Applying reinforcement learning (RL) to foreign exchange (Forex) trading remains challenging because realistic environments, well-defined reward functions, and expressive action spaces are all required simultaneously. Many existing studies simplify these elements through basic simulators, single scalar rewards, and limited action representations, making learned policies difficult to diagnose and limiting practical relevance. This paper introduces a modular RL framework for foreign exchange trading that addresses these limitations. The framework comprises three components. First, a friction-aware execution engine enforces strict anti-lookahead semantics—observations are taken at closet, orders are executed at opent+1, and positions are marked-to-market at closet+1—while incorporating realistic transaction costs, including spread, commission, slippage, rollover financing, and margin-triggered liquidation. Second, a decomposable 11-component reward architecture uses fixed, pre-specified weights with perstep diagnostic logging, facilitating systematic ablation and component-wise attribution analysis. Third, a 10-action discrete interface with legal-action masking defines explicit trading primitives (scaling, reduction, closure, and reversal) while enforcing margin-aware feasibility constraints during both training and evaluation. Three controlled experiment families are evaluated on EURUSD to analyze learning dynamics rather than generalization. Within this controlled setting, reward component interactions exhibit strongly non-monotonic effects—adding penalty terms does not consistently improve outcomes—with the full-reward configuration achieving the highest terminal training Sharpe (0.765) and cumulative return (57.09%). In the action-space comparison, the extended 10-action interface improves cumulative return while increasing turnover and reducing Sharpe relative to a conservative 3-action adapter, indicating a return–activity trade-off under a fixed training budget. In scaling experiments, all scaling-enabled variants reduce drawdown relative to no scaling, and the combined configuration achieves the strongest endpoint return.

Background: Organized colorectal cancer (CRC) screening programs have been widely implemented across Europe; however, robust population-level evaluations of their real-world effectiveness, particularly for programmes based exclusively on faecal immunochemical testing (FIT), remain limited. The Galician CRC screening programme was progressively implemented between 2013 and 2019. Methods: We conducted a population-based ecological time-series study using data from the Galician Tumour Registry (ICD-10 C18–C21) for 2015–2023. Age-standardized mortality (ASMR) and incidence (ASIR) rates were analyzed. Structural changes associated with program implementation were evaluated using interrupted time-series (ITS) models, estimating annual percent change (APC) before and after implementation and the net change in slope (ΔAPC). Absolute and relative changes in ASMR and ASIR were calculated by comparing 2015–2017 and 2019–2023. Analyses were performed for the overall population and for individuals aged 50–69 years. Results: Between 2015 and 2023, overall CRC mortality declined significantly (APC −3.00%; 95% CI −3.37 to −2.63). ITS analysis demonstrated a marked modification of mortality trajectories following program implementation. Mortality shifted from an increasing pre-implementation slope (APC +13.70%; 95% CI 10.12, 17.39) to a significant annual decline post-implementation (APC −3.62%; 95% CI −4.47, −2.76), yielding a ΔAPC of −17.32. In individuals aged 50–69 years, the structural change was more pronounced (ΔAPC −19.88), with post-implementation mortality decreasing by −8.08% annually (95% CI −10.43, −5.66). Incidence showed a comparable structural modification. Overall APC changed from +15.26% (95% CI 5.48, 25.95) before implementation to −2.48% (95% CI −5.29, 0.41) afterwards (ΔAPC −17.74). In the screening-eligible population, APC shifted from +21.32% (95% CI 4.60, 40.71) to −3.74% (95% CI −7.62, 0.30), corresponding to a ΔAPC of −25.06. Descriptively, ASMR declined from 41.92 to 35.91 per 100,000 (−14.33%), and ASIR from 98.37 to 85.16 per 100,000 (−13.42%) between 2015–2017 and 2019–2023. Relative reductions were larger in individuals aged 50–69 years and were more pronounced for colon cancer than for rectal cancer. Conclusions: Implementation of an organized FIT-based screening program was associated with a structural change in CRC mortality and incidence trends, particularly among individuals aged 50–69 years.

Imperialism as a system of exploitation profoundly shaped the natural and human landscapes of colonized regions. This paper examines how the Second World War accelerated imperial transformations in East Africa, focusing on the Tanganyika Groundnut Scheme (1947–1951) as a case of business-imperial collaboration. Set against Britain’s post-war reconstruction needs, the scheme shows how corporate interests, especially Unilever’s, shaped colonial development through industrial agriculture. Drawing on archival research and recent historiography, the article argues that the scheme was not merely a failed agricultural experiment, but a structural expression of business-imperial partnership with lasting environmental and institutional legacies. Its ecological damage and social disruption reflect broader patterns of environmental violence inherent in imperial capitalism. This study contributes to imperial environmental history in two ways: first, by tracing the long-term ecological impacts of post-war industrial development in colonial spaces; second, to show that imperial agro-economic practices were not temporally isolated events, and the effects of even short-lived, failed ventures such as the Tanganyika Groundnut Scheme continued into the post-colonial period.

Despite significant government intervention, the incidence of technology transfer in the United States of America is still less than desired. There appears to be policy options to address this problem, but policymakers have not pursued them. One conjecture is that notions of what are politically legitimate actions are restraining policymakers from pursuing certain kinds of technology transfer policies. This paper presents the results of an examination of two questions. First, how have the current conceptualizations of political legitimacy potentially contributed to the lack of public policies that directly mitigate circumstances that hinder the transfer of technologies from universities and federal laboratories to the private sector? And second, what alternative conceptualization of political legitimacy can potentially encourage lawmakers to pursue such public policies? The primary conclusions are that the concept of political legitimacy has been reified on a normative basis and does not align with the behaviors of constituents. Moreover, current conceptualizations are potentially restraining policymakers in several ways that curb their willingness to pursue certain kinds of technology transfer policies. The paper presents an alternative conceptualization of political legitimacy that might enable policymakers to pursue a more diverse array of public policies that will more directly address factors that curtail technology transfer.

Background: Haematological derangements, including thrombocytopenia are common in burn patients and are usually attributed to SIRS or sepsis. However, rare haematological malignancies may present with similar laboratory findings, posing a diagnostic challenge. We report a case of a patient with a minor burn injury managed at home who presented six days post-injury with bicytopenia, leucocytosis and active bleeding. Initial differentials included sepsis, DIC, NSAID-induced gastrointestinal bleed, and toxic shock syndrome, with malignancy not initially suspected due to low incidence. Methods: This case is a retrospective view on the patients clinical notes, blood investigations , surgical investigation and documentation of their management. Results: Subsequent evaluation revealed Acute Promyelocytic Leukaemia (APL), a rare but potentially curable subtype of Acute Myeloid Leukaemia and this was the cause of the patient’s bleeding tendency. This poses the question of whether a blood smear should be done routinely for haematological abnormalities in burns patients too. Conclusion: Muganza et al [1] demonstrated that platelet decline predicts poor outcomes in severe burns, while prior malignancy increases sepsis risk [3]. This case highlights the importance of considering haematologic malignancy in atypical presentations and the potential value of routine peripheral blood smear screening in patients with persistent cytopenias. Derangements in blood counts should be investigated broadly, without the assumption of infection as the sole cause, as prompt diagnosis and treatment of conditions like APL can substantially improve clinical outcomes, even in critically ill patients.

Large language models (LLMs) are increasingly used by retail traders to interpret information and design complex strategies, yet existing adoption constructs do not capture the decision-time experience of being cognitively scaffolded by an LLM. We define Perceived Cognitive Assistance (PCA) as the trader’s felt expansion of cognitive capability at the moment of trading decision when an LLM is available, and we report initial content validation of a PCA item pool. Study 1 specified the PCA content domain using a two-tier qualitative corpus (8 interviews and 44 YouTube narratives on LLM-assisted trading, plus 24 qualitative and mixed-method studies on robo-advice and social trading). Reflexive thematic analysis yielded five facilitative assistance facets and one adjacent risk facet (over-reliance), and these were translated into a 16-item PCA pool. Study 2 used a naïve-judge sort-and-rate task with 48 retail traders to test whether items show definitional correspondence to PCA and definitional distinctiveness from similar constructs: perceived usefulness, perceived ease of use, trust in the LLM, and trading self-efficacy. The resulting 9 item set is ready for subsequent factor-analytic and predictive validation. This study advances our understanding of how large language models shape retail trading behaviour by identifying and empirically grounding Perceived Cognitive Assistance as the decision-time psychological experience through which LLMs cognitively scaffold traders, clarifying how LLM use differs from generic technology adoption, trust, or self-efficacy effects.

Protein hydrolysate-based biostimulants have been widely investigated for their potential to enhance seedling growth; however, integrated interpretations of dose-dependent morphophysiological and photochemical responses remain limited for Theobroma cacao L., particularly during the nursery phase. This study aimed to evaluate dose-dependent physiological modulation in cacao seedlings of the Catongo and TSH 1188 genotypes under increasing concentrations of a protein hydrolysate-based biostimulant. The experiment was conducted in a randomized block design arranged in a 2 × 6 factorial scheme, corresponding to two genotypes and six biostimulant concentrations. Morphological traits, biomass accumulation, morphophysiological indices, chlorophyll indices, and chlorophyll a fluorescence parameters were assessed. The results revealed clear dose- and genotype-dependent responses, with predominantly quadratic patterns across most variables. Intermediate concentrations were associated with coordinated improvements in vegetative growth, root development, and photosystem II (PSII) functional performance, whereas higher concentrations were linked to reduced physiological balance. Overall, the findings indicate that protein hydrolysate-based formulations modulate cacao seedling performance through dose-dependent physiological adjustment, contributing to a more integrated understanding of biostimulant action during the nursery phase.

The Monk Parakeet is a psittacid species native to South America and introduced into many territories due to their demand as pets. It has proven to be successful in establishing in new territories, capable of reproducing, propagating, and exhibiting a population growth in introduction sites during few decades. It is considered a pest due to negative impacts, especially for its damage to agriculture and urban infrastructure. Global distribution of this species has been described, however considering that this species is highly adaptable and effective in colonizing new environments, many of these distribution maps may be outdated. We used eBird, a free online birding database, to locate sightings of this species globally and compared it with the reported range for the species. Additionally, we overlaid gathered data on species distribution with economic and demographic human data, to explore if there is a correlation between parakeet abundance and wealth and to assess human exposure to their impacts. We collected data since 1900 up to 2024 and compared observed differences in Monk Parakeet presence. Our results indicate that Monk Parakeets have invaded at least 31 countries, being present in capital cities, cities, towns, and rural territories. The number of cities where the species was sighted increased significantly since 1985, on average a 150% by decade. We found a significant and positive correlation between the number of cities invaded and the GDP per capita for the countries. We present new evidence on Monk Parakeet's rapid global expansion and deliver an updated map of Monk Parakeet global distribution, relevant for planning and implementing control measures.

Acute Lymphoblastic Leukemia (ALL) is the most prevalent form of childhood cancer, accounting for approximately 25% of all pediatric malignancies worldwide. Conventional diagnosis relies on a trained hematopathologist manually examining Giemsa-stained bone marrow smear slides, a process that is both time-consuming and inherently subjective, with inter-observer variability of up to 20% reported in clinical literature. This paper presents Marrow-Find, a fully integrated AI-powered web application designed to automate the classification, visual explanation, and quantification of leukemia cells from bone marrow microscopic images. The system employs a ResNet50 deep convolutional neural network, pre-trained on ImageNet and fine-tuned on the C-NMC 2019 and ALL-IDB benchmark datasets, to classify bone marrow cells into four clinically significant categories: Benign, Pre-B ALL, Pro-B ALL, and Early Pre-B ALL. The model achieves an overall test accuracy of 94.8% with a weighted F1-score of 0.954 and a Cohen's Kappa of 0.931. To address class imbalance, a Conditional Generative Adversarial Network (cGAN) generates realistic synthetic bone marrow cell images for minority classes. Gradient-weighted Class Activation Mapping (Grad-CAM) produces visual heatmap overlays on each prediction, enabling pathologists to verify the morphological features influencing the model's decision. A Watershed-based segmentation algorithm automatically delineates individual cell boundaries and counts blast cells per image. All five components are integrated into a Flask web application with secure user authentication, prediction history, PDF report generation with QR codes, and an AI chatbot, making specialist-level leukemia diagnostics accessible from any standard web browser.

The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that guarantees fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends NIST SP 800-207 and the CISA Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. We present the formal system model, threat model, architectural specification, and a multi-scenario evaluation spanning energy-sector OT, smart manufacturing, and vehicle-to-everything (V2X) environments. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, demonstrate that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity.