Bio-based melamine formaldehyde resins for flame-retardant polyurethane foams.

The polyurethane (PU) foams can be functionally tailored by modifying the formulation with different additives. One such additive is melamine (MA) formaldehyde resin for improving their flame-retardant properties. In this work, the glycerol-modified (GMF), sodium alginate (SGMF)- and lignosulfonate-modified melamine formaldehyde (LGMF) were prepared and used as flame retardants reacting with isocyanate to prepare the corresponding rigid polyurethane foams (GMF-PU, SGMF-PU and LGMF-PU). The thermomechanical properties and flame-retardant properties of the foams were characterized. The results showed that the specific compression strength of GMF-PU, SGMF-PU and LGMF-PU increased substantially compared to the foams from physical addition of MA, sodium alginate and lignosulfonate, all of which were greater than that of the foam without any flame retardant (PPU). Meanwhile, the cell wall of the foam pores became thicker and the closed pore ratio increased. The sodium alginate and lignosulfonate played a key role in enhancing foam thermal stability. The limiting oxygen index values and cone calorimetry results indicated the flame-retardant efficiency of GMF-PU, SGMF-PU and LGMF-PU was significantly enhanced relative to PPU. Meanwhile, the heat and smoke release results indicated sodium alginate and lignosulfonate could reduce the amount of smoke generation to different degrees during the combustion of the foam.

Oceanobacillus picturae alleviates cadmium stress and promotes growth in soybean seedlings.

Cadmium (Cd) is a heavy metal that significantly impacts human health and the environment. Microorganisms play a crucial role in reducing heavy metal stress in plants; however, the mechanisms by which microorganisms enhance plant tolerance to Cd stress and the interplay between plants and microorganisms under such stress remain unclear. In this study, Oceanobacillus picturae (O. picturae) was isolated for interaction with soybean seedlings under Cd stress. Results indicated that Cd treatment alone markedly inhibited soybean seedling growth. Conversely, inoculation with O. picturae significantly improved growth indices such as plant height, root length, and fresh weight, while also promoting recovery in soil physiological indicators and pH. Metabolomic and transcriptomic analyses identified 157 genes related to aspartic acid, cysteine, and flavonoid biosynthesis pathways. Sixty-three microbial species were significantly associated with metabolites in these pathways, including pathogenic, adversity-resistant, and bioconductive bacteria. This research experimentally demonstrates, for the first time, the growth-promoting effect of the O. picturae strain on soybean seedlings under non-stress conditions. It also highlights its role in enhancing root growth and reducing Cd accumulation in the roots under Cd stress. Additionally, through the utilization of untargeted metabolomics, metagenomics, and transcriptomics for a multi-omics analysis, we investigated the impact of O. picturae on the soil microbiome and its correlation with differential gene expression in plants. This innovative approach unveils the molecular mechanisms underlying O. picturae's promotion of root growth and adaptation to Cd stress.

Effects of salt stress on soil enzyme activities and rhizosphere microbial structure in salt-tolerant and -sensitive soybean.

Salt is recognized as one of the most major factors that limits soybean yield in acidic soils. Soil enzyme activity and bacterial community have a critical function in improving the tolerance to soybean. Our aim was to assess the activities of soil enzyme, the structure of bacteria and their potential functions for salt resistance between Salt-tolerant (Salt-T) and -sensitive (Salt-S) soybean genotypes when subject to salt stress. Plant biomass, soil physicochemical properties, soil catalase, urease, sucrase, amylase, and acid phosphatase activities, and rhizosphere microbial characteristics were investigated in Salt-T and Salt-S soybean genotypes under salt stress with a pot experiment. Salt stress significantly decreased the soil enzyme activities and changed the rhizosphere microbial structure in a genotype-dependent manner. In addition, 46 ASVs which were enriched in the Salt-T geotype under the salt stress, such as ASV19 (Alicyclobacillus), ASV132 (Tumebacillus), ASV1760 (Mycobacterium) and ASV1357 (Bacillus), which may enhance the tolerance to soybean under salt stress. Moreover, the network structure of Salt-T soybean was simplified by salt stress, which may result in soil bacterial communities being susceptible to external factors. Salt stress altered the strength of soil enzyme activities and the assembly of microbial structure in Salt-T and Salt-S soybean genotypes. Na+, NO3--N, NH4+-N and Olsen-P were the most important driving factors in the structure of bacterial community in both genotypes. Salt-T genotypes enriched several microorganisms that contributed to enhance salt tolerance in soybeans, such as Alicyclobacillus, Tumebacillus, and Bacillus. Nevertheless, the simplified network structure of salt-T genotype due to salt stress may render its bacterial community structure unstable and susceptible.

Comparing the Salt Tolerance of Different Spring Soybean Varieties at the Germination Stage.

Salinization is a global agricultural problem with many negative effects on crops, including delaying germination, inhibiting growth, and reducing crop yield and quality. This study compared the salt tolerance of 20 soybean varieties at the germination stage to identify soybean germplasm with a high salt tolerance. Germination tests were conducted in Petri dishes containing 0, 50, 100, 150, and 200 mmol L-1 NaCl. Each Petri dish contained 20 soybean seeds, and each treatment was repeated five times. The indicators of germination potential, germination rate, hypocotyl length, and radicle length were measured. The salt tolerance of 20 soybean varieties was graded, and the theoretical identification concentration was determined by cluster analysis, the membership function method, one-way analysis of variance, and quadratic equation analysis. The relative germination rate, relative germination potential, relative root length, and relative bud length of the 20 soybean germplasms decreased when the salt concentration was >50 mmol L-1, compared with that of the Ctrl. The half-lethal salt concentration of soybean was 164.50 mmol L-1, and the coefficient of variation was 18.90%. Twenty soybean varieties were divided into three salt tolerance levels following cluster analysis: Dongnong 254, Heike 123, Heike 58, Heihe 49, and Heike 68 were salt-tolerant varieties, and Xihai 2, Suinong 94, Kenfeng 16, and Heinong 84 were salt-sensitive varieties, respectively. This study identified suitable soybean varieties for planting in areas severely affected by salt and provided materials for screening and extracting parents or genes to breed salt-tolerant varieties in areas where direct planting is impossible. It assists crop breeding at the molecular level to cope with increasingly serious salt stress.

Salt altered rhizosphere fungal community and induced soybean recruit specific species to ameliorate salt stress.

Different crop genotypes showed different adaptability to salt stress, which is partly attributable to the microorganisms in the rhizosphere. Yet, knowledge about how fungal communities of different genotypes in soybean respond to salt stress is limited. Here, qPCR and ITS sequencing were used to assess the response of rhizobial fungal communities of resistant and susceptible soybean to salt stress. Moreover, we isolated two fungal species recruited by resistant soybeans for validation. The assembly of fungal community structure might be strongly linked to alterations in fungal abundance and soil physicochemical properties. Salt stress derived structural differences in fungal communities of resistant and susceptible genotypes. The salt-resistant genotype appeared to recruit some fungal taxa to the rhizosphere to help mitigating salt stress. An increase of fungal taxa with predicted saprotrophic lifestyles might help promoting plant growth by increasing nutrient availability to the plants. Compared with the susceptible genotypes, the resistant genotypes had more stronger network structure of fungi. Lastly, we verified that recruited fungi, such as Penicillium and Aspergillus, can soybean adapt to salt stress. This study provided a promising approach for rhizospheric fungal community to enhance salt tolerance of soybean from the perspective of microbiology and ecology.

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location.

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Detection of type III effector-induced transcription factors that regulate phytohormone content during symbiosis establishment in soybean.

Soybean is a pivotal protein and oil crop that utilizes atmospheric nitrogen via symbiosis with rhizobium soil bacteria. Rhizobial type III effectors (T3Es) are essential regulators during symbiosis establishment. However, how the transcription factors involved in the interaction between phytohormone synthesis and type III effectors are connected is unclear. To detect the responses of phytohormone and transcription factor genes to rhizobial type III effector NopAA and type III secretion system, the candidate genes underlying soybean symbiosis were identified using RNA sequencing (RNA-seq) and phytohormone content analysis of soybean roots infected with wild-type Rhizobium and its derived T3E mutant. Via RNA-seq analysis the WRKY and ERF transcription factor families were identified as the most differentially expressed factors in the T3E mutant compared with the wild-type. Next, qRT-PCR was used to confirm the candidate genes Glyma.09g282900, Glyma.08g018300, Glyma.18g238200, Glyma.03g116300, Glyma.07g246600, Glyma.16g172400 induced by S. fredii HH103, S. fredii HH103ΩNopAA, and S. fredii HH103ΩRhcN. Since the WRKY and ERF families may regulate abscisic acid (ABA) content and underlying nodule formation, we performed phytohormone content analysis at 0.5 and 24 h post-inoculation (hpi). A significant change in ABA content was found between wild Rhizobium and type III effector mutant. Our results support that NopAA can promote the establishment of symbiosis by affecting the ABA signaling pathways by regulating WRKY and ERF which regulate the phytohormone signaling pathway. Specifically, our work provides insights into a signaling interaction of prokaryotic effector-induced phytohormone response involved in host signaling that regulates the establishment of symbiosis and increases nitrogen utilization efficiency in soybean plants.

Genome-wide signatures of the geographic expansion and breeding of soybean.

Soybean is a leguminous crop that provides oil and protein. Exploring the genomic signatures of soybean evolution is crucial for breeding varieties with improved adaptability to environmental extremes. We analyzed the genome sequences of 2,214 soybeans and proposed a soybean evolutionary route, i.e., the expansion of annual wild soybean (Glycine soja Sieb. & Zucc.) from southern China and its domestication in central China, followed by the expansion and local breeding selection of its landraces (G. max (L.) Merr.). We observed that the genetic introgression in soybean landraces was mostly derived from sympatric rather than allopatric wild populations during the geographic expansion. Soybean expansion and breeding were accompanied by the positive selection of flowering time genes, including GmSPA3c. Our study sheds light on the evolutionary history of soybean and provides valuable genetic resources for its future breeding.

An effective spatial relational reasoning networks for visual question answering.

Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship reasoning network model is proposed, which can combine visual object semantic reasoning and spatial relationship reasoning at the same time to realize fine-grained multi-modal reasoning and fusion. A sparse attention encoder is designed to capture contextual information effectively in the semantic reasoning module. In the spatial relationship reasoning module, the graph neural network attention mechanism is used to model the spatial relationship of visual objects, which can correctly answer complex spatial relationship reasoning questions. Finally, a practical compact self-attention (CSA) mechanism is designed to reduce the redundancy of self-attention in linear transformation and the number of model parameters and effectively improve the model's overall performance. Quantitative and qualitative experiments are conducted on the benchmark datasets of VQA 2.0 and GQA. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art approaches. Our best single model has an overall accuracy of 71.18% on the VQA 2.0 dataset and 57.59% on the GQA dataset.

Genome-Wide Identification of Candidate Genes Underlying Soluble Sugar Content in Vegetable Soybean (Glycine max L.) via Association and Expression Analysis.

Soluble sugar is a major indicator of the intrinsic quality of vegetable soybean [Glycine max (L.) Merr. ]. The improvement of soluble sugar content in soybean is very important due to its healthcare functions for humans. The genetic mechanism of soluble sugar in soybean is unclear. In this study, 278 diverse soybean accessions were utilized to identify the quantitative trait nucleotides (QTNs) for total soluble sugar content in soybean seeds based on a genome-wide association study (GWAS). A total of 25,921 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected for GWAS. Totally, thirteen QTNs associated with total soluble sugar content were identified, which were distributed on ten chromosomes. One hundred and fifteen genes near the 200-kb flanking region of these identified QTNs were considered candidate genes associated with total soluble sugar content in soybean seed. Gene-based association analysis and haplotype analysis were utilized to further identify the effect of candidate genes on total soluble sugar content. Totally, 84 SNPs from seventeen genes across four chromosomes were significantly associated with the total soluble sugar content. Among them, three SNPs from Glyma.02G292900 were identified at two locations, and other eighty-one SNPs from sixteen genes were detected at three locations. Furthermore, expression level analysis of candidate genes revealed that Glyma.02G293200 and Glyma.02G294900 were significantly positively associated with soluble sugar content and Glyma.02G294000 was significantly negatively associated with soluble sugar content. Six genes (i.e., Glyma.02G292600, Glyma.02G292700, Glyma.02G294000, Glyma.02G294300, Glyma.02G294400, and Glyma.15G264200) identified by GWAS were also detected by the analysis of differential expression genes based on soybean germplasms with higher and lower soluble sugar content. Among them, Glyma.02G294000 is the only gene that was identified by gene-based association analysis with total soluble sugar content and was considered an important candidate gene for soluble sugar content. These candidate genes and beneficial alleles would be useful for improving the soluble sugar content of soybean.

Pre-training Model Based on Parallel Cross-Modality Fusion Layer.

Visual Question Answering (VQA) is a learning task that combines computer vision with natural language processing. In VQA, it is important to understand the alignment between visual concepts and linguistic semantics. In this paper, we proposed a Pre-training Model Based on Parallel Cross-Modality Fusion Layer (P-PCFL) to learn the fine-grained relationship between vision and language. The P-PCFL model is composed of three Encoders: Object Encoder, Language Encoder, and Parallel Cross-Modality Fusion Encoder, with Transformer as the core. We use four different Pre-training missions, namely, Cross-Modality Mask Language Modeling, Cross-Modality Mask Region Modeling, Image-Text Matching, and Image-Text Q&A, to pre-train the P-PCFL model and improve its reasoning and universality, which help to learn the relationship between Intra-modality and Inter-modality. Experimental results on the platform of Visual Question Answering dataset VQA v2.0 show that the Pre-trained P-PCFL model has a good effect after fine-tuning the parameters. In addition, we also conduct ablation experiments and provide some results of Attention visualization to verify the effectiveness of P-PCFL model.

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey.

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL.

Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network.

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

Regulation of melanoma malignancy by the RP11-705C15.3/miR-145-5p/NRAS/MAPK signaling axis.

Melanoma is a common lethal skin cancer. Dissecting molecular mechanisms driving the malignancy of melanoma may uncover potential therapeutic targets. We previously identified miR-145-5p as an important tumor-suppressive microRNA in melanoma. Here, we further investigated the roles of long non-coding RNAs (lncRNAs) in melanoma. We identified RP11-705C15.3, a regulator of miR-145-5p, as an oncogenic lncRNA in melanoma. RP11-705C15.3 competitively bound miR-145-5p, relieved the repressive roles of miR-145-5p on its target NRAS, upregulated NRAS expression, and activated MAPK signaling. In vitro functional assays revealed that ectopic expression of RP11-705C15.3 promoted melanoma cell proliferation, inhibited apoptosis, and promoted migration and invasion. Silencing of RP11-705C15.3 repressed melanoma cell proliferation, induced apoptosis, and repressed migration and invasion. Notably, the roles of RP11-705C15.3 in melanoma cell proliferation, apoptosis, migration and invasion are reversed by miR-145-5p overexpression. In vivo functional assays revealed that RP11-705C15.3 promoted melanoma tumor growth and metastasis, which were also reversed by miR-145-5p overexpression. Furthermore, we investigated the expression of RP11-705C15.3 in clinical melanoma tissues and found that RP11-705C15.3 was increased in melanoma tissues. High expression of RP11-705C15.3 was positively correlated with thickness, ulceration, metastasis, and inferior overall survival. Taken together, our findings suggest RP11-705C15.3 as a novel oncogene in melanoma, and highlight that the RP11-705C15.3/miR-145-5p/NRAS/MAPK signaling axis may be potential therapeutic targets for melanoma.

Multi-Modal Explicit Sparse Attention Networks for Visual Question Answering.

Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other fields. Today, advanced VQA approaches model dense interactions between image regions and question words by designing co-attention mechanisms to achieve better accuracy. However, modeling interactions between each image region and each question word will force the model to calculate irrelevant information, thus causing the model's attention to be distracted. In this paper, to solve this problem, we propose a novel model called Multi-modal Explicit Sparse Attention Networks (MESAN), which concentrates the model's attention by explicitly selecting the parts of the input features that are the most relevant to answering the input question. We consider that this method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. The experimental results on the benchmark dataset VQA v2 demonstrate the effectiveness of our model. Our best single model delivers 70.71% and 71.08% overall accuracy on the test-dev and test-std sets, respectively. In addition, we also demonstrate that our model can obtain better attended features than other advanced models through attention visualization. Our work proves that the models with sparse attention mechanisms can also achieve competitive results on VQA datasets. We hope that it can promote the development of VQA models and the application of artificial intelligence (AI) technology related to VQA in various aspects.

The Degradation and Repolymerization Analysis on Solvolysis Liquefaction of Corn Stalk.

One of the most effective and renewable utilization methods for lignocellulosic feedstocks is the transformation from solid materials to liquid products. In this work, corn stalk (CS) was liquified with polyethylene glycol 400 (PEG400) and glycerol as the liquefaction solvents, and sulfuric acid as the catalyst. The liquefaction conditions were optimized with the liquefaction yield of 95.39% at the reaction conditions of 150 °C and 120 min. The properties of CS and liquefaction residues (LRs) were characterized using ATR-FTIR, TG, elemental analysis and SEM. The chemical components of liquefied product (LP) were also characterized by GC-MS. The results indicated that the depolymerization and repolymerization reaction took place simultaneously in the liquefaction process. The depolymerization of CS mainly occurred at the temperature of <150 °C, and the repolymerization of biomass derivatives dominated at a higher temperature of 170 °C by the lignin derivatives repolymerization with cellulose derivatives, hemicellulose derivatives and PEG400 and self-condensation of lignin derivatives. The solvolysis liquefaction of CS could be classified into the mechanism of electrophilic substitution reaction attacked by the hydrogen cation.

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree.

The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.

An Effective Dense Co-Attention Networks for Visual Question Answering.

At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model's basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks.

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

Modification of Rigid Polyurethane Foams with the Addition of Nano-SiO2 or Lignocellulosic Biomass.

Many achievements have been made on the research of composite polyurethane foams to improve their structure and mechanical properties, and the composite foams have been widely utilized in building insulation and furniture. In this work, rigid polyurethane foams (RPUFs) with the addition of different fillers (nano-SiO2, peanut shell, pine bark) were prepared through the one-step method. The effects of inorganic nano-SiO2 and organic biomass on foam properties were evaluated by means of physical and chemical characterization. The characterization results indicate that the compressive strength values of prepared foams could fully meet the specification requirement for the building insulation materials. The inorganic and organic fillers have no effect on the hydrogen bonding states in composite RPUFs. Furthermore, compared to the biomass fillers, the addition of nano-SiO2 greatly influenced the final residual content of the fabricated foam. All composite foams exhibit closed-cell structure with smaller cell size in comparison with the parent foam. The prepared composite foams have the potential for utilization in building insulation.