Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree.

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system's lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%.

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks.

One of the critical technologies to ensure cyberspace security is network traffic anomaly detection, which detects malicious attacks by analyzing and identifying network traffic behavior. The rapid development of the network has led to explosive growth in network traffic, which seriously impacts the user's information security. Researchers have delved into intrusion detection as an active defense technology to address this challenge. However, traditional machine learning methods struggle to capture complex threats and attack patterns when dealing with large-scale network data. In contrast, deep learning methods have the advantages of automatically extracting features from network traffic data and strong generalization capabilities. Aiming to enhance the ability of network anomaly traffic detection, this paper proposes a network traffic anomaly detection based on Deep Residual Shrinkage Network (DRSN), namely "GSOOA-1DDRSN". This method uses an improved Osprey optimization algorithm to select the most relevant and essential features in network traffic, reducing the features' dimensionality. For better detection performance of network traffic anomalies, a one-dimensional deep residual shrinkage network (1DDRSN) is designed as a classifier. Validation is performed using the NSL-KDD and UNSW-NB15 datasets and compared with other methods. The experimental results show that GSOOA-1DDRSN has improved multi-classification accuracy, precision, recall, and F1 Score by approximately 2 % and 3 %, respectively, compared to the 1DDRSN model on two datasets. Additionally, it reduces the time computation costs by 20 % and 30 % on these datasets. Furthermore, compared to other models, GSOOA-1DDRSN offers superior classification accuracy and effectively reduces the number of features.

Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction.

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58.

Correlations between experiments and simulations for formic acid oxidation.

Electrocatalytic conversion of formic acid oxidation to CO2 and the related CO2 reduction to formic acid represent a potential closed carbon-loop based on renewable energy. However, formic acid fuel cells are inhibited by the formation of site-blocking species during the formic acid oxidation reaction. Recent studies have elucidated how the binding of carbon and hydrogen on catalyst surfaces promote CO2 reduction towards CO and formic acid. This has also given fundamental insights into the reverse reaction, i.e. the oxidation of formic acid. In this work, simulations on multiple materials have been combined with formic acid oxidation experiments on electrocatalysts to shed light on the reaction and the accompanying catalytic limitations. We correlate data on different catalysts to show that (i) formate, which is the proposed formic acid oxidation intermediate, has similar binding energetics on Pt, Pd and Ag, while Ag does not work as a catalyst, and (ii) *H adsorbed on the surface results in *CO formation and poisoning through a chemical disproportionation step. Using these results, the fundamental limitations can be revealed and progress our understanding of the mechanism of the formic acid oxidation reaction.

Nanocomposite Concept for Electrochemical In Situ Preparation of Pt-Au Alloy Nanoparticles for Formic Acid Oxidation.

Herein, we report a straightforward approach for the in situ preparation of Pt-Au alloy nanoparticles from Pt + xAu/C nanocomposites using monometallic colloidal nanoparticles as starting blocks. Four different compositions with fixed Pt content and varying Pt to Au mass ratios from 1:1 up to 1:7 were prepared as formic acid oxidation reaction (FAOR) catalysts. The study was carried out in a gas diffusion electrode (GDE) setup. It is shown that the presence of Au in the nanocomposites substantially improves the FAOR activity with respect to pure Pt/C, which serves as a reference. The nanocomposite with a mass ratio of 1:5 between Pt and Au displays the best performance during potentiodynamic tests, with the electro-oxidation rates, overpotential, and poisoning resistance being improved simultaneously. By comparison, too low or too high Au contributions in the nanocomposites lead to an unbalanced performance in the FAOR. The combination of operando small-angle X-ray scattering (SAXS), scanning transmission electron microscopy (STEM) elemental mapping, and wide-angle X-ray scattering (WAXS) reveals that for the nanocomposite with a 1:5 mass ratio, a conversion between Pt and Au from separate nanoparticles to alloy nanoparticles occurs during continuous potential cycling in formic acid. By comparison, the nanocomposites with lower Au contents, for example, 1:2, exhibit less in situ alloying, and the concomitant performance improvement is less pronounced. On applying identical location transmission electron microscopy (IL-TEM), it is revealed that the in situ alloying is due to Pt dissolution and re-deposition onto Au as well as Pt migration and coalescence with Au nanoparticles.

Spatio-Chemical Heterogeneity of Defect-Engineered Metal-Organic Framework Crystals Revealed by Full-Field Tomographic X-ray Absorption Spectroscopy.

The introduction of structural defects in metal-organic frameworks (MOFs), often achieved through the fractional use of defective linkers, is emerging as a means to refine the properties of existing MOFs. These linkers, missing coordination fragments, create unsaturated framework nodes that may alter the properties of the MOF. A property-targeted utilization of this approach demands an understanding of the structure of the defect-engineered MOF. We demonstrate that full-field X-ray absorption near-edge structure computed tomography can help to improve our understanding. This was demonstrated by visualizing the chemical heterogeneity found in defect-engineered HKUST-1 MOF crystals. A non-uniform incorporation and zonation of the defective linker was discovered, leading to the presence of clusters of a second coordination polymer within HKUST-1. The former is suggested to be responsible, in part, for altered MOF properties; thereby, advocating for a spatio-chemically resolved characterization of MOFs.

On the facile and accurate determination of the Pt content in standard carbon supported Pt fuel cell catalysts.

We introduce a new and straight-forward methodology to accurately determine the Pt content in polymer membrane electrolyte fuel cell (PEMFC) catalysts consisting of carbon supported Pt nanoparticles (Pt/C). The method is based on an indirect Pt proof (IPP) consisting of the oxidative removal of the carbon support, the digestion of the Pt in aqua regia followed by a replacement reaction to form Cu ions (CuCl2). The Pt content is then determined via the Cu-ions with the help a complexometric indicator using a simple titration. The procedure is fast and does not require any expensive equipment. Thus, it can be implemented in any standard chemistry laboratory. The advantages and disadvantages of the IPP method are evaluated in a comparison to alternative methods for the determination of the Pt content in supported catalysts, i.e. inductively coupled plasma mass spectrometry (ICP-MS) and UV/Vis spectroscopy (UV/Vis). It is demonstrated that the IPP method delivers reliable and accurate results and is less influenced than for example ICP-MS by side effects such as excess in nitric acid or organic impurities. Furthermore, during the procedure up to 60% of the Pt material is recovered during the IPP procedure.

Efficacy of liraglutide in treating type 2 diabetes mellitus complicated with non-alcoholic fatty liver disease.

Type 2 diabetes mellitus (T2DM) complicated with non-alcoholic fatty liver disease (NAFLD) is difficult to treat. The present study explored the efficacy of (liraglutide) Lira in treating T2DM complicated with NAFLD. A total of 127 patients suffering from T2DM complicated with NAFLD were enrolled in the present study, and randomly assigned to a Lira group (liraglutide injection: 0.6-1.2 mg/day, 12 weeks, n=52) or a Metformin (Met) group (oral metformin: 1000-1500 mg/day, 12 weeks, n=75). During the treatment phase, the values for fasting plasma glucose (FPG), 2 h plasma glucose (2hPG), glycated hemoglobin (HbA1c), aspartate aminotransferase (AST)/alanine aminotransferase (ALT), and adiponectin (APN) decreased in both the Lira and Met groups, and the levels of Δ2hPG, ΔAST/ALT, and ΔAPN in the Lira group were significantly lower than those in the Met group. The values for total cholesterol (TC), triglycerides (TG), low-and high-density lipoproteins (LDL and HDL), ALT, AST, weight, body mass index (BMI), waist to hip ratio (WHR), and C-reactive protein were markedly increased in both groups, and levels of ΔAST, ΔALT, Δweight, ΔBMI, ΔWHR, and ΔCRP (C-reactive protein) in the Lira group were significantly higher than those in the Met group. An analysis of treatment efficacy showed that liraglutide was better than metformin in its ability to significantly decrease the ALT levels in patients with combined T2DM and NAFLD. Furthermore, liraglutide was more effective than metformin at ameliorating the severity of T2DM complicated with NAFLD, and produced its effects by alleviating liver inflammation and improving liver function.

[Genetic differentiation of Isaria farinosa populations in Anhui Province of East China].

Isaria farinosa is an important entomopathogenic fungus. By using ISSR, this paper studied the genetic heterogeneity of six I. farinosa populations at different localities of Anhui Province, East China. A total of 98.5% polymorphic loci were amplified with ten polymorphic primers, but the polymorphism at population level varied greatly, within the range of 59.6%-93.2%. The genetic differentiation index (G(st)) between the populations based on Nei's genetic heterogenesis analysis was 0.3365, and the gene flow (N(m)) was 0.4931. The genetic differentiation between the populations was lower than that within the populations, suggesting that the genetic variation of I. farinosa mainly come from the interior of the populations. The UPGMA clustering based on the genetic similarities between the isolates revealed that the Xishan population was monophylectic, while the other five populations were polyphylectic, with the Yaoluoping population being the most heterogenic and the Langyashan population being the least heterogenic. No correlations were observed between the geographic distance and the genetic distance of the populations. According to the UPGMA clustering based on the genetic distance between the populations, the six populations were classified into three groups, and this classification was accorded with the clustering based on geographic environment, suggesting the effects of environmental heterogeneity on the population heterogeneity.

Progesterone regulation of the mammalian ortholog of methylcitrate dehydratase (immune response gene 1) in the uterine epithelium during implantation through the protein kinase C pathway.

Implantation requires coordination between development of the blastocyst and the sex steroid hormone-regulated differentiation of the uterus. Under the influence of these hormones, the uterine luminal epithelium becomes receptive to attachment of the hatched blastocyst. In this study we sought to identify genes regulated by progesterone (P4) in the uterine epithelium. This resulted in the identification of one novel P4-regulated gene that had been previously found in lipopolysaccharide-stimulated macrophages and called immune response gene-1 (Irg1) and which is the mammalian ortholog of the bacterial gene encoding methylcitrate dehydratase. In adult mice Irg1 expression was limited to the uterine luminal epithelium where it is expressed only during pregnancy with a peak coinciding with implantation. Irg1 mRNA expression is regulated synergistically by P4 and estradiol (E2) but not by E2 alone. In macrophages Irg1 is induced by lipopolysaccharide through a protein kinase C (PKC)-regulated pathway. Now we demonstrate that the PKC pathway is induced in the uterine epithelium at implantation by the synergistic action of P4 and E2 and is responsible for the hormone induction of Irg1. These results suggest that the PKC pathway plays an important role in modulating steroid hormone responsiveness in the uterine luminal epithelium during the implantation window and that Irg1 will be an important marker of this window and may play an important role in implantation.