Balanced Trade-off Research Articles

Optimal control framework for cost-effective, intelligent, renewable energy-driven communities in Norway, enhanced by ANN and grey wolf method

The present work introduces a novel yet smart energy system to decarbonize the energy mix, provide cost-effective green energy, and help to achieve sustainable energy production/storage/usage. Smart hydronic units and multiple controllers are the backbone of this idea, which aims to monitor energy conversion among components, the grid, and users through an innovative rule-based framework. This clever integration results in smaller components, bidirectional grid connection, and increased penetration of renewable energy sources into the local energy network. The system is driven by photovoltaic thermal panels and a biomass heater integrated with a heat pump and internal combustion engine. The TRNSYS-MATLAB framework evaluates the system's practicality from all aspects. Multi-objective optimization based on the grey wolf approach equipped with artificial intelligence is added to find the most optimal state. Compared to the conventional system in Norway (hydropower + wind), the proposed smart integration achieves up to 55 % and 5.4 % reduction in energy costs and emission index. In particular, the carbon emission index is lowered to 36.3 g/kWh, and the energy cost is decreased to 38.4 $/MWh. Also, the parametric analysis indicates a conflictive change among key metrics when altering a variable, necessitating the multi-objective optimization to achieve a balancing trade-off. The optimization reduces the emission index, investment cost, and energy cost by about 1.8 g/kWh (5 %), 156,000 $/year (2.5 %), and 1.5 $/MWh (4 %) while improving the efficiency by about 1.7 % compared to the design condition. The suggested system generates 17,830 MW/year of clean energy and can mitigate carbon dioxide emissions by 552,000 kg per year compared to the Norwegian energy mix.

Catalytic Synergy between Pd Nanoclusters and Ligand-Functionalized Layered Silicates for Improved Formic Acid Dehydrogenation.

The synthesis and stabilization of Pd nanoclusters on a support, as well as simultaneously achieving optimal catalytic activity, remain challenging tasks. Functionalizing the support surface with specific ligands offers a promising solution, but it often requires carefully balancing trade-offs between the reaction yield and catalyst stability. Here, we used two different ligands (propylamine and propylthiol) to functionalize the layered silicate's interlayer surface for Pd nanocluster synthesis and stabilization. For dehydrogenating formic acid, Pd nanoclusters on aminopropyl groups achieved a catalytic activity ∼27-fold higher than that of thiopropyl groups at 70 °C. Our density functional calculations compared the adsorption energetics and bonding characteristics of single Pd atoms and Pd13 nanoclusters on amino- and thio-functionalized silicate surfaces. Pd-N bonds were predicted to be weaker with minimal covalency, while Pd-S bonds exhibit greater covalency due to higher 4d-3p hybridization, resulting in better stability. However, Pd13 clusters undergo severe structural deformation on thiol-functionalized surfaces, resulting in a smaller overall surface area and diminished catalytic stability.

Using an improved U-Net++ with a T-Max-Avg-Pooling layer as a rapid approach for concrete crack detection

The monitoring of concrete structures has advanced remarkably with the aid of deep learning technologies. Since concrete is multi-purpose and low-cost, it is extensively used for construction purposes. Concrete is very enduring. Nevertheless, it tends to crack which endangers the integrity of the structure and results in complications. The current study offers a new image segmentation approach for detecting cracks in concrete by making use of an optimized U-Net++ architecture. The proposed model gives the features of the T-Max-Avg Pooling layer which effectively combines the advantages of traditional max and average pooling using a learnable parameter to balance feature extraction dynamically. This innovation both improves the output accuracy and processing speed and captures the fine details. In addition, it mitigates noise and transcends the limitations of conventional pooling methods. Moreover, using learnable pruning and shortening skip connections in U-Net++ reduce redundant computations, making the model faster without compromising accuracy. In comparison with other models like Mask R-CNN and VGG-U-Net, the proposed model had considerably faster inference times (21.01 ms per image) and fewer computational requirements (40G FLOPs), making it very suitable for real-time monitoring applications. The DeepCrack and Concrete Pavement Crack datasets were employed to assess the model thoroughly which yielded an MIoU score of 82.1%, an F1 score of 90.12%, a Dice loss score of 93.7%, and an overall accuracy of 97.65%. According to the results, the enhanced U-Net++ with T-Max-Avg Pooling provided a balanced trade-off between segmentation accuracy and computational efficiency. This indicates its considerable potential for automated real-time crack detection in concrete structures by employing resource-constrained environments including drones and mobile platforms.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.

Automated diabetic retinopathy (DR) screening using artificial intelligence has the potential to improve access to eye care by enabling large-scale screening. However, little is known about differences in real-world performance between available algorithms. This study compares the diagnostic accuracy of two AI screening platforms, IDx-DR and RetCAD, for detecting referable diabetic retinopathy (RDR). Retinal images from 758 patients with diabetes were collected during screening from various clinics in Poland. Each patient was graded by three graders with 320 patients graded by Polish and 438 patients graded by Indian graders, with the majority decision serving as the reference standard. The images were evaluated independently by the IDx-DR and RetCAD algorithms. Sensitivity, specificity, positive and negative predictive values, and agreement between algorithms and human graders were calculated and statistically compared. IDx-DR demonstrated higher sensitivity of 99.3% but lower specificity of 68.9% for RDR detection compared to RetCAD which had 89.4% sensitivity and 94.8% specificity. The positive predictive value was higher for RetCAD (96.4% vs 48.1% for IDx-DR) while the negative predictive value was higher for IDx-DR (99.5% vs 83.1% for RetCAD). Both algorithms achieved high sensitivity (> 95%) for sight-threatening diabetic retinopathy detection. In this direct comparison using the same patient cohort, the two algorithms showed differences in their operating parameters for RDR screening. IDx-DR prioritized avoiding false negatives over false positives while RetCAD maintained a more balanced trade-off. These results highlight the variable performance of current artificial intelligence screening solutions and suggest the importance of considering algorithm performance metrics when deploying automated diabetic retinopathy screening programs, based on available healthcare resources.

Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.

Trustworthy V2G scheduling and energy trading: A blockchain-based framework

The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G's potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework's potential to support auxiliary grid services.

Imbalanced graph learning via mixed entropy minimization

Imbalanced datasets, where the minority class is underrepresented, pose significant challenges for node classification in graph learning. Traditional methods often address this issue through synthetic oversampling techniques for the minority class, which can complicate the training process. To address these challenges, we introduce a novel training paradigm for node classification on imbalanced graphs, based on mixed entropy minimization (ME). Our proposed method, GraphME, offers a ‘free imbalance defense’ against class imbalance without requiring additional steps to improve classification performance. ME aims to achieve the same goal as cross-entropy-maximizing the model’s probability for the correct classes-while effectively reducing the impact of incorrect class probabilities through a “guidance” term that ensures a balanced trade-off. We validate the effectiveness of our approach through experiments on multiple datasets, where GraphME consistently outperforms the traditional cross-entropy objective, demonstrating enhanced robustness. Moreover, our method can be seamlessly integrated with various adversarial training techniques, leading to substantial improvements in robustness. Notably, GraphME enhances classification accuracy without compromising efficiency, a significant improvement over existing methods. The GraphME code is available at: https://github.com/12chen20/GraphME.

A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME-SHAP Integration.

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making.

Handoffs and Equity: Impact of a Patient Distribution Model on Handoffs for Black Patients.

Hospital medicine patient distribution models (PDM) assign patients to inpatient services on hospital admission. Models balance tradeoffs including patient handoffs, physician wellness, subspecialty care, and other factors to ensure optimal outcomes; however, equity is rarely considered. Handoffs during inpatient care can result in medical error and worse patient outcomes. This study evaluates the impact of a PDM that prioritizes use of specialty care services and an overflow service (OS) during high census on racial inequities in handoff frequency. A single-center retrospective cohort study of inpatient encounters on hospital medicine services from July 2017 to December 2019 was conducted. The primary exposures included being discharged by a general medicine service (GMS) or cared for by an OS. The primary outcome was handoffs per day of stay, analyzed by multivariable regression adjusted for age, gender, race, ethnicity, insurance, discharge from GMS, and care from OS. A total of 4165 inpatient hospitalizations with the majority of their stay on a hospital medicine service were reviewed. Patients discharged by GMS (78.2% vs. 58.1%, p < .001) and cared for by OS (78.7% vs. 67.0%, p < .001) were more likely to identify as Black. Multivariable analysis showed a handoff risk ratio of 1.53 (p < .001) for OS patients and 1.06 (p = .01) if discharged from GMS, but race alone did not significantly affect risk of handoffs. The PDM prioritization drove increased handoffs disproportionately for Black patients. Multivariable analysis showed that race alone did not contribute to increased handoffs suggesting the creation of a systemic bias in patient care.

Kinematic calibration of industrial robot using Bayesian modeling framework

Positioning accuracy of an end-effector is a crucial metric for evaluating industrial robot performance. Uncertainties in joint angles and joint backlash deviate actual angles from the designed nominal values to negate positioning accuracy. Most existing parameter identification methods overlook or not properly account for such uncertainties, leading to usually overconfident identification results. To this gap, the present study introduces a kinematic calibration methodology employing Bayesian parameter estimation to achieve identification of joint variables. New formulas based on data features of industrial robots for constructing the likelihood function are proposed, and model selection is applied to assess various likelihood functions for a tradeoff balance between complexity and accuracy. To evaluate the robustness of the proposed approach, identification of joint variables is conducted under different measurement noises. The position response of kinematic model is predicted based on the identified joint uncertainty information. The efficacy is verified through rigorous scrutiny involving both a numerical example and an engineering application. Results indicate that the proposed method exhibits satisfactory kinematic parameter identification accuracy and robustness. In addition, the uncertainty of parameters can be measured and the prediction of trajectory uncertainty intervals is realized simultaneously, which promotes the application of industrial robots in high-precision scenes.

Harnessing nano-synergy: A comprehensive study of thermophysical characteristics of silver, beryllium oxide, and silicon carbide in hybrid nanofluid formulations

Nanofluids, promising to improve heat transfer efficiency, encounter stability and durability challenges, hindering their industrial applications. The emerging concept of hybrid nanofluids, alongside surfactant-driven stability research, presents a promising solution to tackle challenges in heat transfer, potentially revolutionizing thermal management systems and advancing nanomaterial science. This comprehensive study investigates the thermophysical properties of simple and hybrid nanofluid formulations composed of silver (Ag), beryllium oxide (BeO), and silicon carbide (SiC) nanoparticles dispersed in water. Hybrid nanofluids were prepared with varying volumetric ratios of 20:80, 40:60, 60:40, and 80:20 and examined across temperatures ranging from 15 to 45 °C. The influence of surfactants on stability was explored to augment thermal characteristics. Results revealed that the surfactants have a significant effect on stability and the specific mixing ratios can lead to more favourable thermal characteristics. Thermal conductivity enhancements, evaluated via the transient hot-wire method, demonstrated improvements of 7.43 %, 7.17 %, and 5.31 % for Ag/SiC (60:40), Ag/BeO (60:40), and SiC/BeO (80:20) hybrid nanofluids, respectively, compared to water. Viscosity measurements revealed Newtonian behaviour for the Ag, SiC, and BeO nanofluids, with a minimum viscosity enhancement of 3.01 % observed for the BeO nanofluid. Hybrid Ag/SiC nanofluid demonstrated a maximum viscosity enhancement of 4.90 % for 20:80 formulation. Density analysis showed maximum augmentation of 0.25 %, 0.097 %, and 0.0775 % for the Ag, SiC, and BeO nanofluids respectively at 0.025 vol% while hybrid Ag/SiC nanofluid exhibited a maximum of 0.23 % density increase for the 80:20 composition. The statistical models were also developed to predict properties against temperature. Furthermore, the cost analysis identified Ag nanofluid as the most expensive option, while the SiC/BeO hybrid was the most economical. However, the Ag-SiC (60:40) hybrid nanofluid offered a balanced trade-off between properties and cost.

Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model’s performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model’s efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads.

Optimization of Secondary Chlorination in Water Distribution Systems for Enhanced Disinfection and Reduced Chlorine Odor Using Deep Belief Network and NSGA-II

This research explores the strategic optimization of secondary chlorination in water distribution systems (WDSs), in order to enhance the efficiency of disinfection while mitigating odor and operational costs and promoting sustainability in water quality management. The methodology integrates EPANET simulations for water hydraulic and quality modeling with a deep belief network (DBN) within the deep learning framework for accurate chloric odor prediction. Utilizing the non-dominated sorting genetic algorithm-II (NSGA-II), this methodology systematically balances the objectives of chloride dosage and chloramine formation. It combines a chloric odor intensity assessment, a multi-component kinetic model, and dual-objective optimization to conduct a comparative analysis of case studies on secondary chlorination strategies. The optimal configuration with five secondary chlorination stations reduced chloric odor intensity to 1.20 at a cost of USD 40,020.77 per year in Network A while, with eight stations, chloric odor intensity was reduced to 0.88 at a cost of USD 71,405.38 per year in Network B. The results demonstrate a balanced trade-off between odor intensity and operational cost on one hand and sustainability on the other hand, highlighting the importance of precise chlorine management to improve both the sensory and safety qualities of drinking water while ensuring the sustainable use and management of water resources.

Predicting Mental Health Disorders based on Sentiment Analysis

The increasing prevalence of mental health disorders such as anxiety, depression, and stress calls for innovative, data- driven approaches to early detection and intervention. This research investigates the use of sentiment analysis to predict mental health disorders from social media text, utilizing machine learning techniques to enhance the accuracy and interpretability of mental health classification. We developed and evaluated multiple machine learning models, including Random Forest, Extra Trees, AdaBoost, and Logistic Regression, using a curated dataset of user-generated content reflecting various mental health states. Our rigorous experimentation demonstrates the potential of sentiment analysis combined with advanced machine learning models in identifying mental health conditions. The study highlights that while Random Forest and Extra Trees excel in prediction accuracy, Logistic Regression offers a balanced trade-off between performance and model stability, making it suitable for real-world applications. These findings contribute to the growing field of AI-driven mental health assessment, offering a foundation for integrating multimodal data, model interpretability, and ethical implementation. Keywords: Mental health disorders, Sentiment analysis, Natural language processing, Machine learning

DRGNN: Disentangled representation graph neural network for diverse category-level recommendations

Graph neural networks (GNNs) have significantly advanced recommender systems (RecSys) by enhancing their accuracy in complex collaborative filtering scenarios. However, this progress often comes at the cost of overlooking the diversity of recommendations, a factor in user satisfaction. Addressing this gap, this paper introduces the disentangled representation graph neural network (DRGNN). DRGNN integrates diversification into the candidate generation stage using two specialized modules. The first employs disentangled representation learning to separate item preferences from category preferences, thereby mitigating category bias in recommendations. The second module, focusing on positive sample selection, further reduces category bias. This approach not only maintains the high-order connectivity strengths of GNNs but also substantially improves the diversity of recommendations. Our extensive validation of DRGNN on three comprehensive web service datasets, Taobao, Amazon Beauty and MSD, shows that it not only matches the state-of-the-art methods in accuracy but also excels in achieving a balanced trade-off between accuracy and diversity in recommendations.

Economic inclusion and ethnonational exclusion of Arabs in Israeli higher education: two tales of one policy

ABSTRACT Higher education systems worldwide strive for diversity, equity, and inclusion. However, policies meant to support these values often – intentionally or not – construct new forms of labeling, discrimination, and exclusion. This study examines this dynamic through the case of access and participation plans for Arabs in Israeli higher education. An analysis of citizenship discourses used throughout the policy narrative is applied to state policy documents and interviews with policymakers and implementors. The analysis reveals two narratives describing a single policy: a ‘shared interests’ narrative focused on economic development and growth, and a ‘balancing tradeoffs’ narrative balancing economy and security. These narratives help identify the policy as a ‘multiple diversity regime’ with social, economic, civic, and ethnonational dimensions. Diversity, equity, and inclusion values are promoted through social, economic, and civic convergence, while labeling, discrimination, and exclusion arise through economic and ethnonational convergence. This study contributes to higher education research by accounting for the complex and multi-dimensional dynamic of diversity policies, especially in deeply divided societies. It calls for higher education systems and institutions to take on a more comprehensive approach, going beyond the social and economic dimensions of diversity, to pursue substantial ethnic and national equity and inclusion.

BCED-Net: Breast Cancer Ensemble Diagnosis Network using transfer learning and the XGBoost classifier with mammography images.

Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models-namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny-followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks-namely Resnet50, EfficientnetB3, and ConvNeXtTiny-that were classified using the XGBoost classifier. The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

Wire-to-two-drop plasma thruster: Experimental and numerical investigation of electroaerodynamic jet flow for micro aerial vehicle propulsion

Conventional micro aerial vehicles (MAVs) have primarily relied on complex, flapping-wing mechanisms for propulsion, often exhibiting limitations in terms of reliability and efficiency. To overcome these challenges, this study explores the potential of electroaerodynamic (EAD) thrusters as a novel propulsion system. By accelerating air molecules through ion collisions, EAD jet flow generates thrust, offering advantages such as noiseless operation and zero emissions due to its moving-part-free design. This research presents a comprehensive experimental and numerical investigation of a wire-to-two-drop thruster configuration to elucidate its electromechanical performance, plasma flow dynamics, and EAD jet characteristics. Experimental measurements of key parameters, including current, thrust, power, and effectiveness, were correlated with numerical simulations, demonstrating excellent agreement with a maximum error below 5%. These findings align strongly with established theoretical frameworks, revealing an inverse square root relationship between effectiveness and thrust. To optimize thruster performance, optimal operating voltages were identified at approximately 8.2, 9.4, and 11.6 kV for inter-electrode gap distances of 10, 15, and 20 mm, respectively, achieving a balanced trade-off between thrust and effectiveness. Detailed numerical visualizations of the plasma flow field, including velocity distribution, jet morphology, potential distribution, and electric field lines, provided valuable insights into the thruster's operation. Building upon these insights, a proof-of-concept EAD flier was constructed and tested, incorporating a serrated emitter electrode and lightweight materials. This flier achieved a mass of 0.5 g and generated a thrust of 0.77 g at 15 kV, resulting in a thrust-to-weight ratio of 1.54 and successful liftoff. This demonstration highlights the potential of EAD propulsion for practical MAV applications.

Is Spartina alterniflora eradication project in Chongming Island a nature-based solution?

Invasive species profoundly impact ecosystems globally, not only threatening biodiversity by displacing native species but also disrupting ecological balances and imposing significant economic burdens. This has led countries to actively combat these pervasive threats. In China, Spartina alterniflora, an invasive species, has taken over more than 70 % of the coastal zone in just 50 years, especially in salt marsh. This study aims to assess the Spartina alterniflora eradication project in Dongtan of Chongming Island, Shanghai, based on the IUCN Global Standard for Nature-based Solutions (NBS). To ensure the comprehensiveness and diversity of the assessment process, stakeholders with diverse backgrounds were invited. The Dongtan project was qualitatively evaluated using the IUCN self-assessment tool for its adherence to the IUCN NBS Standard, identifying its strengths, weaknesses, improvement areas, and contributions to the national eradication campaign. The results show that the Dongtan Project closely aligns with the NBS standard, achieving an 88 % match, and demonstrates excellence in addressing societal challenges, enhancing biodiversity, and offering multiple benefits. Nevertheless, improvements can be made in Economic Feasibility (Criterion 4), Inclusive Governance (Criterion 5), and Balance Trade-offs (Criterion 6). The study underscores the importance of employing NBS standards for self-assessment in the project design phase, which facilitates the identification and refinement of potential design issues. The success of the Dongtan Project, exemplified by its effective Spartina alterniflora eradication strategies, serves as a compelling model for similar national campaigns.

Commercial fishery no-take zones for African penguins minimize fisheries losses at the expense of conservation gains

Abstract The African penguin population has declined precipitously in recent decades, and if current rates of decline persist, this species could become extinct in the wild by 2035. Resource extraction of small pelagic fish prey by the purse-seine fishery around African penguin breeding colonies has been identified as a demographically meaningful threat to African penguins. Consequently, long-term, effective no-take zones around breeding colonies have been endorsed by an expert panel of scientists constituted by the South African government. Here, we consider the six largest South African penguin colonies that currently hold 76% of the global population. We evaluate the adequacy of different no-take zone options using a trade-off mechanism recommended by the expert panel. For all six colonies except Bird Island, Algoa Bay, which is subject to the least fishing pressure, the current no-take zone delineations are assessed as having little benefit to the African penguin and little to no cost to the purse-seine fishery. Four of the six current no-take zones include ≤50% of the African penguins’ core foraging areas. Alternative no-take zones that approximate a more balanced trade-off offer more impactful alternatives to the current fisheries restrictions. Given the urgent need to implement evidence-based conservation interventions for the endangered African penguin, we recommend the substitution of the current no-take zones with those proposed herein.