Browse Articles
Discover research articles across all indexed journals
Influence of soil porosity on hydraulic properties in dike failure induced by overtopping
Impact of MiRNAs on Wnt-related gene activity in breast cancer
Addressing significant challenges for animal detection in camera trap images: a novel deep learning-based approach
Abstract Wildlife biologists increasingly use camera traps for monitoring animal populations. However, manually sifting through the collected images is expensive and time-consuming. Current deep learning studies for camera trap images do not adequately tackle real-world challenges such as imbalances between animal and empty images, distinguishing similar species, and the impact of backgrounds on species identification, limiting the models’ applicability in new locations. Here, we present a novel two-stage deep learning framework. First, we train a global deep-learning model using all animal species in the dataset. Then, an agglomerative clustering algorithm groups animals based on their appearance. Subsequently, we train a specialized deep-learning expert model for each animal group to detect similar features. This approach leverages Transfer Learning from the MegaDetectorV5 (YOLOv5 version) model, already pre-trained on various animal species and ecosystems. Our two-stage deep learning pipeline uses the global model to redirect images to the appropriate expert models for final classification. We validated this strategy using 1.3 million images from 91 camera traps encompassing 24 mammal species and used 120,000 images for testing, achieving an F1-Score of 96.2% using expert models for final classification. This method surpasses existing deep learning models, demonstrating improved precision and effectiveness in automated wildlife detection.
Influencing factor analysis and clinical efficacy of early intervention in severe acute pancreatitis with persistent organ failure
GIS-based integration of marine data for assessment and management of a highly anthropized coastal area
Rumen fermentation profile and methane mitigation potential of mango and avocado byproducts as feed ingredients and supplements
Sustainable sodium alginate hydrogels incorporating banana leaf activated carbon and organo-clay for enhanced dye removal
Abstract New sodium alginate-based hydrogels using activated carbon from banana leaves and organo-modified montmorillonite for water treatment. Activated carbon extracted successfully from banana leaves and montmorillonite clay was surface-modified using cetyltrimethylammonium bromide as a cationic surfactant. Hydrogels were then synthesized using calcium chloride as the cross-linking agent. They were characterized using FTIR, X-ray diffraction, and scanning electronic microscopy. Characterization intimated the incorporation of components successfully. Adsorption performance was determined using pH, adsorbent dosages, initial dye concentration, and contact time. Sodium alginate-based hydrogels demonstrated remarkable efficacy in removing MB and EBT dyes from synthetic solutions, achieving removal efficiencies of up to 80.3% and 84.9% respectively within 90 min at pH 7. The adsorption process corresponded better to the Freundlich isotherm model. The kinetics of EBT dye removal were described by a pseudo-second-order model. Meanwhile, the kinetics of the removal of MB dyes were described by both pseudo-first order and intraparticle diffusion models. We conducted MTT assays to determine the cytotoxicity of our blends. This showed a dose-dependent drop in viability. Sodium alginate-based hydrogels made the cells least cytotoxic. The developed hydrogels can be used as safe and effective agents for water treatment, as indicated by the results.
Association between triglyceride-glucose index and all cause mortality in critically ill patients with heart failure
Self-weighted dual contrastive multi-view clustering network
Abstract Multi-view Clustering (MVC) has gained significant attention in recent years due to its ability to explore consensus information from multiple perspectives. However, traditional MVC methods face two major challenges: (1) how to alleviate the representation degeneration caused by the process of achieving multi-view consensus information, and (2) how to learn discriminative representations with clustering-friendly structures. Most existing MVC methods overlook the importance of inter-cluster separability. To address these issues, we propose a novel Contrastive Learning-based Dual Contrast Mechanism Deep Multi-view Clustering Network. Specifically, we first introduce view-specific autoencoders to extract latent features for each individual view. Then, we obtain consensus information across views through global feature fusion, measuring the pairwise representation discrepancy by maximizing the consistency between the view-specific representations and global feature representations. Subsequently, we design an adaptive weighted mechanism that can automatically enhance the useful views in feature fusion while suppressing unreliable views, effectively mitigating the representation degeneration issue. Furthermore, within the Contrastive Learning framework, we introduce a Dynamic Cluster Diffusion (DC) module that maximizes the distance between different clusters, thus enhancing the separability of the clusters and obtaining a clustering-friendly discriminative representation. Extensive experiments on multiple datasets demonstrate that our method not only achieves state-of-the-art clustering performance but also produces clustering structures with better separability.
Effects and mechanisms of trifolin on attenuating hypertension-induced vascular smooth muscle cell proliferation and collagen deposition in vivo and in vitro
The effects of two preseason periodizations on peak torque of knee extensors and flexors of a Polish first league soccer team
Effect of jaw width in jaw tracking mode on the radiotherapy dose of partial arc VMAT in patients undergoing left breast-conserving surgery
Structural, thermal, and radiation shielding properties of antimony-doped zinc borate glasses
Identifying breast cancer risk factors and evaluating biennial mammography screening efficacy using big data analysis in Taiwan
Human ostension enhances attentiveness but not performance in domestic pigs
Abstract Humans convey their communicative intentions ostensively, e.g., calling others’ name and establishing eye-contact. Also when interacting with animals, humans use ostension. In some companion-animal species, ostension increases attentiveness and/or alters how animals process human-provided information. However, it is unclear whether domesticated species selected for purposes other than companionship, like pigs, would also be sensitive. We investigated whether pigs are sensitive to human ostension and whether experience with humans modulates pigs’ sensitivity. Fifty-four pigs with varying experience with humans completed ostensive and non-ostensive versions of (1) an object-choice task with directional cues, (2) an A-not-B-task, and (3) a detour task with human demonstrations. We expected pigs to respond to human ostension with increased attentiveness and possibly altered performance. Further, if sensitivity to ostension increases with experience with humans, more intensively socialized pigs should be more attentive and more likely to change in performance than less socialized ones. Results suggest that pigs were more attentive to ostensive than non-ostensive detour demonstrations. Otherwise ostension did not affect attentiveness or performance. This suggests that pigs might be less inclined than species selected for companionship to process human-provided information differently. Attentiveness, however, seems to be enhanced by ostension also in animals selected for production purposes. Moreover, we found that living conditions and experience, e.g., training, influenced pigs’ attentiveness and performance, independently of ostension. These findings highlight the influence of training experience and enrichment on pigs’ cognitive performance.
Quality of life changes after electrochemotherapy: a prospective single-center analysis
Abstract The rising prevalence of cutaneous and subcutaneous tumors has driven interest in electrochemotherapy (ECT) as a potential treatment. However, patient-reported outcomes remain underexplored. This study aims to assess the short-term impact of ECT on the quality of life (QoL) of patients, addressing a gap in the current literature. A prospective study evaluated 62 patients treated with ECT between 2015 and 2022. QoL was measured using the EQ-5D-3L questionnaire, calculating EQ-5D-index and assessing health state (EQ-VAS) and pain (pain-VAS). Subgroup analysis was conducted based on tumor histology, previous radiotherapy, and tumor size. Statistical analysis was performed using SPSS 29.0.0. The median age was 70 years, with a median follow-up of 47 days. Pre-treatment, 38.7% of patients reported pain/discomfort, and 24% had anxiety/depression. Post-treatment, these decreased to 32.2% and 19%, respectively. While the EQ-VAS and EQ-5D-3L scores showed a non-significant increase, pain-VAS decreased. Significant improvements were seen in patients with previous radiotherapy (EQ-VAS, p = 0.047; EQ-5D-index, p = 0.012) and smaller tumors (EQ-VAS, p = 0.035; pain-VAS, p = 0.029). ECT demonstrates a significant short-term benefit in maintaining or improving QoL in patients with cutaneous malignancies.
Enhanced adaptive sliding mode control with termination parallelism cooperative optimization for torque ripple reduction in switched reluctance motor drives
Abstract Switched Reluctance Motor (SRM) technology has gained significant attention in various industries due to its reliability, cost-effectiveness, and efficiency. However, torque ripple remains a major issue, impacting the performance of SRM drives. This paper presents an enhanced Adaptive Sliding Mode Control (ASMC) strategy to reduce torque ripple in SRM drives. The proposed control scheme integrates an outer-loop ASMC controller for speed regulation and an inner-loop ASMC controller combined with a hysteresis controller for precise torque control, with the primary goal of minimizing ripple torque while maintaining the desired motor speed. The design process begins with the development of an 8/6 SRM model in MATLAB/Simulink, followed by the implementation of the control architecture. An advanced optimization technique, Termination Parallelism Cooperative Optimization (TPCO), is used to fine-tune system parameters and improve performance. The results demonstrate significant improvements in torque ripple reduction under different load conditions, achieving reductions of up to 80% under light load and 36.84% under heavy load conditions. These findings highlight the effectiveness of the proposed ASMC strategy in reducing torque ripple and improving efficiency, providing a promising solution for SRM-based drive systems.