Abstract: Autism spectrum conditions have been associated with alterations in synaptic transmission, critical period remodeling, and excitatory–inhibitory balance across distributed neural circuits. Converging evidence from genetic, electrophysiological, and animal model studies suggests that dysregulated activity-dependent synaptic plasticity—particularly altered long-term potentiation or long-term depression within hippocampal-cortical, cortico-striatal, and cerebellar networks—may contribute to reduced cognitive flexibility, repetitive behaviors, and difficulties in social and communicative adaptation. In this framework, core behavioral features of autism may reflect circuit-level persistence of previously-formed neural representations and altered updating of new information, rather than global neural dysfunction.Here we propose that modulating activity-dependent plasticity and excitatory–inhibitory dynamics may represent a plausible strategy for supporting cognitive flexibility in autism. Cannabinoids and terpenes derived from Cannabis sativa interact with multiple neural signaling systems—including CB1 receptors, GPR55, TRP channels, voltage-gated ion channels, serotonergic pathways, and endocannabinoid metabolism—that are known to influence synaptic transmission and plasticity. By engaging these convergent mechanisms, interactions among multiple botanical compounds may influence circuit-level excitability and synaptic plasticity processes implicated in autism.Within this framework, we consider a set of phytocompounds—comprising a dozen cannabinoids and terpenes—with documented interactions across neural signaling pathways that regulate synaptic plasticity and excitability. Together, this perspective provides a mechanistic rationale for how multi-compound cannabinoid–terpene interactions may influence neural circuit dynamics underlying cognitive flexibility in autism.

Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs) by embedding physical laws directly into the neural network training process. This paper presents a comprehensive comparative study of PINNs against traditional numerical methods—Finite Element Method (FEM) and Finite Difference (FD)—for solving second-order boundary value problems. We focus on the canonical problem u″(x)=e^-x on the domain [0,1] with Dirichlet boundary conditions u(0)=1 and u(1)=e^-1, which admits the exact analytical solution u(x)=e^-x. The PINN architecture employs a trial solution formulation that automatically satisfies boundary conditions, utilizes automatic differentiation for computing derivatives, and leverages the L-BFGS optimizer with Sobol quasi-random collocation points. We provide rigorous mathematical derivations of the PINN loss function, trial solution construction, automatic differentiation chain rules, FEM weak formulation with stiffness matrix assembly, and FD central difference schemes. Numerical experiments demonstrate that PINNs achieve comparable accuracy to FEM and FD methods while offering mesh-free flexibility and the ability to incorporate physical constraints naturally. The relative L² error for all three methods remains below 10^-3, validating the effectiveness of physics-informed learning for boundary value problems. This work contributes to the growing body of evidence supporting PINNs as a viable alternative to classical numerical methods in computational physics and engineering.

Abstract: Ulcerative colitis (UC) is a chronic, relapsing-remitting subtype of inflammatory bowel disease (IBD), featured by continuous mucosal inflammation restricted to the colon and rectum. Although the exact pathogenesis of UC has not been fully clarified, intestinal barrier impairment and disrupted mucosal homeostasis are recognized as the central mechanism. Therefore, restoring intestinal mucosal barrier function represents a core strategy for UC prevention and treatment, which aligns with the therapeutic goal of achieving mucosal healing and sustained remission. In this review, we outline the composition and functional significance of the intestinal barrier, explore key mechanisms underlying its disruption, and summarize recent advances in UC-related monitoring strategies. Finally, we explore novel therapeutic approaches aimed at epithelial barrier repair. The review aims to provide insights valuable for both basic research and clinical management of UC.

Abstract: Antibody-dependent enhancement (ADE) is a paradoxical immunological phenomenon in which pre-existing antibodies facilitate viral entry into host cells rather than conferring protection. ADE has been extensively characterised in flaviviral systems, most notably dengue virus (DENV), and presents a significant challenge for vaccine development and antibody-based therapeutic design. In coronavirus infections, ADE operates through both classical Fc gamma receptor (FcγR)-mediated pathways and an intrinsic signalling mechanism involving inhibitory FcγRIIb-mediated suppression of the type I interferon (IFN-I) response. Of critical translational relevance is the proposed cooperative FcγR–angiotensin-converting enzyme 2 (ACE2) entry model for SARS-CoV-2, wherein virus–antibody immune complexes simultaneously engage ACE2 through the viral spike receptor-binding domain (RBD) and FcγRIIa through the antibody Fc region on the same macrophage surface. This cooperative dual-receptor engagement may stabilise virion attachment, augment endosomal uptake, and trigger downstream signalling cascades that suppress antiviral immunity, potentially contributing to severe COVID-19 immunopathology. Feline infectious peritonitis virus (FIPV) represents one of the most rigorously documented biological systems in which antibody-mediated macrophage infection directly determines systemic disease outcome, providing a critical comparative framework for understanding coronavirus ADE across species. This comprehensive review integrates current knowledge of FcγR biology, coronavirus cell entry mechanisms, intracellular signalling cascades, cytokine dysregulation, comparative veterinary immunopathology, and nano-engineered immunomodulatory platforms for ADE-safe vaccine development. We critically evaluate lipid nanoparticle mRNA vaccines, virus-like particles, and polymeric nanoparticle systems as rational strategies to elicit selective neutralising antibody responses while mitigating ADE risk. We also highlight key unresolved mechanistic questions and future research directions essential for the development of safer vaccines and therapeutics against both current and emerging coronaviruses in human and veterinary medicine.

Abstract: In an increasingly integrated global financial system, the effectiveness of macroprudential policy is shaped not only by domestic conditions but also by cross-border spillovers and regulatory interactions. Financial integration enables institutions to circumvent national regulations through regulatory arbitrage, while shocks are rapidly transmitted across economies via capital flows and interconnected financial markets. In response, countries often adopt inward-looking macroprudential measures to shield domestic systems; however, without coordination, such policies can generate offsetting effects, amplify volatility, and, in extreme cases, lead to regulatory conflicts. This has led to growing calls for cross-country macroprudential policy coordination, though its relative effectiveness compared to country-specific approaches remains an open empirical question. This study evaluates the relative effectiveness of coordinated and country-specific macroprudential policies in advanced systemic economies (ASEs) and systemic middle-income countries (SMICs), which collectively dominate global output and financial activity and generate substantial international spillovers. Despite extensive theoretical support for coordination, the empirical literature remains fragmented, with studies typically examining either coordination or domestic policies in isolation. To address this gap, the study develops a novel proxy for macroprudential policy coordination based on the co-movement of national policy indices and integrates it with country-specific measures within a unified empirical framework. Using a Dynamic Common Correlated Effects model and a Panel Structural Vector Autoregression model, the study examines the impact and transmission of both policy types on capital flows, credit growth, and property prices. The findings indicate that both coordinated and domestic macroprudential policies generate cross-country effects, particularly through capital flow reallocation. However, important trade-offs emerge. While domestic policies are effective in curbing excessive credit and housing market growth, coordinated policies tend to support expansion in these sectors. These results highlight that neither approach is universally superior. Instead, an optimal policy framework requires balancing country-specific flexibility with cross-country coordination to mitigate spillovers and enhance global financial stability.

Abstract: Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand---producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with \emph{event-driven ontology simulation}: business events trigger scenario conditions encoded in the enterprise ontology~(EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph $G_{\text{sim}}$; all decisions are derived exclusively from this evolved graph. The core pipeline is \emph{event $\to$ simulation $\to$ decision}, realized through a dual-mode architecture---\emph{skill mode} and \emph{reasoning mode}. Every decision produces a fully traceable audit log. LOM-action achieves 93.82% accuracy and 98.74% tool-chain F1 against frontier baselines Doubao-1.8 and DeepSeek-V3.2, which reach only 24--36% F1 despite 80% accuracy---exposing the \emph{illusive accuracy} phenomenon. The four-fold F1 advantage confirms that ontology-governed, event-driven simulation, not model scale, is the architectural prerequisite for trustworthy enterprise decision intelligence.

Abstract: The integration of real-time web technologies and Generative Artificial Intelligence (AI) into campus infrastructure is creating new paradigms for institutional resource management. In campus dining environments, traditional offline management leads to significant food waste, inefficient resource allocation, and delayed feedback loops. This survey reviews the transition from static Enterprise Resource Planning (ERP) systems to dynamic, AI-optimized ecosystems. We synthesize recent advancements in multiple core areas: (i) frictionless Role-Based Access Control (RBAC) using decentralized access codes, (ii) real-time data synchronization for live inventory telemetry via WebSocket protocols, (iii) the application of Large Language Models (LLMs) for automated, sentiment-driven menu generation using structured prompt engineering, (iv) mathematical modeling of sustainability metrics to reduce organic waste, and (v) fault-tolerant microservice architectures. By bridging the gap between operations management and student end-users, this paper outlines how modern architectural frameworks—such as those utilizing Node.js backends, document- based NoSQL databases, Redis-backed event brokers, and Gemini AI integration—can resolve the critical information lag in institutional dining. We propose the TasteTrack architecture as a comprehensive, scalable solution to these challenges, detailing its mathematical foundations, cryptographic privacy models, and algorithmic workflows.

Abstract:

This study evaluated the effects of light spectral quality on shoot yield and essential oil of Tagetes erecta L. cultivated in controlled growth chambers. Plants were grown for up to 101 days under three LED lighting treatments with different red, blue, and white wavelength ratios and a constant 16 h photoperiod. The F2 treatment (5 red­­:1 blue) produced yields of fresh shoots, early blooming flowers, and oils of 271 ± 28 g/tray, 97.43 ± 13.14 g/tray, and 52.46 ± 5.41 mg/tray, respectively. These values were significantly higher (p < 0.05) than those of the F1 treatment (white:red-phosphor), and represented increases of 1.37-, 1.26-, and 1.38-fold, respectively. Gas chromatography identified three major oil constituents—(E)-β-ocimene (22.9–28.8%), (E)-myroxide (13.9–20.6%), and piperitone (7.3–9.6%)—among a total of 24—25 compounds. Essential oils inhibited from four to five of the seven tested microbial strains, with the notable activity against Escherichia coli and Candida albicans recorded in F2 and F1, respectively. These findings confirm that light spectral quality is a critical factor regulating flower, essential oil yield, and antimicrobial efficacy in T. erecta, and support the use of optimized LED spectra as a practical approach to improve plant’s yield and phytochemical quality.

Abstract: Background: To describe the comparison between Black and White pregnant and postpartum women with COVID-19 regarding the need for hospitalization in the Intensive Care Unit, mechanical ventilation, and outcome to death. Methods: Integrative Literature Review conducted in Cumulative Index to Nursing and Allied Health Literature, Excerpta Medica Database, Latin American and Caribbean Literature in Health Sciences, Medical Literature Analysis and Retrieval System Online via the National Library of Medicine, Science Direct from Elsevier Scientific Publications, SciVerse Scopus, and Web of Science. Results: Black pregnant and postpartum women presented worse clinical outcomes; the risk of death among these women was up to five times higher compared to White women, in addition to inequalities in timely access to intensive care. Conclusions: Black pregnant and postpartum women were disproportionately affected by COVID-19, with a higher risk of severe complications and death, which reinforces the need for care strategies that consider racial inequalities to reduce maternal mortality.

Abstract: Rapid developments in biotechnology, rDNA, and gene editing technologies have transformed both biomedical sciences and environmental biotechnology, especially genome editing, synthetic biology, and reproductive biotechnology, etc., and have revolutionized medicine, agriculture, and environmental sciences, etc. However, such biotechnologies also pose serious bioethical, biosafety, and biosecurity risks and challenges, etc. This review critically discusses and analyzes the current bioethical issues and dilemmas associated with biotechnologies such as CRISPR-Cas9, in vitro fertilization (IVF), cloning, xenotransplantation, PGD, transgenic organisms, etc. The study adopted a literature-based methodology to critically examine the current bioethical debates and discussions on biotechnologies, etc. The study findings revealed serious bioethical issues and dilemmas associated with biotechnologies, such as risk-benefit analysis, justice, human dignity, and eugenics, etc. This review also aims to incorporate Islamic bioethics, focusing on maqasid al-shariah, such as maintaining life, lineage, principle of necessity and dignity, etc. This study concluded that biotechnology has tremendous potential, and its use and development require global governance, ethical literacy, and culturally sensitive approaches, etc.

Abstract: Digital transformation represents a critical challenge for contemporary organizations, yet substantial variation persists in their ability to successfully implement digital initi-atives. This study examines whether digital leadership acts as a key enabling factor by shaping the organizational mechanisms through which transformation is realized. Drawing on leadership theory, innovation climate research, and the dynamic capabili-ties perspective, the study develops and tests a structural model linking Digital Vision Leadership, Innovation Climate, Digital Capability Development, Technology Integra-tion, and Digital Transformation Outcomes. Data from 2,901 respondents were ana-lyzed using structural equation modeling. The results show that Digital Vision Lead-ership significantly influences both Innovation Climate and Digital Capability Devel-opment, while Innovation Climate enables capability development and technology in-tegration. Technology Integration emerges as the primary driver of transformation outcomes, supported by additional direct effects of capabilities and innovation climate. Notably, the direct effect of Digital Capability Development on Technology Integration is not supported, indicating that capabilities require enabling organizational condi-tions to be effectively deployed. The findings demonstrate that digital transformation is not driven by capabilities or technology alone, but by a structured sequence of or-ganizational mechanisms in which leadership and innovation climate determine whether capabilities translate into technology integration. The study contributes by advancing a process-based model of digital transformation and clarifying why digital capabilities alone do not ensure successful technology implementation.

Abstract: Hydrogen production via catalytic steam reforming of hydrocarbons is a promising route for fuel cells and distributed energy systems. In this work, Ni–Cu/γ-Al₂O₃ catalysts were prepared and evaluated for iso-octane steam reforming. The effects of catalyst composition and reaction temperature on activity and hydrogen yield were systematically studied. Results show that Cu incorporation significantly enhances catalytic stability and reduces carbon deposition. At 550 °C and a steam-to-carbon ratio of 2, the Ni₀.₅Cu₀.₅/γ-Al₂O₃ catalyst achieved the highest hydrogen yield and conversion, outperforming monometallic Ni catalysts under identical conditions. This improvement is attributed to better metal dispersion and synergistic interactions between Ni and Cu. Compared with reported catalysts, the developed system exhibits competitive performance under moderate conditions, providing useful insights for designing efficient catalysts for hydrocarbon reforming.

Abstract: The rapid proliferation of large language model (LLM) powered multi-agent systems creates a non-trivial combinatorial optimization problem: routing heterogeneous tasks to the most cost-effective model tier while maintaining quality guarantees. Current production systems rely on static lookup tables, which over-provision expensive models and waste computational budget. We formalize the LLM Cascade Routing Problem (LCRP) as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using the Quantum Approximate Optimization Algorithm (QAOA). We benchmark QAOA against greedy heuristics and simulated annealing using both Google Cirq simulation and real IBM Quantum hardware (156-qubit Heron processors). Experiments across three IBM backends (ibm_fez, ibm_kingston, ibm_marrakesh) on problem instances from 6 to 18 qubits reveal three key findings: (i) shallow QAOA circuits (p=1, depth 52) achieve 15.4% valid assignment rate on real hardware versus 0.8% for deeper circuits (p=2, depth 101), demonstrating that NISQ noise favors shallow ansatze; (ii) hardware constraint satisfaction degrades steeply with problem size, dropping from 37-43% at 6 qubits to 0.2-0.3% at 18 qubits; and (iii) results are reproducible across all three backends with consistent valid rates within plus or minus 1.5%. To our knowledge, this is the first quantum computing formulation of the LLM model routing problem. We provide an open-source implementation and discuss the projected quantum advantage horizon.

Abstract: Weed infestation significantly threatens crop productivity and quality, highlighting the need for accurate and scalable monitoring approaches. Recent advances in unmanned aerial vehicle (UAV) remote sensing and deep learning provide promising tools for field-scale weed detection. This study evaluates and compares two state-of-the-art instance segmentation models, Mask R-CNN and YOLOv8, for species-level weed detection in wheat fields under Mongolian agro-ecological conditions. The experiment was conducted in a 4 ha wheat field in Tuv Province, Mongolia, using high-resolution RGB imagery acquired from UAV flights in July 2025. Three dominant weed species were annotated and analyzed. Model performance was evaluated using mAP@0.5:0.95, Precision, Recall, F1-score, and mask IoU. At IoU thresholds of 0.25 and 0.5, both models demonstrated moderate detection performance (IoU = 0.25: Precision 0.49–0.76, Recall 0.20–0.77, F1-score 0.32–0.75; IoU = 0.5: Precision 0.42–0.67, Recall 0.18–0.75, F1-score 0.28–0.69), with variation among weed species. Mask R-CNN achieved higher Recall and more precise boundary delineation, improving weed coverage estimation, whereas YOLOv8 provided faster inference (≈11 ms per image, ~90 FPS) and higher precision, making it more suitable for large-area and near-real-time monitoring. These findings demonstrate the potential of UAV-based instance segmentation for weed detection in Mongolia and provide practical guidance for model selection in precision agriculture applications.

Abstract: As IoT systems complexity grows, transparent and trustworthy machine-learning Intrusion Detection Systems are crucial. Post hoc explainable AI methods, such as SHAP and LIME, are the most widely used ways to explain how models work, but the degree to which these methods are robust to adversarial conditioning is understudied. In this paper, we propose to create a unified system of evaluating explanation fidelity by using three metrics : sparsity, completeness and robustness based on minimally distorting DeepFool input perturbations. Our study benchmarks SHAP and LIME across three datasets (BoT-IoT, Edge-IIoT, N-BaIoT) using four classifiers: CNN, DNN, LSTM, and RF. Our results demonstrate a consistent trade-off: SHAP achieves stronger causal alignment and higher completeness under attack, whereas LIME exhibits greater rank-stability in terms of top-k feature overlap. However, LIME also produces more spurious attributions and offers less explanatory power than SHAP, especially in the presence of synthetic or non-causal features. Our findings reveal that high model accuracy does not guarantee that the provided explanation is also high-fidelity. This investigation highlights the necessity for robustness-aware XAI in cybersecurity and provides reproducible parameters to guide the adoption of XAI in adversarial environments.

Abstract: Background: The domain of microencapsulation technology is considered to be at the level of an advanced scientific discipline that includes the fields of materials science, pharmaceutical engineering, and food technology in the formulation of very specific matrices of polymeric or lipid nature. Method: In this review, a comprehensive analysis of sixteen different techniques of microparticles preparation has been presented: Solvent Evaporation, Solvent Extraction, Coacervation, Spray Drying, Spray Congealing, Ionic Gelation, Interfacial Polymerization, Air Suspension, Pan Coating, In-situ Polymerization, Supercritical Fluid Technology, Electrospraying, Microfluidics, Sol-Gel Process, Hot Melt Encapsulation, and Salting Out. Each technique has been explained by describing the basic physical and chemical phenomena that govern the process of microparticles formation. Results: The review has been presented with a critical analysis of the operating parameters, along with the core and shell material, as well as the applications of the technique, which are of interest in the field of pharmaceuticals, cosmetics, food, and medicine. Conclusion: The types of drugs that are best suited for the particular technique, as per their physical and chemical properties, i.e., solubility in water, lipid solubility, acid–base properties, as well as their thermoreactive properties, have been discussed in the review. The possibility of scaling up the technique from the laboratory scale to the industrial scale has been evaluated by searching the patent database, as well as the grant status of the patents, presented in the review. The prospective industrial applications of the technique, as well as the current limitations that restrict the scaling up of the laboratory-scale protocol, have been discussed in the review.

Abstract: Background/Objectives: Disease-related malnutrition affects millions of patients worldwide. Nutrition support therapy (NST), namely oral nutritional supplements (ONS), serve as a cornerstone therapeutic intervention. However, treatment effective-ness depends not only on appropriate prescription but also on patient acceptance and adherence. This study evaluates the provision pathway of ONS within a co-payment healthcare system, focusing on patient acceptance patterns, barriers to adherence, and the critical yet underexplored role of pharmacist-patient interactions in determining treatment outcomes. Methods: A cross-sectional observational study was conducted across 100 Croatian community pharmacies during September-October 2025. Pharma-cists prospectively documented 973 patient encounters involving ONS prescriptions requiring co-payment using real-time patient record forms. Data captured patient demographics, diagnoses, prescription patterns, prior knowledge of co-payment re-quirements, acceptance responses, and pharmacist-assessed reasons for refusal. Re-sults: While 65% of all patients knew about co-payment requirements in advance, 51% of first-time users arrived uninformed, leading to dramatically different acceptance patterns (93% immediate acceptance when informed versus 33% when uninformed, p< 0.05). Overall, 8-12% of patients refused or reduced prescribed ONS. Among refus-als, 59% cited financial burden, but critically, 23% appeared not to understand why ONS was prescribed or what benefits to expect, revealing significant communication gaps in the care pathway. Fifteen percent of patients overall required pharmacist ex-planation before accepting their prescription, demonstrating pharmacists' decisive role as gatekeepers of nutritional therapy. Conclusions: Successful ONS provision requires enhanced collaborative practice across prescribers, pharmacists, and patients or their families. Key interventions include comprehensive prescriber-patient communication about co-payment and clinical rationale, specialized pharmacist education in dis-ease-specific nutrition and ONS counseling, and systematic communication protocols between prescribers and pharmacists. The pharmacy dispensing encounter represents an important decision point, where insufficient preparation and coordination may lead to avoidable treatment failures among vulnerable patient populations.

Abstract:

The viscosity stability of the polymer solution is one of the challenges in enhancing oil recovery and zwitterionic copolymer presents excellent viscosity stability and emulsification performance, enabling effective control the oil/water interface mobility and enhancing oil recovery. Herein, a zwitterionic copolymer (P(AM/AMBS/MAPTAC)) containing sulfonic acid group and quaternary amine group was synthesized by segmentation initiation with AM, AMBS and MAPTAC as monomers. The chemical structure of P(AM/AMBS/MAPTAC) was confirmed by FTIR and ¹H NMR. The M_w value of (P(AM/AMBS/MAPTAC)) was 9.91×10⁶, and the apparent viscosity of the solution of 2000 mg/L solution was 24.92 mP·s at 60 ℃ in the 5000 mg/L salt solution. P(AM/AMBS/MAPTAC) with the sulfonic acid group and the quaternary amine group exhibits outstanding salt tolerance and shear resistance. When the salinity was 10000 mg/L and the shear rate was 300 s^-1, the apparent viscosity and the viscosity reduction rates for the P(AM/AMBS/MAPTAC) solution were 23.45 mP·s and 69.23 %, respectively. Moreover, P(AM/AMBS/MAPTAC) exhibited higher emulsion property and higher oil-water interface thickness than HPAM and SPAM because of the synergistic effect of sulfonic acid and quaternary amine groups in the P(AM/AMBS/MAPTAC) molecule. The polymer flooding and the alkali-surfactant-polymer flooding formed by P(AM/AMBS/MAPTAC) had high chemical oil recovery and the oil displacement efficiency was higher than HPAM and SPAM in the polymer flooding and the alkali-surfactant-polymer flooding systems.

Abstract: Relative simultaneity remains a highly debated issue. It is presented as a necessary physical consequence of the Lorentz transformation (LT). However, we demonstrate that the phenomenon is an artefact of 'mixed-coordinate' algebraic representation, which does not guarantee that the stationary system S 4-vector transformed to the moving system S’ are both covariant. A covariant representation of a transformed 4-vector which components are explicit functions of time, requires them to be expressed as functions of the local time in S and in the moving frame S'. While the one step multiplication of LT matrix by the 4-vector in S, yields correct algebraic expressions, the ‘raw’ resulting 4-vector retains the variable t throughout all components. This ‘mixed-coordinate’ representation is incomplete; it is not in a form covariant with the vector in S because its components are not functions of t'. The variable t must be replaced by equating the first component of the transformed vector to ct’ and substituting the resulting expression into all instances of t in the ‘raw’ 4-vector. After this procedure applied to two simultaneous events in S, the apparent time difference between the events in S’ becomes ∆t’=0. The effect of relative simultaneity, which appears in the ‘mixed-coordinate’ representation, is absent. This highlights the role of emergent ‘absolute-like time’ hidden within the structure of LT equations affecting temporal relations, suggests that the "Relative Now" discussed by Eddington in 1927 resulting from widely known conclusion ∆t’≠0 is a mathematical artefact of coordinates convention rather than the physical reality.

Abstract: In the trajectory tracking of an autonomous vehicle, a lane-keeping control loop is fundamental. This involves a correct orientation of the yaw angle, which is achieved by actuating the steering. When addressing this type of control, one possible approach is to consider the design of a robust controller with various performance requirements defined by weighting functions. This procedure usually leads to a high-order controller, which entails a computational cost that burdens the processor dedicated to other high-demand control loops, such as computer vision algorithms. In this work, an interlacing procedure for the implementation of the robust controller will be introduced, which will allow a substantial reduction in computational load. The technique is applied to the state-space controller, allowing its extrapolation to MIMO controllers. Several options will be discussed, and the effectiveness and validity of the method will be evaluated through results based on real path tracking.