Abstract: The classical Rankine and Coulomb theories frequently encounter difficulties in accurate-ly modeling the complex, nonlinear, and displacement-coupled behavior of earth pressure on retaining walls under non-limit states. The present study proposes a “key feature re-finement strategy based on collinearity analysis” and employs the said strategy by apply-ing it to model test data. The strategy identified an optimum set of five physical parame-ters, namely displacement mode (DM), relative displacement (Δ/H), relative depth (Z/H), unit weight (γ), and internal friction angle (φ). A machine learning (ML) model has been developed that integrates Categorical Boosting with SHapley Additive exPlanations (Cat-Boost-SHAP). This model has been found to exhibit a marked enhancement in accuracy (R² = 0.917) when compared to classical theories, while concurrently offering the distinct advantage of explicit interpretability. SHAP analysis has been demonstrated to elucidate the nonlinear influence of each parameter. It is confirmed that displacement mode is iden-tified as the governing factor for spatial pressure distribution, and classical mechanisms such as top down stress relaxation in the rotation-about-the-base (RB) mode and soil arch-ing in the rotation-about-the-base (RT) mode are visualized. Furthermore, a displace-ment dependent mechanical threshold (Δ/H ≈ 0.006) has been identified, which marks the transition from a mode dominated to displacement driven pressure evolution. In addition, the proposed approach is integrated into a graphical user interface (GUI) that is designed to be user friendly, thereby furnishing practitioners with a precise tool for designing re-taining walls. The validation of the model's performance against independent experi-mental results has demonstrated its superior agreement and practical utility under dis-placement-controlled conditions in comparison to conventional methods.

Abstract: The close physiological relationship between the left atrium (LA) and left ventricle (LV) suggests that an index assessing both the cardiac chambers simultaneously could provide useful information about disease severity. Consequently, investigators have proposed the atrioventricular coupling index (LACi), and demonstrated its utility in predicting the likelihood of atrial fibrillation, heart failure, and other cardiovascular events in humans. No studies have been reported in veterinary medicine. Therefore, we measured the LACi in healthy dogs and dogs affected by myxomatous mitral valve disease (MMVD). Two hundred and thirty-three dogs (105 healthy dogs and 128 dogs with MMVD) were retrospectively included in the study. The LACi (LA volume/LV volume*100) at LV end-diastole (LACi-ED) and LV end-systole (LACi-ES) of each dog was measured using a monoplane Simpson’s Method of Discs from the left apical four-chamber view. In healthy dogs, LACi-ED and LACi-ES showed no relationship with bodyweight, heart rate and age (R2 < 0.03, for all variables). In MMVD dogs, LACi-ED and LACi-ES differed between ACVIM stages (P < 0.00 and P < 0.02, for all stages). The LACi-ED and LACi-ES had similar accuracy in identifying MMVD dogs with congestive heart failure (area under the curve of 0.920 and 0.906, respectively). Our data suggest that LACi can be useful in assessing left atrioventricular function in dogs with MMVD but the diagnostic accuracy in identifying dogs with congestive heart failure was not superior to left-atrial-to-aortic ratio. Prospective studies are needed to evaluate the predictive value of this new echocardiographic index in dogs affected by MMVD.

Abstract: This paper presents an edge-reinforced learning platform that combines reinforcement learning, homomorphic encryption, and swarm intelligence to support ultra-low latency IoT sensing and cross-device communication. In conventional IoT architectures, cloud-centric processing and centralized coordination introduce significant delays and expose sensitive data to intermediate entities, making them unsuitable for time-critical and privacy-sensitive applications. The proposed platform relocates intelligence to the network edge, where edge nodes learn adaptive policies for sensing, routing, and computation offloading based on local conditions and limited global feedback. To preserve confidentiality, IoT measurements and model updates are protected using homomorphic encryption, allowing aggregation and decision-making to be performed directly over encrypted data without revealing raw values. In parallel, swarm intelligence mechanisms orchestrate distributed cooperation among devices, enabling robust path selection, task allocation, and congestion avoidance through lightweight, bio‑inspired interactions rather than centralized control. The integrated design is evaluated on realistic IoT scenarios with heterogeneous devices and dynamic traffic patterns. Results show that the edge-reinforced learning platform can significantly reduce end-to-end latency and jitter compared to cloud-based and non-learning edge baselines, while incurring acceptable computational overhead from encryption and maintaining strong privacy guarantees. The framework demonstrates that it is feasible to simultaneously achieve low latency, resilient cross-device coordination, and data confidentiality in large-scale IoT deployments.

Abstract: Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human-wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-frame suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 seconds. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-frame classifier while maintaining high recall. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

Abstract: To improve the efficiency of decentralized pay-ment systems for microservices, this paper proposes the use ofblockchain technology in order to allow for parties to transactwith distrust and remove the need for central intermediaries.In order to do this, this paper proposes the use of automatedsmart contracts and scalable off chain technology to allowfor efficient transactions and reduced computational resourcecosts. It is proposed by empirical testing to show that thesystem outlined in this paper will show significant increase incosts and processing latencies when compared to traditionalcentralized payment processing systems. As a result this paperwill show that the system in this paper is a good alternative totraditional microservice based payments systems for real timemarket paymentsThis research will allow increased scalability and security inthe digital transaction environment.

Abstract: The increasing penetration of converter-interfaced generation raises critical concerns for power system stability, especially during rapid transients and system split events that are not yet adequately addressed in current grid code compliance tests. This paper assesses the resilience of a Virtual Synchronous Machine (VSM) compared with a grid-following photovoltaic (PV) inverter using a combined framework of standardized benchmark tests and realistic system-split scenarios. In benchmark testing, the VSM provided synthetic inertia by delivering a transient power burst from 0.30 p.u. to 0.545 p.u. under a -0.4 Hz/s frequency ramp, corresponding to an equivalent inertia constant of approximately 15s. With the Limited Frequency Sensitive Mode-Underfrequency (LFSM-U) function enabled, it sustained additional active power up to 0.61 p.u. once the frequency fell below 49.8Hz. The PV inverter, by contrast, demonstrated compliance with conventional grid requirements: it curtailed power through LFSM-O during overfrequency conditions. It injected 0.25 p.u. of reactive current during a fault ride-through (FRT) event at 1.129 p.u. voltage. In system-split tests, the VSM absorbed surplus PV generation, stabilizing frequency after a transient rise to 52.8 Hz and preventing voltage excursions exceeding 1.2 p.u. During imbalance stress, it absorbed 1.266 MW against its 1.0~MW rating, corresponding to a 26.6 % overload. These results demonstrate that while the PV inverter contributes valuable voltage support, only the grid-forming VSM maintains frequency stability and ensures secure islanded operation. The novelty of this study lies in integrating standardized compliance tests with system-split scenarios, thereby providing a comprehensive framework for evaluating grid-forming controls from both regulatory and resilience-oriented perspectives and informing the evolution of future grid codes.

Abstract: Neospora caninum, the causative agent of abortion in cattle, has a major economic impact worldwide. This review aims to provide an overview of key advances of the last 5-8 years in understanding host-pathogen interactions, molecular mechanisms, and emerging control strategies. Epidemiological studies have revealed the influence of environmental, genetic, and ecological factors on parasite transmission dynamics, and emphasized the importance of integrated "One Health" strategies. Characteristics of different Neospora strains have been elucidated through animal models and molecular tools such as clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9 (CRISPR/Cas9)-based gene editing, high-throughput sequencing and advanced proteomics, aiming to shed light on stage-specific gene regulation and virulence factors, contributing to the development of interventions against neosporosis. Insights into immune modulation, immune evasion and parasite persistence contributed to the efforts towards vaccine development. In terms of therapeutics, repurposed drugs but also more targeted inhibitors have shown promising efficacy in reducing parasite burden and mitigating vertical transmission in laboratory models. Here, more recent innovations in nanoparticle-based drug delivery systems and immunomodulatory strategies are prone to enhance therapeutic outcomes. However, a significant challenge remains the integration of molecular and immunological insights into practical applications.

Abstract: Background: Pulmonary fibrosis (PF) is an irreversible interstitial lung disease in which TGF-β/SMAD signaling pathway plays a critical role in pathogenesis. Thymus species are known for their anti-inflammatory and antioxidant properties and may suppress PF by modulating this pathway. Therefore, this study aimed to investigate the potential antifi-brotic effects of Thymus syriacus essential oil (TS) on TGF-β/SMAD pathway in bleomycin-induced PF. Metods: PF was induced with bleomycin and TS was administered at concentrations of 50 and 100 mg/ml for 28 days. At the end of the experiment, mRNA and protein levels of TGF-β, Smad2, Col1, and α-SMA in lung tissues were analyzed using real-time PCR and ELISA. TNF-α levels in BALF were measured by ELISA, while tissue ROS levels were determined using 2,7-DHCFDA. Histopathological evaluation was performed using Hematoxylin-Eosin and Masson’s-trichrome staining. Blood samples were ana-lyzed for kidney, liver, and cardiac toxicity markers. The chemical composition of TS was determined by GC-MS. Results: TS-treated groups showed increased body weight and sig-nificantly reduced levels of TGF-β, Smad2, Col1, α-SMA, TNF-α, and ROS compared to the BLM group. PF alterations were markedly attenuated by TS treatment. Carvacrol was identified as major constituent of TS. Conclusion: Overall, TS alleviates pulmonary fibro-sis by suppressing the TGF-β/SMAD2 signaling pathway.

Abstract: Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.

Abstract: Goldbach’s conjecture, one of the oldest and most resilient problems in number theory, has traditionally been approached through additive and combinatorial methods. Despite extensive numerical verification and partial results, a structural explanation for its apparent universality remains elusive. In this work, we propose a reinterpretation of Goldbach’s conjecture within the framework of Viscous Time Theory (VTT), introducing an informational–geometric perspective in which prime numbers are treated as stable coherence attractors in an informational field. Within this framework, the pairing of two primes summing to an even integer is no longer viewed as a purely combinatorial coincidence, but as a coherence-driven event governed by informational balance and minimal decoherence pathways. We introduce measurable informational parameters, notably ΔC (coherence variation) and ΔI (informational imbalance), and show how they provide a natural ordering principle for prime pairing phenomena. The conjecture is thus reframed as a manifestation of structural stability in an informational field, rather than as a purely arithmetic property. While no classical proof is claimed, this approach offers a unifying conceptual model that accounts for the persistence of Goldbach-type pairings and connects number theory with broader informational and geometric principles. The results suggest that Goldbach’s conjecture may be interpreted as a specific instance of a more general coherence pairing mechanism in discrete informational systems. The proposed framework is further supported by large-scale numerical validation up to even integers, revealing smooth scaling behavior, bounded curvature, and stable coherence-field signatures consistent with the theoretical model.

Abstract: Background: Autism spectrum disorder (ASD) affects approximately 1-2% of children worldwide, yet its etiology remains incompletely understood. Emerging evidence suggests that offspring of parents with autoimmune diseases show elevated autism prevalence. Notably, children of parents with psoriasis (OR 1.59), type 1 diabetes (OR 1.49-2.36), and rheumatoid arthritis (OR 1.51) demonstrate particularly strong associations.Hypothesis: I propose that autism is fundamentally an immune-metabolic disorder characterized by TNF-α-mediated mitochondrial dysfunction leading to cerebral energy deficiency. This energy deficit impairs three critical processes: (1) synaptic pruning during neurodevelopment, (2) real-time social cognition including gaze processing and emotion recognition, and (3) protein synthesis of critical synaptic scaffolding molecules. The primary mechanism involves TNF-α pathway dysregulation—through genetic inheritance from parents with autoimmune diseases such as psoriasis, type 1 diabetes, and rheumatoid arthritis, and/or through direct fetal exposure to elevated maternal TNF-α during pregnancy. I further propose that the well-documented "firstborn effect" in autism reflects maternal immune maladaptation during primigravid pregnancies. Additionally, for cases without parental autoimmune history, I propose a speculative secondary mechanism: mitonuclear immune conflict, where paternal immune genes may partially recognize maternal mitochondria as non-self, generating endogenous TNF-α. Implications: This hypothesis unifies disparate observations about autism pathophysiology and suggests that anti-inflammatory interventions targeting the TNF-α pathway may have therapeutic potential, particularly when administered early in neurodevelopment.

Abstract: We present Causal Lorentzian Theory (CLT), a flat-spacetime, Lorentz-invariant field theory of gravitation with explicit causal propagation and exact local energy–momentum conservation. Gravitation is described as a physical field propagating on Minkowski spacetime rather than as spacetime curvature. Matter localization is governed by a conformal localization factor modifying physical clock rates and length scales, while photon propagation occurs through a nonlinear, polarizable and magnetizable quantum vacuum medium. A minimal nonlinear completion is introduced in which gravitational field self-energy acts as a physical source while the propagation operator remains linear and hyperbolic. The theory reproduces all experimentally tested weak-field predictions of General Relativity—including Mercury perihelion advance, gravitational light deflection, and Shapiro time delay—while predicting controlled, testable deviations in strong-field regimes such as photon-ring structure and strong gravitational lensing.

Abstract: The seemingly endless expanse of the sky might suggest that it could support a large volume of aerial traffic with minimal risk of collisions. However, mid-air collisions do occur and are a significant concern for aviation safety. Pilots are trained in scanning the sky for other aircraft and maneuvering to avoid such accidents, which is known as the basic see-and-avoid principle. While this method has proven effective, it is not infallible because human vision has limitations, and pilot performance can be affected by fatigue or distraction. Despite progress in electronic conspicuity (EC) systems, which effectively increases visibility of aircraft to other airspace users, their utility as collision avoidance systems remains limited. This is because they are recommended but not mandatory in uncontrolled airspace, where most mid-air accidents occur, so other aircraft may not mount a compatible device or have it inactive. Besides, their use carries some risks, such as over-focusing on them. In response to these concerns, this paper presents evidence on the utility of using an optical flow-based obstacle detection system that can complement the pilot and electronic visibility in collision avoidance, but that, contrary to them, neither gets tired as the pilot nor depends on what other aircraft mount, as with EC devices. The current investigation demonstrates that the proposed optical flow-based obstacle detection system meets or exceeds the critical minimum time required for pilots to detect and react to flying obstacles using a mid-air collision simulator in various test environments.

Abstract: Rigid hydrocarbon scaffolds play an increasingly important role in modern medicinal chemistry by enabling precise control over molecular geometry, lipophilicity, and target interactions. Adamantane and cubane represent two paradigmatic rigid frameworks with distinct structural and physicochemical characteristics that are highly relevant to computer-aided drug design. Adamantane is a low-strain diamondoid scaffold extensively employed in clinically approved drugs, whereas cubane is a highly strained cubic hydrocarbon that serves as a three-dimensional bioisostere of benzene and offers unique opportunities for molecular innovation. This review provides a comparative analysis of natural adamantane-containing metabolites, synthetic adamantane derivatives, and fully synthetic cubane-based compounds, with a particular focus on computer-aided prediction of biological activity and structure–activity relationships. While adamantane derivatives are well established in antiviral and neuroactive therapeutics, naturally occurring adamantane-type metabolites isolated from plants, marine organisms, and microorganisms display a broad spectrum of biological activities, including anticancer, antiviral, anti-inflammatory, neuroprotective, and cytotoxic effects. In contrast, cubane derivatives—absent from natural biosynthetic pathways—have emerged as promising synthetic pharmacophores enabled by advances in molecular synthesis and in silico screening. The biological potential of structurally diverse adamantane and cubane derivatives bearing amino, nitro, hydroxy, hydroperoxy, halogen, thiol, sulfate, phosphate, and phosphonate functionalities was systematically evaluated using the PASS (Prediction of Activity Spectra for Substances) platform. PASS-guided analysis revealed both complementary and scaffold-specific activity profiles. Aminoadamantanes, including clinically used compounds, showed strong predicted neuroprotective and antiparkinsonian activities, consistent with experimental and clinical data. Notably, phosphonate derivatives of both adamantane and cubane exhibited exceptionally high predicted antiparkinsonian activity, in several cases exceeding that of reference drugs. Selected hydroperoxy and halogenated cubane derivatives demonstrated pronounced predicted antiprotozoal, anti-inflammatory, psychotropic, and antidiabetic activities. Overall, this review highlights the value of rigid hydrocarbon scaffolds combined with computer-aided activity prediction as a strategy for identifying high-priority lead compounds. The results underscore the underexplored pharmacological potential of cubane-based phosphonates and peroxides alongside established adamantane pharmacophores, supporting their further development in neurodegenerative, infectious, and oncological drug discovery.

Abstract: FAM3A, FAM3B, FAM3C and FAM3D are members of “family with sequence similarity 3” (FAM3) gene family, an emerging class of cytokine-like proteins with a unique structural globular beta-beta-alpha fold and distinct biological functions. With widespread expression in tissue, organs and in many cell types, their specific roles in human diseases have been the focus of much research. FAM3A acts as a positive regulator of metabolic health, typically activating canonical pro-survival and metabolic pathways. FAM3B, also called PANDER (PANcreatic DERived Factor) exerts critical physiological functions in the regulation of glycemic levels via promotion of hepatic glucose production and pancreatic beta-cell insulin secretion. FAM3C, also named ILEI (Interleukin-like EMT inducer), is involved as inducer of epithelial-mesenchymal transition (EMT) and cancer metastasis, as well as osteoblast differentiation and bone mineralization. FAM3D is a gut secreted protein and potential regulator of gastrointestinal homeostasis and microbiota-induced inflammation. Here we provide an overview of previous studies supporting that FAM3 proteins can binding to putative membrane receptors and co-partners, including Fibroblast Growth Factor Receptor (FGFR), Leukemia Inhibitory Factor Receptor (LIFR), Formyl Peptide Receptor (FPR1/2), to activate diverse downstream signaling pathways on different cellular contexts. Basic and clinical studies suggest that FAM3 family influence both obesity, diabetes, and other metabolic disorders, thus its expression may have diagnostic potential. The differential and often cancer-specific expression patterns make members of the FAM3 family promising candidates for biomarkers and therapeutic targets of some types of neoplasia.

Abstract: Background/Objectives: Artificial intelligence (AI) tools for chest X-ray interpretation have gained relevance as support systems in diagnostic workflows, particularly in settings with high demand or limited specialist availability. This study evaluated the diagnostic performance of the qXR software (Qure.ai) for detecting high-risk pulmonary nodules, cardiomegaly, and pleural effusion in adult patients at Hospital Clínica Bíblica in San José, Costa Rica. Methods: Three radiologists independently interpreted 225 chest radiographs, serving as the reference standard. qXR results were compared against this standard for each finding. Sensitivity, specificity, Cohen’s kappa, and area under the curve (AUC) were calculated. Predictive values were not used for interpretation due to the artificial prevalence of the sample. Results: qXR showed higher agreement with radiologist assessments for pulmonary nodules and pleural effusion, achieving moderate to substantial concordance. Performance for cardiomegaly was more variable, with lower agreement across evaluators. Overall diagnostic accuracy was acceptable, although the magnitude differed by condition. Conclusions: These findings underscore the importance of validating AI diagnostic tools within local clinical environments and heterogeneous imaging conditions. qXR demonstrated potential as a complementary aid for detecting pulmonary nodules and pleural effusion, while its performance for cardiomegaly should be interpreted with caution. The study does not provide evidence of real-world clinical impact.

Abstract: This paper features a 1000 simulations of a set of 100 levered companies equity returns in a financial market. The goal was to generate a realistic distribution of company values that follow a Zipf-Mandelbrot power law. The returns should exhibit leverage effects, negative skewness, and feature Black Swan events of correlated down-turns. Realistic positive covariance structures of returns, systematic risk, plus evidence of long-memory properties. The Merton Model and two versions of the Platen Benchmark Asset Pricing Model (BAPM), the original model and the Stochastic Benchmark Process (SBP). The required market attributes were successfuly captured but the models proved to be highly sensitive to the chosen parameters. The BAPM model proved to be more flexible than the Merton Model and the SBP version more readily generated the stipulated financial market characteristics.

Abstract: Wheat production in Zimbabwe is strongly influenced by environmental variability, making it difficult for breeders to identify genotypes that are both high yielding and stable across locations. This study evaluated the yield performance and stability of pre-release bread wheat genotypes across contrasting environments in Zimbabwe. A total of 25 genotypes in 2020 and 24 genotypes in 2021 were tested using a randomized complete block design (RCBD) with three replications at three sites Gwebi Variety Testing Centre (GVTC), Harare Research Station (DR&SS), and Panmure, forming six test environments across two winter seasons. Grain yield and key agronomic traits were recorded and analyzed using combined analysis of variance, correlation analysis, and genotype plus genotype-by-environment (GGE) biplot methods. The combined analysis of variance revealed highly significant (p < 0.001) effects of location on all traits in both years, confirming strong environmental influence on wheat performance. Genotypic differences were also significant for most traits in each season. In 2020, genotype × location interaction for grain yield and grain weight was not significant, indicating relatively stable genotype ranking across environments. In contrast, significant genotype × location interaction in 2021 demonstrated strong crossover effects, with genotypes responding differently across sites. When the 15 genotypes common to both years were analyzed together, genotype × location interaction for grain yield was again not significant, indicating that this subset of genotypes expressed greater yield stability across environments. GGE biplot analysis revealed clear differences in genotype adaptation and stability. The mean versus stability view identified G10 and G4 as high yielding with moderate stability, while G5 and G8 were closest to the ideal genotype, combining high yield and wide adaptation. The which-won-where pattern separated the test environments into two main mega-environments, with G3 and G10 performing best in GVTC- and Harare-based environments, while G4, G5, and G8 were superior at Panmure-related environments. Environment E3 (Harare 2020) was identified as the most representative and closest to an ideal test environment, while E1, E2, E5, and E6 were more discriminating and useful for detecting genotype differences. The findings of this study demonstrated that both yield level and stability must be considered when selecting wheat genotypes for Zimbabwe. Genotypes G5 and G8 showed the best combination of high grain yield and stability and are therefore recommended for broad adaptation. Genotypes such as G3 showed high yield but strong environmental sensitivity and are better suited for specific environments. These findings provide valuable guidance for wheat breeding and variety recommendation in Zimbabwe’s diverse production environments.

Abstract: International agricultural trade plays a crucial role in balancing the global food supply and demand while facilitating the cross-regional allocation of cropland resources. This study examines the virtual cropland flows embedded in international wheat trade. Utilizing the telecoupling framework and wheat trade data from eight time points between 1995 and 2023, we developed a global virtual-cropland-flow network. Social network analysis (SNA) was used to characterize the structural features and identify telecoupling systems, whereas the quadratic assignment procedure (QAP) regression was applied to analyze the driving factors. The findings reveal that (1) the virtual cropland network shows structural vulnerability and evolutionary complexity amid increasing connectivity, with an overall rise in density and significant fluctuations in the average clustering coefficient and path length. (2) The network exhibits a distinct telecoupling structure. The sending system has shifted from U.S.-Canada dominance to a multipolar pattern involving Australia, Canada, Kazakhstan, and the United States. The receiving systems mainly comprise Asia, Africa, and Latin America, with China as the core inflow country. The United States and France, supported by major transnational agribusinesses, act as key spillover systems, consistently holding a high betweenness centrality. (3) Economic development and foreign demand significantly promote the establishment and intensification of trade relationships between countries. Geographical distance has a dual effect: it strongly negatively influences trade initiation but can be overcome by high complementarity between countries during trade deepening. (4) Although international wheat trade effectively conserves global cropland resources, it also introduces systemic risks and environmental spillovers in some countries. Developing nations that are highly dependent on wheat imports, such as Egypt, are more vulnerable to network fluctuations. By integrating multidisciplinary perspectives, this study provides a scientific basis for constructing sustainable food trade systems and agricultural resource governance. It offers valuable insights for advancing SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), sustainable land systems, and the optimization of global land governance.

Abstract: The Surviving Sepsis Campaign (SSC) 1-hour bundle has transformed sepsis care in high-income countries. This bundle comprises rapid lactate measurements, blood cultures, broad-spectrum antibiotics, intravenous fluids, and vasopressors. However, in fragile systems such as Turkana County, Kenya, this protocol is largely impractical. This review synthesises current global and regional literature to contextualise the bundle’s limitations and propose evidence-based adaptations. Long travel distances, shortage of essential diagnostics and medicine, limited human resources, and inadequate critical care capacity remain significant systemic barriers. This review advocates for reframing the bundle from a fixed 1-hour metric to an “as soon as possible” (ASAP) framework, emphasising early recognition, timely empirical antibiotics, and pragmatic hemodynamic stabilisation using available resources. Key recommendations include replacing lactate measurements with clinical surrogates (such as capillary refill time), creating locally informed empirical antibiotic protocols, strengthening supply chains, investing in task-sharing and simulation-based training, and embedding community awareness initiatives. These adaptations can achieve meaningful mortality reduction and mitigate antimicrobial resistance.