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Medium-temperature anaerobic digestion experiments lasting for 55 days were conducted using sulfamethoxazole (SMX)-containing chicken manure in sequential batch reactors added with nano-Fe2O3 at a concentration of 300 mg·kg−1·TS or nano-C60 at a concentration of 100 mg·kg−1·TS. The effects of nanoparticles on the anaerobic digestion properties of SMX-containing chicken manure were assessed by measuring the following indicators: biogas production by anaerobic digestion, chemical parameters, enzyme concentrations, and bacterial diversity and changes in antibiotic concentrations over time. The law of bacterial degradation of SMX was analyzed. The results showed that (1) adding either nano-Fe2O3 or nano-C60 promoted biogas production by anaerobic production from chicken manure containing different concentrations of SMX, and the cumulative biogas production in Fe2O3 and nano-C60 increased by 35.4% and 130.7%, respectively. The final cumulative biogas productions in different groups were as follows: 3,712(CK), 4,281(S1), 3,968(S2), 4,061(S3), 4,498(S4), and 4,639(S5) mL and the final concentration of SMX residues varied between 99.79% and 99.94%; (2) Bacterial abundance at the phylum level: on day 1, Firmicutes and Bacteroidota were the main dominant bacterial phyla, with relative abundances of 45.13–68.53% and 26.12–48.32%, respectively. The addition of nanoparticles increased the abundance of Bacteroidota in S4 and S5 significantly. The abundance of Bacteroidota was slightly higher in the group added with nanoparticles than in S2. On day 50, Firmicutes became the dominant bacterial phylum, and its relative abundance varied little across the groups, ranging from 90.87% to 94.54%; (3) At different stages, the bacterial community structure at the genus level was dramatically affected by substrates. As nutrients were being depleted, some bacterial communities lost their original competitive advantages. On day 5, the relative abundance of Prevotella increased. Especially, the relative abundances of Prevotella in S4 and S5 added with nanoparticles were lower than that in S2 by 8–10%. On day 15, the relative abundance of Prevotella in S2 decreased compared with the control group CK. A decrease was also observed in S4 and S5, although to a smaller extent than in S2.

[Objective] To establish a nondestructive detection method for fruit appearance. [Methods] Nectarines were used as the research subject. The IQQU3 smart phone camera was used to capture the picture data, which was then preprocessed. The image annotation program Labelimg was used to label the data. Panning, left-right flipping, and mirror flipping were used to enlarge the data. Using an ratio of 8∶2, the enlarged photos were split into training and test sets. Lastly, the data was trained for 150 epochs using five YOLOv8 models （n, s, m, l, x）. The training results of the five models were compared and analyzed in order to determine which detection model was the best. [Results] The nectarine dataset was constructed, there were 4,205 total photos; YOLOv8 （n, s, m, l, x） the total loss values in the training set were 2.275, 1.778, 1.482, 1.880, and 1.401, respectively, The total loss values of the test set were 2.724, 2.253, 2.057, 2.105, and 2.004, respectively; YOLOv8 （n, s, m, l, x） precision were 94.0%, 98.0%, 97.4%, 97.3%, 97.9%, respectively, The recall were 95.4%, 95.5%, 95.9%, 96.9%, and 96.9%, respectively. In a comprehensive comparison YOLOv8s was the better model, and the average detection accuracy mAP_0.5 was 97.8%. The average precision of fresh, bruise and scar were 96.2%, 98.8% and 98.4%, respectively. The inference time and calculation amount （GFLOPs） were 179.4 ms and 28.4 respectively. [Conclusion] YOLOv8 can effectively detect the quality of fruit appearance, which can be used for non-destructive testing of fruit appearance, and this study can provide new ideas for non-destructive testing of fruits.

To improve the work efficiency and stability of discharger in soybean conditioner, this paper combined the discrete element method to simulate the motion process of soybeans in the discharger, and analyzed the effects of rotor blade number, feeding plate angle, and angle between inlet and outlet of the discharger. The results showed that when the velocity of discharger was 12 r/min, the number of rotor blades had a significant negative correlation with the discharger’s work efficiency and stability. The feeding plate angle had no significant on the work efficiency and stability, but it would cause material residue at the inlet of discharger. The larger the angle between the inlet and outlet led to the lower the work efficiency of discharger, but the work stability was better. The above research showed that when the number of the rotor blades was 6, the feeding plate angle was 40°. The angle between inlet and outlet was 180°, and the work efficiency and stability of discharger reached optimal levels.

Objective To investigate the electrophysiological characteristics and clinical significance of unilateral tinnitus with normal hearing threshold. Methods A total of 34 patients with normal hearing but unilateral tinnitus admitted to our department from May 2022 to February 2023 were recruited as the study subjects. Pure tone hearing threshold detection, distortion product otoacoustic emission (DPOAE), auditory brainstem response (ABR) detection and extended high-frequency pure tone audiometry were performed on both ears (affected side and healthy side) of every patient. The data of the affected side group and those of healthy side group were compared. Results The Ⅰ-wave latency of ABR was significantly shorter (1.60 vs 1.73 ms, P=0.018), and its amplitude was obviously decreased (0.19 vs 0.23 μV, P=0.003) in the affected side group when compared with the healthy side group. In the affected group (34 ears), there were 18 ears (52.94%) having amplitude of wave Ⅲ greater than that of wave Ⅴ, while in the healthy group, no such difference in the amplitude between wave Ⅲ and wave Ⅴ was observed. There was statistical significance between the 2 groups (Chi-square=24.480, P < 0.001). In the 12.5, 14.0, 16.0, 18.0 and 20.0 kHz conditions, the average hearing threshold of extended high-frequency audiometry was all significantly higher in the affected side group than the healthy group (P < 0.05). Conclusion For subjective tinnitus patients with normal hearing threshold, ABR examination is helpful in preliminarily determining whether there are hidden functional changes in the auditory pathway. Decreased Ⅰ-wave amplitude, shortened Ⅰ-wave latency, and wave Ⅲ amplitude greater than wave Ⅴ amplitude can be used as objective reference indicators.

Abstract Acute gastrointestinal injury (AGI) is common in mechanically ventilated (MV) patients, but the potential association between ventilatory pressure parameters and AGI grade and their impact on mortality remains unclear. This study aimed to explore the association between ventilatory pressure parameters and AGI grade, and their interaction on all-cause mortality in MV patients. This study was a secondary analysis of a multicenter, prospective, observational study that enrolled adult patients with an expected duration of mechanical ventilation ≥ 48 h from 14 general intensive care units in Zhejiang Province between March and August 2014. The AGI grade was assessed daily on the basis of gastrointestinal symptoms, intra-abdominal pressures, and feeding intolerance in the first week of admission to the ICU. This study included 331 patients (69.2% men; mean age, 64.6 ± 18.9 years). Multivariate regression analysis showed that plateau pressure (Pplat) (OR 1.044, 95% CI 1.009–1.081, P = 0.013), serum creatinine (OR 1.003, 95% CI 1.001–1.006, P = 0.042) and APACHE II score (OR 1.035, 95% CI 1.021–1.072, P = 0.045) were independently associated with global AGI grade III/IV within 7 days of ICU admission. Moreover, global AGI grade (HR 2.228, 95% CI 1.561–3.182, P

Upon 98% reduction cold-rolling, a single layer AA3003 foil material of 115 μm thick was intermediately annealed at 300–420 °C/1-8 h, and the preferred temperature and time of the intermediate annealing for the laminated AA4343/AA3003/AA4343 composite foil material were determined. All of these samples were cold rolled by 40% to 70 μm thick and finally annealed at a temperature in the range of 300–550 °C for 1–9 h. Their sagging resistance and microstructure were investigated using simulated brazing treatment, optical microscopy (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). For the single layer AA3003 foil, the sagging distance decreased first and then increased with increasing intermediate annealing temperature, and the lowest sagging distance corresponds to the coarsest grain in the recrystallized sample. After the final annealing for the single layer, the sagging distance decreased monotonously. For the laminated foil subjected to the final annealing, the sagging distance curves decreased first and then increased with increasing annealing temperature and time. The improvement of sagging resistance was attributed to the recovery of two types of materials during annealing process. The deterioration of the sagging resistance in the laminated foils was due to the corrosion in the brazing process, resulted from the Si diffusion from the cladding layer into the core layer. It is the coarse grains, few dislocations and minimal diffusion of Si that are crucial for improving the sagging resistance of the laminated foils.

Objective To evaluate the severity of anxiety and depression in patients with chronic spontaneous urticaria (CSU) and explore the correlation between anxiety and depression scores, disease activity, and quality of life. Methods Outpatients with CSU were enrolled at Guangzhou Institute of Dermatology from August 2023 to December 2023. A questionnaire survey was used to collect clinical data and urticaria activity score 7 (UAS7), dermatology life quality index (DLQI), self-rating anxiety scale (SAS), and self-rating depression scale (SDS) from patients. SAS and SDS scores were obtained again after over 8-week treatments. Pearson correlation was used to analyze the correlation between SAS, SDS and UAS7 and DLQI. SAS and SDS were compared before and after the treatments. Results A total of 53 CSU patients were included, of which 15 (28.30%) had anxiety and 16 (30.19%) had depression. The DLQI score was significantly higher in CSU patients with anxiety than in those without anxiety (t=2.58, P=0.013), and the UAS7 score was significantly higher in patients with depression than in that without depression (t=2.29, P=0.026). Pearson correlation analysis showed a positive correlation of SAS with UAS7 and DLQI (r=0.31, P=0.029; r=0.33, P=0.020), while SDS was not related to UAS7 and DLQI (both P>0.05). After over 8-week treatments, SAS and SDS were significantly decreased in 24 patients (t=3.97, P

Diabetic kidney disease (DKD) is one of the main microvascular complications of diabetes mellitus, as well as the leading cause of end-stage renal disease. Intestinal microbiota has emerged as a crucial regulator of its occurrence and development. Dysbiosis of the intestinal microbiota can disrupt the intestinal mucosal barrier, abnormal immunological response, reduction in short-chain fatty acid metabolites, and elevation of uremic toxins, all closely related to the occurrence and development of DKD. However, the underlying mechanisms of how intestinal microbiota and its metabolites influence the onset and progression of DKD has not been fully elucidated. In the current review, we will try to summarize the microecological mechanism of DKD by focusing on three aspects: the intestinal microbiota and its associated metabolites, and the “gut–kidney axis,” and try to summarize therapies targeted at managing the intestinal microbiota, expecting to provide theoretical basis for the subsequent study of the relationship between intestinal homeostasis and DKD, and will open an emerging perspective and orientation for DKD treatment.

There is great exploration potential for ultra-deep reservoirs in Junggar Basin， and reservoir prediction with seismic data is the main technical means of oil and gas exploration at present. However， due to the low signal-to-noise ratio of seismic data of ultra-deep reservoirs， unclear reservoir-seismic data correspondence， and few actual drilling wells， it is difficult to establish an effective initial model for seismic inversion， which restricts the accuracy and reliability of ultra-deep reservoir prediction. Gravity inversion， as an important quantitative interpretation method， can obtain the characteristics of underground density distribution and provide support for geological interpretation. According to the density model， a relatively reliable low-frequency model can be established for seismic inversion， which can overcome the difficulty of applying seismic data in ultra-deep reservoirs to a certain extent. Meanwhile， the acquisition of gravity data is economical and convenient compared with that of seismic data， and it is easier to be applied in practice. Therefore， a new technique applying gravity inversion to seismic data-based reservoir prediction was developed in this article. Firstly， a quasi-neural network gravity inversion technique based on Gaussian radial basis functions was proposed to solve the gravity inversion problem and improve the resolution and reliability of gravity inversion. Then， the density body obtained by gravity inversion was used as training data， and a neural network was trained together with seismic and logging data to establish the initial model of seismic inversion. Finally， seismic inversion was carried out with the initial model constraints. This technique broke through the application bottleneck of single seismic data in ultra-deep reservoir prediction and overcame the limitations of logging constraints， providing a reliable initial model for seismic inversion. The application of this technique to the prediction of ultra-deep clastic rock reservoirs in Junggar Basin was consistent with the existing geological knowledge， indicating that this method had high practical value and application potential for ultra-deep reservoir prediction and could provide technical support for ultra-deep reservoir exploration.

ObjectiveTo provide scientific support for the compilation of high-quality anti-nuclear, biological, and chemical (NBC) medical textbooks in China by retrieving books in the field of biological weapons defense in China, summarizing the publication time and distribution of publishing institutions, and categorizing content and key points of related books.MethodsRelevant subject terms in the field of biological weapons defense were searched through the official website of China National Digital Library and other websites, up until December 31, 2023, and were limited to books. Topic analysis was conducted on the introductions and contents of the books using the latent Dirichlet allocation (LDA) model. The number of topics was determined based on perplexity, and topics were identified according to the intertopic distance map, followed by a qualitative description of the core content of each topic.ResultsA total of 104 books were included in this study, among which four were identified as higher educational textbooks. The volume of publications increased during the periods 2002‒2004 and 2020‒2023. Research institutions accounted for the highest percentage of publishers (37.78%), and 56.67% of the publishers were military institutions. The study identified six topics: "distribution, defense, and response to biological weapons", "category, diagnosis, and treatment of biological warfare agents", "response to biological public health emergencies", "status of nuclear, biological, and chemical weapons internationally", "biosafety risk management and prevention and control", and "technologies and equipment related to biological hazard identification".ConclusionThere are few books in the field of biological weapons defense in China and the content is relatively outdated. In the future, the preparation of teaching materials should be aimed at practical emergency handling techniques for biological weapons, enhance the emphasis on biological weapons detection and biological warfare early warning, improve the fundamental theories at different training levels, and timely update the current research status in the field.

[Objective] This study aimed to establish a preparation process for starch-lipid complex with high resistant starch （RS） content and to explore the effects of process parameters on the anti-digestibility of the complex. [Methods] Using RS content as the indicator, one-way experimental and orthogonal designs were employed to study the effects of temperature and time parameters during the pretreatment of high amylose corn starch （HA） and its complexation with stearic acid （SA）. The physicochemical properties, anti-digestibility, and hydration characteristics of the HA-SA complex prepared by the optimal process were analyzed. [Results] Increasing pretreatment temperature in the gelatinization temperature range, prolonging the pre-treatment time, and complexing temperature above 75 ℃ all led to a significant decrease in RS content of HA-SA （P＜0.05）. The optimal process was as following: HA being pretreated at an annealing temperature of 85 ℃ for 12 h, and then complexed with SA at 75 ℃ for 90 min. The prepared HA-SA showed the V-type crystalline pattern and entire starch granules shape, and was characterized as the type I complex. It also presented the significantly enhanced thermal stability, bettered anti-digestibility, and lowered swelling power than HA （P＜0.05）. [Conclusion] Annealing temperature pretreatment and optimal complexation temperature favored to prepare the starch-lipid complex with higher anti-digestibility and entire granule structure. Excessive thermal strength during pretreatment causing excessive gelatinization could impede the formation of RS in the complexes. And a complexation temperature over 75 ℃ led to the disassociation of the complexation, decreasing the RS content. Annealing pretreatment preserved the entire starch granule of HA, which was one of the reasons for the higher digestibility of HA-SA.

Pancreatic neuroendocrine neoplasm (pNEN) is the most common neuroendocrine tumor in the digestive system. The heterogeneity of pNEN makes it difficult to summarize diagnostic and therapeutic strategies only based on clinical experience. Over the past 20 years, basic research on pNEN has been well developed, which in turn has promoted its theoretical system and diagnostic and therapeutic strategies, significantly improving the therapeutic efficacy of pNEN. In this paper, we review the development of basic research on pNEN in recent years and discuss its role in clinical diagnosis and treatment. On the one hand, the development of basic research can further reveal the nature of the differential biological behavior of pNEN and provide clinical reference; on the other hand, reference to the migratory basic research results in other tumor fields is also conducive to improving the clinical diagnosis and treatment level of pNEN.

Aging behavior and phase transformation of Al–5Zn–1Mg–1Cu(-Si) alloys under different isothermal temperatures were investigated using transmission electron microscope (TEM) and mechanical tests. The aging temperature and Si addition significantly influenced to the types of precipitated phases and mechanical properties of the Al–Zn–Mg–Cu alloys. Adding Si in this alloy changed the precipitated phases from T′ and η′ phases to the dual-sized η and GPB-II phases. With increasing isothermal aging temperature, more GPB-II phases formed in alloys with the same composition, effectively improving the microhardness and mechanical properties. When aging at 150 °C, 0.5 wt%Si-containing alloy reached the highest peak hardness of about 150HV and maintained stable hardness, while an alloy without Si only reached 120HV and later declined by 15HV. The tensile strength and yield strength of the 0.5 wt%Si-containing alloy were higher than those of non-Si alloy by 132 MPa and 165 MPa, increasing 51% and 90%, respectively. This result was due to the presence of fine and dispersed 4–8 nm GPB-II phases in 0.5 wt%Si-containing alloy. The GPB-II phase had a core-shell structure, with the core region mainly enriched in Mg and Si, and the shell region mainly enriched in Cu and Zn. Compared with the stability of T′ and η′ phases, this core-shell structure of GPB-II effectively inhibited its growth and beneficially maintained a smaller-sized GPB-II phase. The strengthening effect of GPB-II phase was better than that of η or T phases when aging at 150 °C or 200 °C. The mechanical properties of high Zn/Mg ratios Al–Zn–Mg–Cu alloys can enhanced by adding Si.

Taking chicken manure containing antibiotic oxytetracycline (OTC) as the research object, nano-Fe2O3 with a concentration of 300 mg/kg TS and nano-C60 with a concentration of 100 mg/kg TS as carriers were used for a 55-day sequential batch mesophilic anaerobic digestion experiment. The gas production performance, chemical parameters, antibiotic content, enzyme concentration, and resistance gene changes during anaerobic digestion were analyzed to clarify the effect of nanoparticles on anaerobic systems containing antibiotic chicken manure and provide a reliable basis for pollution reduction capacity of livestock and poultry manure. The results showed that (1) adding nano-Fe2O3 and nano-C60 promoted anaerobic gas production in chicken manure with different concentrations of OTC. The cumulative gas production from days 1 to 10 was 2,234(T5) > 2,163(T4) > 1,445(T2) > 1,289(T3) > 1,220(T1) > 1,216(CK) mL. The cumulative gas production of T4 and T5 increased by 77.29 and 83.11%, respectively. The final cumulative gas production for each group was 3,712(CK), 3,993(T1), 4,344(T2), 4,447(T3), 4,671(T4), and 4,849(T5) mL. The final OTC residue concentrations were 15.25, 20.40, 56.56, 17.67, and 16.89 μg/L, with degradation rates of 98.31, 98.80, 98.29, 99.07, and 99.11% respectively; (2) antibiotic OTC increased the activities of dehydrogenase, amylase, protease, and urease, while adding nanoparticles increased the activities of dehydrogenase and amylase, with no significant impact on cellulase, urease, and protease activities; (3) antibiotic resistance gene (ARGs) of various types were found in groups T2, T4, and T5, with the multidrug resistance gene (ARGs)-Multidrug accounting for a high proportion of 33.54, 35.63, and 37.43%, respectively, while the proportions of the other four types MLS, tetracycline, glycopeptide, and peptide ranged from 8.18 to 12.98%.

Metal inert gas (MIG) welding of a novel Al-5.82Mg-0.61Zn-0.14Er-0.11Zr alloy was performed using ER5E61 and ER5B71 filler wires. The effect of the two filler wires on microstructures and mechanical properties of MIG welded joints was investigated. The study shows that fully penetrated joints are obtained by MIG welding process filled with ER5E61 and ER5B71 wires. Compared with ER5B71, the weld metal (WM) with ER5E61 exhibits a coarse structure and high solid solution of Mg and Mn elements. After the tensile test, the WM with ER5E61 and ER5B71 wires is composed of elongated coarse grains with a higher proportion of low angle grain boundaries (LAGBs). In addition, the lattice rotation occurring within the grains, indicating that single grain has an unevenly plastic deformation after the tensile test. The average microhardness of the WM is 83.4 HV and 82.9 HV for ER5E61 and ER5B71 wire, which is the lowest microhardness in the MIG welded joint. The average ultimate tensile strength (UTS) and elongation index (EI) of the MIG joints are 327.7 MPa and 8.5% for ER5E61 wire, and 314.3 MPa and 8.2% for ER5B71 wire, suggesting the MIG welded joint with ER5E61 wire achieves a good combination of strength and ductility. Quantitative assessments of the effects of different strengthening mechanisms on the strength reveal that dislocation strengthening and solid solute strengthening are the main factors to obtain a higher strength of welded joints with ER5E61.

Objective Cutterhead torque is a crucial parameter that characterizes the safety of shield tunneling and the operating status of equipment. To address the difficulties in cutterhead torque prediction and excavation parameters timely correction, an advanced prediction method for shield tunneling cutterhead torque based on WaveNet network is proposed. Method The preprocessing method for working condition data is introduced, and the basic structure and construction method of the initial static model based on WaveNet network are proposed. A training set is constructed by extracting historical data of shield construction monitoring within the initial 50 m tunneling distance, and advanced prediction of the cutterhead torque is made after five construction steps based on the shield construction monitoring data of the previous 20 construction steps. With the increase of shield excavation distance, the model is retrained and updated every five construction steps using newly generated data set, thus a long-term dynamic model for cutterhead torque advanced prediction is proposed. Taking the left-line data of the Yufuhe Sta.-Wangfuzhuang Sta. shield tunnel interval of Jinan Rail Transit Line 1 as example, the prediction effect of cutterhead torque is analyzed and verified. Result & Conclusion The cutterhead torque values by advanced prediction for the first 50 m tunneling distance show a basic consistency with the changing trend of actual values, with an average relative error of 10.07%. The initial static model exhibits relatively high prediction accuracy. As the tunneling distance increases, the relative error of the initial static model increases from 10% to about 30%, while that of the continuously updated long-term dynamic model remains stable at around 10%. The update time of the long-term dynamic model is generally distributed between 1 and 6 seconds, with an average time consumption of 3.92 s, meeting the requirement for efficient dynamic model updates.

The industrial internet of things (IIoT) has great potential in achieving automation and intelligent production. However, existing networks struggle to meet the low-latency and high-reliability communication requirements in industrial control scenarios. Motivated by this fact, the power allocation problem of superimposed pilot (SP) for downlink short-packet transmission in IIoT was studied and a lower bound for achievable transmission rates with imperfect channel state information and maximum ratio transmission was derived. Furthermore, the downlink weighted sumrate maximization problem was formulated and transformed into a geometric programming problem aiming to optimize pilot and data power allocation via successive convex approximation method. Simulation results demonstrate the superiority of the proposed power optimization scheme of SP in short-packet transmission.

Sterculia monosperma fruit is the dried and mature seed of Sterculia monosperma, which belongs to genus Sterculia. In China, this plant is a widely distributed arbor with a long planting history in Lingnan area. It is mainly cultivated in Guangdong, Guangxi, Fujian, Yunnan and Taiwan of China; it is also distributed in India, Vietnam and Indonesia, mostly cultivated artificially. S. monosperma fruit has many functions like warming the stomach and killing pests, but its name and characteristics are easily confused with other plants of genus Sterculia. However, the relevant research foundation is relatively weak. Pharmacognostical study can provide reference for its resource development and quality standard formulation. This study identifies the morphological and microscopic characteristics of S. monosperma fruit, as well as the microscopic identification of seed cross-section and powder. The DNA barcode sequences ITS2, psbA-trnH, matK and rbcL of S. monosperma were obtained through bidirectional sequencing, Kimura 2-Parameter (K2P) genetic distance was calculated, and the neighbor joining tree was established for clustering analysis. The results were as follows: (1) The morphological characteristics of S. monosperma fruit included dark red fruit shell and reddish brown or dark chestnut surface. It was hard in texture, with two thick yellowish endosperms inside. (2) The micro-morphological characteristics was that the exotesta was reddish brown, extremely thin, and brittle in texture; the mesotesta was black brown, thick, and hard in texture; the endotesta coat was light yellow and soft in texture. (3) The microscopic characteristics included the structure and arrangement of the exotesta stone cell, the grid cell structure of the mesotesta, the bead thickening of the cell wall of the endotesta cells, and the calcium oxalate cluster crystals. (4) Based on the ITS2 sequence, S. monosperma fruit could be effectively distinguished from other plants in genus Sterculia, while the matK sequence could effectively distinguish S. lanceolata from other plants in genus Sterculia. This study obtained the data on the morphological characteristics, micro-morphological characteristics and microscopic characteristics of S. monosperma fruit. Combined with the ITS2 barcode sequence, S. monosperma fruit can be effectively identified, which provides a scientific reference for the development of its germplasm resources and the formulation of relevant quality standards.

For improving the combustion and emission performance of engines operating at high altitude regions, a two OED optimization method was proposed. The influence of each factor on the target was analyzed and discussed through a primary OED. Based on the primary OED test, factors with less influence were excluded to reduce factors in the secondary OED test. The results showed that low intake pressure led to poor spray mixing, prolonged ignition delay, and incomplete combustion, resulting in deteriorated power output, fuel economy, and emission characteristics. After two OED optimizations, The NOx emissions increased significantly by 40.5 % and 122.5 % in the first and second optimization, respectively, however, the ISFC decreased by 13.4 % and 25.2 %, and CO decreased by 13.0 % and 25.2 % in the first and second optimization, respectively, as well as ultra-low carbon smoke emissions was achieved. The related theories and methods' advantage can be extended to engineering applications and has good practical value.

In this paper, the maximal information coefficient method-variational mode decomposition-bidirectional long short term memory network-adaptive boosting (MIC-VMD-Bi-LSTM-Adaboost) algorithm is used to forecast the power load. Firstly, MIC is used to determine the correlation degree of meteorological parameters influencing power load. Features having a high correlation degree are then chosen to be input vectors. Secondly, the input characteristics are decomposed using VMD, and five distinct IMF components are retrieved in order to remove unnecessary information. Lastly, different assessment indices are computed and the power load is predicted using the Bi-LSTM-Adaboost method. In order to determine the benefit of the approach used in this work, the outcomes of LSTM, Bi-LSTM, and LSTM-Adaboost are compared concurrently.

By selecting five key operational parameters—inlet temperature, operating temperature, anode humidity, cathode humidity, and operating pressure—and using PEMFC power density, membrane temperature difference, and membrane water content as performance indicators, simulations were conducted using 3D CFD modeling and orthogonal experiments to identify the most significant factors influencing PEMFC performance and determine the optimal operational parameter combination. The results show that working pressure has the most significant impact on PEMFC performance. Pressure variations cause significant changes in diffusion and heat conduction within the PEMFC. When the working pressure increases from 1.0 MPa to 3.0 MPa, the power density increases by approximately 11.2 %, but the temperature difference within the membrane increases by about 30.5 %. The difference between operating temperature and inlet temperature directly leads to changes in the temperature difference within the membrane. Moreover, changes in operating temperature greatly affect the water content within the proton exchange membrane, which in turn impacts the PEMFC's power density. When the operating temperature increases from 60 °C to 100 °C, the water content within the membrane decreases by approximately 50.5 %, and the power density decreases by 60.4 %. By optimizing the combination of operating parameters using a comprehensive scoring method, the temperature difference within the membrane can be controlled at 27.78 °C while ensuring the power density is increased to 0.92 W/cm2.

The microstructure and grain refinement of Al-4.5Er-1Zr-1.5Ti master alloy were analyzed by the refinement experiment, OM, SEM and XRD. The results show that the grain size of pure aluminum is reduced from 14,000μm to 202μm by Al-4.5Er-1Zr-1.5Ti master alloy, which is mainly due to the nucleation promoted by Ti2Al20Er, Al3Er and Al3Ti. Plastic deformation further improves the refining effect of the material by improving the primary phase size, and the Al-4.5Er-1Zr-1.5Ti −2ARB can refine the pure aluminum from 202 mμm to 150μm, and the refinement was increased by 25.7 %. The master alloy showed a better refinement effect in Al-5Cu alloy than pure aluminum, with a grain size of 92μm and a refinement improvement of 97.8 %.

Systemic therapies, the ultimate strategies for patients with advanced hepatocellular carcinoma (HCC), are suffering from serious clinical challenges, such as the occurrence and development of drug resistance. Treatment resistance aggravates tumor progression partly by inducing tumor metastasis. Regorafenib-resistant HCC cells exhibit a highly striking metastatic phenotype, but the detailed mechanisms underlying these aggressive behaviors remain elusive. Here, we conduct transcriptome sequencing analysis to identify COL5A2 as a crucial driver of the metastatic characteristics of regorafenib-resistant HCC cells. COL5A2 is aberrantly highly expressed in resistant cells, and its genetic depletion significantly suppresses proliferation, migration, invasion, vasculogenic mimicry (VM) formation and lung metastasis in vitro and in vivo, concomitant with the downregulation of VE-cadherin, EphA2, Twist1, p-p38 and p-STAT3 expressions. LIFR is confirmed to be an essential downstream molecule of COL5A2, and its expression is observably elevated by COL5A2 depletion. Ectopic overexpression of LIFR drastically attenuates the proliferation, migration, invasion and VM of regorafenib-resistant cells and represses the expressions of VM-related molecules and the activation of p38/STAT3 signaling pathway. Interestingly, rescue experiments show that the inhibition of the above aggressive features of resistant cells by COL5A2 loss is clearly alleviated by silencing of LIFR. Collectively, our results reveal that COL5A2 promotes the ability of regorafenib-resistant HCC cells to acquire a metastatic phenotype by attenuating LIFR expression and suggest that therapeutic regimens targeting the COL5A2/LIFR axis may be beneficial for HCC patients with therapeutic resistance.

Abstract Background and aim Combination therapy is the primary treatment for unresectable hepatocellular carcinoma (u-HCC). The hepatic functional reserve is also critical in the treatment of HCC. In this study, u-HCC was treated with combined hepatic arterial infusion chemotherapy (HAIC), tyrosine kinase inhibitors (TKIs), and programmed cell death protein-1 (PD-1) inhibitors to analyze the therapeutic response, progression-free survival (PFS), and safety. Methods One hundred sixty-two (162) patients with u-HCC were treated by combination therapy of HAIC, TKIs, and PD-1 inhibitors. PFS was assessed by Child–Pugh (CP) classification subgroups and the change in the CP score during treatment. Results The median PFS was 11.7 and 5.1 months for patients with CP class A (CPA) and CP class B (CPB), respectively (p = 0.013), with respective objective response rates of 61.1 and 27.8% (p = 0.002) and conversion rates of 16 and 0% (p = 0.078). During treatment, the CP scores in patients with CPA worsened less in those with complete and partial response than in those with stable and progressive disease. In the CP score 5, patients with an unchanged CP score had longer PFS than those with a worsened score (Not reached vs. 7.9 months, p = 0.018). CPB was an independent factor negatively affecting treatment response and PFS. Patients with CPA responded better to the combination therapy and had fewer adverse events (AEs) than those with CPB. Conclusions Thus, triple therapy is more beneficial in patients with good liver function, and it is crucial to maintain liver function during treatment.

Objective: To optimize the operating parameters of wheat milling in roller mill I skin mill to reduce the energy consumption of milling. Methods: Using EDEM discrete element simulation software, Box-Behnken test was designed for key milling factors such as roll pitch, feeding amount, rotational speed and rotational speed ratio to investigate the relationship between operating parameters and milling power. Results: The significance rankings of the effects on the toothed roll crushing power were, in order, speed ratio, roll pitch, feed volume, speed ratio quadratic term, roll pitch quadratic term, interaction of roll pitch and speed ratio, interaction of feed volume and speed ratio, and interaction of roll pitch and feed volume. According to the different requirements of the I-skin mill, the optimal parameter combinations were obtained: the finer powder output with a rolling pitch of 0.67 mm, a feeding amount of 803.60 kg/（cm·d）, a fast roller speed of 537.68 r/min, and a rotational speed ratio of 2.64, and the coarser powder output with the optimal low-power consumption of 0.79 mm, a feeding amount of 803.83 kg/（cm·d）, a fast roller speed of 576.08 r/min, and a rotational speed ratio of 2.23. Conclusion: The error between the simulation results and the measured results is within a reasonable range, and the established discrete element milling model can be used for power prediction in wheat milling.

This study aimed to investigate whether analgesia nociception index (ANI) could be an indicator of perioperative pain during laparoscopic trocar insertion.

CoxBy alloy with high hardness and high melting point has wide range of applications in many fields because of its stable chemical property, high strength, and good thermal stability. In this work, the thermodynamic and electrical properties of five CoxBy alloys (CoB, Co2B, Co3B, Co23B6 and Co5B16), were studied and compared based on a first-principles approach. The elastic constant and related mechanical property of the binary alloys were calculated by using the energy-strain method, and the thermodynamic properties such as the Debye temperature (ΘD) and the coefficient of thermal expansion (α) within a finite temperature were calculated based on the quasi-simple harmonic Debye model. By comparing the mechanical parameters of the binary alloys, it is found that the comprehensive mechanical property of the CoB alloy is the best among the studied alloys; the state density mapping indicates that all five CoxB y alloys have good metallicity and electrical conductivity, and have some potential applications in the field of electrode materials. In Co3B and Co23B6 alloys, there is a resonance peak between the d electron orbital of Co atom and the p electron orbital of B atom, indicating that there is a significant chemical bond between Co—B.The study complements the gap of thermomechanical property parameters of CoxBy binary alloys and provides a theoretical reference for the design and application of Co based or Co—B binary alloy materials.

Livelihood capital is the core element of sustainable livelihood development for rural households. Based on the survey data of 615 growers in the main rubber-producing areas of Hainan and Yunnan province, this paper constructs an evaluation system of rubber growers' livelihood capital within the sustainable livelihood analysis framework and calculates key indicators, such as farmers' livelihood capital index, livelihood activity diversity index, and household income diversity index. The multiple regression model and the mediating effect model are used to explore the influence mechanism of livelihood capital on the sustainable livelihood of rural households. The following three conclusions are drawn: (1) The accumulation of livelihood capital of rural households is conducive to enriching the structure of household livelihood activities and income sources, especially the accumulation of financial capital, which is more likely to improve the diversity of household livelihood activities. Regional differences exist in the impact of livelihood capital structures on rural household livelihood outcomes. Natural capital endowment has a significant effect on the income diversification level in Hainan, but there may be a "curse" effect in Yunnan. (2) Geographical characteristics, production decisions, and farming household characteristics significantly impact sustainable livelihoods. The altitude of the village and the distance from the county seat pose certain challenges for farmers in diversifying their livelihoods. Although the law of rubber tapping production may weaken farmers' production and business activities, it can better stabilize farmers' incomes owing to its strong flexibility in labor arrangements, which may be beneficial for diversifying livelihoods; however, it does not necessarily lead to diversified incomes. While the characteristics of poor households can help rural households gain policy support, building capacity is the key to achieving a sustainable livelihood. (3) There are intermediary effects of livelihood capital on livelihood activities and household income, but the mechanisms of action are different. For the diversity of farmers' livelihoods, 52.70% of the catalytic effect of livelihood capital is achieved by improving the ability to integrate livelihood capital. Optimizing household employment strategies contributed to the promotion of livelihood capital on the income diversification index, with a mediating effect of 30.52%. The main contribution of this study is to clarify how livelihood capital can achieve the goal of diversifying household livelihood activities and income by coordinating livelihood capacity and employment strategies. Therefore, to enhance the comprehensive livelihood capacity of rural households, the coordination capacity of livelihood capital, livelihood capacity building should be enhanced, and employment information channels should be expanded.

Objective: To achieve surface wear life prediction of abrasive blast rollers of grinding machines. Methods: The wear images of the grinding roller surface were acquired by the built image acquisition system, and the texture parameters such as second order moments, entropy value, contrast and correlation in the wear cycle of the grinding roller were obtained based on the grey scale co-generation matrix algorithm, and the obtained texture feature parameters were input into the constructed PSO-based LS-SVM algorithm model to finally predict the wear life of the blast roller. Results: The particle swarm algorithm could optimize the penalty factor and kernel parameters of LS-SVM well, and the PSO-LS-SVM algorithm was far superior to the LS-SVM algorithm model. The wear state of the blast roller surface of the mill could be accurately identified using the PSO-LS-SVM algorithm. Conclusion: The system can accurately predict the service life of the blast rollers.

SignificanceThe crop phenotype is the visible result of the complex interplay between crop genes and the environment. It reflects the physiological, ecological, and dynamic aspects of crop growth and development, serving as a critical component in the realm of advanced breeding techniques. By systematically analyzing crop phenotypes, researchers can gain valuable insights into gene function and identify genetic factors that influence important crop traits. This information can then be leveraged to effectively harness germplasm resources and develop breakthrough varieties. Utilizing data-driven, intelligent, dynamic, and non-invasive methods for measuring crop phenotypes allows researchers to accurately capture key growth traits and parameters, providing essential data for breeding and selecting superior crop varieties throughout the entire growth cycle. This article provides an overview of intelligent identification technologies for crop agronomic traits and morphological structural phenotypes.ProgressCrop phenotype acquisition equipment serves as the essential foundation for acquiring, analyzing, measuring, and identifying crop phenotypes. This equipment enables detailed monitoring of crop growth status. The article presents an overview of the functions, performance, and applications of the leading high-throughput crop phenotyping platforms, as well as an analysis of the characteristics of various sensing and imaging devices used to obtain crop phenotypic information. The rapid advancement of high-throughput crop phenotyping platforms and sensory imaging equipment has facilitated the integration of cutting-edge imaging technology, spectroscopy technology, and deep learning algorithms. These technologies enable the automatic and high-throughput acquisition of yield, resistance, quality, and other relevant traits of large-scale crops, leading to the generation of extensive multi-dimensional, multi-scale, and multi-modal crop phenotypic data. This advancement supports the rapid progression of crop phenomics. The article also discusses the research progress of intelligent recognition technologies for agronomic traits such as crop plant height acquisition, crop organ detection, and counting, as well as crop ideotype recognition, crop morphological information measurement, and crop three-dimensional reconstruction for morphological structure intelligent recognition. Furthermore, this article outlines the main challenges faced in this field, including: difficulties in data collection in complex environments, high requirements for data scale, diversity, and preprocessing, the need to improve the lightweight nature and generalization ability of models, as well as the high cost of data collection equipment and the need to enhance practicality.Conclusions and ProspectsFinally, this article puts forward the development directions of crop phenotype intelligent recognition technology, including: developing new and low cost intelligent field equipment for acquiring and analyzing crop phenotypes, enhancing the standardization and consistency of field crop phenotype acquisition, strengthening the generality of intelligent crop phenotype recognition models, researching crop phenotype recognition methods that involve multi-perspective, multimodal, multi-point continuous analysis, and spatiotemporal feature fusion, as well as improving model interpretability.

Aiming at the problem of parameter design of marine wedge gear propeller shaft clutches, the design and research of marine wedge gear propeller shaft clutches are carried out. The variation of the critical friction coefficient required for the self-locking of the marine wedge gear propeller shaft clutch with the pressure angle and wedge angle of the wedge gear is theoretically analyzed and verified by the simulation. The multi-body dynamic model of marine wedge gear propeller shaft clutches is established, and the engagement process with the tooth error is studied. The results show that the marine wedge gear propeller shaft clutch reduces the accuracy requirement of the tooth alignment process. When there is an error within 0.4° in the engagement process, the engagement can be completed under its own guidance. The formula of self-locking critical friction coefficient derived in this study can be used to guide the design of marine wedge gear propeller shaft clutches.

In response to the issue that traditional energy storage VSGs （virtual synchronous generators） cannot simultaneously possess good disturbance resistance and rapid dynamic response capabilities， a control strategy for energy storage VSGs is proposed， optimizing the dynamic synchronizer using RBF （radial basis function） neural networks. First， a mathematical model for VSG is established， analyzing the impact of rotor inertia and damping coefficient configuration on VSG performance. This analysis reveals the conflicting relationship between parameter configuration and dynamic response versus system dynamic stability. Subsequently， the transient unbalanced power of the rotor is taken as input for a three-layer forward structure RBF neural network algorithm. Through online learning with the RBF neural network algorithm， the optimal transient compensation power is obtained to dynamically adjust the input power of VSG， thereby reducing unbalanced rotor torque and enhancing the transient stability of VSG. Finally， simulation and comparative experiments are conducted to validate the effectiveness of the proposed control strategy.

Abstract For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability.

Aiming at the problem that the existing graph neural network session recommendation algorithm ignores all kinds of auxiliary information, which leads to the inability to accurately model the session sequence, a session recommendation algorithm combining the item transition relations and time-order information (RTSR) is proposed. Firstly, the shortest path sequence between any two nodes is obtained by using the graph network structure, which is encoded as the item transition relations between corresponding items through the gated recurrent unit (GRU), and then the global dependency information of the session is captured from the perspective of the graph by combining the self-attention mechanism. At the same time, a lossless graph coding scheme is designed to alleviate the problem of information loss in the process of session graph coding. The scheme quantifies the time-order information in the session sequence reasonably, and takes it as the weight of the edges in the session graph, and then combines the gated graph sequence neural network to obtain the local dependency information of the session. Finally, with linear combination of global dependency information and local dependency information, and in combination with reverse position information, the user??s preference for item is finally generated, and the recommendation list is given. The performance comparison experiment with mainstream models such as SR-GNN, GC-SAN and GCE-GNN on the public benchmark datasets Gowalla and Diginetica shows that RTSR improves at least 6.13% and 1.58% in average reciprocal ranking respectively, and the recommendation accuracy is also improved accordingly.

A systematic analytical approach has been developed to comprehensively characterize the creep damage material properties of coatings on thin-layer substrates. This involves conducting creep tests using a compound, two-material, miniaturized thin plate specimen (MTPS) with an inverse approach. For demonstration, independent creep tests were performed on miniature specimens of the substrate (P91 steel) and the coating (CoNiCrAlY) at 650 °C, as well as on compound substrate-coating specimens. The reference stress method, coupled with finite element analysis, was utilized to obtain conversion parameters for strain data from the miniature specimen tests. Analytical solutions for the Kachanov creep damage model specific to the two-material substrate-coating system were adopted for inverse optimization. The MTPS testing method and the associated inverse approach have proven effective in accurately determining the complete three-stage creep constants within the coating-substrate system. Results obtained for the P91–CoNiCrAlY coated system have shown a clear demonstration of the accuracy and capability of the method in identifying the full sets of uniaxial creep damage properties, i.e. A, n, m, B, χ, and φ, provided the feasible experimental data are available.

As a challenge to avoid the strength-ductility trade-off for Ti alloys, strength and ductility are contradictory, especially at a high content of primary α phase. This study proposed a new strategy to evade this trade-off dilemma by designing an 81% primary α phase/ultrafine-grained (UFG) secondary α phase lamellar heterogeneous structure(LPSlow-HS) in near-α Ti alloys. Compared to the bimodal structure, the LPSlow-HS offers a remarkable increase in the ultimate tensile strength(1119 MPa) and yield strength(1069 MPa), respectively, while the elongation (15%) is not sacrificed. Owing to the strong heterogeneous deformation-induced(HDI) strengthening, more number of delamination of interfaces, UFG microstructures and strong strain hardening render the Ti alloys high strength. This strategy may provide a new approach for achieving higher HDI strengthening effects in heterogeneous structures (HS) under high soft phases, and offer a feasible method for producing high strength-ductility Ti alloys under high primary α phase.

Aluminium (Al) alloys with high corrosion resistance and bactericidal capability will have significant potential for broader utilization in marine applications. To achieve this, a layer of cerium (Ce)-doped anodic oxidation (AO) film was prepared on the surface of the Al alloy to modify the pore structure of the micro-arc oxidation (MAO) coating. This not only improved its corrosion resistance but also imparted certain antibacterial properties to the coating. Then, 1H, 1H, 2H, 2H- perfluoroalkyltriethyloxy-silane (PFOTES) was used to hydrophobically modify the coating, which more effectively obstructed corrosive ions and bacteria, further elevating its corrosion resistance and antibacterial properties. Electrochemical tests demonstrated that |Z|0.01Hz of the modified coating was 2.86 × 109 Ω·cm2 which was two orders of magnitude higher than before. Furthermore, it remained above 109 Ω·cm2 even after 288 h of immersion. Moreover, the antibacterial effectiveness of the modified coating against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) increased by 5.89% and 19.53%, respectively, to 98.58% and 97.34%. These findings indicate a promising direction for improving the corrosion resistance of MAO coatings on metallic materials, paving the way for large-scale industrialization.

This paper investigates the impact of pre-precipitated η phases and Al3(Er, Zr) particles on the hot workability and recrystallization behavior of an Al–Zn–Mg–Er–Zr alloy. The optimal pre-precipitation heat treatment prior to hot deformation was obtained by heat treatment under different conditions combined with microstructure analysis. Subsequently, hot compression tests were conducted to establish the processing maps, and the influence of pre-precipitation on the microstructure and the hot workability under various thermal deformation conditions were analyzed. Furthermore, the effect of pre-precipitation of η phases and Al3(Er, Zr) particles on the recrystallization was studied. The results demonstrate that after the optimized pre-precipitation treatment, a multitude of Al3(Er, Zr) particles (∼10 nm) and η phases (∼1 μm) appeared in the alloy. Through this pre-precipitation treatment, the flow stress was significantly reduced, and the hot workability of the alloy was significantly enhanced, particularly at low temperatures and low deformation rates. Moreover, the pre-precipitated η phases facilitated recrystallization, refined grains and hence created a more homogeneous microstructure. Concurrently, during the recrystallization process, the precipitated Al3(Er, Zr) particles impeded grain boundary migration, and thus inhibited the growth of recrystallized grains, contributing to grain refinement.

The linear electro-optic effect offers a valuable means to control light properties via an external electric field. Lithium niobate (LN), with its high electro-optic coefficients and broad optical transparency ranges, stands out as a prominent material for efficient electro-optic modulators. The recent advent of lithium niobate-on-insulator (LNOI) wafers has sparked renewed interest in LN for compact photonic devices. In this study, we present an electro-optic modulator utilizing a thin LN film sandwiched between top and bottom gold (Au) film electrodes, forming a Fabry–Pérot (F–P) resonator. This resonator exhibits spectral resonance shifts under an applied electric field, enabling efficient modulation of reflected light strength. The modulator achieved a 2.3 % modulation amplitude under ±10 V alternating voltage. Our approach not only presents a simpler fabrication process but also offers larger modulation amplitudes compared to previously reported metasurface based LN electro-optic modulators. Our results open up new opportunities for compact electro-optic modulators with applications in beam steering devices, dynamic holograms, and spatial light modulators, and more.

To solve the problem of the survival differential policy interception between a spacecraft and a non-cooperative target pursuit game, the pursuit game policy is studied based on reinforcement learning, and the adaptive-augmented random search algorithm is proposed. Firstly, to solve the sparse reward problem of sequential decision making, an exploration method based on the spatial perturbation of parameters of the policy is designed, thus accelerating its convergence speed. Secondly, to avoid the possibility of falling into local optimum prematurely, a novelty degree function is designed to guide the policy update, enhancing the efficiency of data utilization. Finally, the effectiveness and advancement of the exploration method are verified with numerical simulations and compared with those of the augmented random search algorithm, the proximal policy optimization algorithm and the deep deterministic policy gradient algorithm.

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.

Abstract Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.

Abstract Neuron localization is a fundamental step in the neuron morphology reconstruction and quantitative analysis of cell counting and spatial distribution. The recent development of labelling and imaging techniques has resulted in growing demand for automatic neuron localization methods of 3D neuronal microscopy images. However, accurately localizing touching neurons remains a challenge. To address this issue, a novel method utilizing a super‐resolution segmentation network based on 2D Conv‐LSTM and a region growing method are proposed. This network can map and detect individual neurons in higher resolution space, allowing for the separation of closely touching neurons with reduced resource consumption. Subsequently, a region growing method is employed to localize neurons accurately. This method is evaluated using neuronal images generated by TDI‐fMOST. This method achieved neuron localization with an F1 score of 0.91. In comparison, other automatic localization methods achieved F1 scores lower than 0.85. It is also demonstrated that our network has fewer computational requirements compared to 3D neural networks. This method is promising for accurately localizing neurons in large‐scale neuron images.

To enhance the attitude stability of hexapod robots on rough terrain, this paper introduces a layered modular motion control method, termed reinforcement learning integrated with foot impedance control and an optimized reward mechanism. It integrates gait and foot impedance coordinated planning with reinforcement learning. By constructing a discrete gait model and a state space, the gait planning problem is transformed into an optimal sequence decision-making problem based on stability margin, which can be solved using the Markov decision process. Within the reinforcement learning framework, the robot autonomously optimizes gaits and foot trajectories using feedback from foot-ground contact states and base inclination. Our designed reward mechanism enhances gait adaptation, significantly augmenting stability and traversal efficiency. The algorithm accelerates convergence and ensures reliable decision-making by compressing the action space to four dimensions, reducing the need for internal sensor information, thus enhancing practicality. Comprehensive simulation experiments demonstrate that this method significantly outperforms central pattern generator methods in terms of distribution of the roll and pitch angles, distance traveled, number of successes, and mean reward.

Thioredoxin-interacting protein (TXNIP) is a crucial thioredoxin-binding protein that is recognized as a tumor suppressor in diverse malignancies, such as breast cancer, lung cancer, hepatocellular carcinoma, and thyroid cancer. However, the specific role and molecular mechanisms of TXNIP in the pathogenesis and progression of pancreatic cancer cells have not been determined. In this study, we investigate the relationship between TXNIP expression and overall survival prognosis in pancreatic cancer patients. Mechanistic studies are conducted to reveal the role of TXNIP in pancreatic cancer cell proliferation, migration, and regulation during malignancy. Our findings indicate that patients with high TXNIP expression have a more favorable prognosis. In vitro experiments with pancreatic cell lines show that overexpression of TXNIP suppresses the proliferation and migration of pancreatic cancer cells. Furthermore, we find that TXNIP inhibits the activation of the MAPK signaling pathway, thereby decreasing the malignant potential of pancreatic cancer. In conclusion, our study reveals TXNIP as a promising new predictive marker and therapeutic target for pancreatic cancer.

Abstract Background Clusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning. Results This paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster. Conclusions The experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas. Graphical Abstract