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An interesting ring-closing/ring-opening cascade reaction selectively induced by aliphatic primary amine for the synthesis of functionalized aminomaleimides is described. This work features phenolic hydroxyl as a traceless directing group that forms a critical six-membered intermediate and then a lactone ammonolysis/intramolecular amidation sequence. This two-step, one-pot practical protocol has broad substrate scope (39 examples) and delivers structurally diverse aminomaleimides in good to excellent yields.

Negative emotions could increase the risks of traffic accidents. However, the driver’s emotional identification is rarely considered in the current design of intelligent vehicle alarm user terminals (IVAUTs). To solve the problem, this paper tries to design an IVAUT system based on emotional identification technology. Firstly, the transformer network was combined with a convolutional neural network (CNN) into a voice emotional identification system for intelligent vehicle alarm, and an emotional labeling approach was provided. Next, a bimodal fusion model was developed based on decompose-CNNs, which includes an appearance module, an optical flow module, and a bimodal fusion module. The proposed emotional identification approach was proved effective through experiments.

How cooperation emerges and persists in a population of selfish agents is a fundamental question in evolutionary game theory. The research shows that collective strategies with master-slave mechanism (CSMSM) defeat tit-for-tat and other well-known strategies in spatial iterated prisoner's dilemma. A CSMSM identifies kin members by means of a handshaking mechanism. If the opponent is identified as non-kin, a CSMSM will always defect. Once two CSMSMs meet, they play master and slave roles. A mater defects and a slave cooperates in order to maximize the master's payoff. CSMSM outperforms non-collective strategies in spatial IPD even if there is only a small cluster of CSMSMs in the population. The existence and performance of CSMSM in spatial iterated prisoner's dilemma suggests that cooperation first appears and persists in a group of collective agents.

Background Carbon ion-beam irradiation has been widely used to advance crop breeding. The purpose of this study was to explore whether irradiation is suitable for mutation creation of japonica rice (Oryza sativaL.) in northeast China and its potential effects on local germplasm resources. Methods and Results 200 Gy irradiation was applied to screened stable rice mutants, random amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR) molecular markers were applied to detect the DNA polymorphisms of mutants and local varieties. Here, the mutants with a shorter maturation period than the mutagenic parent were screened. Among control (Tonghe899) and mutants, RAPD and SSR primers revealed that a total of 574 bands of which 385 were polymorphic (67.07%), all mutants had polymorphic DNA bands, and the polymorphism information content (PIC) of RAPD and SSR varied from 0.500 to 0.924 and 0.836 to 0.954, respectively. Meanwhile, among mutant and other local varieties, RAPD and SSR primers generated a total of 658 amplified bands with 530 polymorphic bands (80.55%). Notably, the addition of mutants reduced the lowest Jaccard’s similarity coefficient of the local varieties population from 0.65 to 0.62. Conclusions In summary, carbon-ion beam irradiating rice seeds generate mutants that can develop as new cultivars, and it slightly expands the genetic diversity of the selected japonica rice from northeast China. RAPD and SSR markers had good polymorphism and could be used for DNA polymorphism identification and facilitate inter-cultivar identification for japonica rice in northeast China.

Arbuscular mycorrhizal fungi (AMF) have been reported to have a wide distribution in terrestrial ecosystems and to play a vital role in ecosystem functioning and symbiosis with Stipa grasses. However, exactly how AMF communities in the rhizosphere change and are distributed along different Stipa population with substituted distribution and their relationships remain unclear. Here, the changes and distribution of the rhizosphere AMF communities and their associations between hosts and the dynamic differences in the glomalin-related soil protein (GRSP) in the rhizosphere soil of seven Stipa species with spatial substitution distribution characteristics in arid and semiarid grasslands were investigated. Along with the substituted distribution of the Stipa populations, the community structures, taxa, species numbers, and alpha diversity index values of AMF in the rhizosphere changed. Some AMF taxa appeared only in certain Stipa species, but there was no obvious AMF taxon turnover. When the Stipa baicalensis population was replaced by the Stipa gobica population, the GRSP tended to decline, whereas the carbon contribution of the GRSP tended to increase. Stipa grandis and Stipa krylovii had a great degree of network modularity of the rhizosphere AMF community and exhibited a simple and unstable network structure, while the networks of Stipa breviflora were complex, compact, and highly stable. Furthermore, with the succession of zonal populations, the plant species, vegetation coverage, and climate gradient facilitated the differentiation of AMF community structures and quantities in the rhizospheres of different Stipa species. These findings present novel insights into ecosystem functioning and dynamics correlated with changing environments.

With the global pandemic of COVID-19, it has been striking psychological burdens on individuals. Under this background, more and more people get wellbeing by watching live broadcasts. However, the psychological mechanism behind this phenomenon is still a black box. This study finds that when people watch a live broadcast and interact with anchors and other people, an interaction ritual chain is formed, and emotional energy is generated, thus making people experience and understand the meaning of the live interaction ritual chains. Under the effect of the meaning transfer model, people will generate wellbeing. Specifically, the basic meaning of live interaction (emotional meaning and functional meaning) drives people’s generation of wellbeing. The meanings of self-participation, self-display, self-concept, and self-renewal play a role in mediation in enhancing people’s wellbeing with the basic meaning of live broadcast interaction.

The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.

Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.

Transition-metal-based chalcogenides are a series of intriguing semiconductors with applications spanning various fields because of their rich structure and numerous functionalities. This paper reports the crystal structure and basic physical properties of a new quaternary chalcogenide In4Pb5.5Sb5S19. The crystal structure of In4Pb5.5Sb5S19 was determined by both powder and single-crystal X-ray diffraction techniques. In4Pb5.5Sb5S19 crystallizes in the monoclinic system with I2/m space group, and the structure parameters are a = 26.483 A, b = 3.899 A, c = 32.696 A, and β = 111.86°. The polyhedral double chains of Sb3+ and Sb/Pb2+ as the main cations are parallel to each other and form a Jamesonite-like mineral structure through the short chain links of the distorted In, Pb, and Sb polyhedron. In4Pb5.5Sb5S19 exhibits a moderate experimental band gap of 1.42 eV, indicating its potential for application in solar cells and photocatalysis. In addition, In4Pb5.5Sb5S19 exhibits good ambient stability, and differential scanning calorimetry tests demonstrate that it is stable up to 892 K in a nitrogen atmosphere. Moreover, In4Pb5.5Sb5S19 exhibits extremely low thermal conductivity (0.438-0.478 W m-1 K-1 ranging from 300 to 700 K) compared with binary counterparts such as PbS and In2S3. Future chemical manipulation via elemental doping or defect engineering may make the title compound a potential thermoelectric or thermal insulating material.

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.

Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers’ trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver’s behaviour, which could thereby increase a user’s willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.

Saliency detection aims to automatically highlight the most important area in an image. Traditional saliency detection methods based on absorbing Markov chain only take into account boundary nodes and often lead to incorrect saliency detection when the boundaries have salient objects. In order to address this limitation and enhance saliency detection performance, this paper proposes a novel task-independent saliency detection method based on the bidirectional absorbing Markov chains that jointly exploits not only the boundary information but also the foreground prior and background prior cues. More specifically, the input image is first segmented into number of superpixels, and the four boundary nodes (duplicated as virtual nodes) are selected. Subsequently, the absorption time upon transition node’s random walk to the absorbing state is calculated to obtain the foreground possibility. Simultaneously, foreground prior (as the virtual absorbing nodes) is used to calculate the absorption time and get the background possibility. In addition, the two aforementioned results are fused to form a combined saliency map which is further optimized by using a cost function. Finally, the superpixel-level saliency results are optimized by a regularized random walks ranking model at multi-scale. The comparative experimental results on four benchmark datasets reveal superior performance of our proposed method over state-of-the-art methods reported in the literature. The experiments show that the proposed method is efficient and can be applicable to the bottom-up image saliency detection and other visual processing tasks.

The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.

In thermal nanoimprint lithography, polymethyl methacrylate (PMMA) with good thermoplasticity is often used as transferring media. However, due to the special properties of PMMA, the accuracy of microstructure transferred to the surface will be seriously affected. To address this problem, the micro-vibration by piezo-driven is introduced in conventional thermal nanoimprint lithography. Because of the micro-vibration will change the contact stress during the embossing process and reduce the contact time. This will improve the filling rate of PMMA during the embossing process and transfer the pattern of stamp to PMMA with excellent feature fidelity. Compared with the traditional nanoimprint lithography, the filling rate of PMMA is increased by 92% after the introduction of vibration by piezo-driven. Then, a new motion model is built to verify the rationality of introducing vibration. Besides, the effect of frequency and amplitude with filling rate is analyzed by finite element method to obtain the best frequency and amplitude for experiments. Finally, the transferred pattern is observed by experiment.

The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule.

Plant-based natural biotemplates are increasingly gaining popularity for the development of catalytic filters in water treatment owing to their unique features, such as exquisite microstructures, renewable properties, and environmental friendliness. However, the development of catalytic filters, not limited to plant-based filters, to achieve both super-high water flux and excellent treatment efficiency, particularly under gravity-driven conditions, remains challenging. Therefore, herein, an efficient gravity-driven catalytic filter was fabricated using a naturally three-dimensional (3D) porous rattan biotemplate for water treatment. After reducing Ag(NH3)2+ ions via an in situ process that did not involve the addition of reductants or stabilizers, Ag nanoparticles (NPs) were immobilized in the inner microchannel of the porous rattan cane. The intrinsically aligned microchannels in the vascular bundles of rattan (diameter 24–420 μm) acted as reaction compartments for the immobilization and stabilization of Ag NPs. Furthermore, the 3D Ag/rattan filter exhibited excellent gravity-driven catalytic activity (up to 97.2% treatment efficiency for a high water flux of 7471 L m−2 h−1), good recyclability, high water permeability, and stability toward 4-nitrophenol reductions. These excellent properties were attributed to the synergistic effect of the evenly distributed nanosized Ag catalysts and microchannel structures of rattan. Rattan materials are abundantly available and naturally porous, and they can readily serve as cost-effective, nontoxic, biodegradable, and sustainable sources in numerous catalytic applications.

Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.

To overcome various factors that limit crop production and to meet the growing demand for food by the increasing world population. Seed priming technology has been proposed, and it is considered to be a promising strategy for agricultural sciences and food technology. This technology helps to curtail the germination time, increase the seed vigor, improve the seedling establishment, and enhance the stress tolerance, all of which are conducive to improving the crop yield. Meanwhile, it can be used to reduce seed infection for better physiological or phytosanitary quality. Compared to conventional methods, such as the use of water or chemical-based agents, X-rays, gamma rays, electron beams, proton beams, and heavy ion beams have emerged as promising physics strategies for seed priming as they are time-saving, more effective, environmentally friendly, and there is a greater certainty for yield improvement. Ionizing radiation (IR) has certain biological advantages over other seed priming methods since it generates charged ions while penetrating through the target organisms, and it has enough energy to cause biological effects. However, before the wide utilization of ionizing priming methods in agriculture, extensive research is needed to explore their effects on seed priming and to focus on the underlying mechanism of them. Overall, this review aims to highlight the current understanding of ionizing priming methods and their applicability for promoting agroecological resilience and meeting the challenges of food crises nowadays.

The bacterial communities of the root-zone soil are capable of regulating vital biogeochemical cycles and the succession of plant growth. Stipa as grassland constructive species is restricted by the difference features of east–west humidity and north–south heat, which shows the population substituting distribution. The distribution, turnover, and potential driving factors and ecological significance of the root-zone bacterial community along broad spatial gradients of Stipa taxa transition remain unclear. This paper investigated seven Stipa species root-zone soils based on high-throughput sequencing combined with the measurements of multiple environmental parameters in arid and semi-arid steppe. The communities of soil bacteria in root zone had considerable turnover, and some regular variations in structure along the Stipa taxa transition are largely determined by climatic factors, vegetation coverage, and pH at a regional scale. Bacterial communities had a clear Stipa population specificity, but they were more strongly affected by the main annual precipitation, which resulted in a biogeographical distribution pattern along precipitation gradient, among which Actinobacteria, Acidobacteria, Proteobacteria, and Chloroflexi were the phyla that were most abundant. During the transformation of Stipa taxa from east to west, the trend of diversity shown by bacterial community in the root zone decreased first, and then increased sharply at S. breviflora, which was followed by continuous decreasing toward northwest afterwards. However, the richness and evenness showed an opposite trend, and α diversity had close association with altitude and pH. There would be specific and different bacterial taxa interactions in different Stipa species, in which S. krylovii had the simplest and most stable interaction network with the strongest resistance to the environment and S. breviflora had most complex and erratic. Moreover, the bacterial community was mainly affected by dispersal limitation at a certain period. These results are conducive to the prediction of sustainable ecosystem services and protection of microbial resources in a semi-arid grassland ecosystem.

Vegetation and soil microorganisms are important components of terrestrial ecosystems and play a crucial role in ecosystem functioning. However, little is known about the synergistic changes in soil microbial community with aboveground plants in grassland degradation and the role of the microbial community in the process of vegetation restoration succession. In this study, we investigated the characteristics of soil microbial communities and diversities in the different levels of grassland degradation using Illumina MiSeq high-throughput sequencing. The dominant bacteria phyla were: Actinobacteriota, 31.61–48.90%; Acidobacteriota, 7.19–21.73%; Chloroflexi, 9.08–19.09%; and Proteobacteria, 11.14–18.03%. While the dominant fungi phyla were: Ascomycota, 46.36–81.58%; Basidiomycota, 5.63–33.18%; and Mortierellomycota, 1.52–37.69%. Through RDA/CCA, the effects of environmental factors on the differences in the soil microbial community between different sites were interpreted. Results showed that the pH was the most critical factor affecting soil microbial communities in seriously degraded grassland; nevertheless, soil microbial communities in non-degraded grassland and less degraded grasslands were mainly affected by the soil moisture content and soil enzyme activities (sucrase activity, alkaline phosphatase activity and catalase activity). We systematically demonstrated the soil microbial communities of different grassland degradation gradients in Mongolia, which provided valuable information for grassland degradation reduction and vegetation restoration succession.

The process parameters significantly influence the preparation and final properties of outdoor wood mats-based engineering composite (OWMEC). During outdoor use, wood composites are susceptible to destruction by rot fungi. Herein, the role of process parameters such as density and resin content on OWMEC resistance to fungal decay was investigated. The poplar OWMEC samples were exposed to white-rot fungus Trametes versicolor and brown-rot fungus Gloeophyllum trabeum for a period of 12 weeks. The chemical composition, crystallinity, and morphology were evaluated to investigate the effect of process parameters on the chemical composition and microstructure of the decayed OWMEC. With an increase in the density and resin content, the mass loss of the decayed OWMEC decreased. The highest antifungal effect against T. versicolor (12.34% mass loss) and G. trabeum (19.43% mass loss) were observed at a density of 1.15 g/m3 and resin content of 13%. As results of the chemical composition and microstructure measurements, the resistance of OWMEC against T. versicolor and G. trabeum fungi was improved remarkably by increasing the density and resin content. The results of this study will provide a technical basis to improve the decay resistance of OWMEC in outdoor environments.

A round-the-clock photocatalyst with energy-storage ability has piqued the interest of researchers for removing microbial contaminants from indoor environments. This work presents a moderate round-the-clock method for inhibiting the growth of fungus spores on bamboo materials using Ag-modified TiO2 thin films. Photoactivated antifungal coating with catalytic memory activity was assembled on a hydrophilic bamboo by first anchoring anatase TiO2 thin films (TB) via hydrogen bonding and then decorating them with Ag nanoparticles (ATB) via electrostatic interactions. Antifungal test results show that the Ag/TiO2 composite films grown on the bamboo surface produced a synergistic antifungal mechanism under both light and dark conditions. Interestingly, post-illumination catalytic memory was observed for ATB, as demonstrated by the inhibition of Aspergillus niger (A. niger) spores, in the dark after visible light was removed, which could be attributed to the transfer of photoexcited electrons from TiO2 to Ag, their trapping on Ag under visible-light illumination, and their release in the dark after visible light was removed. The mechanism study revealed that the immobilized Ag nanoparticles served the role of “killing two birds with one stone”: increasing visible-light absorption through surface plasmon resonance, preventing photogenerated electron–hole recombination by trapping electrons, and contributing to the generation of ●O2−and ●OH. This discovery creates a pathway for the continuous removal of indoor air pollutants such as volatile organic compounds, bacteria, and fungus in the day and night time.

As a non-thermally stable product, nanoemulsion followed by an encapsulation process was fabricated to enhance the techno-functional performance and sustainable applications of Lavandula angustifolia essential oil (La.EO). Integrated ultrasound-microwave techniques were used to enhance the release of Clevenger-hydrodistillation isolation of La.EO. Refined, bleached, and deodorized sunflower oil (RBDSFo) was used as a high unsaturated lipid-carrier type. Whey Protein Isolate (WPI, 95%) was used as a natural-biodegradable polymer wall material to formulate and stabilize Oil-in-Water nanoemulsion (O/W-NE). DLS, CLSM, and TEM were used to study the techno-functional characteristics of La.EO-nanoparticles. SEM, XRD, and FTIR spectroscopy were used to study the efficiency of the encapsulation process of La.EO. The fabricated formulations using a High-pressure Homogenizer showed a highly efficient at reducing the interfacial tension of O/W-NE interfaces. That led to produce nano-scaled droplets reached 128.8 d.nm with a polydispersity index of 0.151, and a ζ-potential of −42.1 mV. The optimized formulation of nanoemulsion showed a slight change in droplet size, PdI, and ζ-potential through 28 days of storage at ambient temperature. La.EO-nanoemulsion was stable against aggregation and coalescence at thermal destabilizing stresses similar to that can be exposed to in the commercial storage conditions (5, 25 and 45 °C) at neutral pH. The findings of this investigation showed that the thermal stability of La.EO can be enhanced through the encapsulation process within edible bio-polymers using both WPI and RBDSFo. In addition, La.EO nanoparticles demonstrated a great potential to be used to enhance the release of bioactive components coated in O/W-NE, as well as, to be used as pathogenic antibacterial agents.

We compare modification effects of thermostable and mesophilic α-amylases (i.e. TS-αA and MS-αA) under high-performance extrusion treatment. As confirmed by size exclusion chromatography, a portion of the wheat starch extracted from extruded noodles (ENs) was modified into a water-soluble mixture of oligosaccharides and dextrin. X-Ray diffraction and differential scanning calorimeter investigations demonstrated that the appropriate concentrations of both enzymes promote starch gelatinisation, whilst their excess usage presents negative effects due to the difficulty of overlaying or adhering to starch fragments. From these findings, innovative ENs with a well-developed porous structure was successfully created to enhance the rehydration and palatability of ENs. Compared with the overall TS-αA modification results, the use of MS-αA concentration of 1.6‰ is recommended as the optimum choice for producing ENs. The current study is the first to compare the modification effects of TS-αA and MS-αA during the high-performance extrusion and to illustrate their promising applications in ENs.

Tremendous efforts have been dedicated to developing functionalized cellulose materials by synthesis with copper-based metal–organic frameworks (MOF199), also known as HKUST-1. However, few studies have explored the deposition of MOFs on woody materials due to the complex chemical compositions of these materials (cellulose, hemicellulose, lignin) and their difficulty of bonding with MOF crystals. In this article, for the first time, MOF199 was successfully synthesized onto two different woody materials, moso bamboo and balsa wood, via in situ deposition at room temperature. The results show that the diverse surface roughness and the hierarchical structures of woody materials have significant effects on the size of MOF199 crystal. Additionally, bamboo and wood coated with MOF199 exhibited better antibacterial activities against Staphylococcus aureus (S. aureus) than Escherichia coli (E. coli); they could minimize S. aureus colony levels to 2.08 from 8.98 CFU cm−2. This study provides a facile method for the functionalization of woody materials with MOFs for antibacterial applications.

The enormous pressures of energy consumption and the severe pollution produced by non-renewable resources have prompted researchers to develop various environmentally friendly energy-saving materials. Transparent bamboo represents an emerging result of biomass material research that has been identified and studied for its many advantages, including light weight, excellent light transmittance, environmental sustainability, superior mechanical properties, and low thermal conductivity. The present review summarizes methods for preparing transparent bamboo, including delignification and resin impregnation. Next, transparent bamboo performance is quantified in terms of optical, mechanical, and thermal conductivity characteristics and compared with other conventional and emerging synthetic materials. Potential applications of transparent bamboo are then discussed using various functionalizations achieved through doping nanomaterials or modified resins to realize advanced energy-efficient building materials, decorative elements, and optoelectronic devices. Finally, challenges associated with the preparation, performance improvement, and production scaling of transparent bamboo are summarized, suggesting opportunities for the future development of this novel, bio-based, and advanced material.

Because thermal infrared sport targets have rich and complex semantic information, there is a high coupling between different types of features. In view of these limitations, we propose a Non-Glodal decoupled Attention, namely,local U-shaped attention decoupling network (LUANets), which aims to decompose the coupling relationship of different sport target features in thermal infrared images and establish effective spatial dependence between them. This method takes the captured multi-scale initial features according to different levels and inputs them into the local decoupling module with U-shaped attention structure to realize the decomposition of semantic details. At the same time, considering the correlation between different targets, in the process of feature decomposition, using prior knowledge as guiding information many times to establish effective spatial dependence. Secondly, we design a two-way cross-aggregation FPN module to cross-aggregate information flows in the front and back directions to achieve feature interaction while further reducing the coupling between different types of features. The evaluation results on data such as TIIs,SportFCs and FLIR show that the LUANets method we proposed has achieved the best detection performance, with mAP of 68.72%,59.51% and 65.29%, respectively.

Although the binary sulfides Bi2S3, PbS, and SnS have attracted extensive interest as thermoelectric materials, no quaternary sulfides containing Sn/Pb/Bi/S elements have been reported. Herein, we report the synthesis of a new quaternary sulfide, SnPb2Bi2S6, which crystallizes in the orthorhombic space group Pnma with unit cell parameters of a = 20.5458(12) A, b = 4.0925(4) A, c = 13.3219(10) A. SnPb2Bi2S6 has a lillianite-type crystal structure consisting of two alternately aligned NaCl-type structural motifs separated by a mirror plane of PbS7 monocapped trigonal prisms. In the lillianite homologous series, SnPb2Bi2S6 can be classified as 4,4L, where the superscripted numbers indicate the maximum numbers of edge-sharing octahedra in the two adjacent NaCl-shaped slabs along the diagonal direction. The obtained SnPb2Bi2S6 phase exhibited good thermal stability up to 1000 K and n-type degenerate semiconducting behavior, with a power factor of 3.7 μW cm-1 K-2 at 773 K. Notably, this compound exhibited a very low lattice thermal conductivity of 0.69-0.92 W m-1 K-1 at 300-1000 K. Theoretical calculations revealed that the low thermal conductivity is caused by the complex crystal structure and the related elastic properties of a low Debye temperature, low phonon velocity, and large Gruneisen parameters. A reasonable figure of merit (ZT) of ∼0.3 was obtained at 770 K.

Bamboo, as a fast-grown forest resource, could be functionalized by metal–organic frameworks (MOF) with various potential applications. However, the stability of MOF immobilized on bamboo surface remains to be improved. In this work, MOF199, as known as HKUST-1, was in situ anchoring on moso bamboo via regulating pretreatment of bamboo and a green two-step synthesis route. The two-step synthesis route could be completed under room temperature and both precursor solutions can be reused. The results indicated that, with the collaboration of delignification and carboxymethylation pretreatment of bamboo, a dense and well-dispersed MOF199 coating was successfully synthesized, the adhesion between MOF and bamboo surface was also improved. Besides, the quantity and size of MOF199 on bamboo can be tailored by adjusting the carboxyl groups of pretreated bamboo and the concentration of copper nitrate solution. More importantly, results show that the formation of carboxyl-copper (II) complex served as nucleation sites for the growth of MOF199 crystals is the key to prepare uniform MOF layers. The growth of MOF199 endow bamboo with good antibacterial activity against E. coli. This method provides a facile and practical strategy for designing MOF coated woody materials.

Transition-metal-based chalcogenides are a series of intriguing semiconductors with applications spanning various fields because of their rich structure and numerous functionalities. This paper reports the crystal structure and basic physical properties of a new quaternary chalcogenide In

The Travelling Salesman Problem and Vehicle Routing Problem are two similar famous NP-hard problems with simple evaluation functions. While these functions can underestimate the quality of solutions in some cases, surrogate models are widely used in many computationally expensive problems as a supplement and optimization of traditional evaluation methods. By studying two surrogate models based on the spatial structure of the solution, this paper dedicates to introducing an additional level of evaluation on the quality of the individuals’ structure in the genetic algorithms designed for TSP and VRP, aiming to maintain the diversity of the population while optimizing the solution of the genetic algorithm in the meantime.

We propose a multivariable continuous polynomial optimization formulation to find arbitrary maximal independent sets of any size for any graph. A local optima of the optimization problem yields a maximal independent set, while the global optima yields a maximum independent set. The solution is two phases. The first phase is listing all the maximal cliques of the graph and the second phase is solving the optimization problem. We believe that our algorithm is efficient for sparse graphs, for which there exist fast algorithms to list their maximal cliques. Our algorithm was tested on some of the DIMACS maximum clique benchmarks and produced results efficiently. In some cases our algorithm outperforms other algorithms, such as cliquer.

Pneumoperitoneum, the presence of air within the peritoneal cavity, is a comparatively rare but potentially urgent critical finding in patients presenting with acute abdominal pain. When prior laparoscopic treatment can be ruled out as a cause, it can indicate perforation of the wall of a hollow organ, which typically necessitates immediate surgery. Computed tomography (CT) is the gold standard for detecting free intra-abdominal air, yet subtle cases are easy to miss. More crucially though, if there is no initial suspicion of pneumoperitoneum, the scans may not be read immediately as other emergency patients take precedence. Therefore, fully automatic detection would provide a direct clinical benefit. In this work, an algorithm for this purpose is proposed which follows a sliding-window approach and has a deep-learning based classifier at its core. In addition to the baseline method, variants that rely on multi-scale inputs and recurrent layers to increase robustness are presented. In a five-fold cross validation on the training data, consisting in abdominal CT scans of 110 affected patients and 29 controls, our method achieved an area under the receiver-operating characteristic curve of 89% for case-level classification. Due to its high specificity at reasonable detection rates, it shows potential for use in triage, where false alerts are considered particularly harmful as they may disrupt the clinical workflow.

The coke in the coils has a serious impact on the long-term and stable operation of the ethylene plant. Inhibiting coking of the cracking furnace is an important issue. In this paper, the Cr/CeO2 coatings were prepared on the surface of 310S stainless steel by high-velocity oxygen-fuel (HVOF) spraying. The anti-coking performances of the bare 310S specimen and the Cr/CeO2 coatings were tested by using the pyrolysis of naphtha. The results show that the coating with the thickness of 10–15 μm and a very small number of pores was built up by the obviously deformed splats and the coating/substrate interface is jagged bond. The white area on the surface of Cr/CeO2 coatings mainly includes CeO2. However, the gray area is made of Cr or Cr2O3. Compared with the bare 310S specimen, the coking inhibition rates of coatings are 48.7%, 58.9%, 76.1% and 88.9% when the ratio of Cr and CeO2 powder is 19:1, 9:1, 4:1 and 3:2, respectively. With increasing the relative ratio of CeO2, more white areas without coke are exposed. The CeO2 surface can not only inhibit catalytic coking, but also promote the reaction of water vapor and coke.