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ObjectiveTo establish the structural confirmation methods of three suspected new psychoactive substances （NPSs）, and explore a more general qualitative testing method.MethodsInfrared absorption spectroscopy （IR）, gas chromatography-mass spectrometry （GC-MS）, 1H-nuclear magnetic resonance spectroscopy （1H-NMR）, 13C-nuclear magnetic resonance spectroscopy （13C-NMR）, 19F-nuclear magnetic resonance spectroscopy （19F-NMR） and other techniques were used to identify the composition and structure of 5 samples containing suspected NPS submitted by public security bureaus.ResultsNPSs were found in the above 5 samples, and 3 were confirmed as NPS included in the newly listed controlled substances on July 1, 2024, namely 2-（methylamino）-2-（2-methylphenyl）cyclohexan-1-one （2-MDCK）, 2-（ethylamino）-2-（2-fluorophenyl）cyclohexan-l-one （2-FXE）, 1-（3,4-methylenedioxyphenyl）-2-（dimethylamino）pentan-1-one （dipentylone）, respectively. The first two substances were phencyclidine NPS, and the third substance was synthetic cathinone NPS.ConclusionThis study systematically summarizes the distinguishing features of the infrared absorption spectrometry, nuclear magnetic resonance spectroscopy and mass spectrometry of three NPSs, which can provide a reference for the qualitative identification of unknown substances.

In the era of globalized geo-economics, international big channel are changing the way geo-economic elements flow across borders, expanding cooperation spaces such as ports and border-free trade zones to more distant inland areas and shaping new ways of cross-border geo-economic cooperation. As an international land and sea channel connecting China and Myanmar, the China-Myanmar Indian Ocean New Channel (hereinafter "the Channel") is of great importance for promoting geo-economic cooperation between the two countries and the construction of the Belt and Road Initiative. However, owing to various obstacles in geo-economic cooperation, the geo-economic effect of the Channel has not been effectively brought into play. In this study, the obstacles to geo-economic cooperation in the Channel and its collaborative mechanisms were analyzed to provide important scientific and technological support for enhancing its geo-economic effects and strengthening geo-economic cooperation between China and Myanmar. The natural geography of the region with its high mountains and deep valleys and the frequently occurring natural disasters are natural obstacles that limit the geo-economic effect of the Channel. Moreover, the difference in economic development between China and Myanmar is not conducive to the deepening of geo-economic cooperation in the Channel. Complex armed conflicts have disrupted the construction of the Channel and China's investment in its route, and the potential intervention of India and the United States will become an added constraint on its further development. Furthermore, the multiple stakeholders game in the construction of the Channel may bring geo-risks. Contrary to preconceived notions, geo-economic cooperation in the Channel is not a strategy for regional control, but rather to create an international trade corridor deeply related to transportation logistics, supply chains, industrial chains and value chains. To promote geo-economic cooperation in the Channel, its construction should be based on a multi-level collaborative mechanism for geo-economic cooperation. At the national level, the docking of development strategies between China and Myanmar should be strengthened by promoting integration of the Channel into the Belt and Road Initiative, strengthening the docking of China-Myanmar policy communication mechanisms, and formulating a long-term development strategy plan for the Channel. At the channel level, the geo-economic effect of the Channel should be upgraded by promoting the linkage of internal and external infrastructures, strengthening geo-economic element cross-border flow and industrial cooperation, and deepening economic and trade cooperation with Myanmar and the Indian Ocean region. At the stakeholder level, a consultation mechanism involving multi-stakeholder participation should be established by strengthening the docking of multi-stakeholder interests, coordinating the mechanism of China-Myanmar port docking, and creating a three-dimensional risk prevention mechanism for the Channel.

ABSTRACT Background Gastric adenocarcinoma of the fundic gland type (GA‐FG) is a newly described variant of gastric adenocarcinoma with lack of knowledges regarding its genetic features. Methods We performed whole‐genome sequencing (WGS) in formalin‐fixed paraffin‐embedded (FFPE) tumor tissues and matched adjacent noncancerous specimens from 21 patients with GA‐FG, and integrated published datasets from 1105 patients with traditional gastric adenocarcinoma with the purpose of dissecting genetic determinants both common to conventional gastric adenocarcinoma and unique to GA‐FG disease. Results We characterized the genomic architecture of GA‐FG disease, revealing the predominant proportion of C > T substitution among the six types of SNVs. GNAS was the most significantly mutated driver gene (14.29%). 42.8% of samples harbored “Kataegis.” Distinct genomic alterations between GA‐FG and conventional gastric cancer were identified. Specifically, low mutational burden and relatively moderate mutational frequencies of significantly mutated driver genes, coupled with the absence of non‐silent alterations of formerly well‐known drivers such as TP53, PIK3CA and KRAS were identified in GA‐FG patients. Oncogenic signaling pathway analysis revealed mutational processes associated with focal adhesions and proteoglycans in cancer, highlighting both common and specific procedures during the development of GA‐FG and conventional gastric cancer. Conclusion Our study is the first to comprehensively depict the genomic landscape highlighting the multidimensional perturbations in GA‐FG patients. These discoveries offered mechanistic insights for novel diagnostic and therapeutic strategies for patients with such disease.

The scientific and sustainable utilization of marine space resources is of great significance for the high-quality development of China’s marine economy and the Ocean Power Strategy. This paper constructs the index system of marine space resources utilization intensity, analyzes the evolution law of China’s marine space resources utilization intensity and its influencing factors. The research results indicate that: (1) The overall utilization intensity of China’s marine space resources utilization is continuously increasing, and the imbalance in provincial utilization intensity of marine space resources was intensifying. (2) The utilization intensity of marine space resources in Tianjin increased significantly, while the utilization intensity of marine space resources in Shanghai fluctuated greatly. The utilization intensity of marine space resources in Zhejiang, Hainan, and Fujian remained at higher intensity and high intensity. The utilization intensity of marine space resources in Liaoning, Shandong, and Guangdong was at medium intensity and high intensity, while the utilization intensity of marine space resources in Jiangsu and Guangxi was at medium intensity and low intensity. The utilization intensity of marine space resources in Hebei remained at low intensity. (3) The overall utilization intensity of marine production space in 11 coastal provinces and cities was higher than that of marine ecological space and marine living space. The overall utilization intensity of marine living space shows a positive growth in 11 coastal provinces and cities, while the overall utilization intensity of marine ecological space shows a significant downward trend. (4) The economic level has the greatest positive effect on the utilization intensity of marine space resources, and industrial structure, government investment, and opening up also have significant positive effects on the utilization intensity of marine space resources. Coastline resources, marine-related employment, and environmental protection has negative effect on the utilization intensity of marine space resources.

[Objective] Rainfall is the driving force of hydraulic erosion in black soil sloping farmland. It is of great significance to study soil erosion characteristics under natural rainfall conditions for soil erosion control. [Methods] Based on 43 erosive rainfall events observed at different ridge-row runoff plots (3°, 5° and 7°) along the slope in Guangrong Small Watershed, Hailun City, Heilongjiang Province during 2021—2022, K-Mean clustering method was used to compare the characteristics of runoff and sediment transport under different rain patterns and slopes, and the influencing factors on runoff and sediment transport were discussed. [Results] The rainfall of short duration, heavy rain and light rainfall (rain type 3) is the main one, followed by the rainfall of medium duration, medium rain intensity and medium rainfall (rain type 2), and the rainfall of long duration, light rain intensity and heavy rainfall (rain type 1) rarely occurs. Under the same rain pattern, the cumulative runoff depth and sediment transport increased with the increase of slope. Under the same slope, different rainfall types have different responses to cumulative runoff depth. Overall, rainfall type 3 has the highest contribution (50.90%～57.90%), rainfall type 2 has the second contribution (37.60%～44.10%), and rainfall type 1 has the lowest. In the event of rain type 2, rainfall erosivity and slope have direct influence on runoff depth, and runoff depth and slope have direct influence on sediment transport. In the event of rain type 3, rainfall erosivity affects runoff depth and then sediment transport through direct and indirect effects. [Conclusion] For all rainfall-type events, rainfall erosivity and slope are the main controlling factors affecting the runoff depth, and the runoff depth and slope are the main controlling factors affecting the sediment transport.

The development of new technologies such as artificial intelligence has promoted the digital transformation of education around the world. ChatGPT, as a representative of generative artificial intelligence, poses new challenges to the field of education and also puts forward new requirements for teacher quality. Artificial intelligence literacy is the core qualitythat teachers need to adapt to the era of AI. Based on the analysis of the connotation of quality, teacher qualityand artificial intelligence literacy, teacher artificial intelligence literacy refers to the qualitythat teachers, as human-machine collaborative teachers, need to understand and use artificial intelligence educational application products in the context of the artificial intelligence era. In the AI era, teachers'artificial intelligence literacy includes three primary elements: AI knowledge, AI skills, and AI attitude and ethics, as well as fifteen secondary elements like knowledge of AI fact, knowledge of AI principle, skills of identifying AI, skills of analyzing AI, guarantee of student safety, and protection of student privacy, etc.It is effective tocultivate teachers' artificial intelligence literacy from three levels: government, school and teachers themselves.

False data injection attack (FDIA) is a serious threat to the security and stable operation of smart grids. In this paper, a FDIA detection algorithm that combines unsupervised and supervised learning is proposed, solving the problems of scarce labeled data and extremely imbalanced normal and attack samples. Firstly, contrastive learning is introduced to capture the features of a small amount of attack data, and it generates new attack samples to achieve data augmentation. Then, various unsupervised detection algorithms are used to perform feature self-learning on a large number of unlabeled samples, addressing the problem of scarce labeled samples. Finally, the features extracted by the unsupervised algorithm are fused with the historical feature set, and a supervised XGBoost classifier is constructed to identify and output the detection results. The results on the IEEE 30-node system show that the proposed method can enhance the stability of the FDIA detection model under scarce labeled samples and imbalanced data, compared with other FDIA detection algorithms. The proposed method can improve recognition accuracy and reduce false alarm rate.

The similarity between arc fault current waveforms and nonlinear load currents can lead to misjudgments in arc fault identification methods that rely on arc current. In response to this issue, this paper establishes a unified fault criterion based on the conclusion that different types of loads that exhibit similar magnitudes of load side voltage sag during arc faults. Combined the voltage sag criterion with waveform criteria, this paper proposes a novel arc fault detection method. The method takes the load side voltage sag value exceeding the threshold as the detection start condition. After detection starts, the method identifies faults by calculating the degree of load side voltage waveform distortion using Hausdorff distance algorithm. Simulations are conducted on five different loads to determine the startup threshold and detection threshold. Test results show that the fault identification accuracy of the proposed method is 100% under the above thresholds.

Aiming at the contradiction between the rapidity requirement of online calculation of transient stability preventive control and the computational complexity of time-domain equations, a stacking ensemable learning driven optimization method for power system transient stability preventive control was proposed. Firstly, a transient stability estimator based on a stacking ensemble deep belief network was constructed to replace the nonlinear differential algebraic equation solution process required for transient stability determination. Secondly, the trained transient stability estimator was used as a transient stability constraint discriminator, which was embedded in the iterative optimization process of the Aptenodytes Forsteri optimization algorithm. Finally, with the goal of minimizing the cost of preventive control, a stacking ensemble learning driven power system transient stability preventive control optimization algorithm was established. The algorithm realized the efficient judgment of transient stability constraints in preventive control, and improved the decision-making level of preventive control for power generation rescheduling. Based on the IEEE39 bus system, the proposed preventive control method was verified by experiments. The results show that the method has achieved good results in both evaluation accuracy and calculation efficiency.

Error analysis of inertial navigation systems is one of the important tasks in inertial navigation research. Through error analysis, the contribution of each error in the system to the total error can be obtained. This work is particularly important in the rotary modulation inertial navigation system, because it is necessary to evaluate the suppression effect of the rotation strategy on various error sources through error analysis. Firstly, a state space model of the error has been established based on the system error model, and the total navigation error is divided into linear combinations of various error factors through recursion and decomposition. The derived covariance update equation can be used to quickly and accurately obtain the contribution of various errors including initial error and device error to the total system error. Simulation results of strapdown, single-axis and two-axis modulation systems verifies that the error propagation rules calculated by the proposed method are in agreement with the known conclusions. Simulation of different modulation strategies shows that the proposed method is fast and intuitive in evaluating rotation strategies.

Can the spinal cord stimulation (SCS) regulate the autonomic nerves through the endothelin-1 (ET-1) and nerve growth factor (NGF)/p75NTR pathways and thus inhibit the occurrence of atrial fibrillation (AF)? In our research, 16 beagles were randomly divided into a rapid atrial pacing (RAP) group (n = 8) and a RAP + SCS group (n = 8), and the effective refractory period (ERP), ERP dispersion, AF induction rate, and AF vulnerability window (WOV) at baseline, 6 h of RAP, 6 h of RAP + SCS were measured. The atrial tissue was then taken for immunohistochemical analysis to determine the localization of ET-1, NGF, p75NTR, NF-kB p65, and other genes. Our results showed that SCS attenuated the shortening of ERP in all parts caused by RAP, and after 6 h of SCS, the probability of AF in dogs was reduced compared with that in the RAP group. Moreover, the expression of ET-1, NGF, and p75NTR in the atrial tissues of dogs in the RAP + SCS group was significantly increased, but the expression of NF-kB p65 was reduced. In conclusion, SCS promotes the positive remodeling of cardiac autonomic nerves by weakening NFκB p65-dependent pathways to interfere with the ET-1 and NGF/p75NTR pathways to resist the original negative remodeling and inhibit the occurrence of AF.

Uricase (or Urate oxidase), a key enzyme involved in purine metabolism, is commonly used in treating conditions such as gout, hyperuricemia, and tumor lysis syndrome. In this study, a uricase-producing strain (named CSAJ-16) was isolated from the soil sample of Cangshan Mountain, Yunnan Province, China. This strain was identified as Arthrobacter sp. CSAJ-16. Based on the gene sequence alignment, the uricase gene (named aruox) of Arthrobacter sp. CSAJ-16 was amplified and heterologously expressed. The recombinant uricase (ArUOX) was about 32 kDa. The optimal pH and temperature of ArUOX were pH 7 and 20°C, respectively. The ArUOX remained above 50% relative activity after incubation at 37°C for 100 min or at pH 6.0–8.6 for 24 h. Moreover, metal ions such as K+, Mg2+, Ca2+, Ba2+ and Pb2+ can significantly enhance the activity of ArUOX (> 200%). These enzymatic properties indicate that ArUOX has potential applications in pharmaceutical enzymes and uric acid detection kits.

Biotechnology is crucial for the sustainable development of the aquatic seed industry. Therefore, improving the innovation capability in aquatic breeding and making seed sources independent are significant for ensuring food security in China. This study summarizes the development status and problems of aquatic breeding in China and analyzes the status of research and application of seven types of biotechnology for aquatic breeding, namely, transgenic breeding, ploidy breeding, molecular-marker-assisted breeding, genomic selection breeding, genome editing breeding, molecular design breeding, and germline stem cell transplantation; future research and development demand for these technical fields in China is further analyzed. Moreover, future development goals and major tasks are suggested, including collecting, preserving, and accurately identifying excellent germplasm resources of aquatic organisms, profoundly analyzing the genetic basis and regulatory mechanism of critical traits, developing efficient and accurate breeding technologies, and achieving breakthroughs in the creation of new varieties. Furthermore, following suggestions are proposed: optimizing development policies of the aquatic seed industry, promoting technological innovation in aquatic breeding, establishing platforms for aquatic variety creation and transformation, and setting up special projects to accelerate technological innovation and development of the aquatic seed industry.

Objective To investigate the effect of Baduanjin exercise combined with Jue-tone Music therapy on anxiety and depression in patients with psoriasis vulgaris. Methods Totally 88 patients with psoriasis vulgaris who discharged from the hospital were randomly divided into three groups: control group (n=29), intervention group A (n=29) and intervention group B(n=30). The control group received routine follow-up after discharged from hospital, the intervention group A received Baduanjin exercise, and the intervention group B received Baduanjin exercise combined with Jue-tone music therapy. The Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS)were used to assess the anxiety and depression status of patients. Results At one month after discharge form hospital, scores of SAS and SDS in intervention group A and intervention group B were lower than those in the control group, respectively(P＜0. 05), and scores of SAS and SDS in intervention group B were lower than those intervention group A(P＜0. 05). Conclusion Baduanjin exercise combined with Jue-tone music therapy during follow-up is effective to relieve the anxiety and depression of patients with psoriasis. (目的 分析健身气功八段锦联合角调式音乐对寻常型银屑病患者焦虑抑郁症状的干预效果。方法 选取医院收治的寻常型银屑病出院患者88例为研究对象, 采用随机数字表法分为对照组(n=29)、干预1组(n=29)和干预2组(n=30), 对照组采用常规出院随访, 干预1组仅练习健身气功八段锦, 干预2组练习健身气功八段锦联合聆听角调式音乐。采用焦虑自评量表(SAS)和抑郁自评量表(SDS)评估患者焦虑抑郁程度。结果 出院后1个月时, 干预1组和干预2组SAS、SDS评分均低于对照组(P＜0. 05), 且干预2组SAS、SDS评分低于干预1组(P＜0. 05)。结论 在寻常型银屑病患者出院后采用中医特色护理手段, 可帮助患者调节不良情绪, 改善患者焦虑抑郁症状。)

This paper summarized the skin care process of a patient with sigmoid colon malignant tumor after two courses of chemotherapy and targeted therapy with Capecitabine and Cetuximab injection. The application of licorice oil, a traditional Chinese Medicine, instead of normal saline for debridement can promote the softening of scab and relieve the pain of debridement. During the treatment, comprehensive nursing interventions on mental health, dietary and home-based education were carried out to reduce the risk of recurrence. (本文总结1例抗肿瘤药物引起皮肤毒性反应患者的护理经验。应用中药制剂甘草油替代生理盐水进行清创, 促进痂皮软化, 减轻患者清创疼痛; 同时给予患者心理、饮食、居家护理指导, 避免复发。)

This study investigates the impact of soaking, germination, and roasting under various conditions on the phenolic content, antioxidant activities, and fatty acid profile of sunflower seeds of striped and black oil seed varieties. Ferulic acid emerged as the predominant phenolic acid across all samples. The study highlights the influence of different treatments and processing conditions on the levels of phenolic acids and flavonoids. Results revealed that soaking striped seeds for 24 h and roasting black oil seeds for 6 min exhibited the highest total phenolic content (TPC) and total flavonoid content (TFC). Antioxidant activities varied, with striped seeds soaked for 24 h demonstrating the highest ferric-reducing antioxidant power (FRAP) values, while black oil seeds roasted for 6 min exhibited superior 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radical scavenging activity. Oleic acid and linoleic acid were identified as major fatty acids in all processed samples, with striped seeds germinated for 24 h and black oil seeds germinated for 48 h displaying the highest concentrations. Soaking led to a reduction in both unsaturated and saturated fatty acids in both varieties. Roasting further influenced fatty acid concentrations, with a significant increase in striped seeds after 5 and 6 h of roasting, followed by a gradual decrease. Our findings suggest that incorporating appropriate processing methods, such as soaking striped seeds for 24 h and roasting black oil seeds for 6 min, enhances the potential health benefits of sunflower seeds.

Intelligent design is a trend in geotechnical engineering. However, a middle link between demand identification and intelligent design is missing in anchor engineering, resulting in lower efficiency in terms of the interaction between the anchor components, geological body and modeling information. To address this challenge, this paper proposes a method for modeling a three-dimensional anchor engineering information model based on sparse boreholes. This method includes three parts: modeling the geological body, integrated modeling of the anchor components and the geological body, and multiple information assignments based on the anchor model. The feasibility was validated in actual engineering scenarios by using neural networks and rendering engines. The results showed that the prediction accuracy of the strata improved by 17.69%, the modeling efficiency increased by double, and engineering information was fit to the model well. This paper has the application potential to achieve intelligent design in anchor engineering.

Spatial load forecasting faces the problems of multiple characteristic factors and data shortage. A spatial load forecasting method based on double-layer extreme gradient boosting (XGBoost) and data enhancement is proposed. Firstly, the area to be predicted is divided into several sub regions according to the supply range of feeder power. Secondly, a feature selection model based on double-layer XGBoost is constructed. The first layer XGBoost scores and sorts the features. The combined features are loaded into the second layer XGBoost for sub regional load forecasting. The best feature variables of each sub region are selected according to the load forecasting results. Then, the training set samples of each sub region are enhanced by the generative adversarial network (GAN), and the load of sub regions is forecasted through the extreme learning machine (ELM). Finally, the predicted values of sub regions are added to obtain the load of the region to be predicted. Taking local areas of Shanghai as an example, the simulated experiment and comparative analysis are carried out. The results show that the proposed method can solve the problems of characteristic variable selection and data shortage at the same time, and has high prediction accuracy.

Sufficient evidence supports a relationship between certain myeloid neoplasms and exposure to benzene or formaldehyde. DNA methylation could underlie benzene- and formaldehyde-induced health outcomes, but data in exposed human populations are limited. We conducted two cross-sectional epigenome-wide association studies (EWAS), one in workers exposed to benzene and another in workers exposed to formaldehyde. Using HumanMethylation450 BeadChips, we investigated differences in blood cell DNA methylation among 50 benzene-exposed subjects and 48 controls, and among 31 formaldehyde-exposed subjects and 40 controls. We performed CpG-level and regional-level analyses. In the benzene EWAS, we found genome-wide significant alterations, i.e., FWER-controlled P-values

Aiming at the uncertain optimization problem of AC/DC hybrid distribution network under the coordination of source, grid load and storage, an AC/DC hybrid distribution network operation scheme based on two stage affinely adjustable robust optimization was proposed in this paper. Firstly, the controllable model of distribution generation (DG), the battery energy storage model with cycle life degradation and the ZIP load model were considered, and the optimization model of AC/DC hybrid distribution network economic operation was constructed. Then the ellipsoid set was used to describe the uncertainty of DG output, and the linear decision-making mechanism of decision variables and uncertainty variables of DG output in the optimization model was formed by combining linear affine strategy, Finally, the robust optimization model of AC/DC hybrid distribution network was transformed into a deterministic mixed integer second-order cone programming model by dual transformation. The effectiveness and advantages of the affinely adjustable robust optimization method proposed in this paper were compared and verified in the two modified IEEE bus system.

As the typical provincial transmission power grid,Jiangsu power grid is faced with the pressures and challenges induced from system-integration of rapid developments of renewable energy during the energy transition period,which needs to be upgraded in "source-network-load-storage" and other aspects. Through the analysis of energy development from load,power supply,grid structure in Jiangsu,challenges faced by Jiangsu power grid such as insufficient transfer abilities of important transmission sections are put forward. Considering the large-scale integrated renewable energy and structure characteristics of Jiangsu transmission power grid,transmission-capacity demands of the Yangtze-river-crossing section during the "14th Five-Year Plan" and future are emphatically analyzed. Afterwards embedded DC transmission technology that both the transmission-and receiving-ends are located in Jiangsu AC network and distributed on both sides of the Yangtze River are proposed. Comparing with the traditional long-distance and high-capacity DC transmission technology constructed under the interconnection mode,the embedded DC transmission technology proposed in this paper usually uses the existing overhead transmission lines or existing/reserved AC cable channels to construct the AC-DC hybrid power grid in Jiangsu,greatly improving transmission capacities of important sections with limited transmission corridor spaces and power supply potential abilities of the existing power system to satisfy the development demands of Jiangsu power system.

Multiple Input Multiple Output (MIMO) detection algorithm is used to compensate the Mode Dependent Loss (MDL) in Mode Division Multiplexing (MDM) optical fiber channel. The problem of MIMO detection algorithm is that the computational complexity is too high and the performance is degraded with a large number of modes. This paper proposes to apply Massive-MIMO detection algorithms to the fiber channel, The algorithms include Minimum Mean Square Error (MMSE) , Conjugate Gradient (CG) , Gauss Seidel (GS) , and Alternating Direction Method of multipliers based Infinity-Norm (ADMIN) , and Optimized Coordinate Descent based BOX constraint (OCDBOX) . The result shows that the OCDBOX has the best Bit Error Rate (BER) performance in MDL-impaired MDM optical fiber communication system while higher complexity. The ADMIN has the second best BER performance and lower computational complexity. Therefore, the ADMIN detection algorithm can represent a good candidate in the MDM system with MDL.

Abstract After the smooth cordgrass Spartina alterniflora invaded coastal China, native birds started avoiding the green desert, and bird diversity declined. After many years, a few passerine birds began to enter and use smooth cordgrass, but only birds with a nonspecialised habitat. In this study, we found that a native reed‐specific bird, the parrotbill Calamornis heudei, flocked and sang in a smooth cordgrass habitat throughout the overwintering period near Sheyang Port in Yancheng, Jiangsu Province. This observation suggests that native obligate birds may be forced to adapt to exotic smooth cordgrass habitats after long‐term coexistence, which would, obviously, affect the distribution, feeding and reproduction of birds. The concern is that this could be an ecological trap, leading to unknown consequences. More research is required to examine the process occurring along the Chinese coast.

During the oil and gas drilling process, inefficient cuttings transport may elevate cuttings concentration, leading to higher Equivalent Circulating Density (ECD) and easily triggering a series of wellbore stability issues. In this paper, a new type of annulus cleaning tool is developed, incorporating the H- B non-Newtonian fluid model and a coupled CFD-DEM to construct a numerical model for analyzing the cuttings transport behavior within the multi-layer wellbore annulus using the cleaning tool. The model, validated against experiments, reveals factors affecting Cuttings Transport Ratio (CTR). Through Box- Behnken Design (BBD), Response Surface Methodology (RSM), and Analysis of Variance (ANOVA), a correlation function for CTR is derived to optimize the cleaning tool parameters. Findings show an initial increase then decrease in CTR with nozzle quantity, diameter, and angle. Decreasing hydraulic diameter enhances CTR, whereas nozzle diameter variation has minimal impact at smaller diameters. After optimization, CTR reaches 95.76% with 4 nozzles of 10mm diameter, 32° angle, and 57mm hydraulic diameter. These results guide cleaning tool optimization design , improving cuttings transport efficiency, and ensuring safe oil and gas production.

Background: Clonorchis sinensis is a zoonotic liver fluke that inhabits the bile ducts of the human liver for prolonged periods, leading to cholangiocarcinoma. Recent research indicates associations between altered biliary microbiota and bile duct disorders. However, the impacts of C. sinensis infection on bile duct epithelium and subsequent effects on biliary microbiota remain unknown. Methods: Feline bile duct samples were collected from both uninfected and C. sinensis-infected cats. Histopathological examination was performed to assess epithelial changes, fibrosis, mucin and cell proliferation using hematoxylin-eosin staining and immunohistochemistry. Additionally, biliary microbiota composition was analyzed through 16S rRNA gene sequencing. Statistical analyses were conducted to compare the microbial diversity and relative abundance between infected and uninfected samples. Results: Histopathological analysis of infected feline bile ducts revealed prominent epithelial hyperplasia characterized by increased cell proliferation. Moreover, periductal fibrosis and collagen fibrosis were observed in infected samples compared to uninfected controls. Biliary microbial richness decreased with disease progression compared to uninfected controls. Streptococcus abundance positively correlated with disease severity, dominating communities in cancer samples. Predictive functional analysis suggested that C. sinensis may promote bile duct lesions by increasing microbial genes for carbohydrate metabolism, replication, and repair. Conclusions: This study provides comprehensive insights into the pathological effects of C. sinensis infection on feline bile duct epithelium and its influence on biliary microbiota composition. These novel findings provide insight into C. sinensis pathogenesis and could inform therapeutic development against human clonorchiasis. Further research is warranted to elucidate the underlying mechanisms driving these changes and their implications for host-parasite interactions.

The detection of multi-feature fusion is a crucial approach to address the issue of series arc fault detection. Effective feature selection plays a vital role in enhancing the accuracy of the classifier and reducing system complexity. In this study, a feature selection algorithm based on Fisher-mutual information is proposed to tackle the problem of feature selection in multi-feature fusion detection. This algorithm utilizes the characteristics of arc fault voltage source to construct a feature pool. The Fisher-score algorithm and mutual information algorithm are employed to construct an optimal feature subset. The feature subset undergoes rough selection by retaining key features of the classifier and fine selection by eliminating redundant features. Experimental results and comparisons with related methods demonstrate that the proposed feature selection method significantly enhances the classifier's recognition accuracy, reduces classification and recognition time, diminishes the feature dimension, and outperforms other existing methods.

In order to solve the problems of unreasonable node distribution and complex connection of pressure energy lines in the current mine ventilation network pressure energy distribution diagram, a new method with independent disjoint roads as the main skeleton, and the node distribution method of the mine ventilation network pressure energy graph distributed on both sides of the remaining connected nodes is proposed. According to the spatial relationship between independent disjoint roads and the remaining nodes and the remaining nodes, the drawing method of the branch shape between the nodes is analyzed and the drawing algorithm is designed. The drawing results of the drawing algorithm show that the algorithm can use the existing data to reasonably convert the mine ventilation network diagram into the pressure energy distribution diagram of the mine ventilation network, and the nodes in the pressure energy distribution diagram are distributed in an orderly manner, and the pressure energy lines between the nodes are connected normally, which can not only reflect the network relationship between nodes, but also reflect the pressure energy distribution of nodes. The algorithm can provide a reference for intelligent mine ventilation and underground fire prevention and risk avoidance.

Key Clinical Message Common urinary symptoms may arise from metastases from uncommon sites. In patients with a history of cancer, the focus should be on the currently affected organ and the status of the underlying malignancy.

Febrile seizures (FSs) are convulsions caused by a sudden increase in body temperature during a fever. FSs are one of the commonest presentations in young children, occurring in up to 4% of children between the ages of about 6 months and 5 years old. FSs not only endanger children’s health, cause panic and anxiety to families, but also have many adverse consequences. Both clinical and animal studies show that FSs have detrimental effects on neurodevelopment, that cause attention deficit hyperactivity disorder (ADHD), increased susceptibility to epilepsy, hippocampal sclerosis and cognitive decline during adulthood. However, the mechanisms of FSs in developmental abnormalities and disease occurrence during adulthood have not been determined. This article provides an overview of the association of FSs with neurodevelopmental outcomes, outlining both the underlying mechanisms and the possible appropriate clinical biomarkers, from histological changes to cellular molecular mechanisms. The hippocampus is the brain region most significantly altered after FSs, but the motor cortex and subcortical white matter may also be involved in the development disorders induced by FSs. The occurrence of multiple diseases after FSs may share common mechanisms, and the long-term role of inflammation and γ-aminobutyric acid (GABA) system are currently well studied.

In view of the problem that quadrature amplitude modulation has 1.53 dB capacity gap at the Shannon limit in high signal noise ratio, the paper proposes a joint shaping method of geometry and probability based on mutual information neural estimation. Geometric shaping and probability shaping are combined to improve the mutual information of the communication system. In this paper, the mutual information neural estimation is used to calculate the mutual information of the system, and the encoder of the transmitter is trained for the purpose of maximizing the mutual information, so as to realize geometric shaping and probability shaping. Through the simulation of Additive White Gaussian Noise (AWGN) channels with different signal noise ratios, it is proved that the performance of joint shaping of geometry and probability based on mutual information neural estimation is better than that of geometric shaping or probabilistic shaping separately. In AWGN channel with signal-to-noise ratio of 10 dB, the mutual information of the system has a gain of 0.041 7 bit/symbol over geometric shaping, and a gain of 0.027 9 bit/symbol compared over probability shaping.

In order to obtain the characteristic parameters of dampness status of resin impregnated paper (RIP),the time domain dielectric response technique is performed on the dampness status evaluation,and the RIP moisture contents are intensively studied based on polarization and depolarization current (PDC) method. Firstly,RIP insulation samples with different moisture contents are prepared in the laboratory and the PDC curves are measured. Then,the optimization algorithm combining genetic algorithm and interior point method is introduced,and the parameters of extended Debye model are obtained by Matlab simulation. Finally,two characteristic parameters of dampness status are extracted,which are the relaxation contribution coefficient of the maximum time constant branch A 9 and the stable depolarization charge quantity Q d-5000. Conclusions from the research results are as follows. The PDC curve moves upward as the moisture content increases. The dampness of the RIP insulation results in the increase of its conductivity and the decrease of its insulation resistance,absorption ratio andpolarization index. The parameters of the 9-branch extended Debye model are sensitive to the RIP insulation dampness status,and the A 9 shows a linear relationship with moisture content. The depolarization charge quantity is sensitive to moisture content,and the Q d-5000 shows an exponential relationship with moisture content.

Coconut is naturally sealed with coconut water inside. Microbial contamination occurs only after the seal is broken during extraction. This study evaluated changes in the microbiological, physicochemical, and chemical properties of coconut water during postharvest storage at ambient and refrigerated temperatures. Initial microbial counts ranged from 2 to 5 log CFU/mL, while physicochemical factors, including total soluble solids (TSSs), pH, and sugar content (4–7 °Brix, 5.0–5.5, 4–6% g/100 mL), were consistent. The dynamic changes in the physicochemical properties of coconut water stored under both conditions exhibited a clear correlation with the increased microbial populations. Fructose was the primary sugar, with citric and malic acids as major acids, while the predominant volatile compounds were ethanol, ethyl acetate, ethyl ester, acetic acid and octanoic acid. Storage conditions led to similar microbial and physicochemical changes, but ambient temperature accelerated spoilage 10 times faster than refrigeration. Sucrose decreased steadily, whereas fructose and glucose remained stable until a precipitous decline coincided with lactic acid bacteria (LAB) reaching >6 log CFU/mL on the final day of storage. Weissella cibaria and Leuconostoc spp. are the main species in coconut water. The presence of specific volatile compounds, including octanoic acid, acetic acid, ethyl acetate, and butyl phenol, is associated with the activities of Lactobacillus, particularly Weissella. There was a clear relationship among microbial groups and populations, total titratable acidity (TTA), and sensory criteria. Remarkably, TTA was closely correlated with total plate count (TPC) (>5 log CFU/mL) and an unacceptable sensory rating.

Cryptographic cores are the key components to enforce information security properties related to confidentiality and integrity. Since the security of a cryptographic core implementation and the security of the cryptographic algorithm itself from the mathematical perspective are two different problems, cryptographic cores may contain hidden security vulnerabilities such as design flaws and side channels. Security analysis methods based on functional verification rely heavily on the quality of test vectors and usually have low test coverage, which is difficult to meet the security verification requirements of security-critical components like cryptographic cores. This work proposes a cryptographic core security verification and vulnerability detection method from the perspective of information flow security. The proposed method can accurately measure all logical information flows in cryptographic core designs to formally verify security properties such as confidentiality and integrity. It can detect potential security vulnerabilities in cryptographic implementations by capturing harmful information flows. Experimental results show that our information flow security verification method is effective in detecting sensitive information leakage caused by the design vulnerabilities and side channels in cryptographic cores.

The current electricity transaction process in the electricity market has problems such as high risk, time-consuming, and low efficiency. In order to solve such problems, an improved smart contract electricity transaction model based on blockchain is proposed. The blockchain is a decentralized database with advantages such as non-tampering and encryption security. The blockchain has the advantages of decentralization and high securing. The blockchain and smart contract are applied in the proposed model to improve the efficiency and security of the electricity transaction process. Firstly, the existing problems of the current electricity transaction model are analyzed. Risk assessment is carried out. A decentralized electricity transaction scenario is constructed. Secondly, the reliability coefficient is combined with smart contracts to establish an improved smart contract electricity transaction model based on blockchain. Finally, the feasibility of the model is verified by simulating electricity transaction. The results show that the improved smart contract electricity transaction model proposed is more efficient than the existing transaction model. The model effectively reduces the risk of being attacked and maintain the power market transaction order, thus providing a new idea for the electricity market transaction mode.

Sepsis-associated encephalopathy (SAE) is an acute neurological deficit caused by severe sepsis without signs of direct brain infection, characterized by the systemic inflammation and disturbance of the blood–brain barrier. SAE is associated with a poor prognosis and high mortality in patients with sepsis. Survivors may exhibit long-term or permanent sequelae, including behavioral changes, cognitive impairment, and decreased quality of life. Early detection of SAE can help ameliorate long-term sequelae and reduce mortality. Half of the patients with sepsis suffer from SAE in the intensive care unit, but its physiopathological mechanism remains unknown. Therefore, the diagnosis of SAE remains a challenge. The current clinical diagnosis of SAE is a diagnosis of exclusion; this makes the process complex and time-consuming and delays early intervention by clinicians. Furthermore, the scoring scales and laboratory indicators involved have many problems, including insufficient specificity or sensitivity. Thus, a new biomarker with excellent sensitivity and specificity is urgently needed to guide the diagnosis of SAE. MicroRNAs have attracted attention as putative diagnostic and therapeutic targets for neurodegenerative diseases. They exist in various body fluids and are highly stable. Based on the outstanding performance of microRNAs as biomarkers for other neurodegenerative diseases, it is reasonable to infer that microRNAs will be excellent biomarkers for SAE. This review explores the current diagnostic methods for sepsis-associated encephalopathy (SAE). We also explore the role that microRNAs could play in SAE diagnosis and if they can be used to make the SAE diagnosis faster and more specific. We believe that our review makes a significant contribution to the literature because it summarizes some of the important diagnostic methods for SAE, highlighting their advantages and disadvantages in clinical use, and could benefit the field as it highlights the potential of miRNAs as SAE diagnostic markers.

As for the tight sandstones, pore-scale fluid flow ability is one of the most fundamental parameters that determine potential development capacity when compared to conventional reservoirs due to relatively tiny spaces. The determination of pore-throat structures and their impact on fluid flow behavior could improve the forecast ability for development. Therefore, the main purpose of this paper is to study the effects of differences in microscopic pore structures on moveable fluid saturation in tight sandstone reservoirs. Casting thin sections (CTS), scanning electron microscope (SEM), constant-rate mercury injection (CRMI), nuclear magnetic resonance (NMR), etc. are used as experimental methods to qualitatively describe the differences in accumulation space types and to quantitatively compare and analyze the differences in microscopic pore structures between reservoirs for Chang 6 formation in the Jiyuan and Heshui areas. The different characteristic parameters are chosen to analyze their correlations with moveable fluid saturation. Thus, as a result, due to good correlations with moveable fluid saturation, parameters such as throat radius, pore-throat radius ratio, and pore-throat amount allocation ratio are chosen to characterize the differences in microscopic pore structures between reservoirs in those areas. Differences in throat radius, pore-throat radius ratio, and a pore-throat amount allocation ratio of microscopic pore structure of Chang 6 members in these two areas have led to differences in moveable fluid saturation to some extent. This research provided the evidence about the coupling law between pore-scale fluid flow capacity and pore-throat configuration in tight sandstone reservoirs, and the results can provide theoretical guidance for field development of oil and gas fields.

Telomerase reverse transcriptase is a subunit of telomerase, which plays an important role in activating telomerase and regulating cell cycle. Human telomerase reverse transcriptase(hTERT) is mainly expressed in germ cells, embryonic stem cells and tumor cells, only a few expressed in normal cell. hTERT is mainly located in nucleus and also distributed in mitochondria. hTERT migration from nucleus into mitochondria is called the mitochondrial translocation of hTERT. Mitochondrial translocation of hTERT functions in the process of apoptosis, immortalization and drug resistance. Control of hTERT is a potential breakthrough in the treatment of diseases.

Existing polyhedral compilation tools often use some simple heuristic strategies to find the optimal loop fusion decisions.It is necessary to manually adjust the loop fusion strategy to get the best performance for different programs.To solve this problem,a fusion strategy based on data reuse analysis is proposed for multi-core CPU platform.This strategy avoids unnecessary fusion constraints that affecting the mining of data locality.For different stages of scheduling,the parallelism constraint for diffe-rent parallel levels is proposed.And a tiling constraint for CPU cache optimization is proposed for statements with complex array accesses.Compared with the previous loop fuion strategies,this strategy takes into account the changes in spatial locality when calculating the fusion profits.This strategy is implemented based on the polyhedral compilation module Polly in the LLVM compilation framework,and some test cases in test suites such as Polybench are selected for testing.In the case of single-core testing,compared with the existing fusion strategies,the average performance is improved by 14.9%~62.5%.In the case of multi-core testing,compared with the existing fusion strategies,the average performance is improved by 19.7%~94.9%,and the speedup is up to 1.49x~3.07x.

Background: Millions of people suffer from Alzheimer's disease (AD) and Parkinson's disease (PD) worldwide. Due to their complex pathology, no effective pharmacological treatment has been found to date, despite extensive research. Developing new, effective therapeutic agents to cure these disease remains a major challenge. Although the cause of AD and PD remains illusive, numerous studies indicates that oxidative stress and neuro-inflammation lead to neurodegeneration in the central nervous system and play vital role in AD and PD morbidity and progression. Flavonoids, which are found widely in nature, exhibit anti-oxidative, anti-inflammatory, anti-mutative, anti-microbial, and neuroprotective properties, so have potential to treat these two kinds of diseases. Methods: In this review, we focus on the anti-oxidative and neuroprotective action of flavonoids in attenuating Alzheimer's and Parkinson's disease, and how they might be harnessed in the development of new pharmacological agents to treat these two diseases. Result: Some flavonoid compounds, like hesperidin, naringin, naringenin, tangeretin, nobiletin, silibinin, Epigallocatechin-3-gallate, displayed to be effective in both AD and PD. Conclusion: Considerable studies have demonstrated the anti-AD and anti-PD effects of flavonoids through various in vitro and in vivo models. However, more rigorous studies are needed to be done for flavonoids to develop into effective drugs and apply them to clinical practice.

Background: Preclinical studies often provide the evidence base for clinical studies. However, the design and reporting of preclinical trial results are inadequate, resulting in poor reproducibility and clinical translatability. We aimed to systematically evaluate the methodology and reporting quality of animal studies of acupuncture for cancer pain. Methods: About 7 databases were searched for animal research articles on acupuncture for cancer pain from the beginning of the database to January 31, 2022. ARRIVE guidelines, STRICTA, and SYRCLE risk of bias tools were used to assess the reporting quality and risk of bias of the selected studies. Results: A total of 18 studies were evaluated. Of the 22 items on the SYRCLE tool, only 6 items had a positive reporting rate of more than 50%. Of the 39 items in the ARRIVE guidelines, 14 were rated excellent, and the least frequently reported checklist items were 7. Out of the 17 STRICTA checklist items analyzed, 10 were considered appropriately reported in more than 80% of the studies, while 4 were correctly reported in less than 20%. Conclusions: Some crucial points in the design, implementation, and reporting of the experiments included in the study were not well developed, which could significantly affect the clarity, reproducibility, and translatability of the experiments. There is a need to fully implement scientific tool guidelines for future experimental studies in order to improve the quality of preclinical studies and facilitate effective translation of their results to the clinic.

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China’s power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China’s power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China’s power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed.