Search Results (20,223)

Public participation in planning, though a foundational democratic principle, faces persistent implementation challenges across diverse planning systems. This paper examines participatory planning practice in Ireland and Serbia—two countries representing distinct planning traditions (discretionary and conformance-based, respectively) yet confronting shared structural pressures. Through comparative analysis of four local land use planning instruments (the Development Plan and Local Area Plan in Ireland; the Municipal Spatial Plan and General Regulation Plan in Serbia), the study investigates how institutional design and legislative frameworks shape the depth and quality of participatory practice. Methodologically, the research triangulates statutory regulations, public hearing documentation, and non-statutory participation records across two planning scales (county/municipal and local/sub-municipal). A four-dimensional analytical framework—informing, consultation, collaboration, and monitoring—guides the systematic comparison of participatory mechanisms across the selected cases. Findings reveal that, while both systems remain predominantly at the informing and consultation levels, critical differences emerge in how participation is structured and documented in institutional practice. Ireland’s discretionary system enables multi-channel information dissemination, feedback-oriented consultation, and non-statutory collaborative experimentation beyond legal minimums. Serbia’s conformance-based system confines participation largely to statutory procedures, with objection-based consultation and limited collaborative mechanisms, though distinctive features, such as the public hearing session, provide direct opportunities for deliberation absent in the Irish context. The study contributes to European comparative planning scholarship by demonstrating that participatory depth is shaped less by the formal existence of legal provisions than by the interplay between institutional design, procedural arrangements, transparency, and responsiveness. Full article

Ceratitis capitata (Diptera: Tephritidae), or medfly, is an invasive pest widespread in Argentina, where standardized management methods, such as cultural and chemical controls, are commonly implemented. The success in controlling medfly populations depends on implementing preventive, sustainable, long-term, and eco-friendly eradication/control strategies across all invaded environments. One strategy may involve augmentative biological control using parasitoids adapted to local conditions, such as Ganaspis pelleranoi (Brèthes) (Hymenoptera: Figitidae), a Neotropical-native parasitoid that mostly forages on tephritid larvae in a broad range of fallen fruit. Two hypotheses were tested in the current study: (1) G. pelleranoi females are more efficient in controlling medfly larvae infesting different fruits as the density of released females progressively increases, and (2) such parasitoid-induced host mortality capacity remains when host density is increased. Parasitism (reproductive effects) and additional host mortality (non-reproductive effects) were the indicator variables of parasitoid-induced host ability. Trials were performed in field cages (semi-field conditions) using two medfly-multiplier host fruit species, namely sour orange and peach, and with variations in both parasitoid release and host larval densities. Three major findings were highlighted: (1) G. pelleranoi females successfully parasitized host larvae on peach and sour orange, regardless of their strongly differing physical features, although medfly larvae in peaches were significantly more susceptible to the parasitoid; (2) medfly mortality significantly increased in both peach and sour orange relative to the gradual increase in released G. pelleranoi females, regardless of the increase in host density offered to parasitoids; and (3) G. pelleranoi females induced a substantially high host die-off rate when the additional mortality was added to the analysis, which was not revealed when parasitism alone was regarded as a medfly mortality variable. Such outcomes may provide relevant information for implementing an augmentative biological control against medfly using indigenous parasitoid species within an eco-friendly fruit fly pest management approach. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. Full article

Super-resolution reconstruction of remote sensing images has significant application value in fields such as smart cities, land monitoring, and traffic management. However, current super-resolution methods often overlook the differences between semantic and texture feature representations. This limitation makes it difficult to collaboratively preserve semantic structures and fine details during reconstruction, thereby affecting overall reconstruction quality. To address these challenges, this paper proposes a novel remote sensing image super-resolution network based on feature decoupling and differential window design, termed FDWD-Net. Specifically, we introduce an Adaptive Energy-driven Channel Selection module and a Multi-Directional Gradient-based Semantic–Texture Decoupling module to identify informative channels from the feature maps and decouple them into semantic and texture representations for independent optimization. Furthermore, we design a Differential Window-based Cross-scale Interaction module that dynamically adjusts window sizes based on local texture complexity, enabling adaptive feature modeling and effective multi-scale information interaction. Experimental results confirm that our method surpasses existing mainstream models on several remote sensing datasets. It also performs better in preserving structures and restoring detailed information. Full article

Background: Magnesium sulfate (MgSO₄) added to local anesthetics has been investigated as an adjuvant in regional anesthesia, but its role in ultrasound-guided erector spinae plane block (ESPB) remains uncertain. Methods: We conducted a PRISMA 2020-compliant systematic review of randomized controlled trials evaluating MgSO₄ added to the local anesthetic solution in ESPB. In the predefined core comparison (MgSO₄ added to local anesthetic vs. local anesthetic alone in adult postoperative surgery), four trials (225 participants enrolled; 160 contributing to the comparison) informed the qualitative synthesis. Results: Eight randomized controlled trials were included. In the predefined core comparison, 24 h pain intensity was reported heterogeneously and was frequently not extractable as continuous data, precluding pooling. Opioid consumption or rescue analgesia more often favored MgSO₄; however, outcome metrics, analgesic drugs, and assessment windows were not harmonized, and these effects were not consistently accompanied by reductions in pain intensity at 24 h, limiting their interpretation as true analgesic benefit. Safety reporting was frequently incomplete and often lacked structured adverse event tabulation. Risk of bias varied across domains, and GRADE certainty for all core outcomes was very low. Conclusions: Current randomized evidence does not support routine use of MgSO₄ as an adjuvant in ESPB. Future trials using standardized ESPB techniques, harmonized magnesium dosing strategies, and core outcome sets are required to determine whether magnesium provides clinically meaningful incremental analgesic benefit. Full article

Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the privacy–utility trade-off in anonymization process. The proposed approach integrates FOX-based global exploration with RUN-based local search using a hybrid adaptive control strategy and intensive mutation search to improve solution diversity in highly constrained solution spaces. Results: The experimental study on the Adult and Bank Marketing datasets shows that PA-FRIM exhibits stable convergence behavior compared to competing methods. The results indicate that full privacy is achieved on the Adult dataset with a violation value of 0.00, and information loss is minimized with an NIL measure of 0.5686. From the analytical utility perspective, PA-FRIM ensures data usability, even in the constrained region, achieving classification accuracies of 89.61% on the Bank Marketing dataset and 84.90% on the Adult dataset. Conclusions: By using a multi-metric evaluation strategy, PA-FRIM provides a robust optimization framework that eliminates privacy violations while maintaining high analytical performance. Full article

Parasitic plants are ecologically important because they can exert a profound influence on the surrounding ecosystem. Yet the ecology and host specificity of most parasitic plant species remain poorly known or undocumented. Cistanche phelypaea is a local and elusive parasitic plant in the Canary Islands. We carried out the first qualitative assessment of this plant’s ecology on the islands by examining 10 subpopulations over a 7-year period. We examined aspects of the plant’s ecology, distribution, and specificity for eight potential host species. Our observations suggest that four species are hosts: Afrosalsola divaricata, Bassia tomentosa, Suaeda vera, and Traganum moquinii, all of which are shrubby Amaranthaceae; however, host specificity varies across the plant’s range. Afrosalsola divaricata was inferred to be the predominant host and was parasitised wherever it co-occurred with the parasite (50% of sites). Cases of inferred parasitism on more than one host species at a given site were rare. Eight of the ten subpopulations occur in areas of high footfall or close to urbanisation; some disturbance, if managed sensitively, appears to favour recruitment and population dynamics. Based on our observations, we suggest that the integration of species distribution models (SDMs) with targeted surveys would be a promising route for scaling up from site-level observations to island-wide inference. We lay the groundwork for practical recommendations informed by such surveys; together with our long-term observations on host range, this offers a template for parasitic plant conservation more broadly. Full article

Pesticidal plants are vital for pest management in sub-Saharan Africa, yet knowledge about them is culturally and geographically unevenly documented. This study examined ethnobotanical knowledge and the distribution of pesticidal plants among six ethnic groups (Maasai, Chagga, Iraqw, Pare, Nguu, Zigua) in northern Tanzania. Data related to ethnobotanical knowledge were collected from questionnaire surveys involving 266 participants, 24 focus group discussions, 26 key informant interviews, complemented by field verifications across the six ethnic groups. Pesticidal plant coordinates were accessed from herbarium voucher specimens from The National Herbarium of Tanzania. Chi-square tests of independence assessed associations between ethnic groups and knowledge transmission pathways. Penalized logistic regression analysis was conducted to assess the influence of demographic factors on reported knowledge of pesticidal plants. Spatial overlay was conducted to examine the distribution of pesticidal plant species occurrences across agroecological zones and rainfall gradients. The results revealed a significant association between ethnic group and the source of pesticidal plant knowledge. Across all ethnic groups, knowledge was predominantly acquired through family/community traditional sources, with the highest frequencies recorded among the Maasai, Iraqw, and Zigua. Knowledge is mainly transmitted orally, particularly among the Maasai, Iraqw, and Zigua. A total of one hundred and six distinct species were recorded across the six ethnic groups surveyed, with Tephrosia vogelii and Solanum incanum being the most frequently cited. Leaves were the most frequently used plant part across all ethnic groups, with notably high usage among the Chagga, Iraqw, and Maasai. Perceptions of the declining population of pesticidal plants were the highest among the Maasai. Spatial mapping revealed pesticidal plant hotspots in the Northern Rift and Volcanic Highlands agroecological zones, and they fall within zones receiving moderate to relatively high rainfall. The findings highlight that ethnobotanical knowledge of pesticidal plants in northern Tanzania is strongly shaped by ethnic affiliation, oral knowledge transmission, and localized ecological availability, with clear spatial hotspots aligned to specific agroecological zones and high-rainfall areas. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

Political polarization and policy uncertainty have become increasingly consequential for regional economic adjustment, yet their joint role in shaping socioeconomic resilience remains underdeveloped in the literature. This study advances the debate by conceptualizing regional resilience as the outcome of a multi-layer socioeconomic system in which external policy disturbances, institutional fragmentation, and structural adaptive capacity interact over time. Using balanced panel data for 16 Korean regions from 2004 to 2023, the analysis develops an integrated empirical framework that combines panel local projections, threshold estimation, structural moderation tests, dynamic robustness checks, and forward-looking machine-learning prediction. The results show that policy uncertainty is associated with lower regional socioeconomic resilience and that this effect persists over time. More importantly, political polarization does not simply accompany weaker resilience; it amplifies the transmission of uncertainty shocks, especially once institutional fragmentation crosses a critical threshold. Structural conditions further shape this process. Digital transformation, industrial diversification, and financial depth reduce vulnerability, whereas trade exposure intensifies it. These findings indicate that resilience is not determined by economic structure alone, nor by institutional instability in isolation. It emerges from the interaction between disturbance, amplification, and adaptive capacity within a regional system. The predictive analysis reinforces this interpretation. Variables identified as central in the econometric models also carry forward-looking information about future vulnerability states. This study therefore contributes not only by combining methods, but by linking explanation and prediction within a single systems-oriented account of regional resilience. The Korean case shows how institutional coherence and structural adaptability jointly condition resilience under uncertainty. Full article

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study presents a Transformer model with geometric enhancement. The model combines local geometric features and global attention to improve species recognition in forest environments. It uses geometric information with biological meaning, including point cloud normals, local density, vertical structure, and growth direction. A focal loss with class balance is also introduced to reduce the impact of species distributions with long tails. Experiments on the ForSpecial20K dataset show that the proposed method performs better than representative models based on convolution, graph methods, and Transformer architectures. It achieves higher overall accuracy (78.20%), higher mean class accuracy (73.48%), and a higher Macro-F1 score (73.21%). Results from confusion matrices and visual analysis of similar species further verify the effectiveness of the geometric features and the loss design. These results suggest that modeling structural information of forests helps improve robustness and generalization. The proposed method offers a practical solution for tree-level species mapping, fusion of LiDAR data from multiple sources, and fine-scale forest inventory. It also shows the value of combining high-resolution LiDAR data with deep learning for forestry applications. Full article

Accurate short-term photovoltaic (PV) power forecasting is essential for power system scheduling and market operations. Existing studies have shown the value of numerical weather prediction (NWP), graph-based spatial modeling, and temporal sequence learning, but the boundary of their contributions remains fragmented across many practical forecasting frameworks. In particular, adjacent multi-point NWP information is often not explicitly organized according to its spatial relationships, while historical similar-day power is rarely integrated with graph-structured meteorological features in a unified model. To address this gap, this study develops a short-term PV power forecasting framework that combines multi-point NWP graph construction with similar-day-guided Transformer fusion. First, predicted irradiance from the target site and neighboring NWP points is organized as a graph, and a Graph Convolutional Network (GCN) is used to extract local spatial meteorological features. Second, similar days are identified through a two-stage selection strategy based on Euclidean distance and Pearson correlation, and the corresponding historical power sequences are aggregated as temporal guidance. Finally, the graph-extracted NWP features, similar-day power, and predicted humidity are fused by a Transformer-based temporal modeling module to generate day-ahead PV power forecasts. Experimental results show that the proposed framework outperforms TCN-Transformer, Transformer, GCN, LSTM, and BP on the studied dataset, and maintains favorable performance on additional PV stations. These results indicate that the joint integration of graph-structured multi-point NWP information and historical similar-day power is effective for short-term PV power forecasting. Full article

In response to the subjective issues, inconsistent quality standards, high labor intensity and low sorting efficiency during the drying process of green pepper, an improved YOLOv11 algorithm was proposed for quality detection. A multi-scale edge enhancement module (MEEM) is introduced into the backbone network, replacing the original basic C3K2 module with C3K2-MEEM to enhance the extraction of detailed features in images of dried green Sichuan pepper and prevent missed detections, false detections, and boundary confusion. The LRSA module is integrated into the 10th layer of the backbone network to improve the clarity of the tumor-like texture of the Sichuan pepper and reduce the influence of impurities, automatically allocating attention based on feature similarity to preserve local information. In the neck layer, the DPCF module is added to the FPN+PAN feature fusion stage to achieve multi-scale feature collaboration, meeting the detection requirements of dried green Sichuan pepper. The results show that the accuracy recall rate, mean average precision, and model size of the improved MLD-YOLOv11 algorithm are 92.1%, 96.6%, 95.6%, and 11.06 MB, respectively. Compared with the training results of the original YOLOv11 model, the average accuracy of the improved model has increased by 2.2 percentage points, and GFLOPs have definitely decreased by 2 G, with parameter reduction of approximately 3.10%. Compared with other mainstream models, the MLD-YOLOv11 model has significant advantages in terms of mean average precision, model size, and floating point operations per second, making it more suitable for industrial applications and providing an efficient, accurate, and lightweight solution for the quality detection of dried green Sichuan pepper. Full article