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24 pages, 1569 KB  
Article
RR-MPF: A Reliable Routing Algorithm Based on Markov Parallel Forecasting for STINs
by Yaowen Qi, Yong Wang, Rui Ding, Xianren Kong and Li Yang
Future Internet 2026, 18(10), 527; https://doi.org/10.3390/fi18100527 (registering DOI) - 30 Sep 2026
Abstract
Satellite–Terrestrial Integrated Networks (STINs) undergo frequent topology reconfiguration due to the high mobility of satellite nodes, which can disrupt active routing paths across time slot boundaries, increase packet drop, and degrade end-to-end reliability. This paper proposes RR-MPF (Reliable Routing with Markov Parallel Forecasting), [...] Read more.
Satellite–Terrestrial Integrated Networks (STINs) undergo frequent topology reconfiguration due to the high mobility of satellite nodes, which can disrupt active routing paths across time slot boundaries, increase packet drop, and degrade end-to-end reliability. This paper proposes RR-MPF (Reliable Routing with Markov Parallel Forecasting), a routing algorithm that selects paths sustaining high reliability across successive time slots. RR-MPF quantifies time-varying link reliability through toughness, delay, jitter, and packet loss metrics. A Markov chain model, executed in a parallel background thread, estimates the probability that each link remains viable in the next time slot, providing future-state awareness without adding online latency. An enhanced Ant Colony Optimization (ACO) then solves a cross-slot weighted optimization that jointly maximizes current and predicted path reliability, yielding path selections that are robust to imminent topology changes. Numerical results show that RR-MPF reduces the average end-to-end delay by up to 23.9%, the delay jitter by 6.4–21.3%, and the packet drop rate by 6.8–34.4%, while improving the overall path reliability by up to 14.0% compared with three benchmark algorithms (iVACO, DPSO-TA, and GA-CG). Full article
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16 pages, 2092 KB  
Article
Mixed Elastohydrodynamic Lubrication for Rough-Surface Contacts with Application to Angular Contact Ball Bearings
by Xiaoming Zong, Zehao Li, Mingyi Tang, Renshan Xia, Jiaoyan Ma, Lei Zhang, Xu Yang, Han Li, Zhi Xu and Ming Ma
Lubricants 2026, 14(10), 376; https://doi.org/10.3390/lubricants14100376 (registering DOI) - 30 Sep 2026
Abstract
This study presents a numerical framework for analyzing mixed elastohydrodynamic lubrication (EHL) of rough contact surfaces under grease lubrication, motivated by the need for reliable lubrication design in tribological components such as mechanical face seals and rolling bearings operating in space environments. Considering [...] Read more.
This study presents a numerical framework for analyzing mixed elastohydrodynamic lubrication (EHL) of rough contact surfaces under grease lubrication, motivated by the need for reliable lubrication design in tribological components such as mechanical face seals and rolling bearings operating in space environments. Considering surface roughness effects, the proposed method integrates an EHL model derived from the Ostwald constitutive equation with the Kogut–Etsion (KE) elastic–plastic asperity contact model. The methodology is demonstrated through a case study of a vacuum grease-lubricated double-row angular contact ball bearing employed in a spacecraft antenna rotation mechanism under low-speed and heavy-load conditions. The governing equations were non-dimensionalized and solved numerically to obtain the lubricant film thickness and pressure distributions under various rotational speeds and axial preloads. The friction torque generated by viscous shear of the lubricant and asperity contact, and the asperity load ratio, were also determined. The novelty of this work lies in two aspects: (i) the integration of the Ostwald grease rheology model and the KE elastic–plastic asperity contact model into a unified mixed EHL framework; and (ii) a systematic investigation of grease-lubricated bearing behavior at low rotational speeds (11.5–55.2 rpm) with explicit consideration of surface roughness. The results indicate that rotational speed and axial preload exert limited influence on film thickness. The film pressure along the rolling direction increases with speed, whereas the asperity contact pressure decreases with speed and increases with preload; the asperity load ratio follows the same trends. The fluid pressure exhibits a single peak on the inlet side of the contact, and no outlet film constriction is observed in the thickness profile. The friction torque decreases with increasing speed, and the asperity load ratio follows the same trend. These findings demonstrate that appropriate adjustment of preload and an increase in rotational speed can reduce both friction torque and the asperity load ratio, thereby improving lubrication conditions and extending bearing service life. Full article
(This article belongs to the Special Issue Modeling and Simulation of Elastohydrodynamic Lubrication)
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27 pages, 14929 KB  
Article
Global Future Modeling of the Monophagous Pest Pagiophloeus tsushimanus (Coleoptera: Curculionidae): An Integrated Approach Using Random Forest and the CLIMEX Model
by Qiang Wu, Kaitong Xiao, Xiaosu Deng, Haijian Hu, Juan Li, Zhenxi Lin and Hang Ning
Insects 2026, 17(10), 1008; https://doi.org/10.3390/insects17101008 (registering DOI) - 30 Sep 2026
Abstract
Pagiophloeus tsushimanus Morimoto, 1982, a camphor tree weevil, is an emerging invasive wood-boring pest that has resulted in a progressive decline of Cinnamomum camphora plantations in affected areas. Native to Tsushima Island, Japan, P. tsushimanus has also spread to eastern China. With global [...] Read more.
Pagiophloeus tsushimanus Morimoto, 1982, a camphor tree weevil, is an emerging invasive wood-boring pest that has resulted in a progressive decline of Cinnamomum camphora plantations in affected areas. Native to Tsushima Island, Japan, P. tsushimanus has also spread to eastern China. With global climate change, the geographic range, occurrence frequency, and severity of this pest are continuously increasing, particularly in affected areas of China. Therefore, to limit the continued spread of this pest, the Random Forest (RF) algorithm and the CLIMEX model were combined to map the potential global distribution of P. tsushimanus. The results indicated that the potential range of C. camphora was jointly regulated by moisture availability (BIO18) and human modification indicators (PD and gHM). In contrast, the geographic distribution limits of P. tsushimanus were primarily defined by cold stress thresholds at higher latitudes and by heat and dry stress limitations in its traditional subtropical core range. Under current climatic conditions, the host-constrained suitable habitat of P. tsushimanus covered 4238.03 × 104 km2, mainly in East Asia, the southeastern United States, southeastern South America, and the Mediterranean coast of Europe. Under future climate change scenarios, P. tsushimanus and its host, C. camphora, exhibit a highly convergent poleward range expansion. New suitability hotspots for P. tsushimanus were projected to emerge in central and northern China, western and central Europe, and the northeastern United States. Owing to increased heat and drought stress in low-latitude subtropical core areas, the global suitable area under future climate change contracted by 17.99% overall, and highly suitable habitat decreased by 39.22%. The suitability maps of P. tsushimanus generated under host-constrained conditions in this study provide a potential early-warning layer for global forest health. We recommend that forestry departments in newly suitable temperate zones implement strict phytosanitary inspections of imported C. camphora nursery stock and that the timber and forest-product industries establish active monitoring programs to prevent the accidental introduction and establishment of this cryptic wood-boring pest. Full article
(This article belongs to the Special Issue Insect Diversity: Coleoptera)
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36 pages, 9491 KB  
Article
An InterpreTable Fuzzy-Logic Framework for Diagnosis and Fault-Signature Analysis of Operational, Corrosion, Structural, and Metallurgical Failure Scenarios in an Aged Pipeline Network
by Jonathan Josué Cid-Galiot, Alberto Alfonso Aguilar-Lasserre, José Pastor Rodriguez-Jarquin, José Ernesto Domínguez-Herrera and Isaí Pardo-Escandón
Corros. Mater. Degrad. 2026, 7(4), 62; https://doi.org/10.3390/cmd7040062 (registering DOI) - 30 Sep 2026
Abstract
Aged pipeline networks face interacting operational, electrochemical, structural, and metallurgical degradation mechanisms that are often assessed independently, limiting diagnosis of system-wide failure propagation. This study develops an integrated, interpreTable fuzzy-logic framework to detect, quantify, and isolate failure scenarios in a 572 km pipeline [...] Read more.
Aged pipeline networks face interacting operational, electrochemical, structural, and metallurgical degradation mechanisms that are often assessed independently, limiting diagnosis of system-wide failure propagation. This study develops an integrated, interpreTable fuzzy-logic framework to detect, quantify, and isolate failure scenarios in a 572 km pipeline network. The methodology combines 596 historical SCADA and SAP records, cathodic-protection and soil measurements from 907 evaluation points, 4651 ultrasonic-inspection anomalies, mechanical and metallographic characterization of API L X65 steel, and validated expert knowledge. Four Mamdani fuzzy inference systems were constructed to represent operational capacity, cathodic-protection performance, mechanical integrity, and metallurgical degradation. Their outputs were combined into a unified fault-signature matrix comprising 28 failure scenarios. Scenario FS-3 produced the broadest systemic response by activating all diagnostic residuals. Metallurgical scenarios FS-22 to FS-28 activated 61% of the matrix, showing their extensive influence on pipeline integrity. Structural scenarios FS-15 to FS-21 showed progressively broader signatures as wall deterioration increased. Meanwhile, FS-1, FS-2, FS-5, FS-6, and FS-7 remained localized and were more readily isolated. The proposed framework preserves diagnostic traceability through explicit input variables, fuzzy rules, and residual signatures. It provides an interpreTable basis for failure classification, diagnostic signature breadth, maintenance prioritization, and operator decision support. Its conclusions are limited to the analyzed network and require external validation before application to other pipeline systems. Full article
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49 pages, 25955 KB  
Review
From Conventional Biomaterials to Smart Bioactive Interfaces: Surface Engineering Strategies for Next-Generation Orthopedic Implants
by Sílvia Rodrigues Gavinho, Thacilla Menezes, Joana Soares Regadas and Manuel Pedro Fernandes Graça
Appl. Sci. 2026, 16(19), 9708; https://doi.org/10.3390/app16199708 (registering DOI) - 30 Sep 2026
Abstract
The long-term success of orthopedic implants depends not only on their mechanical performance but also on their ability to establish a stable and biologically active interface with surrounding tissues. Despite the widespread clinical use of metallic, ceramic, and polymeric biomaterials, implant failure remains [...] Read more.
The long-term success of orthopedic implants depends not only on their mechanical performance but also on their ability to establish a stable and biologically active interface with surrounding tissues. Despite the widespread clinical use of metallic, ceramic, and polymeric biomaterials, implant failure remains associated with insufficient osseointegration, bacterial infection, wear, corrosion, and adverse immune responses. This review provides a comprehensive overview of conventional biomaterials used in orthopedic implants and critically examines current surface engineering strategies developed to improve implant performance and longevity. Particular emphasis is placed on coating technologies, including sol–gel processing, electrochemical deposition, plasma spraying, physical and chemical vapor deposition, and CoBlast™, highlighting their influence on coating adhesion, bioactivity, and clinical performance. Recent developments in bioactive, antibacterial, immunomodulatory, and stimuli-responsive coatings are discussed, together with advances in therapeutic ion incorporation, extracellular matrix-inspired functionalization, and smart drug-delivery systems. Furthermore, the emerging role of osteoimmunomodulation, additive manufacturing, and patient-specific implant design is examined as a key driver for the next generation of orthopedic devices. By integrating materials science, surface engineering, and biological mechanisms, this work highlights current challenges and future opportunities in orthopedic implant technology, offering valuable insights for the development of safer, longer-lasting, and more biologically responsive implant systems. Full article
(This article belongs to the Special Issue Prosthodontics: Advanced Materials, Technologies and Applications)
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37 pages, 2078 KB  
Article
Cassava Starch–Beeswax Edible Coatings Enriched with Pomegranate Peel Extract Improve the Microbiological Stability and Quality of Coalho Cheese
by Bárbara Camila Firmino Freire, Renata Cristina Borges da Silva Macedo, Nícolas Oliveira de Araújo, Bárbara Jéssica Pinto Costa, Tallyson Nogueira Barbosa, Flamênia Shirley Ribeiro Silva, Heithor Syro Anacleto de Almeida, José Lucas Girão Rabelo, Gabrielle Cavalcante Barbosa Lopes, Paulo de Tarso de Paula Santiago, Savyo Mikael Lacerda Gomes, André Nogueira Cardeal dos Santos, Andrelina Noronha Coelho de Souza, Ariclécio Cunha de Oliveira, Vânia Marilande Ceccatto, José Ednésio da Cruz Freire, Ricardo Henrique de Lima Leite and Karoline Mikaelle de Paiva Soares
Macromol 2026, 6(4), 86; https://doi.org/10.3390/macromol6040086 (registering DOI) - 30 Sep 2026
Abstract
Coalho cheese is a highly consumed fresh dairy product in northeastern Brazil; however, its high moisture content and susceptibility to microbial contamination limit shelf life and compromise product safety. In this context, active edible coatings have emerged as sustainable alternatives for food preservation. [...] Read more.
Coalho cheese is a highly consumed fresh dairy product in northeastern Brazil; however, its high moisture content and susceptibility to microbial contamination limit shelf life and compromise product safety. In this context, active edible coatings have emerged as sustainable alternatives for food preservation. This study aimed to develop and characterize biodegradable edible coatings based on cassava starch, beeswax, and pomegranate peel extract and to evaluate their effectiveness in preserving coalho cheese during refrigerated storage. Films were characterized regarding physicochemical, optical, mechanical, barrier, and microstructural properties and subsequently applied as coatings to cheese samples stored at 7 ± 1 °C for 15 days. The incorporation of pomegranate peel extract and beeswax significantly affected film color, microstructure, and mechanical properties, while maintaining overall structural integrity. Formulations containing both additives exhibited improved flexibility and enhanced resistance to rupture, suggesting a combined effect of the lipid and phenolic components. Sensory evaluation performed 24 h after coating demonstrated high initial consumer acceptance, with overall acceptance scores above 6.7 on a nine-point hedonic scale and purchase intention comparable to the uncoated control. Microbiological analyses revealed that the coatings effectively delayed the growth of molds, yeasts, and aerobic mesophilic bacteria throughout storage. The formulation containing cassava starch, beeswax, and pomegranate peel extract (CSEB) showed the greatest preservation efficacy, reducing fungal populations by approximately 1.8 log cycles and maintaining significantly lower bacterial counts than the control after 15 days. In addition, coated cheeses exhibited lower titratable acidity and greater physicochemical stability. Overall, the results demonstrate that cassava starch-based active coatings incorporating beeswax and pomegranate peel extract represent a promising strategy for improving the microbiological stability and quality preservation of coalho cheese. Full article
(This article belongs to the Special Issue Advances in Starch and Lignocellulosic-Based Materials)
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18 pages, 8016 KB  
Article
Modeling and Experimental Validation of Forces in Low-Frequency Vibration-Assisted Drilling Considering Bone Anisotropic Effects
by Ying Han, Jun Wang, Xianzheng Zhou, Yimiao Chen and Qinhe Zhang
Materials 2026, 19(19), 4186; https://doi.org/10.3390/ma19194186 (registering DOI) - 30 Sep 2026
Abstract
This study focuses on modeling and experimental validation of drilling forces in low-frequency vibration-assisted bone drilling (LVAD), with explicit consideration of bone anisotropic effects. A mechanistic force model is established by integrating contributions from the main cutting edges and chisel edge, and a [...] Read more.
This study focuses on modeling and experimental validation of drilling forces in low-frequency vibration-assisted bone drilling (LVAD), with explicit consideration of bone anisotropic effects. A mechanistic force model is established by integrating contributions from the main cutting edges and chisel edge, and a direction-dependent anisotropic coefficient is introduced to characterize the anisotropic shear strength of cortical bone. The influences of feed rate, spindle speed, drill diameter, vibration amplitude, frequency, and drilling orientation on drilling forces are analyzed theoretically and experimentally. Results show that drilling forces increase with feed rate and drill diameter and decrease with spindle speed and vibration amplitude. LVAD reduces drilling forces by up to 16.52% compared with conventional drilling. Perpendicular drilling produces lower forces than oblique drilling, and forces in the yz-plane are higher than those in the xz-plane due to bone anisotropy. The proposed model is well verified by experiments, providing a theoretical basis for parameter optimization in clinical bone drilling. Full article
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22 pages, 17160 KB  
Article
Operator-Based Nonlinear Optimized Multi-Input Control Design and Its Application to a Vibrating Plate with Reduced-Sensor Implementation
by Zizhen An and Mingcong Deng
Appl. Sci. 2026, 16(19), 9709; https://doi.org/10.3390/app16199709 (registering DOI) - 30 Sep 2026
Abstract
In nonlinear mechatronic systems with multiple coupled actuators, the allocation of control inputs affects both the feedback-system structure and the required actuator effort. This paper develops an operator-based nonlinear multi-input control framework within the robust right coprime factorization (RRCF) structure for systems with [...] Read more.
In nonlinear mechatronic systems with multiple coupled actuators, the allocation of control inputs affects both the feedback-system structure and the required actuator effort. This paper develops an operator-based nonlinear multi-input control framework within the robust right coprime factorization (RRCF) structure for systems with more actuator inputs than controlled outputs. The original multi-input plant is represented through a reduced-output formulation, and the control allocation is described by a mapping that selects a right inverse of the input-coupling. Specifically, the actuator inputs are determined by minimizing the quadratic voltage-based control-effort objective subject to the prescribed coupling relation and actuator constraints. Under the ideal allocation condition, the nominal Bezout Identity is preserved, while robust stability in the presence of plant perturbations, coupling uncertainty, and allocation errors is guaranteed when the generalized Lipschitz condition derived for the optimized RRCF system is satisfied. The proposed framework is applied to a vibrating plate actuated by multiple piezoelectric elements. For this application, the constrained two-input allocation problem is reduced to a scalar piecewise optimization problem and solved by a finite-candidate selection procedure. Comparative experiments show that the proposed approach achieves stronger vibration suppression, a lower voltage-based control-effort metric, and a more balanced allocation among the actuators. These results demonstrate the effectiveness of integrating constrained multi-input allocation with the operator-based robust control framework. Full article
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22 pages, 3911 KB  
Article
Wild Nepeta sibirica L. from Eastern Kazakhstan: Botanical Characterization, Essential-Oil Composition, Chemogeographic Context, and Preliminary Bioactivity
by Anar Myrzagaliyeva, Milena Rašeta, Talant Samarkhanov, Yerlan Suleimen, Zharkyn Ibatayev, Moldir Sharipova, Nargiza Adilova, Gulnur Mamytbekova and Yusufjon Gafforov
Plants 2026, 15(19), 2992; https://doi.org/10.3390/plants15192992 (registering DOI) - 30 Sep 2026
Abstract
Nepeta sibirica L. is a medicinal and aromatic species of Lamiaceae whose volatile chemistry varies across its Eurasian range. In this study, we integrated voucher-anchored botanical characterization, GBIF-supported distributional context, essential-oil composition, chemogeographic comparison, and preliminary bioactivity assessment of a wild population from [...] Read more.
Nepeta sibirica L. is a medicinal and aromatic species of Lamiaceae whose volatile chemistry varies across its Eurasian range. In this study, we integrated voucher-anchored botanical characterization, GBIF-supported distributional context, essential-oil composition, chemogeographic comparison, and preliminary bioactivity assessment of a wild population from the Altai–Tarbagatai Ridge, Eastern Kazakhstan. Hydrodistillation yielded 0.3% (w/w) essential oil. GC–MS analysis of the essential oil detected and tentatively identified 62 volatile constituents, accounting for 95.5% of the total composition. A dominant nepetalactone peak represented 70.6% of the oil and was tentatively assigned as cis–trans-nepetalactone based on electron-ionization mass-spectral and retention-index data obtained on an achiral column. Germacrene D (6.5%) and cis-β-ocimene (2.8%) were the principal secondary constituents. Chemogeographic comparison indicated similarity to previously reported nepetalactone-rich material from the Altai region. The essential oil exhibited limited DPPH radical-scavenging activity, reaching a maximum inhibition of 12.88 ± 0.56% at 24.19 µg/mL; an IC50 could not be determined within the tested concentration range. In the Artemia salina assay, the essential oil caused 100% mortality at all tested concentrations (0.01–0.10 mg/mL) after 24 h, precluding LC50 estimation within the tested range. These findings document a nepetalactone-dominant essential-oil profile in a voucher-confirmed wild population of N. sibirica from Eastern Kazakhstan and provide preliminary quantitative data on its DPPH radical-scavenging activity and A. salina lethality. Full article
(This article belongs to the Special Issue Phytochemical Diversity and Bioactivity of Medicinal and Wild Plants)
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14 pages, 1864 KB  
Article
A Model for Core Responses and Species-Specific Reprogramming to Whole-Genome Duplication in Plants
by Yuyao Li, Jiaqing Yuan, Zhiyong Xiong and Kanglu Zhao
Life 2026, 16(10), 1643; https://doi.org/10.3390/life16101643 (registering DOI) - 30 Sep 2026
Abstract
Whole-genome duplication (WGD) is a fundamental evolutionary force in plants, driving diversification and offering immense potential for crop improvement. While WGD’s molecular effects are well-studied in individual species, the consistency of these responses across diverse lineages remains largely unknown. Here, we integrated multi-omics [...] Read more.
Whole-genome duplication (WGD) is a fundamental evolutionary force in plants, driving diversification and offering immense potential for crop improvement. While WGD’s molecular effects are well-studied in individual species, the consistency of these responses across diverse lineages remains largely unknown. Here, we integrated multi-omics datasets from established WGD events across 20 plant species, encompassing both experimentally induced and naturally occurring polyploids, to elucidate shared molecular adaptations to genome doubling. Our analysis identified 68 orthologous core WGD-responsive genes, predominantly involved in stress response, hormone signaling, and cellular homeostasis. Interestingly, despite considerable divergence in individual metabolites across species, their enriched pathways consistently converged on osmotic adjustment, redox regulation, and membrane remodeling. This convergent cascade of core transcriptional regulation and metabolic outputs thus defines a foundational adaptive program across diverse species. However, concurrently, extensive transcriptional and metabolic reprogramming exhibited strong species specificity. Based on this duality, we propose a novel model of core responses and species-specific reprogramming to WGD. This model provides crucial insights into polyploid stabilization and trait diversity, by highlighting an endogenously activated stress-buffering state that is pivotal for WGD establishment. Moreover, the identified core genes offer promising molecular markers for targeted polyploid breeding strategies. Full article
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36 pages, 7618 KB  
Article
Assessing Block-Scale Urban Heat Risk Across Local Climate Zones: Associations with Urban Morphology and Implications for Climate-Resilient Planning in Tianjin’s Six Central Districts, China
by Yuanyuan Sun, Yang Yu, Kunzhuo Wang, Yuxiang Sun, Zheng Ling, Yuqiao Zhang, Junhua Shu, Jianghua Shen and Yangyang Deng
Sustainability 2026, 18(19), 10000; https://doi.org/10.3390/su181910000 (registering DOI) - 30 Sep 2026
Abstract
Extreme heat increasingly threatens public health and urban sustainability, creating a need for fine-scale assessments that connect spatial risk patterns with climate-resilient planning. This study assessed heat risk across 1639 blocks in Tianjin’s six central districts, China. A block-scale heat-risk index (HRI) was [...] Read more.
Extreme heat increasingly threatens public health and urban sustainability, creating a need for fine-scale assessments that connect spatial risk patterns with climate-resilient planning. This study assessed heat risk across 1639 blocks in Tianjin’s six central districts, China. A block-scale heat-risk index (HRI) was constructed within the Hazard–Exposure–Vulnerability–Adaptability (HEVA) framework by integrating remote-sensing, population, built-environment, socioeconomic, and public-service data using a modified CRITIC weighting method. Differences in HRI among Local Climate Zones (LCZs) were examined, and three machine-learning models were compared. Among the three evaluated models, Random Forest showed the best overall test-set performance, and supplementary five-fold spatial block validation retained the same model ranking and major feature-importance ordering despite lower absolute predictive performance. Feature importance, partial dependence plots, and SHapley Additive exPlanations were subsequently used to interpret feature contributions and nonlinear associations. HRI was lower in the resource-rich urban core and higher in peripheral transitional zones, accompanied by clear spatial differences in adaptive-resource provision. The urban core generally exhibited higher adaptive-resource provision, whereas peripheral areas showed more limited provision. Under the adopted HRI formulation, higher values of this component contributed mathematically to lower composite HRI, rather than demonstrating a realised risk-reduction effect. Built LCZs generally exhibited higher HRI than land-cover LCZs; LCZ 5 (open mid-rise) had the highest median HRI, whereas LCZ G (water) had the lowest. Higher sky view factor and vegetation cover were generally associated with lower HRI, while larger blocks, taller buildings, and greater distances from water bodies were associated with higher HRI. These findings support differentiated block-scale interventions, heat-response resource allocation, and climate-resilient urban regeneration. Full article
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42 pages, 6680 KB  
Article
Hybridizing Artificial Electric Field Algorithm with Lévy Flight and Chaotic Dynamics for Enhanced Optimisation Performance
by Indu Bala and Lewis Mitchell
J. Exp. Theor. Anal. 2026, 4(4), 34; https://doi.org/10.3390/jeta4040034 (registering DOI) - 30 Sep 2026
Abstract
The Artificial Electric Field Algorithm (AEFA) suffers from premature convergence and local minima entrapment, limiting its effectiveness in complex optimisation scenarios. To address these limitations, we propose the Lévy-Flight and Chaos-based Artificial Electric Field Algorithm (LCAEFA), which [...] Read more.
The Artificial Electric Field Algorithm (AEFA) suffers from premature convergence and local minima entrapment, limiting its effectiveness in complex optimisation scenarios. To address these limitations, we propose the Lévy-Flight and Chaos-based Artificial Electric Field Algorithm (LCAEFA), which synergistically combines Lévy flight distribution for enhanced global exploration and chaotic dynamics for improved search diversity. The Lévy flight mechanism enables particles to perform strategic long-distance jumps guided by power-law distributions, while ten distinct chaotic maps introduce controlled perturbations that prevent stagnation in local optima. This dual enhancement creates an optimal balance between exploration and exploitation phases throughout the optimisation process. LCAEFA is rigorously evaluated on six benchmark functions spanning unimodal, multimodal, and fixed-dimensional categories, demonstrating superior convergence rates and solution quality compared to the original AEFA. Furthermore, we validate LCAEFA’s practical applicability by employing it as a trainer for Multilayer Perceptron (MLP) neural networks across five MLP training benchmarks: two real-world medical classification datasets (breast cancer, heart disease), one synthetic classification benchmark (XOR), and two synthetic function approximation tasks (sigmoid, cosine). Comparative analysis against ten state-of-the-art heuristic algorithms reveals that LCAEFA achieves up to 100% classification accuracy on the XOR benchmark and 88% on the breast cancer dataset. Statistical validation through Wilcoxon signed-rank tests confirms the significance of performance improvements. The integration of Lévy flight and chaotic dynamics successfully transforms AEFA into a robust optimiser capable of handling diverse optimisation challenges with enhanced convergence characteristics and superior solution quality. Full article
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32 pages, 40652 KB  
Article
Multi-Scale Landslide Displacement Prediction and Multi-Level Early Warning for the Three Gorges Reservoir Area Using Multi-Source Sensing and Gated-Attention Multimodal Fusion
by Liangwu Xu, Xiangjin Ran, Lili Yao and Zhaoji Lin
Sensors 2026, 26(19), 6207; https://doi.org/10.3390/s26196207 (registering DOI) - 30 Sep 2026
Abstract
Landslides are severe global geological hazards, and landslide displacement prediction is essential for early warning. Focusing on landslides induced by the coupling of periodic reservoir water level fluctuations and local short-term rainstorms in the Three Gorges Reservoir Area (TGRA), existing early-warning methods suffer [...] Read more.
Landslides are severe global geological hazards, and landslide displacement prediction is essential for early warning. Focusing on landslides induced by the coupling of periodic reservoir water level fluctuations and local short-term rainstorms in the Three Gorges Reservoir Area (TGRA), existing early-warning methods suffer from coarse rainfall spatial representation, static multimodal-feature fusion, and insufficient deformation-stage-based hierarchical warning mechanisms. Based on multi-source sensing data including Global Positioning System (GPS) displacement monitoring, reservoir water level sensors, ground rain gauges, meteorological radar, and multi-timescale rainfall forecast maps, this study integrates multimodal time-series records of multiple landslide sites from 2007 to 2024. Particle Swarm Optimization–Kriging (PSO–Kriging) interpolation and rainfall-map gridding were employed to construct a dual-source fine-grained rainfall reconstruction scheme. The Bidirectional Long Short-Term Memory (BiLSTM)–Attention network extracted deep temporal features, and a gated-attention fusion mechanism realized 1 d–15 d multi-scale landslide displacement prediction. Combined with the five-stage landslide creep theory, a four-level early-warning system was established. Experimental results show that the proposed multimodal-fusion model achieves a Root Mean Square Error (RMSE) of 3.42 ± 1.27 mm for the 1 d prediction horizon. The model F1-score reaches 89.3 ± 1.9% for short-term warning and 81.7 ± 1.2% for medium-and-long-term warning, which fits well with long-term reservoir-monitoring scenarios. This framework provides feasible technical support for reservoir landslide hazard prevention using multi-source sensing. Full article
(This article belongs to the Special Issue Sensor-Enabled Analysis and Control of Networked Control Systems)
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34 pages, 12797 KB  
Systematic Review
Artificial Intelligence for Irrigation Optimization in Rice Farming: A Systematic Review
by Junias Léandre Kra and Levente Tamás
AgriEngineering 2026, 8(10), 413; https://doi.org/10.3390/agriengineering8100413 (registering DOI) - 30 Sep 2026
Abstract
The scarcity of global water resources has made irrigation optimization a key challenge for sustainable agriculture. Rice cultivation, one of the most water-intensive cropping systems, offers significant opportunities for improving water-use efficiency through artificial intelligence (AI) technologies. This systematic review evaluates the application [...] Read more.
The scarcity of global water resources has made irrigation optimization a key challenge for sustainable agriculture. Rice cultivation, one of the most water-intensive cropping systems, offers significant opportunities for improving water-use efficiency through artificial intelligence (AI) technologies. This systematic review evaluates the application of AI techniques for irrigation optimization in rice farming within the context of Agriculture 4.0, with the aim of identifying the principal AI approaches, application domains, research trends, and future directions. The review was conducted following the PRISMA 2020 guidelines using publications retrieved from ScienceDirect, IEEE Xplore, and the ACM Digital Library published over the past five years. Fourteen studies meeting the predefined eligibility criteria were selected for qualitative synthesis and bibliometric analysis. The results indicate that machine learning and deep learning approaches, particularly Long Short-Term Memory (LSTM) networks and Random Forest (RF) models, dominate the current literature. These methods are primarily applied to irrigation scheduling, crop water requirement prediction, soil moisture estimation, and yield forecasting. The analysis also reveals a strong geographical concentration of research in Asia, which accounts for 64.3% of the selected studies. Furthermore, data availability remains limited, with half of the datasets accessible only upon request and only 7.1% publicly available, highlighting an important barrier to reproducibility and broader adoption of AI-based irrigation solutions. Emerging research directions include the integration of remote sensing, Internet of Things (IoT) technologies, and intelligent decision support systems to enable adaptive, data-driven irrigation management. Overall, this review provides a comprehensive overview of the current state of AI-enabled irrigation optimization in rice farming, identifies existing research gaps, and outlines future opportunities for developing sustainable intelligent irrigation systems. Full article
(This article belongs to the Special Issue Agriculture 4.0: Internet of Things and Digital Agriculture)
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46 pages, 2303 KB  
Review
Opioid Peptide Hybrids as Multifunctional Analgesic Ligands: Emphasis on Intracellular Signaling
by Igor Cecherz, Jakub Waśkiewicz, Alicja Dudek, Dariusz Polatyński, Karol Wtorek and Justyna Piekielna-Ciesielska
Pharmaceuticals 2026, 19(10), 1550; https://doi.org/10.3390/ph19101550 (registering DOI) - 30 Sep 2026
Abstract
Opioid peptide hybrids are multifunctional molecules in which an opioid pharmacophore is covalently integrated with a second opioid or non-opioid pharmacophore within a single molecular entity. Their development is driven by a major challenge in analgesic pharmacotherapy: although μ-opioid receptor (MOR) agonists remain [...] Read more.
Opioid peptide hybrids are multifunctional molecules in which an opioid pharmacophore is covalently integrated with a second opioid or non-opioid pharmacophore within a single molecular entity. Their development is driven by a major challenge in analgesic pharmacotherapy: although μ-opioid receptor (MOR) agonists remain among the most effective agents for the treatment of moderate-to-severe pain, their therapeutic use is limited by adverse effects and risks associated particularly with prolonged or high-dose exposure, including tolerance, constipation, respiratory depression, physical dependence, and abuse liability. Especially, chronic and neuropathic pain involves complex interactions among multiple receptor systems, neurotransmitter networks, and intracellular signaling pathways. Accordingly, hybrid peptide ligands provide a rational multitarget strategy aimed at achieving analgesia through the coordinated modulation of complementary pharmacological targets rather than selective modulation of a single receptor system. This approach may enhance analgesic efficacy and potentially improve the therapeutic profile of opioid-based treatments by reducing some of the dose-limiting adverse effects associated with conventional opioid analgesics. In this review, we discuss the functional and pharmacological interactions between opioid receptors and other receptor systems involved in pain perception and modulation, with emphasis on those for which peptide-based hybrid ligands with demonstrated antinociceptive activity have been developed. Full article
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