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Collective intelligence is an important component of the new generation of artificial intelligence (AI). It plays a decisive role in stimulating and converging innovative forces as well as coupling and integrating large-scale intelligent systems. It is of great significance for promoting deep integration of AI and traditional industries and enabling the sustainable development of the national economy. This study summarizes the overall technical framework of collective intelligence and its major research areas, including:multi-agent systems and optimal decision-making, unmanned swarm systems, open source collective intelligence software, and federated learning. Moreover, it analyzes how these core technologies can be applied in industrial scenarios, in order to establish intelligent processing loops of perception‒cognition‒decision‒action, to support platform economy with distributed intelligence, and to reshape industrial development and digital economy ecosystems. Based on the subjects and application modes of the technical framework, this study analyzes the core industries related to collective intelligence, particularly the software service industry, the smart city industrial cluster, and the intelligent agriculture and port industries based on unmanned swarm systems, by highlighting their significant requirements and empowerment approaches for collective intelligence technologies. Furthermore, this study presents suggestions on how to utilize collective intelligence technologies to foster development of rated industries. It is suggested that we should continuously promote the establishment of open source communities of collective intelligence, enhance the intellectual core of the AI technological innovation ecosystem, and accelerate the domestic substitute of unmanned swarm systems through integrated system research.

Conversion-type transition-metal phosphide anode materials with high theoretical capacity usually suffer from low-rate capability and severe capacity decay, which are mainly caused by their inferior electronic conductivities and large volumetric variations together with the poor reversibility of discharge product (Li3P), impeding their practical applications. Herein, guided by density functional theory calculations, these obstacles are simultaneously mitigated by confining amorphous FeP nanoparticles into ultrathin 3D interconnected P-doped porous carbon nanosheets (denoted as FeP@CNs) via a facile approach, forming an intriguing 3D flake-CNs-like configuration. As an anode for lithium-ion batteries (LIBs), the resulting FeP@CNs electrode not only reaches a high reversible capacity (837 mA h g-1 after 300 cycles at 0.2 A g-1) and an exceptional rate capability (403 mA h g-1 at 16 A g-1) but also exhibits extraordinary durability (2500 cycles, 563 mA h g-1 at 4 A g-1, 98% capacity retention). By combining DFT calculations, in situ transmission electron microscopy, and a suite of ex situ microscopic and spectroscopic techniques, we show that the superior performances of FeP@CNs anode originate from its prominent structural and compositional merits, which render fast electron/ion-transport kinetics and abundant active sites (amorphous FeP nanoparticles and structural defects in P-doped CNs) for charge storage, promote the reversibility of conversion reactions, and buffer the volume variations while preventing pulverization/aggregation of FeP during cycling, thus enabling a high rate and highly durable lithium storage. Furthermore, a full cell composed of the prelithiated FeP@CNs anode and commercial LiFePO4 cathode exhibits impressive rate performance while maintaining superior cycling stability. This work fundamentally and experimentally presents a facile and effective structural engineering strategy for markedly improving the performance of conversion-type anodes for advanced LIBs.

The operation and maintenance (O&M) period is a critical and resource-intensive phase of the life cycle of railway line infrastructure. The O&M of a railway line during this period requires a large amount of investment and labor. Thus, there is an urgent need for railway managers to seek more efficient management tools and methods to improve management efficiency and reduce maintenance costs. Infrastructure–building information model (I-BIM) technology, characterized by its intuitive representation and high information-integration capabilities, has gained considerable traction with infrastructure industry researchers. In the context of existing railway lines, the absence of corresponding I-BIMs or similar technology owing to the immaturity of technical development at the time led to the use of manual modeling processes, which have limitations such as high cost, slow speed, and low efficiency. This paper presents an innovative, high-speed, semiautomated method for generating I-BIMs for existing railway lines and leveraging asset management data without incurring additional data collection costs. This approach leverages key modules such as line trajectory computation, element model generation, the linking of O&M semantic information, and the visualization of asset health indexes, which can enable the swift and semiautomated creation of an O&M-oriented I-BIM for railway lines. This process elevates traditional two-dimensional database tables into more intuitive, operation-oriented, and semantically rich models. This results in the provision of more efficient support for maintenance management decisions related to railway lines. The proposed method addresses the limitations of manual modeling processes and can pave the way for enhanced O&M efficiency in the railway infrastructure sector. Practical Applications: The absence of BIMs during the design and construction phase of existing railway lines, coupled with the high costs and error rates associated with manual modeling, constitute substantial barriers to the implementation of BIMs in operation and management. By leveraging the proposed semiautomated modeling approach, which relies on management data, site managers and researchers can rapidly establish basic I-BIMs. Although the asset ledgers and station equipment plans used in this approach originate from National Railway Administration, similar data sets should serve as fundamental inputs for railway line operation and maintenance worldwide. By deploying this method, field managers can enhance traditional two-dimensional (2D) asset ledgers into a three-dimensional (3D) I-BIM, thereby offering a more intuitive understanding of the assets within their jurisdiction. Owing to the integration of geographic information system maps into the model, personnel can directly query asset-related geographic information, potentially reducing the frequency and cost of off-site surveys. For researchers, using this method to establish a real-world model rapidly offers convenient access for further BIM-based applications, such as the integration of real-time data from on-site sensors to formulate digital twins. [ABSTRACT FROM AUTHOR]

An extremely hazardous heavy metal called cadmium (Cd) is frequently released into the soil, causing a considerable reduction in plant productivity and safety. In an effort to reduce the toxicity of Cd, silicon dioxide nanoparticles were chosen because of their capability to react with metallic substances and decrease their adsorption. This study examines the processes that underlie the stress caused by Cd and how SiO2NPs may be able to lessen it through modifying antioxidant defense, oxidative stress, and photosynthesis. A 100 μM concentration of Cd stress was applied to the hydroponically grown wild rice line, and 50 μM of silicon dioxide nanoparticles (SiO2NPs) was given. The study depicted that when 50 μM SiO2NPs was applied, there was a significant decrease in Cd uptake in both roots and shoots by 30.2% and 15.8% under 100 μM Cd stress, respectively. The results illustrated that Cd had a detrimental effect on carotenoid and chlorophyll levels and other growth-related traits. Additionally, it increased the levels of ROS in plants, which reduced the antioxidant capability by 18.8% (SOD), 39.2% (POD), 32.6% (CAT), and 25.01% (GR) in wild rice. Nevertheless, the addition of silicon dioxide nanoparticles reduced oxidative damage and the overall amount of Cd uptake, which lessened the toxicity caused by Cd. Reduced formation of reactive oxygen species (ROS), including MDA and H2O2, and an increased defense system of antioxidants in the plants provided evidence for this. Moreover, SiO2NPs enhanced the Cd resistance, upregulated the genes related to antioxidants and silicon, and reduced metal transporters' expression levels. [ABSTRACT FROM AUTHOR]

Purpose: Low-grade gliomas (LGG) are common brain tumors with high mortality rates. Cancer cell invasion is a significant factor in tumor metastasis. Novel biomarkers are urgently needed to predict LGG prognosis effectively. Methods: The data for LGG were obtained from the Bioinformatics database. A consensus clustering analysis was performed to identify molecular subtypes linked with invasion in LGG. Differential expression analysis was performed to identify differentially expressed genes (DEGs) between the identified clusters. Enrichment analyses were then conducted to explore the function for DEGs. Prognostic signatures were placed, and their predictive power was assessed. Furthermore, the invasion-related prognostic signature was validated using the CGGA dataset. Subsequently, clinical specimens were procured in order to validate the expression levels of the distinct genes examined in this research, and to further explore the impact of these genes on the glioma cell line LN229 and HS-683. Results: Two invasion-related molecular subtypes of LGG were identified, and we sifted 163 DEGs between them. The enrichment analyses indicated that DEGs are mainly related to pattern specification process. Subsequently, 10 signature genes (IGF2BP2, SRY, CHI3L1, IGF2BP3, MEOX2, ABCC3, HOXC4, OTP, METTL7B, and EMILIN3) were sifted out to construct a risk model. Besides, the survival (OS) in the high-risk group was lower. The performance of the risk model was verified. Furthermore, a highly reliable nomogram was generated. Cellular experiments revealed the ability to promote cell viability, value-addedness, migratory ability, invasive ability, and colony-forming ability of the glioma cell line LN229 and HS-683. The qRT-PCR analysis of clinical glioma samples showed that these 10 genes were expressed at higher levels in high-grade gliomas than in low-grade gliomas, suggesting that these genes are associated with poor prognosis of gliomas. Conclusion: Our study sifted out ten invasion-related biomarkers of LGG, providing a reference for treatments and prognostic prediction in LGG. [ABSTRACT FROM AUTHOR]

ObjectivesDynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) plays an important role in the differential diagnosis between radionecrosis and recurrence of brain metastases (BMs) after gamma knife radiosurgery (GKRS). While the perfusion condition of preliminary hyperperfusion and hypoperfusion BMs when recur has not been studied, as well the separating performance of quantitative DSC-PWI in both kinds of BMs.MethodsFrom February 2017 to October 2019, quantitative DSC-PWI was performed in patients with untreated BMs in this observational study. Patients were assigned to hyperperfusion and hypoperfusion group according the quantitative cerebral blood volume (qCBV). During follow-up after GKRS, patients with a diagnostic pitfall of radionecrosis and recurrence accepted second quantitative DSC-PWI. Final diagnosis was based on the histological results or follow-up results. Receiver operating curve analysis was used to explore the performance of qCBV.ResultsTwenty-nine patients (mean age: 61.3 ± 9.4 years old; male/female: 13/16) were assigned to the group of hypoperfusion group, and 26 patients (mean age: 58 ± 10.4 years old; male/female: 14/12) to hyperperfusion group. The mean qCBV values between hypoperfusion and hyperperfusion groups when recurred were not significantly different (3.17 ± 0.53 ml/100 g vs. 3.27 ± 0.47 ml/100 g, p = 0.63). qCBV was feasible to separate radionecrosis and recurrence in both groups (AUC=0.94 and AUC=0.93, separately).ConclusionBoth premilitary hyperperfusion and hypoperfusion BMs would transform to a high microvascular density when recurs. qCBV is feasible to distinguish radionecrosis and recurrence among both kinds of BMs after GKRS.