This study investigates the relationship between municipal management and sustainable tourism in an urban protected area, specifically the Los Pantanos de Villa Wildlife Refuge in Lima, Peru. The research adopts a quantitative, correlational, non-experimental, cross-sectional design, focusing on a sample of 67 employees from the Municipal Authority. A probabilistic sampling technique was employed to select the sample from a population of 80 workers. Data were collected through two separate questionnaires, each tailored to measure one of the key variables, with responses recorded on a Likert scale ranging from 1 to 5. The study area, Los Pantanos de Villa, is an urban protected area situated in a densely populated region where challenges such as pollution, waste management, and urban sprawl exert significant pressure on environmental sustainability. Findings revealed that 88.06% of respondents assessed municipal management in the protected area as "good," while 76.12% rated sustainable tourism positively. Statistical analysis revealed a Pearson correlation coefficient of 0.590, with a p-value of 0.000, indicating a significant positive correlation between effective municipal management and the promotion of sustainable tourism. These results emphasize the crucial role of municipal governance in enhancing both environmental stewardship and sustainable tourism development within urban protected areas. Effective management practices can contribute to balancing the dual objectives of ecological conservation and urban development, thereby fostering a sustainable tourism model in highly urbanised contexts. This study underscores the importance of governance frameworks in mitigating urban pressures and advancing sustainability in Natural Protected Area (NPA).
The selection of optimal text embedding models remains a critical challenge in semantic textual similarity (STS) tasks, particularly when performance varies substantially across datasets. In this study, the comparative effectiveness of multiple state-of-the-art embedding models was systematically evaluated using a benchmarking framework based on established machine learning techniques. A range of embedding architectures was examined across diverse STS datasets, with similarity computations performed using Euclidean distance, cosine similarity, and Manhattan distance metrics. Performance evaluation was conducted through Pearson and Spearman correlation coefficients to ensure robust and interpretable assessments. The results revealed that GIST-Embedding-v0 consistently achieved the highest average correlation scores across all datasets, indicating strong generalizability. Nevertheless, MUG-B-1.6 demonstrated superior performance on datasets 2, 6, and 7, while UAE-Large-V1 outperformed other models on datasets 3 and 5, thereby underscoring the influence of dataset-specific characteristics on embedding model efficacy. These findings highlight the importance of adopting a dataset-aware approach in embedding model selection for STS tasks, rather than relying on a single universal model. Moreover, the observed performance divergence suggests that embedding architectures may encode semantic relationships differently depending on domain-specific linguistic features. By providing a detailed evaluation of model behavior across varied datasets, this study offers a methodological foundation for embedding selection in downstream NLP applications. The implications of this research extend to the development of more reliable, scalable, and context-sensitive STS systems, where model performance can be optimized based on empirical evidence rather than heuristics. These insights are expected to inform future investigations on embedding adaptation, hybrid model integration, and meta-learning strategies for semantic similarity tasks.
The effects of polycarboxylate superplasticizer (PCE) on the rheological properties and workability of cement-based composites were investigated by testing parameters such as static yield stress, dynamic yield stress, plastic viscosity, slump flow, bleeding rate, and penetration depth. The correlation between the dosage of PCE and the rheological parameters of fresh cement-based composites was analyzed. The results indicated that with an increase in the PCE dosage, the static yield stress, dynamic yield stress, and plastic viscosity of fresh cement-based composites decreased, demonstrating that PCE can improve the rheological properties of these composites. As the PCE dosage increased, the slump flow and bleeding rate of fresh cement-based composites also increased, but the rate of change decreased at higher dosages. Additionally, with an increase in PCE dosage, the penetration depth gradually increased, while the penetration depth difference ($\Delta {H}$) decreased. Furthermore, the compressive strength of cement-based composite cubes slightly decreased with an increase in PCE dosage.
To mitigate safety risks in subway shield construction within water-rich silty fine sand layers, a risk immunization strategy based on complex network theory was proposed. Safety risk factors were systematically identified through literature review and expert consultation, and their relationships were modeled as a complex network. Unlike traditional single-index analyses, this study integrated degree centrality, betweenness centrality, eigenvector centrality, and clustering coefficient centrality to comprehensively evaluate the importance of risk factors. Results indicated that targeted immunization strategies significantly outperformed random immunization, with degree centrality (DC) and betweenness centrality (BC) immunization demonstrating the best performance. Key risk sources included stratum stability, allowable surface deformation, surface settlement monitoring, and shield tunneling control. Furthermore, the optimal two-factor coupling immunization strategy was found to be the combination of DC and BC strategies, which provided the most effective risk prevention. This study is the first to apply complex network immunization simulation to safety risk management in subway shield construction, enhancing the risk index system and validating the impact of different immunization strategies on overall safety. The findings offer scientific guidance for risk management in complex geological conditions and provide theoretical support and practical insights for improving construction safety.
The accurate determination of the conduit water starting time constant ($T_w$) is critical for optimizing hydro turbine performance and dynamic control in hydropower plants. Instead of relying on conventional calculation methods, machine learning (ML) techniques, specifically long short-term memory (LSTM) networks and multilayer perceptron (MLP) models, have been employed to identify $T_w$. The dataset used for model training and validation comprises real operational data collected from two hydropower plants. The effectiveness of both algorithms in $T_w$ identification has been evaluated through simulation, with Python serving as the primary programming environment. The findings indicate that, despite its more complex architecture, LSTM does not necessarily yield superior results. In contrast, MLP, as a relatively simpler model, demonstrates greater accuracy in estimating $T_w$, suggesting that intricate network structures are not always required for precise identification. Additionally, an optimization function ($F_\text{opt}$) has been utilized to assess the reliability of the identified $T_w$ values by comparing them with actual hydro turbine responses. The results underscore the practicality of MLP in hydropower system modeling, providing a computationally efficient alternative for conduit water starting time constant identification. These insights contribute to improving real-time turbine control and enhancing the efficiency of hydropower generation.
Image segmentation plays a crucial role in medical imaging, remote sensing, and object detection. However, challenges persist due to uncertainty in region classification, sensitivity to noise, and discontinuities in object boundaries. To address these issues, a novel segmentation framework is proposed, integrating Complex Pythagorean Fuzzy Aggregation Operators (CPFAs) with a level-set-based optimization strategy to enhance both precision and adaptability. The proposed model leverages complex Pythagorean fuzzy membership functions, incorporating both magnitude and phase components, to effectively manage overlapping intensity distributions and classification uncertainty. Additionally, geometric constraints, including gradient and curvature-based regularization, are employed to refine boundary evolution, ensuring accurate edge delineation in noisy and complex imaging conditions. A key contribution of this work is the formulation of a complex fuzzy energy functional, which synergistically integrates fuzzy region classification, phase-aware boundary refinement, and geometric constraints to guide segmentation. The level-set method is utilized to iteratively minimize this functional, facilitating smooth transitions between segmented regions while preserving structural integrity. Experimental evaluations conducted across diverse imaging domains demonstrate the robustness and versatility of the proposed approach, highlighting its efficacy in medical image segmentation, remote sensing analysis, and object detection. The integration of complex fuzzy logic with geometric optimization not only enhances segmentation accuracy but also improves resilience to noise and irregular boundary structures, making this framework particularly suitable for applications requiring high-precision image analysis.
Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data.
Accurate prediction of soil fertility and soil organic carbon (SOC) plays a critical role in precision agriculture and sustainable soil management. However, the high spatial-temporal variability inherent in soil properties, compounded by the prevalence of noisy data in real-world conditions, continues to pose significant modeling challenges. To address these issues, a robust hybrid deep learning model, termed RTCNet, was developed by integrating Recurrent Neural Networks (RNNs), Transformer architectures, and Convolutional Neural Networks (CNNs) into a unified predictive framework. Within RTCNet, a one-dimensional convolutional layer was employed for initial feature extraction, followed by MaxPooling for dimensionality reduction, while sequential dependencies were captured using RNN layers. A multi-head attention mechanism was embedded to enhance the representation of inter-variable relationships, thereby improving the model’s ability to handle complex soil data patterns. RTCNet was benchmarked against two conventional models—Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA), and a Transformer-CNN hybrid model. Under noise-free conditions, RTCNet achieved the lowest Mean Squared Error (MSE) of 0.1032 and Mean Absolute Error (MAE) of 0.1852. Notably, under increasing noise levels, RTCNet consistently maintained stable performance, whereas the comparative models exhibited significant performance degradation. These findings underscore RTCNet’s superior resilience and adaptability, affirming its utility in field-scale agricultural applications where sensor noise, data sparsity, and environmental fluctuations are prevalent. The demonstrated robustness and predictive accuracy of RTCNet position it as a valuable tool for optimizing nutrient management strategies, enhancing SOC monitoring, and supporting informed decision-making in sustainable farming systems.
Ensuring the integrity of goods during cold chain transportation remains a critical challenge in logistics, as it is essential to preserve product quality, freshness, and compliance with stringent safety standards. Strategic decision-making in this context requires the prioritization of customer requirements and the optimal allocation of limited operational resources. In response to these demands, an integrated Multi-Criteria Decision-Making (MCDM) model was developed by combining the Best-Worst Method (BWM), Quality Function Deployment (QFD), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) approach. Within this framework, BWM was utilized to determine the relative importance of user requirements, which were then mapped onto specific operational resources through QFD to identify critical resource elements and derive their corresponding weights. These weights, subsequently treated as evaluation criteria in the MARCOS method, were applied to assess the performance of Third-Party Logistics (3PL) providers. The proposed methodology was validated through a case study involving eight user requirements and seven key resources. The findings indicated that precise temperature control and delivery speed were the most critical user requirements, whereas advanced temperature sensors and vehicles with cooling systems were identified as the most significant resources. Based on the MARCOS evaluation, Provider 1 emerged as the most optimal 3PL alternative. This integrated decision-making model offers a systematic and data-driven approach for aligning customer priorities with resource capabilities, thereby enabling logistics providers to enhance service quality, operational efficiency, and strategic competitiveness in temperature-sensitive supply chains. The model also demonstrates practical scalability and adaptability across diverse cold chain scenarios.
The rice crisis represents a significant threat to food security and economic stability in Southeast Asia, a region where rice serves as the primary staple for the majority of the population. This crisis is exacerbated by a confluence of factors, including climate change, crop failures, and restrictive export policies, as exemplified by the El Niño phenomenon and India’s 2023 rice export ban. Rising rice prices have been linked to increased social unrest, with the potential to trigger widespread demonstrations across affected nations. To proactively address this issue, the restlessness indicator was introduced as a predictive tool, integrating key variables such as rice prices, consumption patterns, and per capita income. This study employs a Spatio-Temporal Autoregressive (STAR) model to forecast restlessness values across six Southeast Asian countries—Indonesia, the Philippines, Thailand, Vietnam, Malaysia, and Cambodia—from 2024 to 2028. The STAR (5,1) model was identified as the optimal framework, achieving a Mean Absolute Percentage Error (MAPE) of 15.1%. The forecasting results indicate that none of the analyzed countries are projected to enter a state of unprecedented restlessness during the specified period, suggesting that no severe rice crisis is anticipated within this timeframe. These findings provide critical insights for policymakers and stakeholders, enabling the development of preemptive strategies to mitigate potential food security challenges. The study underscores the utility of the restlessness indicator as a robust tool for monitoring and forecasting rice-related crises, contributing to the broader discourse on sustainable food systems in Southeast Asia.
Efficient bidirectional energy exchange between an alternating current (AC) grid and a direct current (DC) source has been enabled through advanced power converter topologies. In this study, a single-stage AC-DC dual active bridge (DAB) converter employing phase-shift modulation (PSM) was investigated, with a particular focus on performance within the overmodulation regime. Bidirectional switching modules were implemented on the AC side to facilitate seamless energy transfer. Two conventional modulation strategies—sinusoidal and triangular—and a novel back-calculated modulation method were examined for their performance in both linear and overmodulation operating regions. The proposed back-calculation method incorporates an off-line generated reference current waveform designed to approximate linear control characteristics while substantially minimizing current harmonic distortion under overmodulated conditions. This approach extends the linear relationship between the reference current and power transfer capability beyond the conventional modulation limits, thereby enhancing converter performance in high-demand scenarios. Simulation-based analysis demonstrated that, in the linear region, the proposed method reduced average current total harmonic distortion (THD) by at least 45% when compared to conventional sinusoidal and triangular modulation techniques. Moreover, within the overmodulation regime, the linear correlation between the reference current and power transfer was extended by approximately 16.5%. The current harmonic distortion remained below 5% and 8% at modulation ratios of 108% and 112%, respectively, underscoring the robustness of the proposed strategy. These results suggest that the proposed PSM method is highly effective in achieving improved power exchange with reduced harmonic content in both linear and overmodulated operation, thereby offering a viable solution for high-performance AC-DC power conversion in smart grids and renewable energy systems.
Benzene is a toxic airborne contaminant and a recognized cancer-causing agent that presents substantial health hazards even at minimal concentrations. The precise prediction of benzene concentrations is crucial for reducing exposure, guiding public health strategies, and ensuring adherence to environmental regulations. Because of benzene's high volatility and prevalence in metropolitan and industrial areas, its atmospheric levels can vary swiftly influenced by factors like vehicular exhaust, weather patterns, and manufacturing processes. Predictive models, especially those driven by machine learning algorithms and real-time data streams, serve as effective instruments for estimating benzene concentrations with notable precision. This research emphasizes the use of recurrent neural networks (RNNs) for this objective, acknowledging that careful selection and calibration of model hyperparameters are critical for optimal performance. Accordingly, this paper introduces a customized version of the elk herd optimization algorithm, employed to fine-tune RNNs and improve their overall efficiency. The proposed system was tested using real-world air quality datasets and demonstrated promising results for predicting benzene levels in the atmosphere.