: The recovery of low- and medium-temperature waste heat using Organic Rankine Cycles (ORCs) is increasingly important for improving the efficiency and sustainability of industrial and energy systems. In compact ORC turboexpanders, high specific power output and large pressure ratios often require single- or two-stage turbines operating in transonic or supersonic regimes. Under these conditions, stator blade design is complicated by strong compressible-flow effects and, for organic working fluids, by real-gas thermodynamic behaviour. Conventional supersonic stator design methods, such as the method of characteristics, are mainly applicable to the diverging supersonic portion of the blade passage, while the converging region is typically defined using empirical or heuristic prescriptions. This paper presents a physics-informed neural-network-based inverse design method for supersonic turbine stator blades. The proposed framework generates the complete inter-blade passage, including both the converging and diverging regions, starting from a prescribed mean-line geometry and Mach number distribution. The velocity field is obtained by solving the governing equations of steady, inviscid, adiabatic, irrotational compressible flow within a PINN formulation. A hard boundary-condition strategy is used to impose the specified mean-line velocity distribution exactly, while real-fluid thermodynamic effects are incorporated through lookup tables for the speed of sound and density. The blade contours are then reconstructed from stream-function isolines predicted from the computed velocity field. The method is demonstrated for two working fluids: air, treated as a perfect gas, and toluene undergoing transcritical expansion. The resulting blade passages are first validated using inviscid CFD simulations, which show close agreement between the prescribed and computed mean-line Mach number distributions. Turbulent CFD simulations of the final blade cascades confirm smooth acceleration through the inter-blade passage, with no strong internal shocks and only weak fishtail shocks downstream of the trailing edge. For both fluids, the post-expansion ratio is approximately unity and the exit flow angle remains close to the prescribed blade metal angle, indicating well-matched supersonic stator designs. The results demonstrate that the proposed PINN-based inverse design method provides a systematic and physically consistent approach for generating supersonic stator blade profiles for both ideal-gas and real-gas turbine applications.

Abstract: Background: Empathy, compassion, self-compassion, and resilience are essential to medical practice and education. While some evidence shows that these traits may decline during medical school, few studies have examined all these capacities in the same cohorts or trends within an academic year. This study examines first-year longitudinal findings on cohort and within-year changes in these constructs among medical students. Methods: 98 students (58.2% female; MS1 25.5%, MS2 25.5%, MS3 20.4%, MS4 26.5%) from a large West Coast school participated in at least one wave of an online survey distributed 4 times during the 2023-2024 academic year. Validated measures assessed empathy (IRI), compassion (SCBCS), self-compassion (Neff SCS), and resilience (CD-RISC-10). Linear Mixed Models analyzed between-cohort differences over time with gender and race/ethnicity as covariates. Results: Compared to MS4 students, MS2 and MS3 students had significantly lower cognitive empathy and self-compassion, with marginally lower compassion and higher resilience (p = 0.06). Women reported higher compassion toward others but lower self-compassion and resilience than men. Conclusions: Lower empathy and compassion were observed as early as the second year of medical school, suggesting erosion factors, such as academic pressure and standardized testing, may impact trainees earlier than previously reported.

Abstract: Introduction: Tetrazole and thiazolidine-4-one derivatives are important heterocyclic scaffolds with diverse pharmacological activities, including antimicrobial and antioxidant effects. This study focuses on the design and synthesis of novel Schiff base–derived analogues using a green synthetic approach to improve biological efficacy and reduce environmental impact. Methods: Schiff bases (2a–2h) were synthesized using tetrabutylammonium iodide as a green catalyst in aqueous medium. These were further converted into tetrazole (3a–3h) and thiazolidine-4-one (4a–4h) derivatives using sodium azide and thioglycolic acid. Structures were confirmed by FTIR, ¹H NMR, and ¹³C NMR spectroscopy. Antioxidant activity was evaluated using the DPPH assay, while antimicrobial activity was assessed by the zone of inhibition method. Molecular docking was performed against Penicillin-Binding Protein 4 (3ZG8), CYP51 (5V5Z), and 1OAF. Results: Compounds 2a, 2b, 3a, and 4a showed strong antifungal activity, exceeding standard drugs. Compounds 2d, 3b, and 4b exhibited superior antibacterial activity. Several derivatives demonstrated higher antioxidant activity than ascorbic acid. Docking studies confirmed stable ligand–protein interactions, with compound 4f showing the highest binding affinity. Discussion: Substituent variation influenced biological activity. Electron-donating and withdrawing groups affected potency. Docking results supported experimental findings and confirmed target interactions. The green synthesis improved efficiency and reduced environmental risk. Conclusion: These derivatives show promising antimicrobial and antioxidant potential. Compound 4f emerged as a lead candidate for further optimization and drug development.

Abstract: m⁶A is a ubiquitous reversible post-transcriptional RNA methylation modification in eukaryotic cells, which has been positive effect on regulating follicles development in animals. However, the role of m⁶A modification profiling in regulating the development of healthy and atresia small yellow follicle have not yet been studied in poultry. In this study, we conducted a comparative analysis of the m⁶A methylation profiles of healthy and atresia follicles Zi goose during the period of peak egg-laying. Here, we discovered that 23,342 and 25,552 m⁶A peak between healthy small yellow follicles group (HSYF) and atresia small yellow follicle groups (ASYF), which were mainly enriched in 3'-UTR and stop codon regions. We found that 1174 differential upregulated peaks and 1250 differential downregulated peaks were identified in ASYF group, these differential peaks were covered 1141 and 1233 genes, including METTL14, WTAP, IGF2BP3 and CYTB. Motif analysis demonstrated that these m⁶A peaks exhibit the RRACH and DRACH conserved consensus sequence. Importantly, Zi goose follice transcriptome was extensively methylated and a positive correlation between the m⁶A peak and gene expression levels. The combined analysis of MeRIP-seq and RNA-seq revealed that a total of 78 DMGs were shared in HSYF and ASYF groups, such as BMP5, PPARGC1A, NGF, SCD5, which were mainly involved in TGFβ signaling pathway, MAPK signaling pathway, PPAR signaling pathway and ECM receptor interaction. Furthermore, METTL14 plays a regulatory role in Zi goose granulosa cell development, which was verified by in vitro experiments. We found that knockdown of METTL14 dramatically prevented GCs apoptosis, promoted GCs proliferation, increased the production and secretion of steriod hormone, enhanced the expression levels of genes related to steroid hormone synthesis in granulosa cell. Conversely, overexpression of METTL14 resulted in opposite outcomes. Additionally, we also observed that knockdown of METTL14 increased the activities of antioxidant enzyme (SOD, GSH and CAT), decreased the activities of MDA in goose GCs. Conversely, overexpression of METTL14 inhibited the activities of antioxidant enzymes, increased the activities of MDA. In summary, these data collectively demonstrated that m⁶A methylation was widely distributed in the process of geese follicle growth and development, and futher confirm the significant role of METTL14 influences on granulosa cell development of Zi geese. These findings can be a considerable efficient way to faciliate the laying egg performance of Zi goose through molecular marker assisted breeding technology.