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Three groups of composite cementitious materials (CCMs) were prepared by separate grinding, mixed grinding, and cascade grinding using iron and steel solid waste (ISSW) materials (granulated blast furnace slag (GBFS), iron ore tailings (IOTs), steel slag (SS) and desulfurization gypsum (DG)), and the influence on the strength of CCMs was investigated under different water-binder ratios (w/b). Then, the hydration hardening mechanism of the CCMs was revealed via hydration heat analysis, phase composition and structural analysis of hydration products. The results show that the properties of the CCMs prepared by cascade grinding (CGC) is better than that prepared by separate grinding and mixed grinding. When the mass percentage of IOTs and SS in the CGC was 2:3, the mix proportion of CGC is SS+IOTs: GBFS: DG=36:51.5:12.5, mixed with 1.0 % Na2SO4 and 1.2 % Na2SiO3ˑ9 H2O of the CGC, the compressive strength (CS) of mortar sample containing CGC in 28-days and 180-days reach 52.3 MPa and 70.7 MPa respectively under the conditions of standard curing and low water-binder ratios. The hydration degree of pure cement is higher than that of CGC, but types of the hydration products were similar with cement, mainly were Ca(OH)2, iolite, ettringite (AFt) and C-S-H gel. The CGC can greatly reduce the hydration heat of CCMs system in early age, and is suitable for massive concrete structures in which elevated temperature can be kept for a long time.

In this study, a composite modifier (CMCDL) containing coal gasification residue, dicyandiamide waste slag, and lake sediments was used to modify steel slag at high-temperature. Five kind of raw materials was analyzed in physicochemical properties, mineral composition and microstructure, cementitious properties and soundness, and particle size analysis, the same to steel slag after high-temperature modification (SHTM). The new mineral phases were found that tricalcium aluminate (C3A), gehlenite (C2AS), magnetite (Fe3O4), spinels (MgFe2O4), quartz (SiO2), celite (C6AF2) and diopside (CMS2) formed in the SHTM when the mass fraction increase from 10% to 30% in CMCDL. The HTM with CMCDL increases the glass phase and hydrated mineral (C3A, C3S, C2S) and in raw steel slag, promoting the disassembled of RO phase, turning the phase from FeO into MgFe2O4 and Fe3O4. MgFe2O4 and Fe3O4 have similar crystal structures, and they form mixed spinels. When the content of CMCCL (coal gasification residue: dicyandiamide waste slag: lake sediments =7:9:4) is 20%, the temperature is 1250 °C, result showed that the mass fraction of f-CaO in SHTM using CMCCL decreases from 4.81% to 1.86%, with a decrease of 61.3%, the 28-day activity index of SHTM increases up to 17.6% compared with raw steel slag, reaching 86.2%. Therefore, the CMCDL-blended SHTM can be applied harmlessly in cement and concrete, enabling low-energy fine grinding of steel slag.

The study of the cementitious material activity of industrial solid wastes is a key issue in the comprehensive utilization of its green building materials. In this study, mechanical activation was used to prepare cementitious materials from vanadium-titanium iron ore tailings (VTIOT) as the main raw materials, and the effects of grinding time, the key parameter, on the grinding characteristics of VTIOT, the mechanical properties of the VTIOT cementitious material, and the hydration mechanism were investigated by means of surface area analysis, particle size analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), alkali leaching, mechanical testing, hydration heat analysis, and thermogravimetric analysis (TGA). The results show that: the grinding time and specific surface area of VTIOT exhibit a linear correlation. Prolonging the grinding time, the fractal dimension of the particles gradually increases. The activity of Si4+ and Al3+ elements increases rapidly, reaching a Si4+ and Al3+ content of 96 mg·L−1 at 90 min of grinding. With a VTIOT mixing amount of 30 %, a mortar paste ratio of 1:3, and a water cement ratio of 0.5, the 50 min ground VTIOT mortar demonstrates 7 d and 28 d compressive strengths of 29 MPa and 38.7 MPa, corresponding to the highest values of the activity index, measured at 71.4 % and 75.2 %, respectively. Under standard curing conditions, the hydration products of cementitious materials at 7 d are ettringite (AFt), calcium silicate hydrate (C-S-H), alumina, ferric oxide, monsulfate (AFm), calcium hydroxide (Ca(OH)2), and calcium silica (CS). In the hydration process of 7 d, the exothermic peak and exothermic amount of the 50 min VTIOT cementitious materials are greater than those observed for other grinding times, but lower than those of pure reference cement.

Abstract The rational design of efficient bimetallic nanoparticle (NP) catalysts is challenging due to the lack of theoretical understanding of active components and insights into the mechanisms of a specific reaction. Here, we report the rational design of nanoreactors comprising hollow carbon sphere‐confined PtNi bimetallic NPs (PtNi@HCS) as highly efficient catalysts for hydrogen generation via ammonia borane hydrolysis in water. Using both density functional theory calculations and molecular dynamics simulations, the effects of an active PtNi combination and the critical synergistic role of a hollow carbon shell on the molecule diffusion adsorption behaviors are explored. Kinetic isotope effects and theoretical calculations allow the clarification of the mechanism, with oxidative addition of an O–H bond of water to the catalyst surface being the rate‐determining step. The remarkable catalytic activity of the PtNi@HCS nanoreactor was also utilized for successful tandem catalytic hydrogenation reactions, using in situ‐generated H2 from ammonia borane with high efficiency. The concerted design, theoretical calculations, and experimental work presented here shed light on the rational elaboration of efficient nanocatalysts and contribute to the establishment of a circular carbon economy using green hydrogen.

The field of evolutionary multitasking optimization (EMTO) has been a highly anticipated research topic in recent years. EMTO aims to utilize evolutionary algorithms to concurrently solve complex problems involving multiple tasks. Despite considerable advancements in this field, numerous evolutionary multitasking algorithms continue to use a single evolutionary search operator (ESO) throughout the evolution process. This strategy struggles to completely adapt to different tasks, consequently hindering the algorithm’s performance. To overcome this challenge, this paper proposes multitasking evolutionary algorithms via an adaptive bi-operator strategy (BOMTEA). BOMTEA adopts a bi-operator strategy and adaptively controls the selection probability of each ESO according to its performance, which can determine the most suitable ESO for various tasks. In an experiment, BOMTEA showed outstanding results on two well-known multitasking benchmark tests, CEC17 and CEC22, and significantly outperformed other comparative algorithms.

Abstract H2 generation from methanol‐water mixtures often requires high pressure and high temperature (200–300 °C). However, CO can be easily generated and poison the catalytic system under such high temperature. Therefore, it is highly desirable to develop the efficient catalytic systems for H2 production from methanol at room temperature, even at sub‐zero temperatures. Herein, carbon nanotube‐supported Pt nanocomposites are designed and synthesized as high‐performance nano‐catalysts, via stabilization of Pt nanoparticles onto carbon nanotube (CNT), for H2 production upon methanol dehydrogenation at sub‐zero temperatures. Therein, the optimal Pt/CNT nanocomposite presents the superior catalytic performance in H2 production upon methanol dehydrogenation at the expense of B2(OH)4, with the TOF of 299.51 min‐130 oC. Compared with other common carriers, Pt/CNT exhibited the highest catalytic performance in H2 production, emphasizing the critical role of CNT in methanol dehydrogenation. The confinement of Pt nanoparticles by CNTs is conducive to inhibiting the aggregation of Pt nanoparticles, thereby significantly increasing its catalytic performance and stability. The kinetic study, detailed mechanistic insights, and density functional theory (DFT) calculation confirm that the breaking of O─H bond of CH3OH is the rate‐controlling step for methanol dehydrogenation, and both H atoms of H2 are supplied by methanol. Interestingly, H2 is also successfully produced from methanol dehydrogenation at −10 °C, which absolutely solves the freezing problem in the H2 evolution upon water‐splitting reaction.

In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

Hydrogen (H2) production is a key challenge for green carbon-free sustainable energy. Among the H2 evolution methods from H-rich materials, ammonia borane (AB) solvolysis stands as a privileged source under ambient and sub-ambient conditions given its stability, non-toxicity, and solubility in protic solvents, provided suitable and optimized nanocatalysts are used. In this paper dedicated to Prof. Avelino Corma, we comparatively review AB hydrolysis and alcoholysis (mostly methanolysis) in terms of nanocatalyst performances and discuss the advantages and inconveniences of these two AB solvolysis methods including AB regeneration.

When entering a ferromagnet, a spin-singlet supercurrent decays rapidly, while a spin-triplet supercurrent can extend over much longer distances. Here, the authors observe long-range, spin triplet supercurrent in lateral Josephson junctions constructed using the van der Waals metallic ferromagnet Fe3GeTe2 as the weak link.

As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

Australia has ambitions to become a major global hydrogen producer by 2030. The establishment of Australia’s and the world’s hydrogen economy, however, will depend upon the availability of affordable and reliable hydrogen storage. Geological hydrogen storage is a practical solution for large scale storage requirements ensuring hydrogen supply can always meet demand, and excess renewable electricity can be stored for later use, improving electricity network reliability. Hosting thick, underground halite (salt) deposits and an abundance of onshore depleted gas fields, Australia is well placed to take advantage of geological hydrogen storage options to support its ambition of building a global hydrogen hub export industry. Using the Bluecap modelling software, we identify regions in Australia that are potentially profitable for large scale hydrogen production and storage. We use the results of this work to suggest high-potential regions for hydrogen development, supporting policymaker and investor decisions on the locations of new infrastructure and hydrogen projects in Australia.

Steel slag (SS) is a kind of industrial solid waste, and its accumulation brings certain harm to the ecological environment. In order to promote the building material utilization of SS, high-temperature modification (HTM) of SS is performed using a composite modifier (CMSFR) containing silicon calcium slag (SCS), fly ash (FA), and reservoir sediment (RS). Then, the authors investigated the effect of CMSFR on the cementitious properties and volume soundness of SS mixture after HTM (SMHTM). After that, the mineral composition and microstructure of SMHTM were investigated through X-ray fluorescence analysis (XRF), X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometry (EDS), and particle size analysis. It was found that the free CaO (f-CaO) content obviously decreased, and the cementitious properties improved in SMHTM. When the CMSFR content was 20% (SCS: FA: RS = 9:7:4), and the modification temperature (MT) was 1,250°C, the mass fraction of f-CaO in SMHTM dropped from 4.81% to 1.90%, down by 60.5%; the 28-day activity index of SMHTM increased to 85.4%, 14.3% higher than that of raw SS, which meets the technical requirement of Steel slag powder used for cement and concrete (GB/T 20491-2017): the activity index of grade I SS powder must be greater than or equal to 80%. As the mass fraction of CMSFR grew from 10% to 30%, new mineral phases formed in SMHTM, including diopside (CMS2), ceylonite (MgFe2O4), gehlenite (C2AS), tricalcium aluminate (C3A), and magnetite (Fe3O4). The HTM with CMSFR promotes the decomposition of RO phase (a continuous solid solution composed of divalent metal oxides like FeO, MgO, MnO, and CaO) in raw SS, turning the FeO in that phase into Fe3O4. The above results indicate that the SMHTM mixed with CMSFR can be applied harmless in cement and concrete, making low-energy fine grinding of SS a possibility.

Introduction: The vanadium-titanium iron ore tailings (VTIOTs) has a significant amount of discharge, and serious pollution, which makes it difficult to use directly as a resources resource.Methods: High-strength fired water permeable brick (HSFWPB) was prepared using VTIOTs as the main raw material. The orthogonal test, X-ray diffraction analysis (XRD), scanning electron microscope (SEM), energy dispersive spectrometry (EDS), and synthetic precipitation leaching procedure (SPLP) were obtained to study the basic properties and firing mechanism of HSFWPB containing VTIOTs, and an economic benefit evaluation was conducted on the project investment of HSFWPB containing VTIOTs.Results: The results show that when the content of VTIOTs in HSFWPB is 78%, the firing temperature (FT) is 1080°C and the holding time is 120 min, the compressive strength and permeability coefficient of the fired product reach 70.4 MPa and 0.055 cmˑs−1, respectively, which meet the requirements of Cc60 grade products in Water permeable brick (JC/T 945–2005). The products of HSFWPB containing VTIOTs after firing are diopside (CaMgSi2O6) and augite (Ca (Mg, Fe, Al) (Si, Al)2O6), with the increase of FT and the extension of HT, the low melting point elements of P, Na, K dissolve out, and the content of Fe and Al in the firing products increases, most of Ca2+ in diopside is replaced by Fe3+, so that augite becomes the main crystal phase. With the increase of FT, the pores in the fired products are changed from: closed pores with different sizes→ connected pores with irregular diameters→ irregular collapse pores→ small and uniform circular pores, which provides a guarantee for the permeability of HSFWPB containing VTIOTs.Discussion: The full investment payback period of the project of HSFWPB containing VTIOTs is 1.77 years. When the production reaches 48.585% of the design output, it reaches a breakeven point, and the project has strong risk resistance ability. The research has improved the utilization rate of VTIOTs and provided a new approach for the large-scale application of VTIOTs.

Introduction: In our study, various test methods were adopted to explore the mineralogical characteristics, grindability, particle morphology, particle size distribution, and environmental leaching toxicity of iron ore tailings (lOTs).Methods: The methods include petrographic analysis, X-ray diffraction (XRD), scanning electronic microscopy (SEM), synchronous thermal analyzer (DSC-TGA), electron probe X-ray microanalyser (EPMA), and synthetic precipitation leaching Procedure (SPLP).Results: The results show that the used IOTs contained 14.54% iron which was mainly stored in magnetite and pyrite. The content of iron silicate accounted for 11.82% of the total iron content. Most of it existed in silicate minerals, including grunerite and hornblende. A small amount of iron was contained in biotite, chlorite and augite. Besides, the grindability of lOTs was much better than that of granulated blast furnace slag (GBFS). After grinding for 140 min, the mass fraction of lOTs particles with the particle size less than 5 um was 44.19%. These particles could be used to fill in the voids in the cement-powder-packed structure. lOTs also contained a large number of submicron and nanoscale particles.Discussion: With great strength and environmental friendliness, the composite cementitious material (CCM) prepared from lOTs can be well applied to the building material field and environmental remediation.

Introduction: In order to promote the comprehensive utilization of iron and steel solid waste, steel slag (SS), granulated blast furnace slag (GBFS), and flue gas desulfurization gypsum (FGDG) were used as raw materials to replace cement to prepare the full solid waste premixed solidified agent (PSA). Then PSA, iron ore tailings (IOTs), and water were mixed to prepare building foundation pit backfilling materials (BFPBM).Methods: Various tests were conducted to investigate the effects of SS fineness, SS content and mixing ratio of PSA on the properties of PFPBM, including mechanical property test, X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometry (EDS) and synthetic precipitation leaching procedure (SPLP). The stability of the BFPBM was tested and the hydration mechanism of the PSA was analyzed.Results: The results show that when the specific surface area (SSA) of SS is 457 m2ˑkg-1, the paste mass concentration (PMC) is 79%, the mixing ratio of PSA is 20%, and the mix proportion of PSA is SS: FGDG: GBFS = 58:10:32. In this case, water-reducing agent (WRA) accounting for 0.18% of the total amount of PSA is added. The 28d compressive strength of BFPBM is greater than 0.40 MPa with the maximum value of 6.22 MPa, and the mixture slump of BFPBM is greater than 215 mm, which meets the index requirements of Chinese National Standard T/CECS 1037-2022 Technical Standard for Backfilling Project by Using Premixed Fluidized Solidified Soil.Discussion: According to the mechanism study, the mineral phases of the PSA after hydration are: C2S, C3S, Ca (OH)2, ettringite (AFT), C-S-H gel, and RO phase. C2S, C3S, CaSO4 and other substances gradually disappeared with the hydration, while the content of AFt and C-S-H gel substances increased. BFPBM prepared from iron and steel waste has high strength and environmental friendliness, making it have good application prospects in foundation pit backfilling and mine filling.

Introduction: In order to improve the utilization efficiency of industrial waste discharged in the process of iron and steel metallurgy, a kind of material that can replace cement for mine filling is sought. In the test, steel slag (SS) and vanadium-titanium slag (VTS) were used as the primary raw materials to prepare cementing agents (CA). Then, combine it with vanadium-titanium iron ore tailings (VTIOTs) to make mine cemented paste backfill material (CPBM).Methods: The composition, properties and hydration mechanism of CPBM are studied through various tests, in-cluding mechanical property test, hydration heat test, X-ray diffraction (XRD), scanning electronic mi-croscopy (SEM), and fourier transform-infrared spectroscopy (FT-IR).Results: The results show that when the following conditions are met, the slump is 216 mm, and the 28-day flexural strength and compressive strength of CPBM reach 4.25 and 9.41 MPa, respectively, which meets the requirements of Chinese National Standard GB/T 39489-2020 Technical specification for the total tailings paste backfill: the mass percentage SS: VTS: phosphogypsum (PG): dicyandiamide waste slag (DWS) = 31:59:6:4; the content of compound phosphoric acid (CPA) accounts for 4% of the dry material; the cement sand ratio of CPBM is 1:4; the paste mass concentration (PMC) is 80%; and the content of water reducing agent (WRA) is 0.18%.Discussion: Mechanism studies show that the hydration product of the CA is mainly ettringite (AFt) and C-S-H gel. The addition of CPA promotes the hydration of active minerals in SS and VTS. The existence of PG pro-motes the formation of AFt, and the formation of AFt further promotes the fracture of [AlO4]5− and [SiO4]4− along the bridge oxygen in VTS and SS.

Abstract Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with an extremely low survival rate. New and effective approaches for treatment are therefore urgently needed. Here, we successfully developed M1-like macrophage-derived extracellular vesicles (M1EVs) that overcome multiple challenges via guidance from two macrophage-related observations in clinical specimens from GBM patients: enrichment of M2 macrophages in GBM; and origination of a majority of infiltrating macrophage from peripheral blood. To maximize the synergistic effect, we further functionalized the membranes of M1EVs with two hydrophobic agents (the chemical excitation source CPPO (C) and the photosensitizer Ce6 (C)) and loaded the hydrophilic hypoxia-activated prodrug AQ4N (A) into the inner core of the M1EVs. After intravenous injection, the inherent nature of M1-derived extracellular vesicles CCA-M1EVs allowed for blood-brain barrier penetration, and modulated the immunosuppressive tumor microenvironment via M2-to-M1 polarization, which increased hydrogen peroxide (H2O2) levels. Furthermore, the reaction between H2O2 and CPPO produced chemical energy, which could be used for Ce6 activation to generate large amounts of reactive oxygen species to achieve chemiexcited photodynamic therapy (CDT). As this reaction consumed oxygen, the aggravation of tumor hypoxia also led to the conversion of non-toxic AQ4N into toxic AQ4 for chemotherapy. Therefore, CCA-M1EVs achieved synergistic immunomodulation, CDT, and hypoxia-activated chemotherapy in GBM to exert a potent therapeutic effect. Finally, we demonstrated the excellent effect of CCA-M1EVs against GBM in cell-derived xenograft and patient-derived xenograft models, underscoring the strong potential of our highly flexible M1EVs system to support multi-modal therapies for difficult-to-treat GBM.

Infrared and visible image fusion combine data information from different sensors to achieve a richer description of the same scene. In order to highlight the salient features of the infrared image and the visible image in the fusion image and obtain a fusion image with good performance, an end-to-end infrared and visible image fusion algorithm is proposed in this paper. The contrast attention module and visible image cascade part are introduced in the generator, so that the fusion image can focus on the detail information in the visible image and the contrast information in the infrared image. And in order to retain more structural contour information in the original image, the contour loss is added to the content loss function. In addition, the contrast and detail information in infrared and visible images are balanced by two discriminators. And a goal-guided reward function is introduced into the discriminator, which further facilitates the generator to produce effective fused images. Finally, extensive fusion experiments on public datasets verify the advantages of the proposed algorithm compared with other classical algorithms, and ablation experiments demonstrate the effectiveness of the improved part of the algorithm.

Peanuts are prone to insect damage, breakage, germination, mildew, and other defects, which makes the quality of peanuts uneven. The difference in peanut pod quality makes the price and economic benefit also have a big difference. The classification of peanut pods according to quality is an important part of improving the product grade and market competitiveness. Real-time, accurate, and non-destructive quality detection of peanut pods can effectively improve the utilization and commercial value of peanuts. The strong subjectivity of manual detection and the low efficiency and low accuracy of mechanical detection have caused considerable wastage. Therefore, the present study proposed a new convolutional neural network for the peanut pod quality detection algorithm (PQDA) based on an improved ResNet. Compared to previous models, this model is more practical with high accuracy, lightweight, and easy nesting. Firstly, the detection and classification effects of ResNet18, AlexNet, and VGG16 are compared, and ResNet18 was determined to be the best backbone feature extraction network for model training. Secondly, three models were designed to optimize and improve the algorithm. The KRSNet module was added to the algorithm to make the model lightweight. The CSPNet module was added to the algorithm to improve the learning efficiency of each feature layer. The Convolutional Block Attention Module (CBAM) was added to the algorithm to improve its ability to capture more feature information about peanut pods. The experimental ablation results show that the precision of the improved model PQDA reaches 98.1%, and the size of parameters is only 32.63 M. Finally, the optimized model was applied to other peanut pod varieties for generalization experiments, and the accuracy reached 89.6% and 90.0%, indicating the effectiveness of the proposed peanut pod quality detection model. Furthermore, the model is suitable for deployment on embedded resource-limited devices, such as mobile terminals, to achieve the real-time and accurate detection of peanut pod quality.

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Scholars have paid considerable attention to the factors that affect the safety states of construction workers. However, only a few studies have focused on the safety assessment and security alerts of individual workers. In this study, the term ‘frequency statistics’ refers to the factors considered by domestic and foreign experts and scholars. The statistical results were combined with the interpretation of these factors to determine 22 factors that negatively influence the safety status of construction workers, which were used as the research object. The initial weight of the research results was integrated into the BackPropagation neural network, using the improved analytic hierarchy process to establish an early warning model for the unsafe status of construction workers. The mean squared error meets the requirements of the model and the prediction accuracy meets the requirements of the sample. The model can effectively provide an early warning and correct the initial weighting of the results. The early warning model was then applied to a project that involved the construction of a primary school in Suzhou. The follow-up results show that the safety status of the workers significantly improved. These results show that the early warning model was successfully used in the safety assessment to provide security alerts to individual workers. The data obtained by comprehensively considering both workers and experts are universal, unlike those obtained by considering only one of these two groups. Among the indicators, safety awareness, protection measures, and team cohesion most strongly negatively affected the safety statuses of the construction workers. The results of the early warning model combined with the sensitivity analysis are targeted and applicable in the practice of safety monitoring.

Correlation filter (CF) based tracking algorithms have tremendously contributed to the field of visual tracking due to the high computational efficiency and competitive performance. Nonetheless, most CF-based trackers are vulnerable to the influence of occlusion and boundary effect, which results in suboptimal performance. In this article, we propose saliency guided visual tracking via correlation filter with log-Gabor filter to robustify its performance under occlusion and boundary effect challenges. Firstly, we propose the CF with log-Gabor filter to get a robust appearance model. The log-Gabor filter is adopted to preprocess the sequence to gain the log-Gabor feature, which provides important cues for tracking since it encodes the texture information. Secondly, considering the prior information, we embed the novel saliency guided adaptive spatial feature selection to filter learning to preserve the spatial structure in the lower manifold and mitigate boundary distortion. Thirdly, the occlusion estimating strategy, performing on-line evaluation of tracking, triggers the motion estimation module to optimize the optimal location. Experiments on benchmark databases demonstrate the enhanced discrimination and interpretability of the proposed tracker and its superiority over other trackers.

Experimental and numerical study on upward, vertical flame spread was carried out on expanded polystyrene (EPS) at two different altitudes of 50 m and 3658 m to understand the effect that altitude has on flame spread of melted material. The experiments show that, with the altitude increase, the flame spread rate would decrease. At low altitude, the flame spread rate accelerated at later time, while at high altitude, the stagnant stage of pyrolysis front was formed. The numerical simulation could well give the trend of pyrolysis front evolution and the different flame spread behaviors with altitudes. It was found that, at low altitude, Raxincreased slowly in the turbulent period with relatively large entrainment air flow rate, mainly due to the increased and then decreased mass loss rate in the wall fire zone. While, at high altitude, the simulated increasing entrainment air flow rate, combined with the larger preheating length δf and the smaller flame heat flux qf'', resulted in the stagnancy of pyrolysis front easily. It was hypothesized that extinction occurred eventually at high altitude due to competition between flame spread rate νf and preheating length δf.

The laboratory polyethylene wire with different core diameters of 3 mm, 5 mm, 6 mm, 7 mm, 8 mm, 10 mm and 12 mm was used to study the flame spread characteristics. The flame spread could be divided into initial oscillation stage, stable oscillation stage and burn-out stage. With the increase of the copper core diameter, the flame width and flame area increased firstly and then decreased. For wires with intermediate copper core diameters of 6 mm, 7 mm, 8 mm and 10 mm, the relationship between flame area and core diameter was established as A~dc, the total mass loss rate k with the copper core diameter dc followed as k~dc, and the combustion mass loss rate and dripping mass loss rate gradually equaled to each other at stable oscillation stage. By analyzing the temperature field during the process of flame spread, the temperature gradient was almost same for diameters from 3 mm to 10 mm, which demonstrated the radiation heat would be influenced by flame height. Combined with the increased flame spread, the dominant conduction heat transfer mechanism was built. When the diameter was 12 mm, the smaller conduction heat transfer, which illustrated the core material Cu played an cool effect on flame, thus the dominant heat transfer of Cu was changed from a heat source to a heat sink.

Image fusion technology can process multiple single image data into more reliable and comprehensive data, which play a key role in accurate target recognition and subsequent image processing. In view of the incomplete image decomposition, redundant extraction of infrared image energy information and incomplete feature extraction of visible images by existing algorithms, a fusion algorithm for infrared and visible image based on three-scale decomposition and ResNet feature transfer is proposed. Compared with the existing image decomposition methods, the three-scale decomposition method is used to finely layer the source image through two decompositions. Then, an optimized WLS method is designed to fuse the energy layer, which fully considers the infrared energy information and visible detail information. In addition, a ResNet-feature transfer method is designed for detail layer fusion, which can extract detailed information such as deeper contour structures. Finally, the structural layers are fused by weighted average strategy. Experimental results show that the proposed algorithm performs well in both visual effects and quantitative evaluation results compared with the five methods.

The goal of this paper is to investigate the security performance of the multi-antenna unmanned aerial vehicle (UAV)-enabled mobile relaying system with a multi-antenna destination in the presence of a multi-antenna eavesdropper. To improve the secrecy outage probability performance, we propose a location-based three dimensional (3D) beamforming scheme without the channel state information of the destination and the eavesdropper. To this end, we first derive the expression of the secrecy outage probability for uniform linear arrays (ULAs) and uniform circular arrays (UCAs), which is valid for arbitrary values of system parameters. The expression is practicable in the UAV-enabled mobile relaying system as the system parameters are time-variant with the movement of the UAV. Using this expression, we study the secrecy outage probability minimization problem by optimizing the 3D beamforming direction along with the trajectory of the UAV, subject to the mobility constraint and location constraint. To solve this problem, an efficient iterative algorithm is proposed. The numerical results demonstrate the effectiveness of our algorithms by comparing to other benchmark schemes, they also show the system with UCAs outperforms the system with ULAs on secrecy outage probability performance.

Abstract Nanovaccines have attracted booming interests in vaccinology studies, but the profound impacts of their delivery mode on immune response remain unrealized. Herein, immunostimulatory CpG‐modified tumor‐derived nanovesicles (CNVs) are used as a nanovaccine testbed to initially evaluate the impacts of three distinct delivery modes, including mono‐pulse CNVs, staggered‐pulse CNVs, and gel‐confined CNVs. Fundamentally, delivery mode has enormous impacts on the immunomodulatory effects, altering the spatiotemporal distribution of nanovaccine residence and dendritic cell–T cell interaction in lymph nodes, and finally affecting subsequent T cell‐mediated immune performance. As a result, the gel‐confined delivery mode offers the best therapeutic performance in multiple tumor models. When extending evaluation to examine how the various delivery modes impact the performance of liposome‐based nanovaccines, similar trends in intralymph node distribution and antitumor effect are observed. This work provides a strong empirical foundation that nanovaccine researchers should position delivery mode near the top of their considerations for the experimental design, which should speed up nanovaccine development and facilitate efficient selection of appropriate delivery modes in the clinic.

The electric energy system in Indonesia is undergoing with the challenges of fast-increasing electricity demand, carbon constraints, and rising costs. Using our model of the Australian and Indonesian electrical grids (either separately or interconnected) that incorporates operational flexibility in capacity expansion planning, we first show that meeting the projected demand for Java and Bali—the main Indonesian grid, with 100% locally integrated renewables by 2050 would be challenging. However, a submarine high-voltage DC (HVDC) link connecting Indonesia’s Java-Bali power grid to the Australian National Electricity Market (NEM) grid through the Northern Territory would help alleviate this situation, given Australia’s abundant renewable energy resources. Then, our model reveals that the Australian NEM could also profit from additional renewables if connected to the Northern Territory through a ground HVDC transmission line to gather intermittent wind and solar generation, which would be curtailed otherwise if unused by Indonesia through the submarine link. Despite the expensiveness of long HVDC links, the wholesale electricity cost of the integrated 100% renewable Australasia power system could be reduced by over 16%, from $AUD177/MWh with only local renewables to $AUD148/MWh with integrated HVDC transmission. The model retrieved the optimal international HVDC link with capacity of 43.8 GW, and the optimal regional HVDC transmission line with a capacity of 5.5 GW. To the best of our knowledge, this is the first detailed model on power system decarbonisation planning for both Australian NEM and Indonesian Java-Bali power grid considering HVDC interconnections. Keywords: Decarbonisation, Climate change mitigation, HVDC, Grid interconnection

Abstract The joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed sensing. It aims to recover a set of jointly sparse vectors, i.e., those that have nonzero entries concentrated at a common location. Meanwhile l p $l_{p}$ -minimization subject to matrixes is widely used in a large number of algorithms designed for this problem, i.e., l 2 , p $l_{2,p}$ -minimization min X ∈ R n × r ∥ X ∥ 2 , p s.t. A X = B . $$\begin{aligned} \min_{X \in\mathbb {R}^{n\times r}} \Vert X \Vert _{2,p}\quad \text{s.t. }AX=B. \end{aligned}$$ Therefore the main contribution in this paper is two theoretical results about this technique. The first one is proving that in every multiple system of linear equations there exists a constant p ∗ $p^{\ast}$ such that the original unique sparse solution also can be recovered from a minimization in l p $l_{p}$ quasi-norm subject to matrixes whenever 0 < p < p ∗ $0< p

Abstract In signal processing theory, l 0 $l_{0}$ -minimization is an important mathematical model. Unfortunately, l 0 $l_{0}$ -minimization is actually NP-hard. The most widely studied approach to this NP-hard problem is based on solving l p $l_{p}$ -minimization ( 0 < p ≤ 1 $0< p\leq1$ ). In this paper, we present an analytic expression of p ∗ ( A , b ) $p^{\ast}(A,b)$ , which is formulated by the dimension of the matrix A ∈ R m × n $A\in\mathbb{R}^{m\times n}$ , the eigenvalue of the matrix A T A $A^{T}A$ , and the vector b ∈ R m $b\in\mathbb{R}^{m}$ , such that every k-sparse vector x ∈ R n $x\in\mathbb{R}^{n}$ can be exactly recovered via l p $l_{p}$ -minimization whenever 0 < p < p ∗ ( A , b ) $0< p< p^{\ast}(A,b)$ , that is, l p $l_{p}$ -minimization is equivalent to l 0 $l_{0}$ -minimization whenever 0 < p < p ∗ ( A , b ) $0< p< p^{\ast}(A,b)$ . The superiority of our results is that the analytic expression and each its part can be easily calculated. Finally, we give two examples to confirm the validity of our conclusions.

This paper examines the feasibility of preparing C30 concrete with replacing part of cementitious materials with lead-zinc tailings (LZT). The optimal fineness of LTZ is determined based on fineness measurement of the ground powder and the strength tests of mortars. C30 concrete is mixed partially replacing cement, fly ash (FA) and ground blast furnace slag (GBFS) with the LTZ with the optimal fineness, respectively. The related properties of the concrete are measured. The concrete with LTZ partially replacing cement, FA or GBFS can meet the requirements of C30 pump concrete. The concrete with LTZ shows good durability. Based on the results of permeability, carbonation resistance and autogenous shrinkage tests, it indicates that the ground LTZ show good micro-filler effect and pozzolanic reactivity in concrete since more than 60 % particles of LTZ is smaller than 5 µm.

For comprehensive utilization of solid wastes, the autoclaved aerated concrete (ACC) were prepared by using lead-zinc tailings (LZT) as mainly siliceous materials. The effects of fineness and dosage of LZT on the AAC properties, hydration products and microstructures of AAC were analyzed by the mechanical test, X-ray diffraction analysis (XRD) and scanning electron microscope (SEM). The results show that the ACC containing 62 % LZT (in mass percentage) with a surface area of 325 m2 kg-1 (grinding time of 25 min) can achieve a bulk density of 587 kg m-3 and compressive strength of 4.94 MPa, which is in line with the requirement of A3.5, B06-class AAC products regulated by autoclaved aerated concrete building blocks (GB/T 11969-2008). The mobility of slurry and hydration activity increase with decreasing fineness of LZT. However, the small size of LZT powder and high thickness of the slurry are bad to form good pore structure, which would influence the properties of AAC. When the blending percentage of LTZ is too high, the unreacted LTZ particles increase and accumulate within the system, which reduces the space among them and thus influences the growth and crystallization of hydration products. The main hydration products in the AAC are C-S-H gels and tobermorite- 11 Å.

How to treat more industrial solid waste friendly and environmental have become the global issues. In this study, direct reduction-magnetic separation (DRMS) process was employed to recovery iron from the nickel smelting slag (NSS) with calcium carbide slag (CCS) as the additive and coke dust (CD) as the reductant. Response surface methodology (RSM) approach using central composite design (CCD) was adopted for optimizing the effects of reduction temperature, reduction time and CCS dosage on the iron content of iron concentrate powder (ICP) and iron recovery. The results were modeled and the impact factors were evaluated. All the plots show that there were positive correlations between investigated impact factors and responses in a certain range but if exceeded, the positive correlations would be broken down. The overall optimization reaction conditions were converged as around 1287 ºC, 2.8 h and CCS dosage of 57.19 %. Under these proven optimization reaction conditions, the iron content of ICP and iron recovery were reached up to 91.30 % and 95.44 % respectively. The micro phase of reduced samples were investigated by X-ray diffraction analysis (XRD), in reduction process, and the microstructure of reduced samples under the optimal condition were investigated by SEM for further discussion and analyzation. The experimental results and microstructure analysis suggests that utilizing CCS and CD in DRMS process were viable.

Orthogonal tests were conducted in this paper to study the mechanical properties of the heightened temperature curing steel slag (SS)-ground blast furnace slag (GBFS) cementitious materials without clinker based on four factor: A) the mass ratio of SS/GBFS; B) the mixing milling time of the cementitious materials; C) the mass ratio of Ca(OH)2/gypsum (from flue gas desulfurization waste, FGDW); D) curing temperature. The hydration products and microstructure of the heightened temperature curing SS-GBFS system was also studied. When the four factors are 1:2, 480 m2 kg-1, 2:1 and 35 ºC, respectively, the highest compressive strength was observed with the values of 18.36 MPa, 26.89 MPa and 45.32 MPa at the ages of 3 days, 7 days and 28 days, respectively. The influence of the four factors on the mechanical properties is in the order of D > A > B > C. The results from X-ray diffraction (XRD) and scanning electron microscope (SEM) indicate that ettringite and C-S-H form from the reactions between C2S from SS and amorphous phases from GBFS, and FGDW during hydration, which greatly contributes to the early-age strength. As the curing time increases, more ettringite forms due to the reaction of SO42- from FGDW, OH- from Ca(OH)2 and AlO2- from GBFS, and the formed ettringite (AFt) intervened with C-S-H gels, which provides the framework of the microstructure and thus improves the strength of the specimens.

In order to promote the application of molybdenum (Mo) tailings in concrete industry, this paper studies the grinding characteristics of Mo tailings, the mechanical and hydration properties of mortars containing Mo tailings by X-ray diffraction (XRD), scanning electron microscope (SEM), laser particle size analysis, specific surface area analysis and strength test. The optimal grinding time is 80 mins to obtain the ground Mo tailings (-0.16 mm) with a specific surface area of 501 m2 kg-1 as mineral admixtures to the cementitious materials. With the coarse Mo tailings (+0.16 mm) as fine aggregate, composite mortars with a good workability were obtained with the compressive strength of 83.5 MPa and the flexural strength of 14.4 MPa after cured for 28 days. The comprehensive utilization rate of Mo tailings is 71 % in the composite mortars. The hydration of the cementitious composite containing Mo tailings at early age is primarily due to the hydration of the cement clinker (CC). While the large amount of quartz remains inert, the pozzolanic reaction of ground Mo tailings leads to the strength increases in long term. The main hydration phases in the cementitious composite are calcium silicate hydrates (C-S-H gels) and ettringite (AFt).

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m.

This paper studied the effects of wind speed, spacing and groove width on the combustion and flame merging characteristics of two n-heptane line fires. The experimental results show that the range of intermittent merging stage will increase with groove width; the greater the wind speed, the greater the probability of merging. At the non-merging stage, the influence of wind speed on the flame merging probability can be ignored. Under the influence of wind speed, mass loss rate per unit area, ṁ", of the downstream pool fire is greater than that of the upstream pool fire, at the stages of fully merging and intermittent merging. While when the flame spacing increases to the non-merging stage, ṁ" of the upstream pool fire begins to be gradually greater than that of the downstream pool fire. On the other hand, the downstream flame ṁ" increases firstly and then decreases, while the upstream flame ṁ" shows an increasing trend, with spacing distance. The smaller the groove width, the greater the value of ṁ". When the spacing is 0, ṁ" is the smallest. It is found that with the increase of spacing, the flame length under all tested conditions increases firstly and then decreases, and the flame inclination angle decreases firstly and then increases. [ABSTRACT FROM AUTHOR]

In this paper, we propose a parameterized three-operator splitting algorithm to solve nonconvex minimization problems with the sum of three non-convex functions, where two of them have Lipschitz continuous gradients. We establish the convergence of the proposed algorithm under the KurdykaŁojasiewicz assumption by constructing a suitable energy function with a non-increasing property. As applications, we employ the proposed algorithm to solve low-rank matrix recovery and image inpainting problems. Numerical results demonstrate the efficiency and effectiveness of the proposed algorithm compared to other algorithms. [ABSTRACT FROM AUTHOR]