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The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

19 pages, 730 KB

Open AccessArticle

How Human–AI Interaction Impacts Sustainable Learning Resilience: Evidence from Western China’s Underdeveloped Higher Education

by Shengnan Ning, Dexiang Yang, Xiaoling He and Xiaowen Jie

Sustainability 2026, 18(12), 6102; https://doi.org/10.3390/su18126102 (registering DOI) - 13 Jun 2026

Abstract

Despite the promise of human–AI interaction in enhancing learning outcomes, its contribution to fostering sustainable learning resilience, particularly in underdeveloped regions, remains insufficiently examined. Prior research has inadequately investigated the psychological processes underlying the relationship between human–AI interaction and the development of resilience. To address these gaps, this study adopts the Cognition–Affect–Conation (CAC) framework to explore how task–technology fit and system quality collectively shape the dynamics of sustainable learning resilience, mediated by perceived value and trust. Survey responses were collected from 617 students across 34 universities in Western China, using both online and offline methods. The findings indicate that task–technology fit and system quality substantially influence students’ perceptions of value and trust in human–AI interactions, which in turn strengthen their sustainable learning resilience. Additionally, these mechanisms exert a significant positive influence on different academic disciplines. This research advances the understanding of how human–AI interactions facilitate sustainable learning resilience and provides actionable insights for implementing equitable technology solutions in higher education, particularly in resource-constrained environments. Full article

(This article belongs to the Section Sustainable Education and Approaches)

29 pages, 2813 KB

Open AccessArticle

A Conceptual Framework for Sustainable Vertical Growth in the Housing Sector: A Case Study of the Dammam Metropolitan Area

by Saqr Mohammed Al-Absi, Ali M. Alqahtany and Umar Lawal Dano

Sustainability 2026, 18(12), 6101; https://doi.org/10.3390/su18126101 (registering DOI) - 13 Jun 2026

Abstract

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

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24 pages, 16109 KB

Open AccessArticle

Broadband Simulation-Based EMC Modeling and EMI Assessment of a GaN-Based Phase-Shift Full-Bridge Converter for EV DC Powertrains

by Sofiane Khelladi, Nassim Rizoug, Cristina Morel and Abdelchafik Hadjadj

Actuators 2026, 15(6), 340; https://doi.org/10.3390/act15060340 (registering DOI) - 13 Jun 2026

Abstract

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

(This article belongs to the Special Issue Electrothermal and Electromagnetic Modeling of Actuator Power Cable Harnesses)

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21 pages, 967 KB

Open AccessArticle

Unlocking Private Investment for Sustainable Infrastructure in the Pacific Islands: Japan’s JCM and ESG Innovation

by Noriyuki Segawa, Suliasi Vunibola and Viliame Kasanawaqa

Sustainability 2026, 18(12), 6100; https://doi.org/10.3390/su18126100 (registering DOI) - 13 Jun 2026

Abstract

Developing countries in which infrastructure development is heavily dependent on overseas development aid face significant sustainability challenges, including financing gaps and inadequate maintenance. Increasing private-sector investment is crucial for addressing these challenges. This paper proposes an innovative framework linking environmental, social, and governance (ESG) principles with a revised joint credit mechanism (JCM) to attract private investment in infrastructure development, particularly in Pacific Island countries facing the climate crisis. Under the revised JCM, by allocating generated carbon credits to participating Japanese companies, rather than the Japanese government, corporations can monetise credits through market transactions, creating compelling economic incentives for private-sector engagement. In ESG-advanced markets, credits serve as strategic instruments for corporate value enhancement beyond revenue generation, while corporations require continuous credit acquisition to sustain investor confidence. Our revised framework provides a sustainable solution to both financing gaps and infrastructure maintenance challenges. Our analysis demonstrates that integrating market dynamics and corporate incentives into bilateral climate mechanisms holds substantial potential for mobilising private capital for sustainable climate infrastructure finance. This approach represents a promising departure from traditional donor-dependent models, effectively aligning corporate interests with sustainable development objectives while advancing national emission reduction commitments. Full article

23 pages, 4833 KB

Open AccessArticle

Production-Level Mitigation of Mn(VII) via a Novel Quaternary Hybrid Nanocomposite: Structural Elucidation, Experimental Optimization, and Advanced Ionic Simulation

by Raouf Hassan, O. A. Mohamed, M. Rashad and Ahmed S. Elshimy

Nanomaterials 2026, 16(12), 742; https://doi.org/10.3390/nano16120742 (registering DOI) - 13 Jun 2026

Abstract

This study was conducted to investigate a novel quaternary hybrid nanocomposite (QHNC) that can successfully remove Mn(VII) ions from contaminated water. The nanocomposite was analyzed using FTIR, XRD, BET, TGA/DTG and FESEM/EDX techniques to investigate whether the synthesis led to an outcome with optimal properties that will enable it to effectively remove Mn ions from aqueous solutions. Optimal results have been achieved by conducting the analysis at a pH level of 2, using 25 mg of the adsorbent material, an interaction time of 60 min and a concentration of 500 mg/L. The Freundlich isotherm best described the adsorption equilibrium. Further analysis through advanced computational simulations indicated that a sorption process underpins the phenomenon based upon a complex geometry mechanism with a preferential horizontal to inclined orientation of the adsorbate upon the surface. The techno-economic assessment reveals the biosorbent’s viability—with a production cost that is highly competitive at USD 9.95/kg, yet with a stable removal efficiency of nearly 60% over five cycles. Such factors lead to a treatment cost of around USD 7.3 for 1 m³ of 500 mg/L Mn(VII)—confirming both the economic viability and scalability for advanced tertiary wastewater remediation applications. Full article

(This article belongs to the Section Environmental Nanoscience and Nanotechnology)

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29 pages, 4396 KB

Open AccessArticle

Synergistic Role of Crosslinker and Silane-Based Additive in Designing Structurally Robust Bio-Based Polyurethane Coatings

by Mayankkumar L. Chaudhary, Kinal Chaudhari, Rutu Patel and Ram K. Gupta

Polymers 2026, 18(12), 1490; https://doi.org/10.3390/polym18121490 (registering DOI) - 13 Jun 2026

Abstract

Bio-based polyurethane (PU) coatings offer sustainable alternatives to petrochemical coatings but often suffer from inferior mechanical performance, durability, and chemical resistance. This work addresses that challenge by integrating a trifunctional bio-based crosslinker (glycerol) and a silane-based additive (hexamethyldisilane (HMDS)) to simultaneously enhance structural robustness and hydrophobicity. Coatings were synthesized using a renewable soybean oil polyol (SOP), glycerol (5, 10, 15 and 20 wt.%), and methylene diphenyl diisocyanate (MDI), followed by the addition of HMDS (10, 20, 30, 40 and 50 wt.%). Mechanical tests identified 10 wt.% glycerol as the optimal content, yielding a maximum tensile strength of 47.18 MPa. Incorporating 10 wt.% HMDS into the optimized formulation greatly increased water contact angle (WCA, 95.76°) and chemical resistance with minimal loss of mechanical performance (38.19 MPa, tensile strength); higher HMDS loadings caused network disruption and reduced strength. Calorimetry and thermogravimetric analyses confirmed that the modified coatings retained high thermal stability. This synergistic crosslinker additive strategy produced a structurally robust, water-resistant bio-based coating, demonstrating a viable high-performance sustainable coating solution for industrial applications. Full article

(This article belongs to the Special Issue Recent Advances in Polymer Coatings)

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27 pages, 65786 KB

Open AccessArticle

Canopy-Adaptive TAD-IRRT* Algorithm for 3D Path Planning of 6-DOF Apple-Harvesting Robots in Dense Orchards

by Lu Han, Wei Chen, Tianzhong Fang and Yunpeng Sun

Actuators 2026, 15(6), 336; https://doi.org/10.3390/act15060336 (registering DOI) - 13 Jun 2026

Abstract

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting (

R^{2} = 0.9849

) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

(This article belongs to the Section Actuators for Robotics)

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24 pages, 12903 KB

Open AccessArticle

TIDE-Net: A Triple-Branch Illumination and Detail Enhancement Network for Underwater Images

by Boyu Pang, Chaoxian Jia and Zhenping Weng

Appl. Sci. 2026, 16(12), 6006; https://doi.org/10.3390/app16126006 (registering DOI) - 13 Jun 2026

Abstract

Underwater images exhibit severe colour distortion, low contrast, and blurred details due to light absorption and scattering, which limits their practical use in marine applications. Existing methods face poor generalisation, high computational costs and weak integration of physical priors. To address these issues, this paper proposes TIDE-Net, a triple-branch illumination and detail enhancement network for underwater images. It decomposed inputs into illumination, reflectance intensity, and chromaticity branches for parallel optimisation, enabling decoupled handling of brightness, texture, and colour degradation. A piecewise colour correction module mitigated complex colour casts without introducing artefacts; a lightweight U-Net branch enhanced fine details while suppressing noise; and a local gain compensation module improved brightness uniformity and reduced halo effects. Experiments on four datasets showed that TIDE-Net outperforms some state-of-the-art methods, achieving a PSNR of 29.44 dB, an SSIM of 0.94, and competitive UIQM/UCIQE scores with only 7.74 M parameters. The results confirmed that the proposed triple-branch strategy effectively balances physical interpretability, restoration quality, and computational efficiency. In conclusion, TIDE-Net provides a robust and lightweight solution suitable for deployment on resource-limited underwater platforms, offering practical value for real-world underwater vision tasks. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

23 pages, 19029 KB

Open AccessArticle

CETransUNet: An Intelligent Landslide Identification Method Based on Collaborative Optimization of Global Context and Dual Attention Mechanisms

by Tianli Sun, Chengsheng Yang, Jifeng Wu, Zewei Liu, Ziqian Wang and Xiaoqiang Cheng

Remote Sens. 2026, 18(12), 1974; https://doi.org/10.3390/rs18121974 (registering DOI) - 13 Jun 2026

Abstract

Accurate landslide identification is crucial for enhancing emergency response capabilities during destructive geological hazards. Although deep-learning-based semantic segmentation has demonstrated effectiveness, substantial variations in landslide scales and environmental similarities continue to challenge existing methods. This paper systematically constructs a new co-seismic landslide dataset for the Yarlung Zangbo River basin based on the 2017 Nyingchi earthquake, effectively filling a critical regional data gap. This paper proposes CETransUNet (coordinate attention and edge-guided attention transformer UNet), a novel landslide detection model that integrates ResNet and Transformer architectures. Specifically, a coordinate attention (CA) module is introduced within the skip connections between the encoder and decoder. This module encodes positional information along both horizontal and vertical spatial directions and dynamically re-weights the feature maps, thereby effectively suppressing background noise caused by semantic gaps and enhancing the model’s ability to localize landslide regions. Additionally, an edge-guided attention (EGA) module is incorporated into the decoder. This module extracts explicit edge priors from the input image using a Laplacian operator and imposes geometric constraints on the predictions via a boundary reverse attention mechanism, thereby significantly alleviating boundary ambiguity and morphological distortion of landslides. Evaluations across datasets from the Yarlung Zangbo River, Iburi-Tobu, and Bijie regions demonstrate that CETransUNet significantly outperforms state-of-the-art models—including TransUNet, SegFormer, and SwinUNet—in terms of IoU, MIoU, and F1-score. Overall, through the synergistic optimization of the coordinate attention and edge-guided attention modules, the CETransUNet model achieves synchronous enhancement of boundary integrity and geometric precision in complex scenarios, providing a reliable technical solution for large-scale intelligent landslide identification. Full article

(This article belongs to the Special Issue Advances in Geological Hazard Characterization and Assessment: Merging Remote Sensing with Direct Surveys)

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11 pages, 4568 KB

Open AccessArticle

Preparation of Eu(III) Luminescent Hybrid Nanomaterials via Oxidation Induced by Gas-Phase Vacuum Evaporation Approach and Their Anti-Counterfeiting Applications

by Wenzhe Wu, Shaofeng Chen, Wei Ling, Yiwei Tang, Yuji Du, Peilin Liang, Shi-Jian Su and Dongcheng Chen

Nanomaterials 2026, 16(12), 741; https://doi.org/10.3390/nano16120741 (registering DOI) - 13 Jun 2026

Abstract

Europium (Eu) is a rare-earth element with unique optoelectronic properties that underpin its applications in displays and lighting, X-ray imaging, anti-counterfeiting, and biomedicine. Conventional methods typically involve the synthesis of europium-based luminescent materials in powder or crystalline form via high-temperature solid-state reactions or solution processes, followed by secondary processing such as spin coating or evaporation to fabricate films or devices. In this work, we report a direct approach to prepare trivalent europium-based luminescent materials using divalent europium bromide (EuBr₂) as the precursor via a gas-phase vacuum evaporation approach (GPVEA). This “deposition-as-synthesis” method enables the fabrication of the hybrid nanoscale films with various blending ratios, which exhibit changes in the fine structure of the emission peaks. The luminescence spectra remain nearly identical across the temperature range from 80 K to 320 K. The photoluminescence emission intensity is stronger in air than in a vacuum. The films show a maximum photoluminescence quantum yield (PLQY) of 8.27% and good photostability, with an emission decay of 3.44% over 50 min under continuous 300 nm excitation. Through patterned design, we demonstrate their value for anti-counterfeiting applications. This work thus provides guidance for the preparation of europium-based luminescent nanomaterials via GPVEA and their application in anti-counterfeiting. Full article

(This article belongs to the Special Issue Quantum Dots in LED and Advanced Display Technologies)

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22 pages, 2900 KB

Open AccessArticle

Sustainable Urban Greening of Tropical Asia: A Lightweight Vegetative Tile for Conventional Sloped Roofs of Sri Lanka

by Gayanthi Krishani Perera John, Abeysiri Munasinghe Madhushika Gihanthi Munasinghe, Rathnayake Kankanamge Nethmi Prabudya Piyasena and Rangika Umesh Halwatura

Urban Sci. 2026, 10(6), 327; https://doi.org/10.3390/urbansci10060327 (registering DOI) - 13 Jun 2026

Abstract

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

(This article belongs to the Section Urban Environment and Sustainability)

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20 pages, 3442 KB

Open AccessFeature PaperArticle

Constraint-Based Disassembly Sequencing Algorithms for Dismantling Applications—A Comparative Study

by Aron Webster, Adam Knight and Xiaodong Jia

Processes 2026, 14(12), 1937; https://doi.org/10.3390/pr14121937 (registering DOI) - 13 Jun 2026

Abstract

With growing interest in automated dismantling operations for hazardous environments, automatically planning safe and efficient disassembly sequences is becoming increasingly important. When a large structure is segmented into parts, the removal order must ensure that each part can be extracted safely without destabilising the remaining structure. This paper presents a comparative study of four algorithms for solving the disassembly sequencing problem in two dimensions: First Feasible Random Search (FFRS), Greedy Search (GS), Height-Decreasing Search (HDS), and Stochastic Tree Search (STS). The present study focuses specifically on sequencing feasibility under geometric and physical constraints, namely connectivity, accessibility, and structural stability. The 2D formulation provides a simplified yet computationally efficient testbed for analysing algorithmic behaviour under varying cutting complexities, with the objective of minimising the total removal trajectory length. Results show that while STS consistently finds optimal or near-optimal solutions, its factorial runtime limits scalability. GS produces high-quality solutions efficiently but can become trapped in infeasible configurations, whereas HDS offers strong reliability and speed at the expense of solution quality. Based on these findings, a hybrid height-based backtracking algorithm is proposed as a promising future direction, combining the efficiency of greedy search with the robustness of stochastic exploration. The results provide insight into the relative strengths and limitations of different sequencing strategies and establish a foundation for future extension to more realistic dismantling scenarios, including 3D and radiologically constrained applications. Full article

(This article belongs to the Section Particle Processes)

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29 pages, 28758 KB

Open AccessArticle

Spatio-Temporal Feature Enhancement for Recognizing Strongly Correlated Sequential Actions in Aircraft Assembly

by Jiaming Shi, Xiang Huang, Guoyi Hou, Chengda Guo, Qingxue Wang and Yumin Chen

Sensors 2026, 26(12), 3781; https://doi.org/10.3390/s26123781 (registering DOI) - 13 Jun 2026

Abstract

The positioning and clamping process in aircraft assembly exhibits pronounced long-term temporal correlations and intense human–machine interactions. Consequently, assembly quality depends heavily on operator compliance and consistency. Capturing long-term, strongly correlated features in complex industrial environments remains a significant challenge. To overcome this, this study proposes a Long-Term Strongly Associated Action Recognition Network (LTSA-Net) tailored for aircraft assembly positioning and clamping tasks. Based on the C3D backbone, the model first incorporates the SimAM attention mechanism and BN modules to significantly enhance focus on critical spatiotemporal features. To address the challenge of capturing long-term temporal dependencies, LTSFEM is designed to extract global temporal information accurately. Furthermore, to balance structural lightweight design with real-time inference requirements, the CWSTB module is integrated to achieve substantial parameter compression. In addition, a dedicated aircraft assembly positioning and clamping dataset was constructed, and a robust training framework was established using the AdamW optimizer and Mixup data augmentation. Experimental results demonstrate that LTSA-Net achieves a recognition accuracy of 98.82% on the LTSA-Dataset, with a per-frame inference time of 42 ms, successfully meeting the dual requirements of high precision and real-time performance in industrial scenarios, and providing a practical technical solution for intelligent monitoring of aircraft assembly processes. Full article

(This article belongs to the Section Industrial Sensors)

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23 pages, 1956 KB

Open AccessArticle

A Hybrid Multi-Agent Control Architecture for Interoperable and Deterministic IoT-Based Swine Precision Feeding

by Vicente López-Sacanell and Lluís Miquel Plà-Aragonés

AgriEngineering 2026, 8(6), 242; https://doi.org/10.3390/agriengineering8060242 (registering DOI) - 13 Jun 2026

Abstract

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article

(This article belongs to the Special Issue Precision Feeding, Digital Twins and Decision-Support Systems for Livestock Production)

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