Search Results (38,439)

Decarbonising greenhouse food production requires improvements in thermal management, energy efficiency, and system integration. Greenhouse energy demand is shaped by coupled heat and mass transfer processes, particularly envelope performance, ventilation, and latent heat associated with humidity control. This article synthesises recent advances in greenhouse microclimate control with emphasis on heat transfer, low-carbon heating and cooling, thermal storage, renewable and waste heat integration, and advanced modelling and control approaches. The review shows that humidity control and latent load management are primary drivers of winter energy use, as moisture removal through ventilation and dehumidification directly increases the sensible heating required to maintain indoor temperature setpoints. When assessed using realistic psychrometric relationships, ventilation and dehumidification can dominate peak heating demand and seasonal consumption. The performance of heat pumps, storage systems, semi-closed greenhouse concepts, and renewable heat pathways depends on how thermal loads are defined, how system boundaries are set, and how technologies are integrated in operation. Digital twins, predictive control, and hybrid physics-data models are increasingly used to manage variability in weather, energy prices, and infrastructure constraints. Greenhouse decarbonisation cannot be treated as a simple substitution of energy sources. System performance depends on coordinated design and operation, including heat recovery, moisture removal, and integration of supply technologies. Semi-closed and heat recovery-based configurations can reduce the ventilation–heating penalty and lower primary energy demand compared with vent-to-dry approaches. Long-term market projections suggest that the commercial greenhouse sector could expand substantially by 2050 under plausible growth scenarios, reflecting increased capital investment rather than a proportional rise in global food output. Net-zero greenhouse production is achievable through combined improvements in thermal management, electrification, and renewable energy integration. However, large-scale deployment depends on consistent modelling assumptions, credible economic assessment, and alignment with heat and CO₂ supply infrastructure. The transition is therefore shaped by system integration and planning as much as by individual technologies. Full article

Objectives: The aim of this retrospective study was to evaluate the long-term clinical and radiographic performance of zirconia dental implants with one-piece and two-piece configurations supporting single-tooth restorations. The primary outcome was implant survival, while the secondary outcome was the assessment of interproximal marginal bone loss (MBL) over time. Materials and methods: A total of 67 implants placed in 55 patients were included, with a mean follow-up of 60.6 months. Forty-five implants were one-piece systems and twenty-two were two-piece systems. All surgical and prosthetic procedures were performed by the same operator, following the manufacturer’s recommendations. Final restorations were delivered three months after implant placement. Marginal bone levels were assessed radiographically at the time of definitive prosthesis delivery (T0) and at the last follow-up examination (T1). Statistical significance level was set at 5% (α = 0.05). Results: The overall implant survival rate was 100% in both groups. One-piece implants showed higher initial MBL values than two-piece implants; however, bone level changes over time were limited in both configurations, with no significant intra-group differences between T0 and T1. Conclusions: Both implant configurations showed excellent clinical and radiographic outcomes, with a 100% survival rate and limited marginal bone loss during long-term follow-up. Marginal bone levels appeared to be influenced more by implant neck design than by implant configuration, suggesting that zirconia implants with a smooth transmucosal design may represent a reliable metal-free option for single-tooth rehabilitation in properly selected patients. Full article

Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article

This paper investigates the cooperative formation control problem in unmanned surface vehicles (USVs) with prescribed performance constraints under complex marine conditions including external disturbances, model uncertainties, actuator faults, and input saturation. A novel fault-tolerant control (FTC) algorithm is developed by integrating cooperative learning neural networks (NNs), distributed disturbance observers, and the backstepping technique. Specifically, the learning NNs adaptively approximate system uncertainties, and the learned weight information is shared among vehicles to enhance cooperative cognition. Additionally, an auxiliary dynamic system and an actuator configuration matrix are designed to compensate for input saturation and propeller failures. Theoretical analysis based on the Lyapunov method proves that all signals in the closed-loop system are bounded, and the formation tracking errors strictly remain within the predefined transient and steady-state performance bounds. Finally, simulation experiments involving a group of four USVs validate the proposed algorithm. The results demonstrate that the USVs can rapidly converge to and maintain the desired quadrilateral formation shape despite time-varying disturbances and actuator efficiency loss. Furthermore, comparative simulation results indicate that the proposed cooperative learning FTC scheme significantly reduces velocity tracking error oscillations compared to traditional non-learning methods, explicitly verifying its superior robustness and fault-tolerant capabilities. Full article

Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. Conventional neural network models trained on specific geometric configurations typically fail to generalize to unseen preform designs, requiring costly retraining for each new geometry. This work proposes a unified geometry-aware deep learning framework that predicts spatial temperature distributions across multiple preform designs using a single neural network model. The approach reformulates temperature prediction as a coordinate-level regression task conditioned on spatial location, geometric descriptors, process parameters, and structural region labels. A domain-bounded training strategy based on extreme feasible preform geometries is introduced, ensuring that predictions for intermediate designs remain within the interpolation regime of the network. The framework is evaluated on six distinct preform geometries, demonstrating that a single model can generalize reliably to new, unseen preform designs when their geometric parameters fall within the bounds of the training data. This is achieved through a domain-bounded training strategy that constructs datasets from the extreme feasible geometries, thereby converting the prediction of any intermediate design into an interpolation task. Since neural networks are inherently limited in their ability to extrapolate beyond the training domain, this formulation is essential for ensuring stable and accurate predictions across the full range of industrially relevant preform configurations. The proposed methodology provides a foundation for geometry-informed surrogate modeling in thermal process control and can be extended to other manufacturing systems characterized by strong geometric variability. Full article

Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) remains challenging due to heterogeneous tumor morphology, intensity variability, and multi-scale structural complexity. This study proposes a DeepLabV3+-based segmentation framework integrating a VGG19-UNet encoder, Atrous Spatial Pyramid Pooling (ASPP), and low-level feature refinement to simultaneously capture hierarchical semantics and boundary-sensitive spatial details. The architecture enhances receptive field coverage without additional downsampling while preserving fine-grained contour information during reconstruction. Extensive evaluation was conducted on the Figshare Brain Tumor Segmentation (FBTS) dataset and the BraTS 2021 and BraTS 2018 benchmarks, focusing on Whole Tumor segmentation across multiple MRI modalities and tumor grades. Under five-fold cross-validation, the proposed model achieved a mean Dice Similarity Coefficient of 0.9717 and Jaccard Index of 0.9456 on FBTS, with stable and competitive performance across FLAIR, T1, T2, and T1CE modalities in both HGG and LGG cases. Boundary-level analysis further confirmed controlled Hausdorff Distance and low Average Symmetric Surface Distance. Statistical validation and ablation analysis demonstrate consistent improvements over baseline U-Net configurations. The proposed framework provides a robust and computationally efficient solution for automated brain tumor segmentation across heterogeneous datasets. Full article

Evidence from recent large-scale disruptions indicates that efficiency-centered supply chain designs struggle to sustain operation under persistent and systemic uncertainty. This study introduces the Response and Adaptive Immune-Inspired Supply Chain Immune System (RAIE–SCIS), a continuous-time dynamic framework that extends existing viability and resilience approaches by explicitly modeling inter-temporal adaptation and operational memory within a control-theoretic structure. The framework represents supply chains as multi-layer control systems where structural protection, adaptive regulation, and memory mechanisms jointly shape system response over time. Viability is assessed using time-dependent indicators, including performance trajectories, recovery time, and an adaptation-based viability index. The model is applied to a carbon capture, utilization, and storage (CCUS) supply chain under heterogeneous disruption scenarios. Results show that immune-enabled configurations increase minimum performance levels by 15–30% and reduce recovery times by up to 25% compared to non-adaptive configurations. These improvements are not uniform across scenarios and depend on disturbance structure and recurrence. The analysis reveals that adaptive regulation introduces a trade-off between recovery speed and variability, while memory mechanisms shape recovery dynamics under recurrent disruptions—effects not captured by static or purely reactive models. Their effects become more pronounced when disturbances accumulate or propagate. Full article

The sustainable enhancement of regional science and technology industrial competitiveness is an important objective of artificial intelligence (AI) policy. However, how different AI policy instruments can be combined to achieve this goal remains insufficiently understood. This study aims to address this issue by identifying the configurational pathways through which combinations of AI policy instruments contribute to the sustainable enhancement of regional science and technology industrial competitiveness. Based on a policy instrument framework, we analyze AI policies issued by provincial-level governments in China and apply fuzzy-set qualitative comparative analysis (fsQCA), which is appropriate for examining the combined effects of multiple policy instruments. The results show that no single policy instrument is sufficient to produce high regional science and technology industrial competitiveness. Instead, sustained competitiveness is achieved through multiple equivalent configurations of policy instruments. Three driving pathways are identified—(supply and demand)-environmental resonance, demand-driven (supply-environmental) assurance, and supply–demand complementarity—covering five specific configurations. The variation across configurations indicates that effective AI policy mixes are contingent on regional resource endowments and development conditions. Technology R&D support, talent cultivation and collaboration, and application demonstration and promotion emerge as the most recurrent core conditions across configurations. Accordingly, local governments should develop coordinated AI policy mixes, align differentiated policy pathways with regional conditions, and prioritize technology R&D support, talent cultivation and collaboration, and application demonstration and promotion to sustain long-term regional competitiveness. Full article

Event detection is crucial for disaster response, public safety, and trend analysis, enabling real-time identification of critical events. Social media platforms provide a vast content source, offering timely and diverse event coverage compared to traditional news reports. However, challenges arise due to the informal and noisy nature of the text, along with the limited availability of ground truth data for training models. This study introduces SOCIAL (Social Media Event Classification using Integrated Artificial Learning and Natural Language Processing), a mathematically grounded framework for real-time social media event detection. SOCIAL integrates a formal representation of social media text with a customized CNN–LSTM architecture, combining convolutional operations for local feature extraction with sequential modeling to capture temporal dependencies, thereby enhancing classification accuracy. Generative AI is employed to create synthetic event-related samples, addressing data scarcity and ensuring a balanced dataset, while the design incorporates quantitative principles to guide embedding selection and model optimization. This study systematically evaluates six experimental configurations with TF-IDF and Word2Vec embeddings. The TF-IDF-based CNN–LSTM model achieved top performance with 98.59% accuracy, 98.13% precision, 99.06% recall, and 0.9719 MCC. Additionally, the $F_{0.5}$, $F_1$, and $F_2$ scores were 98.31%, 98.59%, and 98.87%, respectively, confirming the model’s strong predictive capabilities. TF-IDF integration enhanced event-specific term recognition, reducing misclassifications and improving reliability. These results demonstrate that SOCIAL is not only a fast, accurate, and scalable tool for crisis event detection, but also a formally principled framework for modeling and analyzing social media signals. Full article

Objective: The objective of this study is to evaluate the biomechanical effects of different attachment placement strategies using rectangular attachments on palatal root torque control of maxillary central and lateral incisors with clear aligners. Methods: Three-dimensional finite element analysis was performed to simulate simultaneous 1° palatal root torque of maxillary central and lateral incisors. Six attachment configurations were evaluated: no attachment (control), canine-only, both incisors, central incisor-only, lateral incisor-only, and all anterior teeth. Three-dimensional tooth displacement and torque expression were analyzed across 200 iterative simulations. Model validation was confirmed through mesh convergence analysis and comparison with published studies. Results: Only the control and canine-only groups simultaneously achieved the appropriate torque direction for both incisors. Attachments on central incisors produced reverse torque, with the central incisor-only group showing the most severe magnitude, while the control and canine-only groups achieved expected directions, validating model reliability. Lateral incisors exhibited different responses, including reverse torque in the lateral incisor-only group. The canine-only attachment demonstrated the most balanced torque expression. Increasing anterior attachments was associated with greater extrusion and canine displacement. Conclusions: Attachment placement using rectangular attachments significantly influenced torque expression during palatal root torque. Central and lateral incisors responded differently to attachments, and certain configurations produced reverse torque. For small torque movements (1–2°), a “less is more” approach using rectangular canine attachments for anchorage proved most effective, suggesting that anchorage may be more critical than incisor attachments for anterior torque control. Full article

Recent automated search methods have improved lower bounds for several Ramsey numbers, but the strongest gains often depend on structured seeding and cell-specific heuristic discovery. This leaves open a more fundamental question: Can a useful search structure be transferred across related Ramsey cells rather than rediscovered independently for each target instance? This work proposes a teacher–student framework for transferable structural search in Ramsey graph construction, inspired by the structure-distillation logic of Physics Structure-Informed Neural Networks (Ψ-NNs). The framework builds compressed structural representations from teacher witnesses and search traces, extracts reusable motifs and relations, and reconstructs transfer candidates. These are refined by balanced search and, for weak R(3, s) cells, by exact small-cell supervision. The framework is evaluated as a proof of concept across five Ramsey cells under transfer, matched-compute, search, ablation, and interpretability settings, including a proportional shift-scaling baseline and a greedy triangle-closing baseline that probe the structure-validity frontier from complementary directions. Supplementary experiments cover seed robustness, budget sensitivity, transfer-neighborhood variation, structural-resolution changes, stronger exact supervision, cross-r teacher pooling, single-teacher configurations, and scaling behavior across graph sizes. The results show that the portfolio version of the framework is the strongest balanced transfer method in the current study, while a structure-dominant oracle achieves stronger witness-shape agreement but worse Ramsey-valid construction. These findings reveal a clear structure-validity frontier and suggest that transferable Ramsey search should be evaluated by how well structural priors survive the validity constraints of new cells. Full article

Purpose: This research investigates where formal responsibility for artificial intelligence (AI) lies within organisations and how the presence, absence, or structure of that responsibility affects their ability to govern AI effectively. Method: The study surveys 351 organisations across sectors and regions to examine AI governance roles. It focuses on authority, resources, and organisational integration, using hierarchical cluster analysis to identify governance configurations. Findings: The results indicate that formal AI governance roles are unevenly distributed and often weakly integrated into organisational structures. When these roles exist, they are usually placed below the executive level, lack sufficient authority, and differ greatly in the resources available to them. A cluster analysis reveals four governance configurations—Governance Absence, Symbolic Governance, Operational Governance, and Institutionalised Governance, indicating that governance capacity is primarily influenced by how well these roles are embedded in the structure, rather than just their presence. Implications: The findings suggest that AI governance may be better understood as a structural and organisational design issue, with potential implications for accountability and oversight. However, the relationship between governance configurations and outcomes, such as ethical risk and compliance, remains an area for future research. Originality: The study takes an absence-based approach to AI ethics, establishing a baseline for future research on governance maturity, compliance, trust, and ethical risk. Full article

The reliability of adhesive bonding to CAD/CAM resin composites is influenced not only by material composition and surface treatment but also by the testing methodology used to assess bond strength. However, the impact of different shear bond strength (SBS) test configurations remains insufficiently clarified. This study evaluated the influence of different surface pretreatment protocols and SBS test methods on the bonding performance of a self-adhesive resin cement to two CAD/CAM materials: a conventional particulate-filled composite (Cerasmart 270) and an experimental short glass fiber-reinforced composite (SFRC CAD). Specimens (14 × 12 × 3 mm; n = 80 per material) were ground with 320-grit silicon carbide paper and divided according to surface pretreatment: airborne-particle abrasion (APA) or APA followed by hydrofluoric acid application for 60 s (APA + HF). Each group was further subdivided based on the SBS test method using either resin cement cylinders fabricated with a custom transparent mold (diameter: 3.6 mm; height: 3 mm) or metallic cylinders cemented to the treated surface. Half of the specimens were tested after 48 h of water storage, while the remainder underwent hydrothermal aging by boiling in water for 16 h prior to testing. Material type, SBS test method, surface pretreatment, and aging significantly affected bond strength (p < 0.05). The metallic cylinder method produced higher SBS values than the transparent mold technique, particularly for SFRC CAD. APA + HF tended to reduce SBS in Cerasmart 270, particularly after aging, whereas SFRC CAD showed comparable or higher bond strength values with APA alone. Aging decreased SBS in most groups. Overall, bond strength was influenced by both material type and test methodology. Within the limitations of this study, airborne-particle abrasion alone may be sufficient for SFRC CAD materials, while additional HF treatment may not provide further benefit. These findings highlight the importance of considering both material characteristics and test configuration when interpreting laboratory bond strength data. Full article

The liquid spreading on structured packings plays an essential role in affecting gas–liquid mass transfer in separation columns, yet the synergistic mechanism of surface wettability and textured geometries remains insufficiently understood. This study integrates experimental and computational methods to systematically investigate the liquid spreading characteristics on textured surfaces. The synergistic combination of hydrophilic modification and surface textures markedly enhances liquid spreading performance. Compared with the hydrophilic plane surface, the spherical cap texture increases the interface area and wetted area by 25.2% and 49.6%, respectively, while the pyramid-shaped texture leads to improvements of 24.5% and 48.9%, respectively. Based on Weber number analysis, it is identified that the competition between inertial force and surface tension governs the evolution of liquid spreading regimes. In addition, the results suggest that variations in liquid viscosity and density may further influence spreading behavior by modifying the balance among inertial, viscous, and surface tension forces. The geometric parameters of spherical cap textures are systematically examined, and it is revealed that a spherical cap with a non-uniform staggered configuration (Mode III) enables the efficient liquid spreading. A new non-uniform spherical cap texture is designed to enhance liquid spreading, which enhances spreading performance compared with the original plate, increasing the interface area by 27.3% and the wetted area by 47.4%. Although the liquid film thickness increases slightly, the wetted area ratio is significantly improved, indicating enhanced effective surface coverage. Both simulations and experiments confirm that the new textured structure further enhances liquid spreading performance on the textured surface. This research unveils a strategy to improve liquid spreading through tailored surface textures, opening up new possibilities for the design of efficient packings. Full article