Search Results (534)

The mapping of vineyard cultivars presents a substantial challenge in digital agriculture due to the crop’s high intra-class heterogeneity and low inter-class variability. High-dimensional spectral datasets, such as hyperspectral or spectrometry data, can overcome these difficulties. However, research has yet to fully address the need for optimal spectral feature subsets tailored for grapevine cultivar discrimination, while few studies have systematically examined waveband subsets that transfer effectively across different learning algorithms. This study sets out to address these gaps by introducing a Partial Least Squares (PLS)-based ensemble feature selection framework with Weighted Borda Count aggregation for cultivar discrimination. Using in-field spectrometry data, collected for six cultivars, and 18 PLS-based feature selection methods spanning filter, wrapper, and hybrid approaches, the PLS–ensemble identified 100 wavebands most relevant for cultivar discrimination, reducing dimensionality by ~95%. The efficacy and transferability of this subset were evaluated using five classification algorithms: Oblique Random Forest (oRF), Multinomial Logistic Regression (Multinom), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (CNN). For oRF, Multinom, SVM, and MLP, the PLS–ensemble subset improved accuracy by 0.3–12% compared with using all wavebands. The subset was not optimal for the 1D-CNN, where accuracy decreased by up to 5.7%. Additionally, this study investigated waveband binning to transform narrow hyperspectral bands into broadband spectral features. Using feature multicollinearity and wavelength position, the 100 selected wavebands were condensed into 10 broadband features, which improved accuracy over both the full dataset and the original subset, delivering gains of 4.5–19.1%. The SVM model with this 10-feature subset outperformed all other models (F1: 1.00; BACC: 0.98; MCC: 0.78; AUC: 0.95). Full article

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments. Full article

U.S. hospitals generate considerable food waste, contributing to environmental degradation strategies. This study evaluated the feasibility, impact, and perception of a novel composting program implemented at Rhode Island Hospital over six months beginning in December 2024. Compostable waste bins were installed in the cafeteria with educational signage. Surveys assessing composting knowledge, attitudes, and roles in waste management were distributed to staff, patients, and administrators. Collected food waste was transported to Bootstrap Compost, which provided daily weight data used to estimate greenhouse gas emissions reductions, compare composting with landfill disposal costs, and project annual outcomes. Over the study period, 490.6 kg of food waste were diverted from landfills, corresponding to a reduction of 0.35 metric tons of CO₂-equivalent emissions. While composting was more expensive than landfill disposal ($6.45/kg vs. $0.24/kg), cost neutrality could be achieved with diversion rates at or above 116 kg per day. Surveys revealed strong support for composting but limited awareness of its relevance to healthcare’s environmental footprint. Respondents suggested improvements in education, signage, and infrastructure. This program demonstrated how hospital-based composting initiatives align with healthcare institutions’ environmental stewardship goals while highlighting financial and logistical challenges relevant for pilot–scale efforts. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Semi-autogenous (SAG) milling represents one of the most energy-intensive and variable stages of copper mineral processing. Traditional deterministic models often fail to capture the nonlinear dependencies and uncertainty inherent in industrial operations such as granulometry, solids percentage in the feeding or hardness. This work develops and validates a stochastic model based on Discrete Bayesian networks (BNs) to represent the causal relationships governing SAG Production and SAG Power under uncertainty or partial knowledge of explanatory variables. Discretization is adopted for methodological reasons as well as for operational relevance, since SAG plant decisions are typically made using threshold-based categories. Using operational data from a Chilean mining operation, the model fitted integrates expert-guided structure learning (Hill-Climbing with BDeu/BIC scores) and Bayesian parameter estimation with Dirichlet priors. Although validation indicators show high predictive performance (R² ≈ 0.85—0.90, RMSE < 0.5 bin, and micro-AUC ≈ 0.98), the primary purpose of the BN is not exact regression but explainable causal inference and probabilistic scenario evaluation. Sensitivity analysis identified water feed and solids percentage as key drivers of throughput (SAG Production), while rotational speed and pressure governed SAG Power behavior. The BN framework effectively balances accuracy and interpretability, offering an explainable probabilistic representation of SAG dynamics. These results demonstrate the potential of stochastic modeling to enhance process control and support uncertainty-aware decision making. Full article

Effective spatial and structural monitoring of World Heritage sites often relies on continuous high-spatial-resolution remote sensing imagery, which is often unavailable for specific years due to sensor, atmospheric, and revisit constraints. Super-resolution reconstruction thus becomes crucial for maintaining data continuity for such analyses. Traditional methods are trained on temporally aligned LR-HR pairs; however, their performance significantly declines when applied to unseen years due to temporal distribution shifts. To address this, we propose a temporal invariance-aware training strategy combined with an improved Residual Dense Network (RDN_2_M). We introduce a cross-year masked sample generation algorithm that identifies temporally stable regions via local structural similarity. This constructs explicit invariance-guided training pairs, which helps guide the model to focus on persistent structural features rather than transient appearances and to learn robust representations against inter-annual variations. Experiments on the Bin County Cave Temple (BCCT) Heritage Site dataset show our method, integrating the proposed strategy with the enhanced RDN model (RDN_2_M), significantly improves both the objective metrics and visual quality of reconstructed images. This offers a practical solution to filling temporal data gaps, thereby supporting long-term spatial and structural heritage monitoring. Full article

Echinoderms are marine invertebrates that play important roles in structuring marine benthic ecosystems. DNA barcoding has become a valuable tool for species identification; however, reference DNA barcode libraries for echinoderms remain incomplete. This study aims to: (i) develop a COI-5′ reference dataset for echinoderms from Vietnam by integrating DNA barcodes with morphological data; (ii) evaluate species resolution and barcode gaps using multiple analytical approaches; (iii) assess the consistency of species assignments from BOLD and GenBank for echinoderms collected in Vietnam; (iv) make barcode data publicly available to support global reference database development. Thirty-two echinoderm specimens representing 16 species were analyzed for COI-5′ sequences, and BLAST assignments were highly concordant with those from GenBank and BOLD. Integrative validation confirmed that all taxa were monophyletic in the Neighbor Joining Tree, formed single OTUs in Cluster Sequences, and exhibited clear barcode gaps greater than 3% to the nearest-neighbor species. These results provided species-level resolution for 75% and genus-level resolution for 90% of the records. The dataset, spanning four classes, eight orders, and eleven families, enhances barcode coverage and contributes records (ProcessIDs. BINs; GenBank accessions) to public repositories. This study delivers the first curated COI-5′ reference library, supporting regional baselines for taxonomy, conservation, and biodiversity assessment. Full article

Artificial freshwater bodies receive elemental inputs and face environmental stressors, posing a risk of wetland pollution that could threaten ecological health. In such an inland backwater, its microbial diversity and functional potentials remain uncharacterized. Here, shotgun metagenomic sequencing was performed on environmental DNA samples collected from the Atoud Dam reservoir in southwestern Saudi Arabia. The taxonomic assignments of the sequencing reads identified Pseudomonadota and Actinomycetota as the dominant phyla, while the most prevalent species was Microcystis aeruginosa. Binning assembled contigs recovered 30 metagenome-assembled genomes representing 11 phyla, suggesting potentially novel bacterial taxa and metabolic functions. Functional analysis of gene-coding sequences identified genes associated with mobile genetic elements and xenobiotic biodegradation pathways as the main factors driving the spread of antibiotic resistance genes. Additionally, a community-wide analysis of enzyme-encoding genes involved in regulating the carbon, nitrogen, and sulfur cycles revealed significant annotation of denitrification and thiosulfate oxidation pathways under anoxic conditions, suggesting early signs of eutrophication and a potential risk of algal blooms. Overall, our study provides detailed insights into the genomic capabilities of the microbial community in this previously understudied ecosystem and establishes baseline data for future assessments of microbial biodiversity in other, less-explored ecosystems, thereby facilitating more effective biomonitoring and discovery. Full article

The current work focuses on the study of the long-term evaluation of wind potential and energy production for a specific wind farm siting position over a mountainous region in Hellas. It aims to calculate the probability of exceedance of the twenty-year normalized average annual net production of the wind farm based on ground wind measurements coupled with Copernicus ERA5 data via a measure–correlate–predict (MCP) method. The study proposes an integrated long-term wind resource assessment workflow that couples short-term mast data with a twenty-year ERA5 record via a refined MCP procedure including temporal shifting for complex terrain. It introduces a practical uncertainty framework that jointly treats measurement, MCP, and terrain effects through dRIX and propagates these to energy yield using a bin-wise power curve and Weibull weighting. The proposed methodology is both fast and readily available to end-users and provides a realistic estimate of the energy production and long-term wind distribution in the investigated area. The data and assumptions employed in the calculations are given in detail. The uncertainty of the parameters in the estimation of the wind potential of the broader area and the energy calculation is analyzed. The results of the calculations and the probability of exceedance curve of the normalized twenty-year average annual net production of the wind farm summarize all uncertainty sources, delivering bankable long-term energy projections for the specific case study. Full article

Honey bees are essential both for many global ecosystems and apicultural production. The management of bee colonies remains labour-intensive, which drives a need for automated solutions. This work presents a proof-of-concept system to monitor honey bee activity by identifying the yearly lifecycle stages exhibited by the colony. A non-invasive vibration monitoring system was developed and placed on top of brood frames in Warsaw-type beehives to collect vibration data over a full apicultural season. The recorded vibration signals were analyzed using both Convolutional Neural Networks (CNNs) and classical machine learning approaches such as the extra trees method. Recursive Feature Elimination with Cross-Validation (RFECV) was performed to isolate the most important frequency bins for lifecycle period identification. The results demonstrate that the critical frequencies for recognizing yearly honey bee activity are concentrated below 1 kHz. The proposed machine learning models achieved a weighted accuracy score of over 95%. These findings have significant implications for future bee monitoring hardware design, indicating that sampling frequencies may be reduced to as low as 2 kHz without significantly compromising model accuracy. Full article

The Lorentz invariance violation (LIV) predicted by some quantum gravity theories would manifest as an energy-dependent speed of light, which may potentially distort the observed temporal profile of photons from astrophysical sources at cosmological distances. The dispersion cancellation (DisCan) algorithm offers a powerful methodology for investigating such effects by employing quantities such as Shannon entropy, which reflects the initial temporal characteristics. In this study, we apply the DisCan algorithm to search for LIV effects in the LHAASO observations of GRB 221009A, combining data from both the Water Cherenkov Detector Array (WCDA) and Kilometer Squared Array (KM2A) detectors that collectively span an energy range of ∼0.2–13 TeV. Our analysis accounts for the uncertainties from both energy resolution and temporal binning. We derive $95\%$ confidence level lower limits on the LIV energy scale of $E_{\mathrm{QG},1}/{10}^{19}\mathrm{GeV}>14.6$ (11.2) for the first-order subluminal (superluminal) scenario, and $E_{\mathrm{QG},2}/{10}^{11}\mathrm{GeV}>13.7$ (12.5) for the second-order subluminal (superluminal) scenario. Full article

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

Objectives: Alzheimer’s disease (AD) and other dementias represent a growing public health concern, highlighting the need for reliable biomarkers for early diagnosis and treatment monitoring. This study evaluated the potential utility of BIN1 and TOMM40 genotyping in diagnosing mild cognitive impairment (MCI) and early-stage dementia. Methods: The BIN1 rs744373 and TOMM40 rs2075650 polymorphisms were genotyped in a cohort of 105 individuals diagnosed with MCI or dementia and in 164 cognitively healthy controls. Genotype distributions were compared between the groups, and the potential role of these variants in diagnostic assessment was explored. Results: A significantly higher frequency of the TOMM40 rs2075650 GG genotype was observed in patients with AD compared with cognitively healthy controls. In contrast, no statistically significant differences in genotype distribution were found among individuals with mild MCI, vascular dementia, or mixed dementia. Furthermore, the distribution of BIN1 rs744373 alleles did not differ significantly across the analyzed groups. Conclusions: Data on the effects of BIN1 rs744373 and TOMM40 rs2075650 polymorphisms in MCI and dementia remain limited and inconsistent. In our study, significant differences were observed only for the TOMM40 rs2075650 GG genotype and G allele, which were more frequent in Alzheimer’s disease patients than in controls. No significant associations were found for MCI, vascular dementia, or mixed dementia, nor for the BIN1 rs744373 polymorphism. These results suggest that TOMM40 rs2075650 genotyping may serve as an additional marker for assessing AD risk. Full article

This study integrates Discrete Element Method (DEM) simulations, soil bin experiments, and multi-objective optimization to develop an energy-efficient manure injector shank. Eighteen geometries were first screened using DEM, reducing the set to six designs (S_1–S_6) based on draft force–rupture area performance. The selected designs, varying in rake angle (30°, 45°, 60°), thickness (25 and 30 mm), and width (102, 110, and 118 mm), were tested in a soil bin to measure draft, trench width, spoil cross-sectional area, and soil rupture. Statistical analysis revealed significant differences among designs (p < 0.05), confirming that rake angle, width, and thickness have a strong influence on the soil–tool interaction. A multi-objective optimization framework was then used to minimize draft, trench width, and spoil area while maximizing rupture, with performance quantified through overall desirability values (0–1). Shank S_3 (45° rake, 25 mm thickness, 110 mm width) achieved the highest desirability (0.6676), representing the best trade-off between energy efficiency, minimal surface disturbance, and effective subsurface loosening. This integrated DEM–experimental–optimization approach demonstrates a reliable, data-driven workflow for implement design, reducing reliance on extensive field trials. However, future studies should validate the performance of S_3 and other candidate designs under diverse soil types, moisture levels, and operating conditions to confirm their agronomic and environmental benefits. Full article

Accurate equilibrium-line altitude (ELA) estimates are a valuable proxy for evaluating glacier mass balance conditions and interpreting climate-driven change in the Canadian high Arctic, where sustained in situ observations are limited. A scalable remote-sensing framework is evaluated to extract the snow cover ratio (SCR) and snowline altitude (SLA) on White Glacier (Axel Heiberg Island, Nunavut) and to assess the agreement with in situ ELA measurements. Ten-metre Sentinel-2 imagery (2019–2024) is processed with a hybrid pipeline comprising the principal component analysis (PCA) of four bands (B2, B3, B4, and B8), unsupervised K-means for pseudo-label generation, and a Random Forest (RF) classifier for snow/ice/ground mapping. SLA is defined based on the date of seasonal minimum SCR using (i) a snowline pixel elevation histogram (SPEH; mode) and (ii) elevation binning with SCR thresholds (0.5 and 0.8). Validation against field-derived ELAs (2019–2023) is performed; formal SLA precision from DEM and binning is quantified ($\pm 4.7$ m), and associations with positive degree days (PDDs) at Eureka are examined. The RF classifier reproduces the spectral clustering structure with >99.9% fidelity. Elevation binning at $\mathrm{SCR}\ge 0.8$ yields SLAs closely matching field ELAs (Pearson $r=0.994$, $p=0.0006$; RMSE $=30$ m), whereas SPEH and lower-threshold binning are less accurate. Interannual variability is pronounced as follows: minimum SCR spans 0.46–0.76 and co-varies with SLA; correlations with PDDs are positive but modest. Results indicate that high-threshold elevation-bin filtering with machine learning provides a reliable proxy for ELA in clean-ice settings, with potential transferability to other data-sparse Arctic sites, while underscoring the importance of image timing and mixed-pixel effects in residual SLA–ELA differences. Full article