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.

Abstract: Most audit fee studies treat the relationship between fees and client risk as symmetric. A unit increase and a unit decrease in client risk are assumed to produce equal but opposite fee responses. We examine whether that assumption holds in the U.S. audit market using 4,090 firm-year observations of U.S. listed companies from 2010 to 2022 and a first-difference specification with firm and year fixed effects. The data show that audit fees rise by about 1.06 percent for each one-unit increase in the Audit Analytics Risky Client Score (p < 0.001). The response of fees to risk decreases is not statistically different from zero (coefficient = 0.001, p = 0.708). The implied stickiness differential is 0.0093 (p = 0.058). The stickiness ratio is approximately 0.13. Fees adjust downward at about 13 percent of the rate at which they adjust upward following an equivalent risk movement in the opposite direction. The pattern is robust to a strict definition of risk decreases, holds in both early (2010–2016) and late (2017–2022) sub-samples, and is corroborated by an alternative risk proxy based on loss-status transitions, where fees rise 4.3 percent on entry to loss status and do not adjust on exit. The result has implications for audit pricing models, audit committee oversight, and the way fee dynamics are interpreted by users of audit fee data.

Abstract: Background: Lung cancer is a highly heterogeneous disease in which molecular characte-rization has become essential for guiding personalized therapies. The implementation of next-generation sequencing (NGS) allows the simultaneous detection of multiple genomic alterations, improving tumor profiling and therapeutic decision-making. This study aimed to characterize the molecular landscape of lung cancer using NGS and to evaluate its as-sociation with histological subtypes and programmed death-ligand 1 (PD-L1) expression. Methods: A retrospective observational study was conducted on 96 patients diagnosed with lung cancer between 2023 and 2025. Molecular profiling was performed using the Action OncoKitDx panel. Associations between genetic alterations, histological subtypes, and PD-L1 expression were analyzed using Fisher’s exact test, with p < 0.05 considered statistically significant. Results: Adenocarcinoma was the most common histological subtype (67.7%), followed by squamous cell carcinoma (26%). The most common mutations were KRAS (34.4%), TP53 (29.2%), and EGFR (11.5%). KRAS mutations were significantly associated with adenocar-cinoma (p = 0.001), while the absence of detectable mutations was associated with squa-mous cell carcinoma (p = 0.002). Co-mutations were identified in 22.9% of cases, with KRAS–TP53 being the most common combination. Tumors harboring EGFR mutations showed a significantly lower frequency of co-mutations (p = 0.012). No significant asso-ciations were found between PD-L1 expression and either histological subtypes or the analyzed genetic alterations. Conclusions: Lung cancer exhibits marked molecular heterogeneity, with a predominance of KRAS mutations in adenocarcinoma. The low frequency of co-mutations in EGFR-mutated tumors supports their role as dominant driver alterations. The lack of asso-ciation between PD-L1 expression and genomic alterations highlights the complexity of its regulation and suggests the involvement of multiple biological factors. These findings reinforce the clinical value of NGS in comprehensive tumor profiling and in the develop-ment of precision medicine strategies.

Abstract: Despite the large amount of successful existing methods and frameworks for planning sets of multiple Unmanned Aerial Systems (UAS), there are still lack of coordination frameworks capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UAS to complete Points of Interest (PoIs) visiting missions while ensuring that all generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energetic constraints, and the existing 3D No-FLy Zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions, and is built upon three core components: 1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to the Vehicle Routing Problem (VRP) optimization, resulting in shorter NFZ-safe routes; 2) a trajectory planning module that ensures the generation of kinematically-feasible trajectories for fixed-wing UAS; and 3) a mission supervision module for real-time monitoring and replanning in case of changes in UAS, mission, wind speed, or airspace restrictions. It was implemented and validated by interfacing with professional-grade Visionair Ground Control Station Software and the VECTOR-SIL Software-in-the-Loop simulator, which realistically replicates the behavior of certified fixed-wing autopilots under various weather conditions. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes typical of real-world operational scenarios.

Abstract: The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. This study develops an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. A multi-loop coupled, heat pump based integrated thermal management model is constructed, including a compressor, heat exchangers, expansion valves, and an electro thermal battery sub model. Bench and vehicle level tests confirm that the model predicts refrigerant mass flow rate and heating capacity with mean relative errors of 4.76 % and 4.30 %, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA NMPC strategy is evaluated under NEDC and CLTC P driving cycles. Under the NEDC cycle, the strategy limits cabin temperature overshoot to 0.35°C and battery temperature fluctuation to 0.26°C, while achieving a 6.31 % energy saving under high speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA NMPC approach can improve thermal management performance under the investigated operating conditions.

Abstract: Background: Drug-coated balloons (DCBs) have emerged as a "leave-nothing-behind" strategy in percutaneous coronary intervention (PCI), with potential advantages over drug-eluting stents (DES) in selected patients with acute coronary syndrome (ACS).Methods: We performed a narrative review of randomized controlled trials, registries, and meta-analyses evaluating DCB therapy in ACS, including PEPCAD NSTEMI, REVELATION, BASKET-SMALL 2, AGENT IDE, REC-CAGEFREE I/II, and the ongoing TRANSFORM II trial. Articles were identified through searches of PubMed/MEDLINE, Embase, Scopus, Web of Science, and Cochrane CENTRAL covering January 2005 to February 2026.Results: Across published studies, DCBs have shown outcomes that are non-inferior to those of DES in selected ACS subsets, together with a lower risk of major bleeding attributable to shorter dual antiplatelet therapy (DAPT) requirements. Advances in intravascular imaging and lesion preparation, alongside emerging applications of artificial intelligence (AI) and robotic-assisted PCI, may further improve DCB performance, although evidence specific to DCB use in ACS remains limited for these adjunctive technologies. Conclusions: DCBs are a reasonable alternative to DES in selected patients with ACS, particularly those at high bleeding risk or with lesion subsets in which DES perform less well (small vessels, in-stent restenosis, bifurcations, diffuse disease). Adequately powered randomized trials with long-term follow-up are required before broader recommendations can be made.

Abstract: Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.

Abstract: Background/Objectives: Long-term clinical data on direct posterior composite restorations are scarce. This study evaluated their performance after almost three decades using selected FDI criteria. Temporal changes were tracked across follow-up exams, including within a predefined subcohort. Methods: This observational follow-up involved 21 patients with 57 posterior composite restorations placed in 1995–1996. The 2025 follow-up used FDI criteria to assess functional, esthetic, and biological properties, classifying outcomes as clinically acceptable, intervention needed, or failure. Descriptive analyses were applied to the entire cohort. Longitudinal analyses were conducted on a subcohort of 14 patients with 27 restorations at three time points. Exploratory analyses assessed associations with restoration factors, caries experience, and gingival health. Results: In 2025, 54.4% of restorations were clinically acceptable, 28.1% required intervention, and 17.5% were failures. Functional criteria remained mostly acceptable, though form and contour showed the highest mean values. In the longitudinal subcohort, significant changes over time were observed in anatomical form and occlusal wear. Retention, marginal adaptation, proximal contact, and surface luster did not change significantly. Biologically, restorations available for direct assessment had low incidences of secondary caries, hard-tissue defects, and postoperative sensitivity or pulpal issues. Conclusions: Posterior composite restorations can function for nearly three decades but gradually deteriorate in certain aspects. Long-term changes mainly involve cumulative functional aging of the anatomical form and occlusal wear, rather than widespread biological failure. These findings underline the importance of differentiated long-term assessment and support conservative management approaches where clinically feasible before replacement is undertaken.

Abstract: Chronic migraine is increasingly understood as a network-level disorder in which trigeminovascular nociception is sustained by metabolic, inflammatory, and macro-network dysfunction rather than by an isolated headache mechanism. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), originally developed for type 2 diabetes and obesity, engage receptors expressed in choroid plexus, cortex, hippocampus, thalamus, and hypothalamus. Evidence converges on four mechanistic intersections between GLP-1 signalling and the chronic-migraine cascade: restoration of cerebral insulin signalling and cortical energy supply; modulation of cerebrospinal-fluid secretion and intracranial pressure with downstream relevance for glymphatic dynamics (most clearly validated in idiopathic intracranial hypertension, with explicit caveats for normotensive migraine); suppression of microglial activation in pain-relevant circuits (established preclinically, with one pilot human imaging study); and direct trigeminal-nociceptor effects via TRPV1 inhibition demonstrated for exendin-derived peptides. A 2026 real-world cohort of approximately 11,000 chronic-migraine adults provides a preliminary clinical signal. We propose, and operationalise as falsifiable, the hypothesis that GLP-1RAs are disease-modifying rather than purely symptomatic in chronic migraine, and we describe a placebo-controlled trial with parallel multiple mediation analysis on weight change AND HOMA-IR/TyG delta that can refute the claim within five years. A biochemical scheiding between exendin-based agonists (exenatide, lixisenatide) and human GLP-1 analogues (semaglutide, liraglutide, dulaglutide) generates a falsifiable sub-hypothesis on peripheral TRPV1 inhibition that is testable retrospectively on data that already exist.

Abstract: The study investigates overconfidence bias in the Bangladesh equity market through the relationship between the market returns, and the trading volume in a nonlinear, information-theoretic model. Building upon the traditional literature on returns and volume, the study differentiates between the total market returns and unexpected market returns, the latter being the unexpected information shocks represented under the Market Index Model. Transfer Entropy with bootstrap inference is used to determine directional and asymmetric causality across various market states, including bullish, bearish, crisis, extended crisis, and COVID-19. The findings indicate that the total market returns give weak and inconsistent evidence of overconfidence, which is bi-directional but limited information flow. Conversely, unexpected market returns have a statistically significant directional effect on trading volume, which represents strong evidence of overconfidence. The results also reveal that overconfidence is conditional as it is stronger in normal and bullish market contexts, and weaker during times of crisis. Asymmetric analysis reveals that the overreaction of investors is more pronounced when the market trends are negative, implying that unexpected losses stimulate an amplified trading effect due to the feeling of mispricing and recovery hopes. The results have significant implications on market efficiency, investor behavior and regulatory policies to improve market stability and facilitate informed financial decision-making.