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Toward a relational biodiversity economics: Embedding plural values for sustainability transformation
The prioritization of market over nonmarket values of nature is a key driver of the global biodiversity crisis. Recognizing nature’s diverse values in decisions is a fundamental lever for sustainability transformation. While economic valuation of nature has a long history, it has struggled to recognize the full suite of nature’s values, particularly the broad, relational, intrinsic, and shared values reflecting the complexity of human–nature relationships. We explore opportunities to expand the consideration of values within the economics of biodiversity by reviewing conventional and heterodox economic approaches. We argue that integrating pluralistic values requires a relational biodiversity economics that transcends people–nature dualism and seeks the flourishing of life. We synthesize foundations for such a paradigm in relation to worldviews, values, value indicators, and life frames. Our perspective transcends the dominant economic framing of nature as a passive, largely substitutable asset, to also consider nature as place, self, and harboring agency. This helps to overcome the limitations of conventional economic assumptions, better reflects peoples’ lived experiences, and supports transformations toward more just and sustainable futures.
A disaggregated system dynamics and agent-based modeling of the water-energy-food nexus for optimizing water allocation
Computational identification of PTPRJ peptide targeting the Epstein Barr virus latent membrane protein 1
Hydrogen sulfide pretreatment mitigates the adverse effects of salinity in young carob seedlings
Rapid mobile inspection equipment for metro tunnels based on multi-sensor integration
Abstract The acquisition of tunnel inspection data is fundamental to tunnel operation and maintenance. Existing inspection equipment is either based on laser scanning or CCD cameras. Laser scanning-based devices often struggle to detect cracks, while equipment based on CCD cameras is unable to acquire point cloud to assess tunnel deformations. In order to obtain high-quality, comprehensive data, this paper develops a mobile three-dimensional inspection device CKY-200, integrated with multiple sensors. The CKY-200 addresses the temporal and spatial synchronization issues of CCD cameras and laser scanners. Considering the convenience of inspection, the device progresses on the track powered by electricity. Moreover. This article proposes a tunnel deformation detection algorithm and a crack width measurement method, and verifies the accuracy of the data collected by the equipment. Through on-site experiments and comparison, the CKY-200 provides the advantages of more comprehensive data acquisition while ensuring high precision and speed.
Hybrid geostatistical and deep learning framework for geochemical characterization in historical mine tailings
Abstract Sustainable mine tailings management has become a worldwide priority given increasing critical raw materials (CRMs) demand and growing environmental concerns. While these anthropogenic deposits are often enriched with useful metals, they may also contain hazardous substances and thus provide both opportunities for resource recovery and environmental risk. In this work a hybrid geostatistical–deep learning framework was established to model geochemical distribution in old tailings. This study integrates ordinary kriging (OK) with a one-dimensional convolutional neural network and a bidirectional long short-term memory model (1D CNN and BiLSTM). The hybrid relies exclusively on features derived from the OK spatial covariance structure, computed from covariance matrices over the sampled locations, to inform the deep model and enhance prediction accuracy. The framework, applied to a historical tailings site, significantly outperformed traditional geostatistical methods as it can provide high-resolution predictions across all points of interest, while accounting for spatial heterogeneity. These results highlight the applicability of this strategy in sustainable resource recovery and environmental remediation, in accordance with circular economy concepts.
Scalable flight cancellation prediction with ensemble distributed KNN and feature selection
Assessment of patient satisfaction with the hemodialysis experience using an integrated decision-making approach with rough numbers
Nano-bioremediation of metal-polluted industrial wastewater using myco-synthesized iron oxide nanoparticles derived from Aspergillus niger AUMC 16028
Abstract The presence of heavy metals in wastewater poses serious ecological and environmental issues. Using biogenic nano adsorbents to remove heavy metals from industrial wastewater could be beneficial and serve as an alternative to traditional chemical and physical methods in real-world applications. The aim of this study is to biosynthesize green iron oxide nanoparticles (IONPs) for the removal of heavy metals from industrial wastewater. This process utilizes a cell-free extract derived from heavy metal-resistant fungi that were isolated from various industrial wastewater effluents in Egypt. Several fungal strains were examined for their ability to produce IONPs. A molecular identification of the most powerful fungus was made. The color change, as observed using UV-Vis spectroscopy, indicated that IONPs were being produced. Box-Behnken design (BBD) and Plackett-Burman design (PBD) were used to optimize the mycosynthesis of IONPs. The iron oxide nanoparticles (IONPs) produced through mycosynthesis were characterized using several techniques, including Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDAX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). After characterization, we evaluated their ability to extract heavy metal nanoparticles from both industrial and synthetic wastewater effluents. The results showed that different levels of IONPs were formed by various fungal strains: Aspergillus niger strain F1, A. flavus strain F2, Mucor sp. strain F3, and Alternaria sp. strain F4. Molecular analysis identified the most effective fungus for IONP production as A. niger AUMC 16028. The myco-synthesized IONPs were validated through the analyses conducted. Optimal conditions for IONP myco-synthesis included 8 g/L of yeast extract, a reaction temperature of 40 °C, and a culture period of 6 days. The myco-synthesized IONPs achieved heavy metal removal efficiencies of 92.47% for copper (Cu²⁺), 72.77% for iron (Fe³⁺), 84.76% for manganese (Mn²⁺), 70.28% for zinc (Zn²⁺), and 80.79% for chromium (Cr³⁺) in synthetic wastewater. Furthermore, the removal efficiencies of Zn²⁺ and Fe³⁺ in industrial effluent were 78.75% and 90.74%, respectively. These findings demonstrate that the heavy metals copper, iron, manganese, zinc, and chromium were effectively removed from synthetic wastewater, as well as iron and zinc from industrial wastewater, through the myco-synthesis and optimization of IONPs derived from A. niger AUMC 16028. This research offers a promising, green environmentally friendly, and efficient method for long-term industrial wastewater bioremediation and contributing to additional clean water resources.
Modified RECIST submodels and ordinal regression model predict neoadjuvant chemoimmunotherapy response in locally advanced gastric cancer
Clustering and time series analyses of hybrid immunity to SARS-COV-2 using data from the BQC19 biobank
Eco-Inspired terraform networks emerge from material self-organization
Age estimation of children and adolescents from mandibles using machine learning
Abstract Age estimation is a crucial step in forensic identification, particularly in scenarios where dental structures may be absent. This study aimed to develop and evaluate supervised machine learning models to predict chronological age based on mandibular morphometric measurements in children and adolescents. A sample of lateral cephalometric radiographs from 401 orthodontic patients aged between 6 and 16 years was analysed. Linear and angular mandibular measurements including the total mandibular length (Co-Pog), mandibular ramus height (Co-Go), mandibular body length (Go-Gn), and the gonial angle (Ar-Go-Me) were analysed. Eight supervised machine learning algorithms were trained to predict chronological age based on these measurements and sex. The dataset was split into training (80%) and test (20%) sets, with stratified 5-fold cross-validation to prevent overfitting. Model performance was evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²), with 95% confidence intervals estimated via bootstrapping. The models based on mandibular morphometric features and sex achieved a minimum MAE of 1.54 years (95% CI: 1.33–1.76) and RMSE of 1.93 (95% CI: 1.66–2.18) on the test set. Cross-validation confirmed model stability, with the Gradient Boosting Regressor achieving the best performance, showing a MAE of 1.21 (95% CI: 1.09–1.32) and R² of 0.56 (95% CI: 0.46–0.64). Total mandibular length (Co-Pog) and mandibular ramus height (Co-Go) were the most important predictors. Pairwise comparisons revealed statistically significant differences favoring ensemble methods over linear and simpler tree models. Supervised machine learning models demonstrated promising accuracy for age estimation based on mandibular measurements in growing individuals. Gradient Boosting emerged as the most effective algorithm. However, the generalizability of the models may be influenced by population-specific characteristics and the need for prior knowledge of certain predictor variables. Further external validations are recommended to enhance model applicability across diverse forensic contexts.
Unveiling the genetic diversity in horsegram (Macrotyloma uniflorum L.) genotypes through morphological and microsatellite (SSR) markers
Abstract Horsegram (Macrotyloma uniflorum L.) is a climate-resilient legume crop with significant nutritional value, yet its genetic potential remains underutilised. Understanding the genetic diversity in advanced breeding lines of horsegram (Macrotyloma uniflorum L.) can aid in developing effective selection method for seed yield improvement. This study evaluates the genetic variability and diversity of 22 advanced horsegram breeding lines using both morphological traits and molecular markers (SSR) to enhance yield and adaptability. Eleven morphological traits, including Plant height, No. of primary branches, Days to 50% flowering and maturity duration, Harvest Index, and Seed yield per plant, were analyzed to estimate genetic variability. Heritability and genetic advance percentage of mean were calculated to identify effective selection traits at early stage. Additionally, 30 SSR primers were screened to analyze molecular diversity, of which 9 polymorphic primers were identified. Cluster analysis, dendrogram, and Principal Component Analysis (PCA) were performed to group lines based on both morphological and molecular data. Significant variability was observed across morphological traits, with 1000-seed weight exhibiting high heritability and genetic advance, making it a promising selection trait. positively correlated with most traits except PH and primary branches per plant. Cluster analysis grouped the 22 lines into four morphological clusters, with HPKM-150 identified as the most diverse and high-yielding line. Molecular analysis revealed two main genetic clusters, with the primer MUMS-18 showing the highest polymorphic information content (PIC = 0.70). The study emphasizes significant genetic variability in horsegram, highlighting valuable traits and SSR markers for diversity assessment and future breeding to improve yield and genetics.