Search Results (344)

Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows researchers to classify the individuals and also determine the organizational structure and behavioral patterns of the insects within colonies. The miniaturization of hardware devices for data and image acquisition, coupled with new Artificial Intelligence techniques such as Scene Graph Generation (SGG), has evolved from the detection and recognition of objects in an image to the understanding of relationships between objects and the ability to produce textual descriptions based on image content and environmental parameters. This research paper presents the design and functionality of a distributed computing architecture for image and video acquisition of bees and ants in their natural environment, in addition to a parallel computing architecture that hosts two datasets with images of real environments from which scene graphs are generated to recognize, classify, and analyze the behaviors of bees and ants while preserving and protecting these species. The experiments that were carried out are classified into two categories, namely the recognition and classification of objects in the image and the understanding of the relationships between objects and the generation of textual descriptions of the images. The results of the experiments, conducted in real-life environments, show recognition rates above 70%, classification rates above 80%, and comprehension and generation of textual descriptions with an assertive rate of 85%. Full article

Ants act as keystone species in terrestrial ecosystems, providing important ecosystem services. The large-scale production and application of GO constitute a predominant contributor to its inevitable environmental dispersion. Most GO toxicity studies have focused on plants, animals, and microorganisms, with limited research on ground-dwelling ants. In the study, we used Camponotus japonicus as a model to investigate the toxic effects of GO on ants by integrating physiological characteristics, gut microbiota and transcriptome profiling. Results showed that GO exposure induced mitochondrial dysfunction, as evidenced by mitochondrial ROS accumulation and elevated mitochondrial membrane permeability. Physiological assessments revealed that GO exposure induced oxidative stress. Specifically, GO treatment significantly suppressed superoxide dismutase (SOD) and catalase (CAT) activities, while enhancing peroxidase (POD) and carboxylesterase (CarE) activities and increasing the levels of malondialdehyde (MDA) and trehalose. Gut microbiota analyses showed that GO remarkably reduced the relative abundance of beneficial bacterial symbionts (e.g., Candidatus Blochmannia) and destabilized the whole community structure. Furthermore, transcriptome profiling revealed 680 differentially expressed genes (DEGs) in the ants after GO exposure, most of which were significantly enriched in pathways associated with oxidative phosphorylation. This study suggests that GO may compromise ant-mediated ecosystem function and provides a reference for understanding the environmental risks of GO. Our findings also offer new insights for protecting the ecosystem services of ants. Full article

Accurate and interpretable staging of Alzheimer’s disease (AD) remains challenging due to the heterogeneous progression of neurodegeneration and the complementary nature of imaging and clinical biomarkers. This study implements and evaluates an optimized Hierarchical Multi-Modal Fusion Framework (HMFF) that systematically integrates 3D structural MRI with clinical assessment scales for robust three-class classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD subjects. A standardized preprocessing pipeline, including N4 bias field correction, nonlinear registration to MNI space, ANTsNet-based skull stripping, voxel normalization, and spatial resampling, was employed to ensure anatomically consistent and high-quality MRI inputs. Within the proposed framework, volumetric imaging features were extracted using a 3D DenseNet-121 architecture, while structured clinical information was modeled via an XGBoost classifier to capture nonlinear clinical priors. These heterogeneous representations were hierarchically fused through a lightweight multilayer perceptron, enabling effective cross-modal interaction. To further enhance discriminative capability and model efficiency, a hierarchical feature selection strategy was incorporated to progressively refine high-dimensional imaging features. Experimental results demonstrated that performance consistently improved with feature refinement and reached an optimal balance at approximately 90 selected features. Under this configuration, the proposed HMFF achieved an accuracy of 0.94 (95% Confidence Interval: [0.918, 0.951]), a recall of 0.91, a precision of 0.94, and an F1-score of 0.92, outperforming unimodal and conventional multimodal baselines under comparable settings. Moreover, Grad-CAM visualization confirmed that the model focused on clinically relevant neuroanatomical regions, including the hippocampus and medial temporal lobe, enhancing interpretability and clinical plausibility. These findings indicate that hierarchical multimodal fusion with interpretable feature refinement offers a promising and extensible solution for reliable and explainable automated AD staging. Full article

The stability of ice structures in cold regions and polar environments has been increasingly challenged by global warming and climate change, making the accurate estimation of ice flexural properties essential. However, the flexural failure process of ice is highly complex, and the calculated flexural properties are influenced by multiple factors. Hence, several data-driven artificial intelligence models were developed to predict flexural strength, using classification and regression tree (CART), AdaBoost, and Random Forest methods, while the Elitist Ant System (EAS) was applied to optimize model parameters. The EAS procedure converged rapidly within ten iterations and effectively enhanced overall model performance. Compared with the single CART model, ensemble approaches exhibited higher prediction accuracy and better generalization, with AdaBoost achieving the best performance (R² = 0.736). Feature-importance analysis indicated that the testing method and specimen geometry had the greatest influence on the results, highlighting the importance of careful control of experimental conditions. The proposed ensemble–metaheuristic framework provides an efficient tool for predicting the mechanical behavior of ice and offers useful support for stability assessments of ice structures under changing climatic conditions. Full article

Canagliflozin (CFZ), a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is extensively utilized in the management of type 2 diabetes. Among its various polymorphic forms, the hemi-hydrate (Hemi-CFZ) has been selected as the active pharmaceutical ingredient (API) for CFZ tablets due to its superior solubility. However, during the production, storage, and transportation of CFZ tablets, Hemi-CFZ can undergo transformations into anhydrous (An-CFZ) and monohydrate (Mono-CFZ) forms under the influence of environmental factors such as temperature, humidity, and pressure, which may adversely impact the bioavailability and clinical efficacy of CFZ tablets. Therefore, it is imperative to develop rapid, accurate, non-destructive, and non-contact methods for quantifying An-CFZ and Mono-CFZ content in CFZ tablets to control polymorphic impurity levels and ensure product quality. This research evaluated the feasibility and reliability of using near-infrared spectroscopy (NIR) combined with partial least squares regression (PLSR) for simultaneous quantitative analysis of An-CFZ and Mono-CFZ in CFZ tablets, elucidating the quantifying mechanisms of the quantitative analysis model. Orthogonal experiments were designed to investigate the effects of different pretreatment methods and ant colony optimization (ACO) algorithms on the performance of quantitative models. An optimal PLSR model for simultaneous quantification of An-CFZ and Mono-CFZ in CFZ tablets was established and validated over a concentration range of 0.0000 to 10.0000 w/w%. The resulting model, Y_{An-CFZ/Mono-CFZ} = 0.0207 + 0.9919 X, achieved an R² value of 0.9919. By analyzing the relationship between the NIR spectral signals selected by the ACO algorithm and the molecular structure information of An-CFZ and Mono-CFZ, we demonstrated the feasibility and reliability of the NIR-PLSR approach for quantifying these polymorphic forms. Additionally, the mechanism of PLSR quantitative analysis was further explained through the variance contribution rates of latent variables (LVs), the correlations between LVs loadings and tablets composition, and the relationships between LV scores and An-CFZ/Mono-CFZ content. This study not only provides a robust method and theoretical foundation for monitoring An-CFZ and Mono-CFZ content in CFZ tablets throughout production, processing, storage, and transportation, but also offers a reliable methodological reference for the simultaneous quantitative analysis and quality control of multiple polymorphic impurities in other similar drugs. Full article

An avionic weather radar antenna should be able to operate in multiple modes to cope with the change in resolution and elevation coverage as an aircraft approaches a storm cell that could expand 10 km in elevation. To solve this problem, we propose the addition of four auxiliary antenna (AuxAnt) arrays based on the phased-MIMO antenna structure to the existing avionic weather radar for future field data collection missions. Two types of signals are employed: the Type I signal transmitted by AuxAnt 1 and 2 is designed based on a non-overlapping subarray configuration, with Subarray 1 and 2 dedicated to the transmission of long and short pulses, respectively, so that the near-range blind zone is mitigated. Leveraging the waveform design and beamforming flexibility provided by the phased-MIMO antenna, pulse compressions based on frequency modulation and phase-coding are employed for wide and narrow main beams, respectively. To suppress the range sidelobes, adaptive pulse compression is used at the receiver end in substitute of the conventional matched filter. In contrast, the Type II signal transmitted by AuxAnt 3 and 4 is designed based on the contextual information so that the transmitted beampatterns have specific sidelobe levels at certain directions for interference suppression. The advantages of the proposed signaling strategy are verified with a series of ingeniously devised experiments based on real weather data. Full article

Faults and fractures play a critical role in subsurface systems; they may act as hydrocarbon traps, compartmentalize reservoirs, or serve as conduits for fluid migration across stratigraphic intervals. Consequently, fault delineation from seismic data plays a key role in reservoir characterization. This study presents a workflow for generating ant-tracking attribute volumes using multiple structural attributes to enhance fault/fracture delineation. Our results were thereafter validated with formation microimager (FMI) data. The workflow involves a sequential process comprising seismic data conditioning, structural attribute computation, and ant-tracking volume generation. Variance, curvature, and amplitude contrast attributes were calculated on conditioned 3D seismic data and subsequently used as input for the ant-tracking process. Parameter optimization was conducted through an iterative process of varying individual parameters and qualitatively assessing the results against key seismic features in both vertical sections and time slices. The ant-tracking volumes generated from individual attribute volumes were integrated to produce a composite volume, which served as input for automatic fault extraction. The resultant fault patch orientations were consistent with the formation microimager (FMI) log orientations. The integration of multiple structural attributes within the ant-tracking workflow significantly enhanced fault and fracture delineation by leveraging the complementary strengths of each attribute. Full article

Arboreal ants occupy a thermally dynamic environment, yet the mechanisms integrating nest architecture and worker behavior to maintain colony homeostasis remain understudied. We investigated the interplay among circadian rhythm, nest homeostasis, and worker morphology in Azteca chartifex spiriti, a Neotropical arboreal species that builds large polydomous nests suspended in trees. In ten colonies, we measured internal moisture and temperature gradients in the main nest, which houses most individuals, including the reproductive female, immatures, and numerous workers. In six colonies, we assessed the polymorphism of foraging workers over a 24 h cycle in relation to external temperature variation. The results show integrated thermoregulatory mechanisms that combine passive strategies, derived from nest architecture and moisture gradients from the suspension base to the lower extremity, with active strategies linked to foraging patterns and worker polymorphism. Internal temperature (27.8 ± 2.41 °C) remained buffered relative to external fluctuations, and moisture was significantly higher at the nest’s lower extremity (p < 0.001). Worker size displayed a bimodal distribution during the day that shifted to a unimodal pattern at night, indicating behavioral adjustments to thermal and operational demands. These findings demonstrate that the interaction between physical structure and worker behavior maintains colony homeostasis, providing essential insights into how dominant canopy ants may cope with future climate change scenarios. Full article

Deep learning has shown strong performance in ECG domain adaptation; however, its decision-making process remains opaque, particularly when operating on input spectrograms. Traditional fuzzy inference offers interpretability but is structurally limited to tabular or multi-channel data, making it difficult to apply directly to single-channel two-dimensional spectrograms. To address this limitation, we propose the Fuzzy Energy–Frequency Spectrogram Network (FEFSN), a new fuzzy–deep learning hybrid framework that enables direct fuzzy rule generation in the spectrogram domain. In FEFSN, the Fuzzy Rule Image Generation Module (FRIGM) decomposes an STFT-transformed ECG spectrogram into multiple energy-based channels using an Energy–density Membership Function (EMF), and then applies a Frequency Membership Function (FMF) to produce AND and OR fuzzy rule images for each energy–frequency combination. The generated rule images are subsequently normalized, activated, and combined through learned weights to form a rule-based domain-adapted spectrogram, which is then processed by a CNN. To evaluate the proposed approach, we used the PhysioNet ECG-ID dataset and compared the performance of a standard CNN with and without the FRIGM under identical training conditions. The results show that FEFSN maintains or slightly improves adaptation performance compared to the baseline CNN, despite introducing only a small number of additional parameters. More importantly, FEFSN provides ante hoc interpretability, allowing direct visualization of which energy–frequency regions were emphasized or suppressed during adaptation—an ability that conventional post hoc methods such as Grad-CAM cannot offer. Overall, FEFSN demonstrates that fuzzy logic can be effectively integrated with deep learning to achieve both reliable performance and transparent, rule-based interpretability in ECG spectrogram domain adaptation. Full article

Accurate and robust classification of dementia subtypes using non-invasive electroencephalography (EEG) signals remains a critical challenge for clinicians and researchers in the field of neuroscience. Traditional methods often rely on limited spectral features, overlooking the rich structural and geometric information inherent in EEG dynamics. CONECT (Complex Network Conversion and Topology), a novel framework, is introduced and built upon four core innovations. First, EEG time series are transformed into weighted networks using a novel Angle-Relation Connection (ARC) rule, a geometry-based approach that links time points based on angular monotonicity. Secondly, a tunable edge-weighting function is introduced by integrating amplitude, temporal, and angular components, providing adaptable heuristics adaptable to the most promising biomarker, i.e., curvature-driven features in dementia. Additionally, two new graph-based EEG features, the Weighted Angular Irregularity Index (WAII) and the Curvature-Based Edge Feature Index (CBEFI), are proposed as potential biomarkers to capture localized irregularity and signal geometry, respectively. For the first time in a dementia EEG classification study using the OpenNeuro ds004504 dataset (raw), Ant Colony Optimization (ACO) is applied as a feature selection technique to select the most discriminative features and improve model classification and transparency. The classification results demonstrate CONECT’s potential as a promising, interpretable, and geometry-informed framework for accurate and practical dementia subtype diagnosis. Full article

Background/Objectives: The cavernous segment of the internal carotid artery (ICA) is a critical neurovascular structure with complex cranial nerve relationships. Understanding its morphometric variability is essential for safe microsurgical and endovascular procedures. This study aimed to characterize the morphometry of the cavernous ICA using Magnetic resonance imaging (MRI) and assess associations with demographic variables. Methods: A retrospective observational study was conducted on 135 MRI scans of adult patients, distributed among 79 women and 56 men with an average age of 50.8 years, without cerebrovascular pathology, performed between March 2023 and January 2025. The diameters of the left and right cavernous ICA and the intercarotid distance were measured using RadiAnt DICOM Viewer. Statistical analyses included descriptive statistics, t-tests, correlations, and multivariate regression models adjusted for age and sex. Principal component and cluster analyses were applied to identify morphometric patterns. Results: The mean left and right ICA diameters were both 5.09 ± 0.65 mm, with a mean intercarotid distance of 17.4 ± 4.22 mm. No age-related associations were found (p > 0.05). Male patients showed significantly larger right ICA diameters (p = 0.008). Bilateral symmetry was confirmed (p > 0.05). Two morphometric clusters were identified: Morphotype 1 (larger ICA caliber and narrower spacing) and Morphotype 2 (smaller caliber and wider spacing), showing a significant sex distribution difference (p = 0.012). Conclusions: The cavernous ICA demonstrates stable bilateral symmetry with minor sex-dependent differences. Morphometric characterization supports safer planning of transsphenoidal, endovascular, and skull-base surgeries by reducing the risk of iatrogenic neurovascular injury. Full article

The dual-channel structure resulted from manufacturer encroachment could alter the incentives of downstream retailer to ex ante communicate demand forecast. And different types of channel competition need to be investigated in this dual-channel information sharing scenario. This paper aims to investigate retailer’s ex ante imperfect demand information sharing strategy given that upstream manufacturer has set up direct sales channel (manufacturer encroachment). The imperfect information sharing means the demand information shared is uncertain and has some error relative to the real-world demand condition. It examines two types of channel competition: quantity competition and price competition. Additionally, this study discusses the encroaching manufacturer’s incentives for adjusting channel substitution. The paper adopts a stylized game theoretic model to describe interactions between retailer and the encroaching manufacturer. Contrary to conventional wisdom, the paper shows that under manufacturer encroachment, it is always possible for ex ante demand information sharing. Specifically, in the Cournot competition scenario where retailer channel and the encroaching manufacturer direct channel compete in quantity, the encroaching manufacturer could encourage demand information communication through side payment. Furthermore, in the Bertrand competition scenario, retailer may voluntarily share demand information. In addition, in either quantity or price competition, the encroaching manufacturer has incentives to adjust channel substitution for profit maximization. Full article

This study investigates the accelerating adoption of electric vehicles (EVs) integrated with residential rooftop solar and battery storage in Australia, employing Actor–Network Theory (ANT) to elucidate socio-technical dynamics. Through purposive sampling, semi-structured interviews with 15 EV industry stakeholders were conducted and analysed using NVivo 14. Findings revealed EV–solar–storage adoption as a negotiated process shaped by alignments among human and non-human actors, structured by three interdependent obligatory passage points. First, technological integration hinges on interoperability among inverters, smart chargers, EV supply equipment, batteries, and home energy management systems. These are constrained by factors like off-street parking availability. Second, policy and market frameworks require clear interconnection standards, bidirectional charging protocols, streamlined approvals, and targeted incentives. Third, consumer engagement depends on energy literacy, equitable access for renters, and daytime charging infrastructure. Smart and bidirectional charging positions EVs as flexible energy assets, yet gaps in standards and awareness destabilise networks. This ANT-framed study offers a practice-oriented model for clean mobility integration, proposing targeted interventions such as device compatibility standards, equitable policies, and education to maximise environmental and economic benefits at household and system levels. Full article

A lack of safety awareness in industrial companies can cause substantial harm to people and the environment. This study explores how fading of safety awareness influences safety-related decisions in (petro)chemical companies. Drawing on ethical fading theory, the research aims to better understand the mechanism that causes safety to decline and to identify ways to prevent this process and reduce safety incidents. Semi-structured interviews were conducted within the (petro)chemical industry to explore this phenomenon. The findings suggest that self-interest plays a more significant role in safety incidents than previously assumed and manifests in multiple forms that contribute to the fading of safety awareness. Moreover, self-interest was seldom identified as a formal root cause of incidents, likely because the fading process occurs largely at a subconscious level, as described in ethical fading theory. Finally, the study found that neutralization techniques were frequently used to justify unsafe behavior, both ex ante and ex post. These insights extend existing theory by linking ethical fading to safety management and highlight the need for interventions that address subconscious drivers of unsafe decision-making. Full article

Ulex europaeus L. has been introduced into many countries as an ornamental and hedgerow plant, and it often escapes its intended location, establishing dense, feral thickets. These thickets threaten the structure and function of native flora and fauna in areas where the plant has been introduced. Because of its invasive nature, U. europaeus is considered one of the world’s 100 worst alien invasive species. It exhibits rapid growth, and high biomass accumulation with a high nitrogen fixation ability. Its flowering phenology depends on local conditions and population. It produces a large number of viable seeds and establishes extensive seed banks. These seeds remain viable for a long time due to physical dormancy. Ulex europaeus produces elaiosomes on the seed surface that are likely used solely for seed dispersal by ants. Ulex europaeus has a high level of genetic diversity due to its allohexaploid chromosome sets. This allows the plant to adapt to different habitats and tolerate various climate conditions. It can survive in areas with limited sunlight beneath tall plant canopies. Its shade tolerance surpasses that of other shrub species. Ulex europaeus produces several compounds, including quinolizidine alkaloids, monoterpenes, flavonoids, and cinnamic acid derivatives. These compounds play a role in defensive responses to biotic stressors, including pathogen infections, herbivorous insects, and neighboring plants competing for resources. These life history traits and defensive abilities may contribute to the expansion of U. europaeus populations into new habitats, enabling the plant to thrive as an invasive species. This is the first study to examine the invasiveness of U. europaeus in terms of its growth, reproduction, ability to adapt to different conditions, and defensive responses to biotic stressors. Full article