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Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable’s jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG’s self-noise and the turntable’s speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

Exposure to fine particulate matter can easily lead to health issues. PM 2.5 concentrations are associated with various spatiotemporal factors, which makes the prediction of PM 2.5 concentrations still a challenging task. One of the reasons that makes the accurate prediction by statistical learning method difficult is severe fluctuations in input data. In addition, the abstraction method of space will also affect the prediction results. To address these important issues, a novel hybrid decomposing-ensemble and spatiotemporal attention (DESA) model is proposed to improve the prediction accuracy by decomposing the mode-mixed time series into single-mode series and automatically assign weights to the spatiotemporal factors. In our proposed framework, raw PM 2.5 series are firstly decomposed into simple sub-series via the complete ensemble empirical mode decomposition (CEEMD) method. Then, to keep the results independent of the spatial abstraction method, a data-driven approach called multiscale spatiotemporal attention network is employed to extract spatiotemporal features from the sub-series. Finally, the predictions of each sub-series are processed separately and combined to obtain the final prediction results. The experimental results indicate that the proposed model achieved the better performance with RMSE of 11.15, 17.49, 24.84, and 26.93 for 6-, 12-, 24-, and 36-h forecasting, respectively. The proposed method is expected to be applied in fine prediction of air pollution and controlling programs and therefore provide decision support or useful guidance.

The sustainable disposal of large volumes of contaminated dredged river sediment has become a challenge for municipal management. In this study, a cutting-edge biochar application method was innovated, which converted the polluted dredged sediment into a low-carbon and environmentally friendly building material through an autoclave-free method. As the amount of biochar addition increased from 0 to 2% (w/w), the compressive strength of the dredged sediment-based lightweight concrete (DS-LC) increased from 3.92 to 4.61 MPa. Accordingly, the thermal conductivity decreased from 0.237 to 0.222 W/(m K), the water absorption decreased by 6%, and the water resistance coefficient increased by 33%. Results of X-ray diffraction (XRD) and thermogravimetric (TG) analysis showed that biochar promoted the hydration reaction and the carbonation process. Scanning electron microscopy (SEM) attached with energy-dispersive X-ray spectroscopy (EDX) showed that biochar addition changed the microstructure of the DS-LCs, which made the pore distribution more uniform and densified. Biochar addition also strengthened the immobilization of heavy metals (Cu, Zn, Cr, and As) by approximately 18–27% and combination of biochar and silica fume could increase the heavy metal immobilization by 28–44%. Compared with the traditional concrete material, the DS-LC with biochar addition could not only reduce the carbon emission but also has potential economic benefit for the treatment and utilization of dredged sediment.

Exposure to fine particulate matter can easily lead to health issues. PM concentrations are associated with various spatiotemporal factors, which makes the prediction of PM concentrations still a challenging task. One of the reasons that makes the accurate prediction by statistical learning method difficult is severe fluctuations in input data. In addition, the abstraction method of space will also affect the prediction results. To address these important issues, a novel hybrid decomposing-ensemble and spatiotemporal attention (DESA) model is proposed to improve the prediction accuracy by decomposing the mode-mixed time series into single-mode series and automatically assign weights to the spatiotemporal factors. In our proposed framework, raw PM series are firstly decomposed into simple sub-series via the complete ensemble empirical mode decomposition (CEEMD) method. Then, to keep the results independent of the spatial abstraction method, a data-driven approach called multiscale spatiotemporal attention network is employed to extract spatiotemporal features from the sub-series. Finally, the predictions of each sub-series are processed separately and combined to obtain the final prediction results. The experimental results indicate that the proposed model achieved the better performance with RMSE of 11.15, 17.49, 24.84, and 26.93 for 6-, 12-, 24-, and 36-h forecasting, respectively. The proposed method is expected to be applied in fine prediction of air pollution and controlling programs and therefore provide decision support or useful guidance.

Based on the analysis of heavy mineral assemblages in Cenozoic southwestern Qaidam Basin, we found that different areas have variable heavy mineral assemblage characteristics, which suggested that there were two source areas--the Altyn Moun- tains and the Qimen Tagh-East Kunlun Mountains. In Ganchaigou-Shizigou-Huatugou （Area A）, which was mainly source from the Altyn Mountains, its heavy minerals were mainly composed of zircon, Ti-oxides, and wollastonite in the Paleocene- early Eocene and mainly of unstable minerals, especially amphibole, in the middle Eocene-Oligene. Since the late Oligocene- Miocene, the heavy minerals were still mainly unstable minerals, but the content of epidote increased and the content of am- phibole decreased. In Qigequan-Hongliuquan （Area B）, which was the mixed source from the Altyn Mountains and the Qimen Tagh-East Kunlun Mountains, its heavy minerals were mainly garnet, epidote, and amphibole. The source of Lticaotan- Dongchaishan-Kunbei （Area C） was mainly from the Qimen Tagh-East Kunlun Mountains, heavy minerals in the sediments in Area C were mainly zircon and Ti-oxides in Paleogene and garnet, epidote, and amphibole in Neogene. In Yuejin-Youshashan （Area D）, where the stable minerals and unstable minerals were present simultaneously, the heavy mineral assemblages was controlled by multi-direction source. The variation of heavy mineral assemblages in southwestern Qaidam Basin shows that Altyn Mountains was of low-lying topographic relief in Paleocene-early Eocene, and the rapid uplift of Altyn Mountains started from the middle Eocene. In Paleogene, the Altyn Tagh Fault had a slow strike-slip velocity, but the strike-slip velocity increased greatly since the late Oligocene, leading to a strike-slip displacement above 300 km since Neogene. Meanwhile, the Qimen Tagh-East Kunlun fault zone was under a stable tectonic stage in Paleogene with the Qimen Tagh Mountain being low- lying hills; since the late Oligocene, the fault zone started to activate and the Qimen Tagh Mountain began to uplift rapidly.

One RIL population derived from the cross between Dalibao and BYL8 was used to examine the phenotypes of kernel-related traits in four different environments. Six important kernel traits, kernel length (KL), kernel width (KW), kernel perimeter (KP), kernel area (KA), kernel length/width ratio (KLW), and thousand-kernel weight (TKW) were evaluated in Yangling, Shaanxi Province, China (2016 and 2017), Nanyang, Henan Province, China (2017) and Suqian, Jiangsu Province, China (2017). A genetic linkage map was constructed using 205 SSR markers, and a total of 21 significant QTLs for KL, KW, KP, KA, KLW and TKW were located on 10 of the 21 wheat chromosomes, including 1A, 1B, 2A, 2B, 2D, 3D, 4D, 5A, 5B, and 7D, with a single QTL in different environments explaining 3.495–30.130% of the phenotypic variation. There were four loci for KLW, five for KA, five for KL, three for KP, two for KW, and two for TKW among the detected QTLs. We used BSA + 660 K gene chip technology to reveal the positions of major novel QTLs for KLW. A total of 670 out of 5285 polymorphic SNPs were detected on chromosome 2A. The SNPs in 2A are most likely related to the major QTL, and there may be minor QTLs on 5B, 7A, 3A and 4B. SSR markers were developed to verify the chromosome region associated with KLW. A linkage map was constructed with 7 SSR markers, and a major effect QTL was identified within a 21.55 cM interval, corresponding to a physical interval of 10.8 Mb in the Chinese Spring RefSeq v1.0 sequence. This study can provide useful information for subsequent construction of fine mapping and marker-assisted selection breeding.

Cushioning and antibacterial packaging are the requirements of the storage and transportation of fruits and vegetables, which are essential for reducing the irreversible quality loss during the process. Herein, the composite of carboxymethyl nanocellulose, glycerin, and acrylamide derivatives acted as the shell and chitosan/AgNPs were immobilized in the core by using coaxial 3D-printing technology. Thus, the 3D-printed cushioning–antibacterial dual-function packaging aerogel with a shell–core structure (CNGA/C–AgNPs) was obtained. The CNGA/C–AgNPs packaging aerogel had good cushioning and resilience performance, and the average compression resilience rate was more than 90%. Although AgNPs was slowly released, CNGA/C–AgNPs packaging aerogel had an obvious antibacterial effect on E. coli and S. aureus. Moreover, the CNGA/C–AgNPs packaging aerogel was biodegradable. Due to the customization capabilities of 3D-printing technology, the prepared packaging aerogel can be adapted to more application scenarios by accurately designing and regulating the microstructure of aerogels, which provides a new idea for the development of food intelligent packaging.

Background Exosomes have emerged as vital biomarkers of multiple cancers and contain abundant circular RNAs (circRNAs). However, the potential for exosomal circRNAs to be used in diagnostics and their molecular mechanism of action in colorectal cancer (CRC) remain unclear. Methods CRC-specific exosomal circRNAs were identified by RNA sequencing, exoRBase database and a tissue microarray. The diagnostic performance of plasma exosomal circRNAs was evaluated among cancer-free controls, precancer individuals, CRC patients, and patients with other types of cancer. The corresponding biological functions were mainly assessed using circRNA pull-down, proteomic analysis, and RNA immunoprecipitation assay underlying cellular and mouse models. Results CircLPAR1 was encapsulated in exosomes with high stability and detectability, and its expression in plasma exosomes was remarkably decreased during CRC development but recovered after surgery. Exosomal circLPAR1 showed cancer specificity in CRC diagnosis and increased the diagnostic performance to an area under the receiver operating characteristic curve of 0.875, as determined by analysing its performance in combination with common clinical biomarkers CEA and CA19–9. Additionally, circLPAR1 was downregulated in CRC tissues and was associated with overall survival. Mechanistically, exosomal circLPAR1 was internalized by CRC cells, and it suppressed tumor growth, likely because exosomal circLPAR1 directly bound with eIF3h specifically suppressed the METTL3-eIF3h interaction, decreasing the translation of oncogene BRD4 . Conclusions This comprehensive study highlights plasma exosomal circLPAR1 as a promising predictor in CRC diagnosis and describes its biological regulation of colorectal tumorigenesis. This study provides a new perspective on early diagnosis in the clinic and pathogenesis in disease development.

The COVID-19 pandemic now affects the entire world and has many major effects on the global economy, environment, health, and society. Focusing on the harm COVID-19 poses for human health and society, this study used system dynamics to establish a prevention and control model that combines material supply, public opinion dissemination, public awareness, scientific and technological research, staggered work shifts, and the warning effect (of law/policy). Causal loop analysis was used to identify interactions between subsystems and explore the key factors affecting social benefit. Further, different scenarios were dynamically simulated to explore optimal combination modes. The main findings were as follows: (1) The low supervision mode will produce a lag effect and superimposed effect on material supply and impede social benefit. (2) The strong supervision mode has multiple performances; it can reduce online public opinion dissemination and the rate of concealment and false declaration and improve government credibility and social benefit. However, a fading effect will appear in the middle and late periods, and over time, the effect of strong supervision will gradually weaken (but occasionally rebound) and thus require adjustment. These findings can provide a theoretical basis for improving epidemic prevention and control measures.

Test anxiety is a common issue among college students, which can affect their physical and psychological health. However, effective interventions or therapeutic strategies are still lacking. This study aims to evaluate the potential effects of JYLP-326 on test anxious college students. Sixty anxious students were enrolled and randomly allocated to the placebo group and the probiotic group. Both groups were instructed to take placebo and JYLP-326 products twice per day for three weeks, respectively. Thirty unanxious students with no treatments were assigned to a regular control group. The anxiety, depression, and insomnia questionnaires were used to measure students' mental states at the baseline and the end of this study. 16S rRNA sequencing and untargeted metabolomics were performed to analyze the changes in the gut microbiota and fecal metabolism. The questionnaire results suggested that JYLP-326 administration could relieve the symptoms of anxiety, depression, and insomnia in test anxious students. The gut microbiomes of the placebo group showed a significantly greater diversity index than the control group (p < 0.05). An increased abundance of and at the genus level was observed in the placebo group, and the relative abundance of and decreased. Whereas, JYLP-326 administration could partly restore the disturbed gut microbiota. Additionally, test anxiety was correlated with disordered fecal metabolomics such as a higher Ethyl sulfate and a lower Cyclohexylamine, which could be reversed after taking JYLP-326. Furthermore, the changed microbiota and fecal metabolites were significantly associated with anxiety-related symptoms. The results indicate that the intervention of JYLP-326 could be an effective strategy to alleviate anxiety, depression, and insomnia in test anxious college students. The potential mechanism underlying this effect could be related to the regulation of gut microbiota and fecal metabolites.