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The platformization of urban space is opening new frontiers of capital accumulation, particularly through short-term rentals. Airbnb plays a central role in this process by commodifying housing in tourist destinations. Despite its rapid growth, research on Airbnb in Latin America—especially in small tourist cities—remains limited and largely focused on metropolitan contexts. This article addresses this gap with the objective of analyzing how platform-mediated short-term rentals reorient housing markets beyond traditional urban cores. It is hypothesized that Airbnb expands housing commodification by extending tourism-oriented uses into new residential areas and by redistributing returns unevenly across actors. Using a quantitative and geospatial approach, the results reveal a strong presence of Airbnb in rural and natural areas, from which the highest returns are extracted, as well as a high concentration of accommodation supply among professional hosts. These dynamics reconfigure housing use toward asset-based logics, posing challenges for housing security and social and territorial sustainability in small tourist cities. Full article

(This article belongs to the Special Issue Sustainable Development of Regional Tourism)

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36 pages, 2926 KB

Open AccessReview

Advances in Nanotechnological Strategies for Preserving and Authenticating Bioactive Compounds in Extra Virgin Olive Oil: Nano-Enabled Stabilization, Sensing, and Circular Valorization

by José Roberto Vega Baudrit, Yendry Corrales-Ureña, Karla Jaimes Merazzo, Javier Stuardo Chinchilla Orrego and Mary Lopretti

Foods 2026, 15(8), 1278; https://doi.org/10.3390/foods15081278 (registering DOI) - 8 Apr 2026

Abstract

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

(This article belongs to the Section Food Engineering and Technology)

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33 pages, 875 KB

Open AccessReview

Artificial Intelligence for High-Availability Systems: A Comprehensive Review

by Lidia Fotia, Rosario Gaeta, Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarné

Computers 2026, 15(4), 231; https://doi.org/10.3390/computers15040231 (registering DOI) - 8 Apr 2026

Abstract

High-availability (HA) systems—essential in many contemporary contexts—are designed to guarantee the availability of processes and data for more than 99% of their operational time. These systems are typically implemented as Cloud/Edge infrastructures that are properly maintained by human operators and intelligent agents in order to guarantee the required level of availability. Moreover, we are witnessing the widespread adoption of AI-based automation across many industries. AI-based software agents are increasingly being adopted to introduce more automation in highly available systems, particularly for monitoring and fault detection, fault prediction, recovery, and optimization processes. In this review paper, we discuss the state of the art of AI-based solutions for HA systems. In particular, we focus on the use of AI for the core operational mechanisms of monitoring, failure detection, and recovery. Our discussion begins by reviewing a few key background concepts of HA architectures, then we review recent work on AI-based solutions for monitoring, fault detection and recovery in HA systems. Full article

(This article belongs to the Special Issue Recent Trends in Dependable and High Availability Systems)

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32 pages, 7135 KB

Open AccessArticle

Evolutionary Multi-Objective Prompt Learning for Synthetic Text Data Generation with Black-Box Large Language Models

by Diego Pastrián, Nicolás Hidalgo, Víctor Reyes and Erika Rosas

Appl. Sci. 2026, 16(8), 3623; https://doi.org/10.3390/app16083623 (registering DOI) - 8 Apr 2026

Abstract

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

(This article belongs to the Special Issue Resource Management for AI-Centric Computing Systems)

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12 pages, 800 KB

Open AccessArticle

Preliminary Experimental Study on the Removal of Staphylococcus epidermidis and Pseudomonas aeruginosa from Surgical Instrument Surfaces Under Controlled Conditions

by Edmar Gonçalves Pereira Filho, Stéfanne Rodrigues Rezende Ferreira, Amanda Veiga Paiva Simões, Eli Júnior Pereira Rodrigues, Iorrana Morais de Oliveira, Marillia Lima Costa, Adeliane Castro da Costa, Berendina Elsina Bouwman and Hanstter Hallison Alves Rezende

Microbiol. Res. 2026, 17(4), 77; https://doi.org/10.3390/microbiolres17040077 (registering DOI) - 8 Apr 2026

Abstract

The objective of this study is to evaluate the efficiency of surgical instruments’ manual cleaning versus automated cleaning in an ultrasonic cleaner for the removal of biofilms on surgical forceps contaminated with Staphylococcus epidermidis and Pseudomonas aeruginosa. Subsequently, the residual microbial load was quantified through microbiological culture, aiming to evaluate the effectiveness of biofilm removal under different reprocessing conditions. Cleaning is an essential step in the processing of surgical instruments to ensure the effective removal of dirt and microorganisms. Through adhesion, microorganisms can attach to surfaces and form biofilms, organized structures surrounded by an extracellular matrix consisting of various components, which favor metabolic exchanges, adaptation, resistance, and bacterial dispersion. These biofilms increase the pathogenic potential of microorganisms, contributing to the occurrence of Healthcare-Associated Infections, and to avoid these, it is essential that preventive measures aimed at microbial reduction are adopted. Automated cleaning proved more effective than manual cleaning, and the combined approach achieved the greatest microbial reduction, though persistent contamination was still observed. The ability of adhesion and biofilm formation on the surfaces of surgical instruments is regarded as a challenge for complete microbial removal. These findings enhance the need for more rigorous reprocessing protocols and complementary strategies to ensure greater safety in the use of reusable instruments in clinical practice. Full article

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26 pages, 9959 KB

Open AccessFeature PaperArticle

Sustainable Humidity and Thermal Management in UK Indoor Swimming Pools with Liquid Desiccant Technology

by Alessandro Giampieri, Janie Ling-Chin, Christopher Beeson and Anthony Paul Roskilly

Energies 2026, 19(8), 1823; https://doi.org/10.3390/en19081823 (registering DOI) - 8 Apr 2026

Abstract

Indoor swimming pools require ventilation and precise temperature and humidity control, leading to significant energy consumption. This study investigated the use of liquid desiccant technology to reduce energy consumption for heating and dehumidification of two indoor swimming pools in a UK leisure centre. Through dynamic modelling and techno-economic analysis, this research quantified heat losses in the pools, simulated the performance of liquid desiccant technology, evaluated the economic benefits and cost implications of regenerating the desiccant solution using waste heat, and assessed the feasibility of adopting the technology across the entire UK. The results showed that evaporative losses were the dominant heat loss mechanism for both pools, while the proposed liquid desiccant system effectively maintained optimal temperature and humidity conditions. Additionally, pool water can serve as a heat sink after desiccant regeneration, thereby reducing the energy demand for pool water heating. Energy consumption could be reduced by 68.9–76.7% when using a cooling tower and 77.5–88.1% when using pool water for heat rejection, with internal rates of return that can exceed 15% for the most cost-effective configurations. If the regeneration heat is sourced externally, up to £34.7/MWh could be paid for the heat required while ensuring the cost-effectiveness of the process. These findings suggest that liquid desiccant systems can reduce heating and dehumidification energy in indoor swimming pools when low-temperature heat is available for regeneration. Future research should focus on experimental validation, addressing interactions with chlorine gases, long-term system performance and real-world implementation challenges to ensure commercial deployment. Full article

(This article belongs to the Special Issue Optimising Sorption-Based Low-Grade Heat Recovery and Storage for Buildings and Industry)

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15 pages, 6086 KB

Open AccessArticle

Horizon Calibration in Highly Deviated Wells and Implications for Velocity-Model Building

by Hailong Ma, Liping Zhang, Ting Lou, Yao Zhao, Lei Zhong, Xiaoxuan Chen and Xuan Chen

Appl. Sci. 2026, 16(8), 3628; https://doi.org/10.3390/app16083628 (registering DOI) - 8 Apr 2026

Abstract

Highly deviated wells commonly exhibit large errors in horizon calibration because the logging path follows an inclined borehole trajectory, whereas post-stack seismic processing effectively treats wave propagation as vertical. This mismatch has received limited attention. Here, we performed horizon calibration and velocity-model building for highly deviated wells drilled in the Mahu Sag, Junggar Basin, and obtained three key findings. First, the assumed vertical travel path in post-stack data is the primary cause of the initial mis-tie for highly deviated wells. Second, calibration in the deviated interval requires a strategy distinct from that of vertical wells and may involve substantial stretching or squeezing of the original logs to achieve a consistent time-depth relationship. Third, the map-view projection of a highly deviated well is essentially linear; relative to vertical wells, it provides denser in situ velocity constraints and, with pseudo-well control, supplies 2D velocity information along the well-trajectory plane, thereby improving velocity-field modeling. Validation against drilling data showed that this workflow improved well ties and refined the velocity model, providing practical guidance for geological well planning and reducing drilling risk. Full article

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33 pages, 736 KB

Open AccessArticle

Analysis of Chip Electronic Components’ Typical Yield in Taping Process Based on Virtual Metrology

by Shiqi Zhang, Lizhen Chen, Jiangcheng Fu, Chenghu Yang and Guangli Chen

Sensors 2026, 26(8), 2292; https://doi.org/10.3390/s26082292 (registering DOI) - 8 Apr 2026

Abstract

This study addresses virtual metrology for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework that reformulates the task as the prediction of operating-condition-level typical yield. First, physically relevant features are retained based on process knowledge and analyzed using Pearson correlation, Spearman correlation, and mutual information. We then perform multidimensional equal-frequency binning to partition the observable feature space into locally homogeneous operating condition groups, and define the within-bin median yield as the typical yield, thereby constructing an operating condition dictionary. Based on this dictionary-based representation, low-yield-oriented sample weighting is combined with nested cross-validation and Bayesian optimization for model comparison and hyperparameter tuning. Using desensitized production data from an electronic component taping process, the results under this representation show more stable prediction than direct modeling on unbinned batch samples while also improving tail-oriented fitting relative to unweighted baselines. These findings suggest that, for partially observable manufacturing data, operating condition stratification provides a practical basis for stabilizing VM prediction, while low-yield-oriented sample weighting further improves sensitivity to the low-yield tail, supporting picture yield early warning and process-level decision making. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

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20 pages, 19535 KB

Open AccessArticle

The Effect of Structural States on the Microstructure and Mechanical Properties of Low-Activation Austenitic Steel After Long-Term Thermal Exposure at 700 °C

by Igor Litovchenko, Sergey Akkuzin, Nadezhda Polekhina, Valeria Osipova, Anna Kim, Kseniya Spiridonova and Vyacheslav Chernov

J. Manuf. Mater. Process. 2026, 10(4), 126; https://doi.org/10.3390/jmmp10040126 (registering DOI) - 8 Apr 2026

Abstract

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads to the precipitation of dispersed M₂₃C₆ carbides (M = Cr, W), primarily along grain and deformation twin boundaries. After HTMT, these particles are mainly localized at grain and low-angle boundaries. With increasing aging time, both the size and volume fraction of the particles increase. In both states, the microtwin and substructure are partially retained after aging. Local regions corresponding to the early stages of recrystallization were identified after both treatments. These regions were associated with intense decomposition of the supersaturated solid solution and the coarsening of carbide particles. The mechanical properties were evaluated by tensile testing at 20, 650, and 700 °C. Aging reduced average ductility after both treatments and at all test temperatures, with this trend persisting with increasing aging time. After CR and aging, a significant scatter in elongation to failure was observed, with minimum values of ≈2–3%. This behavior is attributed to the high density of plate-like M₂₃C₆ carbides at grain and microtwin boundaries. Microcrack formation and intercrystalline fracture features were observed, directly linked to the high density of boundary carbides. These effects were less pronounced in the HTMT condition after aging. In this paper, strategies for suppressing carbide precipitation in high-manganese low-activation austenitic steels via chemical composition and thermomechanical processing optimization are discussed. Full article

(This article belongs to the Special Issue Deformation and Mechanical Behavior of Metals and Alloys)

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30 pages, 800 KB

Open AccessFeature PaperArticle

Symmetry-Resolved Phase Transitions of Electromagnetic Degrees of Freedom Under RIS Control

by Carlos Bousoño-Calzón

Mathematics 2026, 14(8), 1239; https://doi.org/10.3390/math14081239 (registering DOI) - 8 Apr 2026

Abstract

The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore–Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RISs). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decomposition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal

SO (2)

symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable insights for excitation, surface programmability, and measurement strategies under practical architectural constraints. Full article

(This article belongs to the Section E: Applied Mathematics)

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18 pages, 35497 KB

Open AccessArticle

Hierarchical YOLO-SAM: A Scalable Pipeline for Automated Segmentation and Morphometric Tracking of Coral Recruits in Time-Series Microscopy

by Richard S. Zhao, Cuixian Chen, Meg Van Horn and Nicole D. Fogarty

Sensors 2026, 26(8), 2291; https://doi.org/10.3390/s26082291 (registering DOI) - 8 Apr 2026

Abstract

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies—Second Edition)

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16 pages, 10939 KB

Open AccessArticle

Numerical Simulation of Multi-Field Evolution in Fractured Production of Horizontal Shale Oil Wells in Jimusar

by Huiyong Yu, Wenhao He, Rui Wang, Wenfu Jiao, Qianhu Zhong, Xinfang Ma and Qing Wang

Appl. Sci. 2026, 16(8), 3625; https://doi.org/10.3390/app16083625 (registering DOI) - 8 Apr 2026

Abstract

The Jimusar shale reservoir exhibits extremely low permeability, classified as an ultra-low porosity and ultra-low permeability formation. Crude oil mobility is poor, and the reservoir demonstrates significant heterogeneity. Conventional horizontal well fracturing development fails to meet requirements, facing issues such as pronounced energy depletion in the formation, unclear oil–water distribution, and changes in formation stress direction. Based on the reservoir properties of the Jimusar shale oil reservoir, this paper establishes a fracture propagation model for horizontal wellbore hydraulic fracturing and a reservoir numerical model. It simulates the evolution of pressure fields, stress fields, and seepage fields at different time points during the fracturing and production phases of horizontal wells. Results indicate the following: (1) When fracturing fluid is injected into the formation, oil saturation around fractures rapidly decreases. During the initial production phase, oil saturation around fractures increases due to the recovery of some fracturing fluid and the sorption effect between fracturing fluid and crude oil. (2) Formation pressure around horizontal wells significantly increases upon fracturing fluid injection. The dual effects of fracture opening and fluid injection cause stress to rise near fractures. During production, both formation pressure and stress decrease near the wellbore, with greater pressure reduction in the near-wellbore zone than in the far-wellbore zone. However, formation stress decreases less near the wellbore due to stress concentration effects from fracture opening, resulting in a smaller reduction than in the far-wellbore zone. (3) The formation surrounding the fracture undergoes dual influences from fracture opening and fracturing fluid injection, causing deflection in the direction of near-wellbore stress. During the initial production phase, the impact of stress deflection gradually diminishes with ongoing production. However, after prolonged production, the deflection of formation stress intensifies. The conclusion states that this understanding clarifies the multi-field evolution patterns in fracturing production for horizontal well clusters, providing theoretical guidance for subsequent shale development processes. Full article

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28 pages, 53387 KB

Open AccessArticle

Isotopic Disequilibrium Between Migmatites and Protolith: Insights from a Variscan Anatectic Complex (NW of Iberian Variscan Belt, Portugal)

by Joana Alexandra Ferreira, Helena C. B. Martins, Maria dos Anjos Ribeiro and José Francisco dos Santos

Geosciences 2026, 16(4), 152; https://doi.org/10.3390/geosciences16040152 (registering DOI) - 8 Apr 2026

Abstract

Isotopic disequilibrium during the formation of high-temperature (HT) metamorphic complexes by anatexis during continental collision is a process that deserves intense discussion since it is fundamental to understand the evolution of continental crust. The axial sector of the Iberian Variscan Belt (IVB) is known by the profusion of synorogenic granites that are sometimes clearly associated with the migmatites composing the HT metamorphic complexes. The Pedregal Migmatitic Complex is located in the autochthonous domain of the IVB and is composed of metatexites and diatexites associated to syntectonic two-mica granites. The anatectic process occurred by dehydration melting of muscovite and biotite with the growth of peritectic minerals such as garnet, K-feldspar, and sillimanite in metatexites; and K-feldspar, sillimanite, and hercynite in diatexites reaching the metamorphic peak at 313.5 ± 0.5 Ma. A process of residuum-melt separation during crustal melting is attested by the Pedregal migmatites, giving origin to metatexites and residual diatexites as indicated by field evidence and their geochemical signature. Zircon oxygen isotopes and inherited zircon ages point to the Douro-Beiras Supergroup metasedimentary sequence (Beiras group) as a possible protolith of the Pedregal diatexites. Conversely, the isotopic composition of the diatexites suggests isotopic disequilibrium caused by residual mineral phases (biotite, monazite and garnet). Full article

(This article belongs to the Section Geochemistry)

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31 pages, 2475 KB

Open AccessArticle

Fuzzy-Logic Workload Orchestration Framework for Smart Campuses in Edge-Cloud System Architecture

by Abdullah Fawaz Aljulayfi

Electronics 2026, 15(8), 1556; https://doi.org/10.3390/electronics15081556 (registering DOI) - 8 Apr 2026

Abstract

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency. Full article

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