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Oxygen vacancies (VO) in metal oxide semiconductors play an important role in improving gas-sensing performance of chemiresistive gas sensors. Nonetheless, there is still a lack of clear understanding of the inherent mechanism of the influence of oxygen vacancies on gas sensing due to generally focusing on the concentration of VO. Herein, oxygen vacancies were rationally modulated in WO3nanoflower structures via an annealing process, resulting in a transformation of VOfrom neutral (VO0) to a doubly ionized (VO2+) state. Density functional theory (DFT) calculations indicate that VO2+is significantly more efficient than VO0for NO2detection in competition with atmospheric O2. Benefiting from a high concentration of VO2+, the WO3-450 (WO3annealed at 450 °C) sensor exhibits excellent sensing performance with an ultrahigh sensitivity (3674.1 to 5 ppm NO2), superior selectivity, and long-term stability (one month). Furthermore, the sensor with the wide range of concentration detection not only can detect NO2gas with parts per million (ppm) but also can detect NO2with parts per billion (ppb) level concentration, with a high sensibility reaching 2.8 to 25 ppb NO2and over 100 to 100 ppb NO2. This study elucidates the oxygen vacancy mediated sensing mechanism toward NO2and provides an effective strategy for the rational design of gas sensors with high sensing performance.

Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

In the competitive automotive market, A‐Si Liquid Crystal Display (LCD) products are gradually losing their cost and performance advantages. Consequently, reducing costs while enhancing critical specifications has become a major challenge for A‐Si LCDs. To address this, we employed Triple Gate technology to cut back on costs, but encountered the issue of relatively low optical performance. To overcome this challenge, we conducted a Design of Experiments (DOE) using a 2^k Full Factorial Design. Through this approach, we constructed a non‐linear mathematical model with multiple objectives and identified optimal solutions for key factors. Our experimental results demonstrate a 15.4% enhancement in contrast ratio (CR) and a 5.4% increase in transmittance (TR). Typical CR values reached 1500, while TR achieved a typical value of 3.9%. Ultimately, these significant improvements in CR and TR have greatly strengthened the market competitiveness of A‐Si LCDs.

The degradation of power converter performance is one of the most critical issues of complex system with the improvement of power capacity and density. Power converter bears severe electrical and thermal stress, resulting in an increase in the probability of failure and significant economic losses. Most research addresses performance evaluation either through reliability theory without physical understanding or through data-driven methods requiring high experimental cost. Few studies focus on predicting system-level performance degradation, which is technically difficult as many components degrade randomly. Identifying the parameters of electronic components based on sensor data has become possible with the development of neural networks and computational power. Therefore, in this article, we propose a novel system-level power degradation predicting framework, which combines the advantages of neural networks in nonlinear fitting and empirical knowledge to predict the degradation of the power converter. In addition, a comprehensive and improved feature parameter screening method is proposed to identify the most critical feature parameters of the power converter systems. Furthermore, the neural network parameter identification method based on the sparrow search algorithm–Elman neural network is introduced to improve prediction accuracy. Finally, the result shows that the proposed method can accurately predict the degradation of the system by using a DC–DC converter as an example.

ABSTRACTFailure of the normal process of cell death pathways contributes to the defection of immune systems and the occurrence of cancers. The key genes, the multimolecular mechanisms, and the immune functions of these genes in pan-cancers remain unclear. Using online databases of The Cancer Genome Atlas, GEPIA2, TISIDB, HPA, Kaplan-Meier Plotter, PrognoScan, cBioPortal, GSCALite, TIMER, and Sangerbox, we identified the key genes from the six primary cell death-related pathways and performed a comprehensive analysis to investigate the multimolecular characteristics and immunological functions of the hub genes in 33 human cancers. We identified five hub genes in the six primary cell death-related pathways (JUN, NFKB1, CASP3, PARP1, and TP53). We found that CASP3, PARP1, and TP53 were overexpressed in 28, 23, and 27 cancers. The expression of the five genes was associated with the development and prognosis of many cancers. Particularly, JUN, NFKB1, CASP3, and TP53 have prognostic values in Brain Lower Grade Glioma (LGG), while PARP1 and CASP3 could predict the survival outcomes in Adrenocortical carcinoma (ACC). In addition, an extensive association between five genes’ expression, DNA methylation, and tumor-immune system interactions was noticed. The five cell death-related hub genes could function as potential biomarkers for various cancers, particularly LGG and ACC. The immunological function analysis of the five genes also proposes new targets for developing immunosuppressants and improving the immunotherapy efficacy of cancers. However, further extensive clinical and experimental research are required to validate their clinical values.

•Methylation levels of MALAT1 and H19 were associated with the response to chemotherapy and prognosis of gastric cancer (GC).•Methylation levels of MALAT1 and H19 in peripheral blood leukocytes could be used to predict the chemotherapy effect and prognosis of GC.•MALAT1 expression was correlated to the microsatellite instability and tumor mutational burden.•MALAT1 and H19 expression were related to multiple immune checkpoint and immune pathways.

A highly stable anion exchange membrane (AEM) is constructed by linking bipyridine to a polyetheretherketone (PEEK) polymer. First, PEEK is prepared by the polycondensation reaction; N-bromosuccinimide and benzoyl peroxide are used for the bromination reaction; and then pyridine or 4,4′-bipyridine is connected to PEEK by the Friedel–Crafts alkylation reaction. The successful preparation of the target product was confirmed by the collaborative analysis of the Fourier transform infrared spectrum and 1H nuclear magnetic resonance spectrum. The ionic conductivity test showed that the highest hydroxide conductivity of PyBPEEK-50% and BiPyBPEEK-50% at 80 °C reached 36.99 and 32.05 mS cm–1, respectively, which proved that they can meet the basic requirements. The alkali stability test showed that the ionic conductivity of the membrane could still reach 88.46% after 750 h of immersion in 1 M NaOH at room temperature, which proved that the cross-linking structure composed of 4,4′-bipyridine effectively weakened the attack of OH–on AEMs. In addition, the cross-linked structure effectively balances the loss of mechanical properties as a result of water absorption and membrane swelling. The swelling rate of BiPyBPEEK-50% at 80 °C is only 4.4%, and the tensile strength can reach 66 MPa. Therefore, to avoid the introduction of too many uncontrollable factors in the complicated synthesis process, this work used a simple method to synthesize AEMs based on the bipyridine cross-linked structure and compared its performance gains and losses through non-cross-linked membranes. The potential application value of this method in the field of fuel cells is verified.

Severe fever with thrombocytopenia virus (SFTSV), an emerging tick-borne bandavirus, poses a significant public health threat in rural China. Since 2021, an increase of local cases has been noted in the rural-urban fringe of Beijing. This study aimed to assess the formation of natural foci in urban areas by conducting a field survey of ticks and hedgehogs from the second to fifth ring roads of Beijing. Our survey revealed a diverse tick population in city parks, including the major SFTSV vector, Haemaphysalis longicornis. Parthenogenetic H. longicornis, known for its role in the rapid spread of SFTSV, was identified in key locations such as Beihai Park and Taoranting Park, near the Forbidden City. Notably, high SFTSV seroprevalence and RNA prevalence were found in hedgehogs and parasitic ticks in the center of Beijing. Phylogenetic analyses of SFTSV RNA and mitochondrial sequences of parthenogenetic H. longicornisticks revealed the existence of diverse lineages of SFTSV and H. longicornisticks within Beijing, suggesting multiple invasion events happened. These findings reveal the circulation of SFTSV in central Beijing, highlighting the need for urgent attention and enhanced surveillance measures.

Water level variations (WLVs) substantially affect the spatial distribution of riparian vegetation. Accurately quantifying the water level fluctuation requirements (WLFRs) of aquatic macrophytes is essential for effective ecological water level management. Nevertheless, studies on the WLFRs of herbaceous plants in large river floodplains, such as Phragmites australis(common reed) and Phalaris arundinacea(reed canarygrass), are limited. Herein, we investigated the WLFRs of the two plants across water bodies with different WLVs in the Yangtze River Basin. We focused on the effects of sub-monthly and sub-daily WLVs on their average monthly water depth requirements (WDRs) during critical growth periods. Our findings indicated: (1) For Phragmites, during the emergence period, the upper limits of WDRs decreased with increased amplitudes of sub-monthly WLVs and with greater frequency and amplitude of sub-daily WLVs, in which sub-daily variations had a more pronounced effect. Conversely, during the rapid growth period, the upper limits increased with larger sub-monthly WLVs amplitudes, but decreased with higher frequency and amplitude of sub-daily WLVs. (2) For Phalaris, during both the emergence and rapid growth periods, intensified sub-daily WLVs led to decreased upper limits of WDRs. In summary, although distinct disparities existed in the WLFRs of Phragmitesand Phalarisacross various water bodies, the trends in WDRs during the emergence and rapid growth periods were consistent for both species. These findings provide essential quantitative data to restore aquatic vegetation and carry out effective ecological water level management in large-scale riparian ecosystems.

For precision mechanical systems, different distribution characteristics of geometric form errors usually lead to different assembly contact states, and in turn, different assembly errors are formed, due to the propagation and accumulation of geometric form errors. Statistically, a batch of part machined by the same precision process possesses the identical distribution characteristics of geometric form errors. It is of great importance to improve assembly accuracy and productivity if the statistic distribution characteristics of geometric form errors can be understood. To solve the problem, two methods of modeling geometric form errors for single parts based on linear combination of basis shapes and modeling statistic geometric form errors for a batch of parts machined by the same process on the basis of principal component analysis are proposed in this article. Besides that, evaluating indicators of model accuracy are also proposed. The results of the case study imply that the two methods can model geometric form errors of single parts and statistically model geometric form errors of a batch of parts, effectively and reliably.

The propagation of variations, such as fixture errors and datum errors resulting from assembly and machining processes, has been extensively studied. However, only a few studies that focus on form error propagation in assembly systems have been implemented. Machining errors, especially form errors, have great impact on assembly accuracy and accuracy stability of precision mechanical systems. With form errors being the research object, a method for calculating mating variation and specifying mating coordinate is proposed to improve the accuracy of the variation propagation model. Taking into account the form error of mating surfaces, the assembly variation propagation of a precision mechanical system is analyzed, and the brief derivation procedure of the variation propagation model is introduced afterwards. The variation propagation model involves a new concept of mating variation specified by the two mating surfaces. An innovative method, the difference surface search based method, is proposed to calculate the mating variation amongst the mating surfaces. The obtained mating variation is then utilized to specify the mating coordinate in the variation propagation model. Moreover, FEM is employed to simulate the contact state of the two mating surfaces to demonstrate effectiveness of the proposed method. Meanwhile, the mating variation and mating coordinate obtained are incorporated into the assembly variation propagation model, which is then verified by a following case study through a comparison between the calculated results and the experimental results. The comparing results indicate that the established model improves the prediction of assembly accuracy. The developed model enables the investigation of various fundamental issues in variation reduction, including variation analysis, process monitoring, accuracy prediction, and accuracy control.

The minimum zone method is currently a traditional means for evaluating form error. However, it cannot reveal the effects of form error on the scale of assembly accuracy. To address this deficiency, however, considering the propagation of geometric error in assembly process, this article proposed an entropy-based evaluation method for plane form error for precision mechanical system, which includes primary evaluation, convex hull search, top plane establishment, local entropy analysis and obtaining comprehensive evaluation index. Experimental results demonstrate that the proposed method can evaluate the plane form distribution error, which suggests the assemblability of parts, with the same form errors accurately and stably.

Offset face gear drive has shown many unique advantages in the area of crossed-axes transmission. In this paper, deviation analysis of an offset face gear using a involute disc wheel as a cutting tool has been discussed to achieve precised machining.Firstly, equations of involute disc wheel and offset face gear are derived based on meshing theory.Then, a grinding machine tool is designed and equations of offset face gear containing machining deviation are derived.At last,figures of deviation cloud of face gear surface are created with MATLAB and importance of different machining deviation is discussed.The results show that the deviation Az along the axis of grinding wheel is the most influential deviation,while Ay along the axis of face gear is the least one.

Composite structures have been widely used in aerospace field due to their low density, high strength, and high stiffness to weight ratio. In the aerospace field, composite laminates are widely used in the skin of the airplane. Due to the complexity of the composite molding process, and the difficulty of machining composite laminates, the manufacturing precision of the skin is usually not very high. Hence the assembly force is applied to the skin to ensure the composite laminates fit the framework in the assembly process of an airplane. However, too large assembly force leads to the assembly out of tolerance owning to the over deformation and leads to the stress of the skin out of tolerance. Therefore, this paper proposes a method of analyzing the allowable assembly force. With the help of mechanics and physics, the mechanical relationship between the assembly forces and the deformation of a skin is modeled. On the basis of the model and the experimental date of a skin assembly process, the mathematical relationship between the assembly forces and the deformation of the skin is established. The allowable assembly forces are determined based on the mathematical relationship and the allowable deformation. This paper provides an optimization methodology of assembly force optimization.

Cutting force and tool temperature play significant roles in the drilling process because they are strongly related to drilling performance such as surface roughness, tool wear and tool breakage etc. For difficult-to-machine materials, like titanium alloys, the drilling operation is considered to be complex due to their mechanical and thermal properties, and it is important to find suitable process parameters to avoid too large cutting force or too high tool temperature, which may deteriorate the drilling performance. This paper is dedicated to studying the effects of process parameters, including spindle speed, feed rate and helix angle, on cutting force and tool temperature in drilling based on finite element simulation. A series of drilling simulations are carried out to generate the simulated cutting force and tool temperature data under different drilling conditions. By data fitting, the steady cutting force and tool temperature are predicted. Based on the Taguchi method, the effects of process parameters on steady cutting force and tool temperature are analysed. This paper can provide a basis for suitable selection of process parameters in drilling.