Search Results (77)

How can the shape of biodiversity inform us about cities’ ecoclimatic fitness and guide their development? Can we use species as the harbingers of climatic extremes? Eco-climatically sensitive species carry information about hydroclimatic change in their distribution, fitness, and preferential gradients of habitat suitability. Conversely, environmental features outside of the species’ fitness convey information on potential ecological anomalies in response to extremes to adapt or mitigate, such as through urban parks. Here, to quantify ecosystems’ fitness, we propose a novel computational model to extract multivariate functional ecological networks and their basins, which carry the distributed signature of the compounding hydroclimatic pressures on sentinel species. Specifically, we consider butterflies and their habitat suitability (HS) to infer maximum suitability gradients that are meaningful of potential species networks and flows, with the smallest hydroclimatic resistance across urban landscapes. These flows are compared to the distribution of urban parks to identify parks’ ecological attractiveness, actual and potential connectivity, and park potential to reduce hydroclimatic impacts. The ecosystem fitness index (EFI) is novelly introduced by combining HS and the divergence of the relative species abundance (RSA) from the optimal log-normal Preston plot. In Shenzhen, as a case study, eco-flow networks are found to be spatially very extended, scale-free, and clustering for low HS gradient and EFI areas, where large water bodies act as sources of ecological corridors draining into urban parks. Conversely, parks with higher HS, HS gradients, and EFIs have small-world connectivity non-overlapping with hydrological networks. Diverging patterns of abundance and richness are inferred as increasing and decreasing with HS. HS is largely determined by temperature and precipitation of the coldest quarter and seasonality, which are critical hydrologic variables. Interestingly, a U-shape pattern is found between abundance and diversity, similar to the one in natural ecosystems. Additionally, both abundance and richness are mildly associated with park area according to a power function, unrelated to longitude but linked to the degree of urbanization or park centrality, counterintuitively. The Preston plot’s richness–abundance and abundance-rank patterns were verified to reflect the stationarity or ecological meta-equilibrium with the environment, where both are a reflection of community connectivity. Ecological fitness is grounded on the ecohydrological structure and flows where maximum HS gradients are indicative of the largest eco-changes like climate-driven species flows. These flows, as distributed stress-response functions, inform about the collective eco-fitness of communities, like parks in cities. Flow-based networks can serve as blueprints for designing ecotones that regulate key ecosystem functions, such as temperature and evapotranspiration, while generating cascading ecological benefits across scales. The proposed model, novelly infers HS eco-networks and calculates the EFI, is adaptable to diverse sensitive species and environmental layers, offering a robust tool for precise ecosystem assessment and design. Full article

Bipartite graphs are widely used for modelling various real-world scenarios characterized with binary relations, such as, scholarly articles recommendation with author-paper relations, and product recommendation with user-product relations. Particularly, maximum butterfly as a special cohesive subgraph of bipartite graphs, is playing an critical role in many promising application such as recommendation systems and research groups detection. Enumerating maximal butterfly has been proved to be a NP-hard and suffers time and space complexity. To conquer this challenge, this paper pioneers a novel problem called maximal butterfly generators search (MBGS) for facilitating the detection of maximal butterflies. The MBGS problem is to find a subgraph $\mathbb{B}$ of G such that maximize the number of butterflies in $\mathbb{B}$ and it is mathematically proved to NP-Hard. To address this problem, an equivalence relation theorem between maximum butterfly generator and maximum butterfly concept is presented. Furthermore, an effective MBGS search algorithm is proposed. Extensive experiments on real-world networks with ground-truth communities and interesting case studies validated the effectiveness and efficiency of our MBGS model and algorithm. Full article

Clitoria ternatea L. (CT) is a perennial herbaceous plant with deep blue flowers native to tropical Asia. This work explores the endometrial pain (EP) regulation of CT flower through a multifaceted approach. Phytochemical screening unveiled the presence of alkaloids, steroids, flavonoids, glycosides, and tannins in CT flower methanolic extract (ME). In the in vitro membrane stabilizing experiment, the ME demonstrated 91.47% suppression of heat-induced hemolysis. Upon carrageenan-induced paw edema assay conducted on male Swiss albino mice at doses of 200 mg/kg and 400 mg/kg, 65.28% and 81.89% inhibition rates, respectively, of paw edema were reported. For the same doses, upon acetic acid-induced-writhing assay, 75.6% and 76.78% inhibition rates, respectively, were observed. For network pharmacology analyses, a protein–protein interaction network was constructed for 92 overlapping gene targets of CT and EP, followed by GO and KEGG pathway enrichment analyses. Network pharmacology-based investigation identified the anti-EP activity of CT to be mostly regulated by the proteins SRC homology, ESR1, and PI3KR1. Physicochemical, pharmacokinetic, and toxicity property predictions for the compounds with stable ligand–target interactions and a molecular dynamics simulation for the highest interacting complex further validated these findings. This work affirmed the anti-EP role of CT flower against EP, suggesting a probable molecular mechanism involved. Full article

In the intricate landscape of the global food system, a nuanced understanding of dynamic evolution patterns and driving mechanisms of food trade network is essential for advancing insights into the African food trade and maintaining the food security of Africa. This paper constructs a framework for analyzing the food trade network from a comparative perspective by comparing and analyzing the evolution of food trade networks in China, the United States, Russia, the European Union, and African countries. The development trend of food trade between China, Russia, the United States, the European Union, and African countries is relatively good. China, the United States, Russia, and the European Union export far more food to African countries than they import, and bilateral food trade plays an important role in alleviating food supply shortages in Africa. The food trade networks between China, the United States, Russia, the European Union, and African countries exhibit a butterfly-shaped structure centered in Africa, and the overall intensity of bilateral trade linkages is gradually increasing. France has the greatest control over the food trade network between China, the United States, Russia, the European Union, and African countries, and the influence of the United States on the food trade network between China, the United States, Russia, the European Union, and African countries is increasing. China’s independence in the food trade network between China, the United States, Russia, the European Union, and African countries is enhanced, but its control ability is limited. The impact of differences in total population, differences in food production, and geographical borders on the trade network between China, the United States, the European Union, and African countries tends to decrease, while the influence of differences in the proportion of agricultural employment, differences in the arable land available for food production, and institutional distance tends to increase. Full article

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. Full article

Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the “butterfly effect” emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact. Full article

Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario. Full article

Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the prevention of groundwater pollution and overexploitation. Moreover, the development of a novel localization strategy project in wireless sensor groundwater networks aims to address the challenge of optimizing sensor location in relation to the monitoring process so as to extract the maximum quantity of information with the minimum cost. In this study, the improved hybrid butterfly artificial gorilla troop optimizer (iHBAGTO) technique is applied to optimize nodes’ position and the analysis of the path loss delay, and the RSS is calculated. The hybrid of Butterfly Artificial Intelligence and an artificial gorilla troop optimizer is used in the multi-functional derivation and the convergence rate to produce the designed data localization. The proposed iHBAGTO algorithm demonstrated the highest convergence rate of 99.6%, and it achieved the lowest average error of 4.8; it consistently had the lowest delay of 13.3 ms for all iteration counts, and it has the highest path loss values of 8.2 dB, with the lowest energy consumption value of 0.01 J, and has the highest received signal strength value of 86% for all iteration counts. Overall, the Proposed iHBAGTO algorithm outperforms other algorithms. Full article

This study explored the potential role of Clitoria ternatea (CT) flower in ameliorating endometrial pain (EP) through network pharmacology and experimental approaches. Phytochemicals of the CT flower were listed from the literature and databases, and 18 suitable actives were screened for bioavailability and drug likeness parameters using SwissADME. For these actives, 279 exclusive target genes were predicted using SwissTargetPrediction. Additionally, 939 exclusive genes for EP were acquired from the DisGenet and GeneCards databases. Ninety-one overlapping gene targets of CT and EP were listed, for which a Protein–Protein Interaction (PPI) network was constructed using STRING. The top three node proteins (SRC, ESR1, and PI3KR1) in the PPI network were identified through Cytoscape (version 3.9.1). Molecular docking analysis of the eighteen actives with the three target proteins showed strong binding interactions of Flavylium, kaempherol, and quercetin with all the targets, suggesting their involvement in EP relief. In addition, Gene Ontology (GO) functions analysis revealed 320 biological processes, 59 cellular components, and 107 molecular functions were enriched with the target genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses identified 106 KEGG pathways, including steroid hormone biosynthesis, endocrine resistance, and endometrial cancer pathways, which were significantly enriched with the target genes. The anti-inflammatory and analgesic effects of CT’s methanolic extract (ME) were investigated through in vitro and in vivo assays. The ME exhibited 91.47% inhibition of heat-induced hemolysis compared to 92.87% by aspirin in the in vitro membrane stabilizing assay. The in vivo carrageenan-induced paw edema study revealed 65.28% inhibition of paw edema by ME compared to 80.38% inhibition by aceclofenac at the end of 4-h treatment. The in vivo acetic acid-induced writhing test demonstrated analgesia by ME by 75.6% inhibition of writhing compared to 77.49% by aceclofenac. These findings suggest CT flower could be a potential natural remedy for EP, warranting further investigation in future studies. Full article

Bilayer hydrogel actuators, consisting of an actuating layer and a functional layer, show broad applications in areas such as soft robotics, artificial muscles, drug delivery and tissue engineering due to their inherent flexibility and responses to stimuli. However, to achieve the compatibility of good stimulus responses and high mechanical properties of bilayer hydrogel actuators is still a challenge. Herein, based on the double-network strategy and using the synchronous ultraviolet (UV) polymerization method, an upper critical solution temperature (UCST)-type bilayer hydrogel actuator was prepared, which consisted of a poly(acrylamide-co-acrylic acid)[MC] actuating layer and an agar/poly(N-hydroxyethyl acrylamide-co-methacrylic acid)[AHA] functional layer. The results showed that the tensile stress/strain of the bilayer hydrogel actuator was 1161.21 KPa/222.07%. In addition, the UCST of bilayer hydrogels was ~35 °C, allowing the bilayer hydrogel actuator to be curled into an “◎” shape, which could be unfolded when the temperature was 65 °C, but not at a temperature of 5 °C. Furthermore, hydrogel actuators of three different shapes were designed, namely “butterfly”, “cross” and “circle”, all of which demonstrated good actuating performances, showing the programmable potential of bilayer hydrogels. Overall, the bilayer hydrogels prepared using double-network and synchronous UV polymerization strategies realized the combination of high mechanical properties with an efficient temperature actuation, which provides a new method for the development of bilayer hydrogel actuators. Full article

The use of fast transients for fault detection in long transmission networks makes the generation of controlled transients crucial. In order to maximise the information that can be extracted from the measured pressure time history (pressure signal), the transients must meet certain requirements. In particular, the manoeuvre that generates the transient must be fast and repeatable, and must produce a pressure wave that is as sharp as possible, without spurious pressure oscillations. This implies the use of small-diameter valves and often pneumatically operated automatic valves. In the present work, experimental transient tests are carried out at the Water Engineering Laboratory (WEL) of the University of Perugia using a butterfly valve and a ball pneumatic-driven valve to generate pressure waves in a pressurised copper pipe. A camera is used to monitor the valve displacement, while the pressure is measured by a pressure transducer close to the downstream end of the pipe where the pneumatic valve is installed. The experimental data are analysed to characterise the valve performance and to compare the two geometries in terms of valve closing dynamics, the sharpness of the generated pressure wave and the stability of the pressure time history. The present work demonstrates how the proposed approach can be very effective in easily characterising the transient dynamics. Full article

In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced Coati Optimization Algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional Coati Optimization Algorithm (COA). We augment its search capabilities through a dual population strategy, adaptive factors, and a stochastic differential variation strategy. The ICOA is employed for the optimal selection of Bi-LSTM parameters, effectively accomplishing the fault prediction task. Our method harnesses the global search capabilities of the COA and the sophisticated data analysis capabilities of the Bi-LSTM to enhance the accuracy and efficiency of fault warnings. In a practical application to a real-world case of induced draft fan fault warning, our results indicate that our method anticipates faults approximately two hours in advance. Furthermore, in comparison with other advanced methods, namely, the Improved Social Engineering Optimizer Optimized Backpropagation Network (ISEO-BP), the Sparrow Particle Swarm Hybrid Algorithm Optimized Light Gradient Boosting Machine (SSAPSO-LightGBM), and the Improved Butterfly Optimization Algorithm Optimized Bi-LSTM (MSBOA-Bi-LSTM), our proposed approach exhibits distinct advantages and robust prediction effects. Full article

Comparing co-occurring species may provide insights into how aspects of ecology may play a role in influencing their microbial communities. During the 2019 commercial shrimp trawl season off coastal Georgia, swabs of skin, gills, cloaca, and gut were taken for three species of batoids (Butterfly Ray, Bluntnose Stingray, and Atlantic Stingray) and one shark species (Atlantic Sharpnose) for high-throughput sequencing of the V4 region of the bacterial 16S rRNA gene. White muscle was analyzed for stable isotopes (δ¹³C and δ¹⁵N) to evaluate potential niche overlap in these four sympatric mesopredators. Significant differences were found in both δ¹³C and δ¹⁵N signatures across species, suggesting a degree of resource partitioning. When examined within tissue type, the host species had a weak effect on β-diversity for cloaca and skin, with no differences found for gill and gut samples. However, network analysis metrics demonstrated a stronger species-specific effect and distinct microbial community relationships were apparent between the shark and batoids, with the former having tighter networks for both internally- and externally-influenced tissues (gut/cloaca and skin/gills, respectively). Despite overlapping habitat use, species’ microbiomes differed in their organizational structuring that paralleled differences in stable isotope results, suggesting a mediating role of species-specific ecology on bacterial microbiomes. Full article

Butterfly pea petal extract (BPE)-loaded water-in-oil-in-water (W/O/W) emulsions were fabricated using nanocrystalline cellulose (NCC) as a hydrophilic stabilizer and polyglycerol polyricinoleate (PGPR) as a hydrophobic emulsifier. The impact of different concentrations of NCC and PGPR in different phase proportions on the emulsion formation, rheology, and stability of an anthocyanin-loaded (pH ≈ 7.0) emulsion was investigated. The mean droplet size of the emulsions increased as the NCC concentration increased, while color intensity (greenness) decreased as the PGPR and NCC concentrations increased. A microscopic examination confirmed that the NCC nanoparticles stabilized the inner W₁/O phase, whereas the excess concentration of non-adsorbing NCC nanoparticles was suspended in the continuous aqueous phase. The rheological results showed that robust emulsion networks were formed when the NCC concentration increased. A network structure between the droplets and the development of the NCC network during the continuous phase were attributed to a gel-like behavior. Over the course of seven days, the emulsions with a higher proportion of NCC remained stable, as in samples 3%P-%N, 5%P-2%N, and 5%P@1%N, the total anthocyanin content decreased from 89.83% to 76.49%, 89.40% to 79.65, and 86.63% to 71.40%, respectively. These findings have significant implications for the accurate formulation of particle-stabilized double emulsions for anthocyanin delivery with higher stability. Full article

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the “oracle” method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method. Full article