Abstract: This work proposes a method to construct the Dirac operator in curved spacetime without introducing a vierbein (tetrad) or an independent spin connection, using only a matrix representation rooted in the basis structure of the four-dimensional gamma-matrix algebra. We introduce sixteen two-index gamma matrices realized as 256 × 256 matrices and embed the spacetime metric directly into matrix elements. In this framework, geometric operations such as covariantization, connection-like manipulations, and basis transformations are reduced to matrix products and trace operations, enabling a unified and transparent computational scheme. The spacetime dimension remains four; the number ``16'' labels the basis elements of the four-dimensional gamma-matrix algebra ((2⁴ = 16). Based on an extended QED Lagrangian, the vertex rule, propagators, spin sums, and traces can be treated in a unified way, which facilitates automation. As validation, we consider Compton scattering, muon-pair production, Møller scattering, and Bhabha scattering. We show that off-diagonal components of the metric can induce characteristic angular dependences in differential cross sections, while the flat-spacetime limit reproduces standard QED results exactly. In a trial calculation with a toy metric containing off-diagonal components, a systematic deviation from the flat result appears near a scattering angle θ ≈ 90^◦ when the coordinate angle is plotted directly, suggesting that metric-induced angular dependence could, in principle, serve as an observational indicator. These results indicate that the proposed matrix representation provides a practical algebraic tool to integrate the Dirac operator in a curved background and quantum electromagnetic processes into a single computational pipeline.

Abstract: The expansion of internet connectivity and mobile technologies has transformed financial services worldwide, positioning digital banking as a key platform for transactions. In Ethiopia, adoption has accelerated through regulatory reforms and national strategies such as Digital Ethiopia 2025 and the National Financial Inclusion Strategy. Despite these developments, empirical studies remain limited, particularly in urban contexts where usage is rapidly increasing. This study applies the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine factors influencing digital banking utilization in Addis Ababa. Using survey data from 405 respondents and Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis shows that facilitating conditions and price value were the strongest predictors of adoption, followed by performance expectancy and social influence, while effort expectancy was not significant. These findings underscored the importance of infrastructure readiness, affordability, and normative influences in shaping digital banking users. The study contributes to technology adoption literature by contextualizing UTAUT2 within Ethiopia’s financial sector and offers practical insights for policymakers, banks, and technology providers seeking to advance digital financial inclusion.

Abstract: Zoogeomorphology, which is the mutual effect of biological activity and landforms, provides a significant yet underused framework for evidence-based wildlife conservation and management. This paper seeks to review international literature on the importance of zoogeomorphological processes toward biodiversity conservation in savanna ecosystems with a focused case study at Maasai Mara National Reserve (MMNR), Kenya. The Maasai Mara happens to be one among many other species-rich savanna landscapes in the world under increasing pressures from climate variability, land-use change, and human activities that create challenges for effective conservation planning. A structured search protocol was used to carry out this review which revealed 86 studies as relevant documentation on how fauna create landforms through processes like trampling, grazing, digging, burrowing, dunging, and wallowing among others influencing soils and hydrology vegetation structure habitat availability as well as species interactions. Evidence has been presented here regarding large mammals playing the role of ecosystem engineers creating heterogeneity in habitats resource distribution as well as population dynamics over different scales. The case study from Maasai Mara brings out these interactions practically by showing how activities of wildlife and livestock around water points floodplains migration corridors significantly demarcate landscape structure ecological viability. Results indicated extensive documentation on zoogeomorphological effects yet confirmed that such events were almost entirely absent from formal integration into conservation planning monitoring frameworks or any regulatory instruments. The study also suggested that management strategies based on insights from zoogeomorphology could enhance ecosystem resilience improve habitat connectivity and foster adaptive conservation under new environmental conditions. It highlighted the imperative need for incorporating landform–biota interactions into wildlife management practices to achieve greater long-term sustainability of savanna protected areas within Kenya and beyond.

Abstract: Background: Recurrent pregnancy loss (RPL) and infertility are associated with significant psychological morbidity, including stress, anxiety, and depression. While these impacts are well-documented globally, their prevalence and severity in the Omani population remain unexplored. This study investigates the mental health outcomes of Omani women with RPL and infertility compared to fertile controls. Objectives: To assess the prevalence of stress, anxiety, and depression in women with recurrent pregnancy loss (RPL) and infertility, and compare these rates to women with no fertility concerns in an Omani population. Design: A prospective, cross-sectional study. Setting: Sultan Qaboos University Hospital and Royal Hospital in Muscat, Oman. Participants: 111 women with RPL, 131 women with infertility, and 210 antenatal controls with no fertility issues. Interventions: No clinical interventions were administered as this was an observational study. Participants completed validated psychological assessments (DASS-42 and BDI-II). Primary and secondary outcome measures: Primary outcomes were the prevalence rates of stress, anxiety, and depression assessed using DASS-42 and BDI-II. Secondary outcomes included sociodemographic correlates and risk factors Results: The study included 111 women in the RPL group, 131 in the infertility group, and 210 controls. Among RPL patients, 31% reported stress, ranging from mild to extremely severe, while 35.9% of infertility patients reported stress, compared to 17.1% in the control group (p = 0.003). Anxiety was present in 45% of RPL patients, 45.5% of infertility patients, and 28.1% of controls (p = 0.019). Depression, measured by the DASS-42, was most prevalent in the RPL group (34.2%), followed by the infertility group (33.6%), and controls (13.8%) (p < 0.001). Similar results were observed with the BDI-II, with depression rates of 23.4% in the RPL group, 19.1% in the infertility group, and 7.6% in controls (p = 0.02). Conclusions: Women with RPL and infertility in Oman experience significantly higher levels of stress, anxiety, and depression compared to women without fertility concerns. This study did not assess the mental health of male partners, highlighting the need for further research on the psychological impact on both partners. Future studies should focus on developing psychological support interventions and evaluating their impact on patient outcomes.

Abstract: Post-inflammatory hyperpigmentation (PIH) is a common pigmentary disorder characterized by excessive melanin production following skin inflammation. Histamine, a key inflammatory mediator, is known to stimulate melanogenesis via H2 receptors; however, the underlying calcium signaling mechanisms remain largely unexplored. In this study, we investigated the role of the ORAI1-STIM1 complex in histamine-induced melanogenesis using B16F10 melanoma cells and normal human epidermal melanocytes (NHEMs). Histamine (10–30 μM) significantly increased melanin content (2.5–2.8-fold), an effect specifically abolished by the H2 antagonist famotidine. Notably, while acute histamine application failed to trigger immediate calcium influx, chronic exposure significantly enhanced store-operated calcium entry (SOCE) capacity by approximately 2.8-fold, providing evidence for a functional remodeling of the Ca2+ signaling machinery. Histamine-induced melanogenesis was significantly suppressed by intracellular calcium chelation, pharmacological inhibition of ORAI1 (BTP-2 or Synta-66), and siRNA-mediated silencing of ORAI1 or STIM1, but not ORAI2, ORAI3, or STIM2. Our findings demonstrate that chronic histamine exposure drives hyperpigmentation through ORAI1-STIM1-mediated SOCE remodeling, establishing this complex as a promising therapeutic target for the treatment of PIH and related inflammatory pigmentary disorders.

Abstract: Retinoic acid receptor (RARg) mRNA is expressed spatially and temporally during mouse embryogenesis and largely within stem and progenitor cells, indicating a role in organ formation. RARg agonism promoted the maintenance of hematopoietic stem cells, and blocked stem cell development as shown for hematopoiesis, zebrafish development, and chondrogenesis. Transgene expression enhanced the generation of induced pluripotent stem cells, indicating a role in ground state pluripotency. RARg is oncogenic in acute myeloid leukemia, cholangiocarcinoma and colorectal, head and neck, hepatocellular, ovarian, pancreatic, prostate, and renal cancers. RARg agonism or overexpression enhanced the proliferation of cancer cells. Conversely, antagonism or inhibition of all-trans retinoic acid synthesis led to the death of cancer cells including cancer stem cells. The pathways regulated by RARg, via canonical activation and repression of gene expression, include Wnt/b-catenin and Notch signaling. RARg also acts as a co-factor to Smad3 and reduced or enhanced TGFb driven and Smad3-mediated events when liganded and non-liganded, respectively. Collectively the findings support the view that RARg plays a crucial role to control stem and progenitor cell behavior.

Abstract: Recently, several studies from developing economies have reported the presence of per- and polyfluoroalkyl substances (PFAS) in water bodies, with a dominance of Perfluorooctanoic acid (PFOA), a potential endocrine disruptor. In this study, an engineered sugarcane bagasse biochar–chitosan composite (SBCT) was designed, synthesized, and evaluated as an adsorption medium for the removal of PFOA from aqueous systems at concentrations up to 500 ppb in water. Batch adsorption experiments were conducted to investigate the effects of initial PFOA concentration, contact time, pH, adsorbent dosage, and temperature. Scanning electron microscopy (SEM) showed that SBCT has a significant porous structure. The composite showed over 90% of PFOA removal from water. Further, the presence of peaks corresponding to C-F bonds after adsorption by Fourier transform infrared (FTIR) Spectroscopy analysis confirms the adsorption of PFOA on SBCT. The protonated amine groups (NH₃⁺) in chitosan enhanced the adsorption of anionic PFOA through electrostatic attraction with carboxyl groups (COO⁻). The Kinetic study revealed that Pseudo-first order best described the adsorption process, with equilibrium adsorption capacity (qeq) of 2.78 mg/g, suggesting that physisorption is the predominant mechanism. The Langmuir Isotherm model gave the best fit, establishing a maximum adsorption capacity (qmax) of 9.08 mg/g. Thermodynamic analysis revealed that the adsorption process was spontaneous and exothermic, consistent with physisorption. The regeneration capacity of the SBCT composite demonstrated exceptional reusability across five adsorption-desorption cycles with methanol. The adsorption kinetics, equilibrium behavior, and regeneration efficiency suggest that SBCT is a viable low-cost adsorbent for batch adsorption-based treatment systems targeting PFOA removal, particularly in decentralized and resource-constrained water treatment applications.

Abstract: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphoma (NHL). In approximately 40% of the patients, the prognosis and clinical evolution is unfavorable. This study is a proof-of-concept computer vision exercise to support the feasibility of predicting the prognosis of DLBCL using only hematoxylin and eosin (H&E) histological images and deep learning. A conventional series of DLBCL of 114 cases was split into two prognostic groups according to the overall survival (curve fitting and slope analysis): patients who died before the first 2 years (“Dead 2-years”, b1 = -0.054), and the others (b1 = -0.003). Twenty different convolutional neural networks (CNN) were used, and explainable artificial intelligence (XAI) was used to identify the areas of the images that the network used for classification. The final model based on DarkNet-19 predicted prognosis groups with high performance (test set accuracy = 96.26%). The other performance parameters were precision (94.46%), recall (95.02%), false positive rate (3.07%), specificity (96.93%), and F1 score (94.74%). XAI, including grad-CAM, occlusion sensitivity, and image-LIME confirmed that the CNN was focusing on the correct areas. Correlation with the clinicopathological characteristics found that the Dead < 2-years group was correlated with stage III-IV, International Prognostic Index (IPI) High + High/intermediate, progressive disease, non-GCB cell-of-origin, CD10-, BCL2+, and EBER+. Analysis of the immune microenvironment, cell cycle, and germinal center markers showed that Dead < 2-years had higher IL10, PD-L1, and CD163, and lower E2F1 protein expressions. In conclusion, the overall survival of DLBCL can be predicted using H&E histological images and deep learning. The trained CNN could be used as pre-trained CNN model for transfer learning in the future.

Abstract: Purpose: This study interrogates the paradox of employer-reported “labour shortages” in labour-abundant African economies. It advances the claim that shortage signals are partly institutional outputs: they arise when screening rules narrow the effective labour pool, rather than reflecting exogenous skill scarcity. Design/methodology/approach: Drawing on labour market segmentation, information economics, and critical institutionalism, we analyse 10,432 job advertisements scraped monthly (January 2024–June 2025) from leading portals in seven Anglophone African countries. A rigorously validated support-vector-machine classifier distinguishes explicit numeric age ceilings from implicit youth-coded cues to construct an Age-Coded Hiring Index (ACHI). We triangulate ACHI with employer-reported workforce-constraint indicators from the World Bank Enterprise Surveys and labour-underutilisation (LU4) from ILOSTAT, estimating fixed-effects and interaction models to test whether age-coded screening predicts shortage complaints most strongly where latent labour supply is greatest. Findings: Age-coded screening is pervasive in vacancy texts: approximately 15–20% of postings impose numeric age caps and a much larger share deploys implicit youth signals. Higher ACHI is robustly associated with stronger shortage complaints net of underutilisation and macro controls, and the relationship steepens under high labour slack, consistent with an institutional mechanism in which screening rules convert latent labour supply into perceived scarcity. Originality/value: Conceptually, the paper reframes “shortage” indicators as partially endogenous to screening rules and to employers’ definition of “suitability,” rather than treating them as market facts. Empirically, it introduces a replicable NLP-based measure of exclusionary screening from vacancy text, enabling cross-country tests of institutional scarcity dynamics in low- and middle-income contexts. Practical implications: The results imply that diagnostic and policy responses to “shortages” should not presume supply failure alone; they should also examine how recruitment criteria restrict the recognised labour pool and thereby shape shortage measurement itself.

Abstract: Artificial Intelligence (AI) has been widely successful and effective in optimizing tasks across various fields such as smart agriculture, healthcare, and education, positioning it as a key enabler in advancing the Sustainable Development Goals (SDGs) set forth by the United Nations. Understanding the perspectives of industry and academic experts in domains such as AI, data science, biotechnology, natural and physical sciences, can provide valuable multidisciplinary insights into the responsible usage of AI. This study analyses 12 opinion essays written by 13 experts from academia and industry, focusing on the opportunities and challenges provided by AI in the advancement of SDGs. These experts provide their unique perspectives shaped by their nationality, professional role, gender, and domain of expertise. The analysis highlights several strong application areas of AI, including enhancing diagnostic accuracy, monitoring cattle and crops to reduce wastage, ensuring accessibility of education, and enhancing gender equality. However, the experts also caution against potential risks and ethical implications associated with AI, such as the risk of algorithmic bias, concerns with over reliance, and inequitable access to AI enabled tools.

Abstract: The aim of this study was to estimate the historical exposure to a selection of polybrominated diphenyl ethers (PBDEs) and Dechlorane Plus (DP) concentrations and the potential sociodemographic and lifestyle associated factors. Study population (n=134) was a subcohort of GraMo Study, recruited in 2003-04 in Granada (Spain). Information on potential exposure associated factors was collected by face-to-face interviews and clinical records review. Historical exposure was estimated by analyzing adipose tissue concentrations of 12 PBDEs and the 2 DPs, by means of gas chromatography coupled to mass spectrometer. Data analyses included multivariable linear regression analyses. Median (Interquartile Ranges) pollutant concentrations ranged from 0.13 (0.09, 0.23) ng/g lipid for BDE-99 to 1.34 (0.92, 2.43) ng/g lipid for BDE-153. The body mass index was inversely associated with anti- and syn-DP, BDE-153, -183, and -197 concentrations. Males exhibited higher levels of BDE-28, -47, -153 and -209 than females. Compared to non-manual workers, manual workers exhibited increased BDE-154, anti- and syn-DP concentrations, but lower BDE-28 levels. These findings highlight the elevated prevalence of PBDE/DP exposure and the heterogeneous exposure patterns observed across the study population. Further research is warranted to elucidate the long-term implications for human health.

Abstract: Frontotemporal lobar degeneration (FTD) is a proteinopathy that induces neuroinflammation and neurodegeneration; Alzheimer´s disease (AD) is characterized by Abeta-42 deposits, microglia overactivation, astroglial alterations and p-Tau accumulation. Identification of neuroinflammatory mediators as predictors of cognitive cognition have gained attention. We compared several biomarkers in plasma as predictors of cognitive impairment between AD and FTD patients (Nfl, p-Tau217, TDP-43 and CX3CR1 and soluble fractalkine levels) by ELISA (pg/ml) and age-matched controls (without cognitive impairment) or HIV-1 seropositive patients. To our knowledge, this is the first study showing that increased plasma CX3CR1 and soluble fractalkine predict cognitive impairment specifically in FTD. In addition, high plasma p-Tau 271 levels correlate with sFK levels and their mini mental scores in FTD. Thus, fractalkine and TDP-43 are exclusive biomarkers of cognitive impairment in FTD. However, Nfl, GFAP and p-Tau271 levels did not differ between AD or FTD patients. Anatomically, we observed hippocampal involution as well as Tau deposits in human FTD postmortem brains. On the other hand, neuroinflammation contributes to dementia; and chemokines as HIV-1 co-receptors facilitate spread of HIV-1 infection inducing apoptosis in the brain. On the other hand, chemokines promote neuronal survival and regulate neuron-glia interactions. Fractalkine is a delta chemokine (also termed CX3CL1), that binds to its CX3CR1 chemokine receptor, that as a membrane-isoform can be released as a soluble form by damaged neurons. We confirmed that fractalkine prevents LPS (an inflammation inductor)-induced apoptosis by decreasing caspase-3 activation in cortical neurons at 7 DIV LPS exposure. Thus, fractalkine may play a dual role: it is associated with cognitive impairment in both FTD and AD, yet it also exerts neuroprotective effects by reducing LPS-induced neuronal apoptosis at 7 DIV.

Abstract: Cybersecurity assurance drifts under change. Tooling updates, policy revisions, monitoring redesigns, and AI-enabled automation can silently change what is measured, how it is measured, and which differences are treated as “the same,” while human workflows adapt under staffing constraints, alert fatigue, incentives, and competing priorities. We introduce a human-centred, proof-carrying approach to security assurance: a certificate layer that freezes one operational record—system boundary, defect definitions, risk scoring ruler, neutrality conventions, audit window, upgrade path, and observation interfaces—so that “improvement under upgrades” has a precise and checkable meaning. Over time, the method combines multiple interacting risk channels into a single decision-ready assurance summary with an explicit improvement margin and an explicit disturbance allowance, designed to remain interpretable during incidents and operational spikes. Across versions and refinements, it enforces a vertical-coherence requirement: upgrade effects must have a finite total footprint so that claims do not drift without bound as systems evolve. We package the framework as four auditable obligations—controlling semantic and policy drift, maintaining a uniform improvement claim, ensuring upgrade coherence, and transporting guarantees to observable evidence—and prove a Master Certificate showing that passing these checks yields version-stable, mechanically verifiable assurance envelopes on the declared episode window. The resulting rates, budgets, and slack are human-centred objects: decision-ready summaries, governance-grade non-regression guarantees, and feasibility diagnostics under organisational constraints.

Abstract: This paper consolidates our previous work and articulates Scheme C, a constitutional architecture designed to resolve deadlocks and instability in fragmented democracies by synthesising previous findings into a self-contained theoretical framework. The scheme's centrepiece is a game-based investiture rule that guarantees the appointment of a prime minister through a strategic nomination process, eliminating the risk of investiture-related collapse. Central to this system is also a bifurcated confidence structure -- assigning the prime minister either type I (majority) or type II (minority) status -- managed by a dynamic no-confidence mechanism. Stability is reinforced by a synchronised electoral rhythm and a Westminster-style dissolution mechanism that protects cohesive assemblies while resulting in contingent, quasi-midterm elections. To ensure continuity, a novel hybrid caretaker office bridges Westminster and Presidential traditions by automatically converting a departing prime minister into a tenure-secured, though authority-attenuated, caretaker. This "converted" logic is balanced by a presidential-style "acting" appointment mode for vacancies, ensuring administrative resilience throughout the electoral cycle. Ultimately, Scheme C provides a resilient architecture that ensures unyielding governmental functionality and rigorous legislative oversight regardless of the underlying electoral system or party landscape.

Abstract: Understanding customer purchasing behavior is essential for businesses to optimize marketing strategies and improve customer retention. This study employs machine learningbased clustering techniques to segment customers based on transactional data. By leveraging Recency, Frequency, and Monetary (RFM) analysis, the study compares multiple clustering algorithms to identify distinct customer groups. Experimental results demonstrate that the proposed approach effectively categorizes customers, enabling data-driven decision-making for targeted marketing. These findings highlight the potential of unsupervised learning methods in enhancing business intelligence and customer relationship management.

Abstract: This study investigated the degradation and contamination behavior of 41 real-world operational Marine Diesel Fuel samples, conforming to ELOT ISO 8217:2024 (DFA category). Samples were sourced directly from land-based supply tanks. To assess fuel degradation, a comprehensive suite of parameters was evaluated, including fuel characteristics such as viscosity and density. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was employed for elemental analysis to determine the content of wear and other metallic contaminants. Elevated concentrations of various metals were detected, suggesting potential leaching from system components within the storage infrastructure. Notable elemental concentrations included Iron (Fe up to 1.38 mg/kg), Copper (Cu up to 0.401 mg/kg), Lead (Pb up to 0.358 mg/kg), Aluminum (Al up to 0.218 mg/kg), Zinc (Zn up to 1.331 mg/kg), Nickel (Ni up to 0.172 mg/kg), Calcium (Ca up to 8.054 mg/kg), Sodium (Na up to 0.332 mg/kg), Phosphorous (P up to 0.602 mg/kg), and Silicon (Si up to 8.249 mg/kg). The presence of these contaminants in marine fuels, if bunkered, poses a significant risk of impaired engine performance, including injector fouling and ash formation. Critically, this study suggests that FAME content is not the primary driver of the observed oxidation and subsequent metallic degradation.

Abstract: We present a geometric framework for understanding the parameter structure of the StandardModel. Starting from the Grassmannian manifold Gr(k,N)—the space of k-dimensional subspaces inan N-dimensional vector space—we demonstrate that two fundamental observables, the weak mixingangle and the gauge-gravity hierarchy, uniquely select the integers (k, n) = (3, 13) with N = k+n =16. This selection is not approximate but exact: no other integer pair satisfies both constraintssimultaneously within experimental tolerances. We provide complete mathematical proofs of globaluniqueness, analyze the robustness of the selection across tolerance variations, and show that theresulting Grassmannian dimension D = k(N −k) = 39 determines the hierarchy between the Planckand electroweak scales. The framework makes over forty parameter-free predictions for StandardModel quantities, with a mean accuracy of 0.1%. We discuss the physical interpretation, connectionsto gauge theory, and implications for the hierarchy problem.

Abstract: This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the Covid-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with a statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the Covid-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets.

Abstract: The growing demand for cost-efficient digital transactions has driven the need for scalable and low-cost payment solutions. Traditional blockchain-based transactions suffer from high fees and slow processing times, making decentralized off-chain payment networks a promising alternative. In this paper, we propose SpeedyMurmurs, an AI-enhanced decentralized routing algorithm that significantly reduces payment processing costs and transaction delays. Our approach optimizes payment routing efficiency through embedding-based path discovery, reducing routing overhead by up to two orders of magnitude and cutting transaction processing times by over 50 percent compared to existing blockchain networks. By leveraging machine learning-driven transaction optimization, our system dynamically selects the most cost-effective paths for digital payments while maintaining user privacy and security. Experimental results demonstrate that SpeedyMurmurs reduces transaction fees and computational costs, making decentralized payment systems more financially viable. This research highlights the role of AI-powered routing strategies in minimizing costs and improving efficiency in modern payment networks.

Abstract:

Background: The outer membrane impermeability of multidrug-resistant (MDR) Gram-negative bacteria, particularly Escherichia coli, remains a primary barrier to antibiotic efficacy. Overcoming this challenge requires strategies that transcend traditional lipophilicity-driven drug design. Methods: This study presents the rational design and in silico validation of ‘Armored-Trojan-1,’ a novel siderophore–beta-lactam conjugate engineered to exploit the bacterial iron-acquisition pathway. Using a generative in silico approach, we designed a high-affinity catechol siderophore with a beta-lactam warhead. To address the metabolic instability limiting previous "Trojan Horse" candidates, we introduced a sterically hindered alpha-methyl ether linker designed to prevent premature periplasmic hydrolysis. Results: Physicochemical profiling indicates that while the candidate exceeds standard passive diffusion thresholds (TPSA > 190 Ų), its polarity is optimized for active transport via the FhuA receptor. A steric and dimensional compatibility audit demonstrates that the molecule fits within the transporter channel without occlusion. Furthermore, structure-based database analysis validates the candidate as a previously undescribed chemical entity. Conclusion: These findings provide a validated computational blueprint for the development of sterically stabilized conjugates, offering a viable strategy to bypass intrinsic resistance mechanisms in Gram-negative pathogens.