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Abstract Synthesis of valuable chemicals from CO2 electroreduction in acidic media is highly desirable to overcome carbonation. However, suppressing the hydrogen evolution reaction in such proton-rich environments remains a considerable challenge. The current study demonstrates the use of a hollow fiber silver penetration electrode with hierarchical micro/nanostructures to enable CO2 reduction to CO in strong acids via balanced coordination of CO2 and K+/H+ supplies. Correspondingly, a CO faradaic efficiency of 95% is achieved at a partial current density as high as 4.3 A/cm2 in a pH = 1 solution of H2SO4 and KCl, sustaining 200 h of continuous electrolysis at a current density of 2 A/cm2 with over 85% single-pass conversion of CO2. The experimental results and density functional theory calculations suggest that the controllable CO2 feeding induced by the hollow fiber penetration configuration primarily coordinate the CO2/H+ balance on Ag active sites in strong acids, favoring CO2 activation and key intermediate *COOH formation, resulting in enhanced CO formation.

Jointing multi-modal image fusion and subsequent high-level tasks is attracting more researches to achieve both mutual promotions. However, owing the feature gap between the two tasks, complicated network structure and training strategies need to be redesigned for specific different datasets. To address these issues, this paper proposes an infrared and visible image fusion via general feature embedding from frozen CLIP and DINOv2 models. The core idea is that the general semantic features from CLIP model are injected into the fusion network with the DINOv2-based segmenter as a constraint. Specially, the feature merging module and injection strategies are design to generate the semantic features that are compatible with fusion features meanwhile aligned with DINOv2 features. Leveraging the generalization ability of these foundation models, the proposed network can be optimized mutually to promote the training process. Comprehensive experiments on the four public datasets demonstrate the effectiveness of our method.

Video object segmentation models have gained impressive performance, but present low interactivity with different prompts, such as click, box or text. Some models combined with SAM in a naive manner to enhance this ability, which achieve a limited performance owing to the coarse mask and inconsistent segmentation propagation. In this paper, we propose ASDeM, which augments SAM with decoupled memory, achieving high-performance on tracking and segmentation in videos. Specifically, to explore the combination of SAM and VOS model, ASDeM fully utilizes the class-agnostic features from SAM to build the memory features for VOS model, and further the object-agnostic temporal propagation with decoupled memory is applied to address the feature staleness problem and the oblivion problem of visual information. Given the prompt for the specific object in a video, people can get satisfactory segmentation and tracking results. The experiments demonstrate the effectiveness of ASDeM on public benchmarks.

The aim of this research is to devise and assess a holistic approach for optimizing the performance of the Internet of Things Sensor Network (IoTSN), thereby enabling it to better adapt to diverse and unpredictable environments. Initially, this study delves into the self-organizing network, achieving intelligent nodal cooperation and allowing the network to autonomously reshape its topology. Subsequently, an adaptive approach is incorporated to intelligently modulate the operational modes and task allocations of nodes by continuously monitoring network load, energy usage, and data transfer efficiency. To validate the efficacy of our method, we utilized the SensorScope Dataset, composed of real-time data from various IoT devices, encompassing diverse environmental metrics such as temperature, humidity, and lighting. Experimental findings reveal that, in contrast to conventional strategies, our integrated self-organization and adaptive approach offer substantial benefits in terms of load balancing, energy consumption, and data transfer latency. More specifically, we recorded a 30% drop in average load, a 25% decrease in energy use, and a 20% reduction in average transmission delay. These outcomes strongly support the effectiveness of our comprehensive strategy in boosting IoTSN performance when juxtaposed with alternative methods. Additionally, we offer an in-depth exploration of the experimental data and identify potential areas for refinement and future research avenues, opening the door to fresh perspectives and advancements in IoTSN intelligence and efficiency. This investigation is pivotal in advancing IoT technology and bolstering the adaptability and performance of IoTSN in a wide range of practical scenarios.

Embracing sustainable strategies that consider Design for Manufacture and Assembly (DfMA) has become a rapidly growing trend in urban development. Continued uncertainty on the sustainability assessment of design could drive a series of indecisive decision-making among design alternatives, further disrupting the potential opportunities toward sustainable DfMA. However, there is a lack of research on sustainable design assessments for DfMA and establishing a sustainable index. This research establishes an integrated value model for the sustainability assessment framework and DfMA sustainability index to address this challenge. This model integrates Building Information Modelling (BIM) with MIVES, a customisable Multi-Criteria Decision Making (MCDM) tool. The pilot case of this framework is the retrofit of a commercial building’s façade system, which demonstrated the capability of the proposed framework. Data collection and analysis include the comparisons between five design alternatives. This research furthers previous studies and has three-fold significance: 1) Establishing reasonable multi-criteria for the sustainable DfMA indices; 2) Adapting the MIVES approach for comparative analysis across three building phases to make it compatible with DfMA; 3) developing a quantitative analysis method for sustainable design assessment of DfMA in the construction industry.

Abstract The recent outbreaks of mpox have raised concerns over the need for effective vaccines. However, the current approved vaccines have either been associated with safety concerns or are in limited supply. mRNA vaccines, which have shown high efficacy and safety against SARS-CoV-2 infection, are a promising alternative. In this study, three mRNA vaccines are developed that encode monkeypox virus (MPXV) proteins A35R and M1R, including A35R extracellular domain -M1R fusions (VGPox 1 and VGPox 2) and a mixture of encapsulated full-length mRNAs for A35R and M1R (VGPox 3). All three vaccines induce early anti-A35R antibodies in female Balb/c mice, but only VGPox 1 and 2 generate detectable levels of anti-M1R antibodies at day 7 after vaccination. However, all three mRNA vaccine groups completely protect mice from a lethal dose of vaccinia virus (VACV) challenge. A single dose of VGPox 1, 2, and 3 provide protection against the lethal viral challenge within 7 days post-vaccination. Long-term immunity and protection were also observed in all three candidates. Additionally, VGPox 2 provided better passive protection. These results suggest that the VGPox series vaccines enhance immunogenicity and can be a viable alternative to current whole-virus vaccines to defend against mpox.

Abstract The attention mechanism has produced impressive results in object tracking, but for a good trade‐off between performance and efficiency, CNN‐based approaches still dominate, owing to quadratic complexity of attention. Here, the SGF module is proposed, an efficient feature fusion block for effective object tracking with reduced linear complexity of attention. The proposed method fuses feature with attention in a coarse‐to‐fine manner. In the low‐resolution semantic branch, the top K regions with highest attention scores are selected; in the high‐resolution detail branch, attention is only calculated within regions corresponding to the top K regions. Thus, the features from the high‐resolution branch can be efficiently fused under the guidance of low‐resolution branch. Experiments on RGB and RGB‐T datasets with reformed FairMOT and MDNet+RGBT trackers demonstrated the effectiveness of the proposed method.

VG2025 is a recombinant oncolytic herpes simplex virus type 1 (HSV-1) that uses transcriptional and translational dual regulation (TTDR) of critical viral genes to enhance virus safety and promote tumor-specific virus replication without reducing virulence. The TTDR platform is based on transcriptional control of the essential HSV-1 immediate-early protein ICP27 using a tumor-specific carcinoembryonic antigen (CEA) promoter, coupled with translational control of the neurovirulence factor ICP34.5 using multiple microRNA (miR)-binding sites. VG2025 further incorporates IL-12 and the IL-15/IL-15 receptor alpha subunit complex to enhance the antitumor and immune stimulatory properties of oncolytic HSVs. The TTDR strategy was verified in vitro and shown to be highly selective. Strong in vivo antitumor efficacy was observed following both intratumoral and intravenous administration. Clear abscopal and immune memory effects were also evident, indicating a robust antitumor immune response. Gene expression profiling of treated tumors revealed increased immune cell infiltration and activation of multiple immune-signaling pathways when compared with the backbone virus. Absence of neurotoxicity was verified in mice and in rhesus monkeys. Taken together, the enhanced tumor clearance, excellent safety profile, and positive correlation between CEA levels and viral replication efficiency may provide an opportunity for using biomarker-based precision medicine in oncolytic virotherapy.

The SAM shows remarkable generalization and transformable capabilities for category-agnostic segmentation. Although the semantics in latent space are explored slightly, more researches are working on instance segmentation. And it’s unclear how to design the appropriate prompts for semantic segmentation, which have large impact the performance. In this paper, by summarizing several existing methods for semantic segmentation with SAM, we propose a learnable prompt method for semantic segmentation based on the SAM model, incorporating the latent semantic features with prompt learning by a grouping approach, referred as GroupPrompter. This enables SAM to perform semantic segmentation with the automatic learned prompts. And the experimental results on ADE20K, Pascal Context and COCO-Stuff datasets validate the effectiveness of the proposed method.

Biopsy is recommended for definitive diagnosis of liver carcinoma, however, this process requires staining and expert pathologists to confirm diagnosis. Here, the authors employ a portable Raman spectroscopy system combined with deep learning to detect carcinoma from normal tissue in real time.

Chronic myeloid leukemia is a multistep, multi-lineage myeloproliferative disease that originates from a translocation event between chromosome 9 and chromosome 22 within the hematopoietic stem cell compartment. The resultant fusion protein BCR::ABL1 is a constitutively active tyrosine kinase that can phosphorylate multiple downstream signaling molecules to promote cellular survival and inhibit apoptosis. Currently, tyrosine kinase inhibitors (TKIs), which impair ABL1 kinase activity by preventing ATP entry, are widely used as a successful therapeutic in CML treatment. However, disease relapses and the emergence of resistant clones have become a critical issue for CML therapeutics. Two main reasons behind the persisting obstacles to treatment are the acquired mutations in the ABL1 kinase domain and the presence of quiescent CML leukemia stem cells (LSCs) in the bone marrow, both of which can confer resistance to TKI therapy. In this article, we systemically review the structural and molecular properties of the critical domains of BCR::ABL1 and how understanding the essential role of BCR::ABL1 kinase activity has provided a solid foundation for the successful development of molecularly targeted therapy in CML. Comparison of responses and resistance to multiple BCR::ABL1 TKIs in clinical studies and current combination treatment strategies are also extensively discussed in this article.

Rice–wheat rotation is a widely adopted multiple-cropping system in the Yangtze River Basin, China. Nitrogen (N) fertilizer is a key factor in regulating crop yield; however, only a few studies have considered the impact of annual N application on the yield, environmental impacts, and economic profits of rice–wheat rotation systems. In this study, a field experiment was conducted in the Jiangsu Province from 2020 to 2022. The rice and wheat seasons included six and five N fertilizer application rates, respectively (Rice: 0, 180, 240, 300, 360, and 420 kg N ha−1; Wheat: 0, 180, 240, 300, and 360 kg N ha−1), combined to form a total of 30 treatments. Life-cycle assessment was used to evaluate the environmental impacts of rice–wheat rotation under different N application treatments, using area, yield, and economic profit as functional units. Ten environmental impact categories were selected, including global warming. The results showed that grain yield did not consistently increase with an increase in N application, and the annual yield was the highest when 300 and 240 kg N ha−1 (R300W240 treatment) was applied in the rice and wheat seasons, respectively. The area-based weighting index of the R300W240 treatment ranked 20th among the 30 treatments, while the yield- and profit-based weighting indices were the lowest among the 30 treatments, decreasing by 14.9% and 28.7%, respectively, compared to the other treatments. The R300W240 treatment was the optimal annual N application strategy for rice–wheat rotation. Among the 10 environmental impacts considered, urea production contributed significantly to over eight environmental impacts, whereas the pollutant losses caused by its application contributed significantly to six environmental impacts. These findings reveal the dependence of the rice–wheat rotation system on the unsustainable use of N fertilizer and indicate that N fertilizer management practices should be further optimized to improve the environmental sustainability of grain production in the future.

We developed a reliable simple analysis of adenosine, creatinine and other nucleosides (cytidine and guanosine) with single measurement based on surface-enhanced Raman spectroscopy (SERS) using DSP-AuNPs with Cu2+ ions probe and applied it to achieve noninvasive screening of lung cancer. The gold nanoparticles (AuNPs) were modified with 3,3′-dithiodipropionic acid di (N-hydroxysuccinimide ester) (DSP), which could react with above mentioned molecules followed with the aggregation of AuNPs at the presence of Cu2+, thus form the plasmonic hot spots to dramatically increase their fingerprint Raman signal. The probe of DSP-AuNPs was applied for the detail analysis of urinary adenosine of healthy people and lung cancer patients. The SERS measurement indicated a higher concentration of urinary adenosine for lung cancer patients than that of healthy people, which was consistent with the results measured by high-performance liquid chromatography (HPLC). In combination of Raman spectroscopy with the orthogonal partial least squares discriminant analysis (OPLS-DA) model, we are able to discriminate the lung cancer patients from healthy people with the urinary test. The results indicated that besides of adenosine, other metabolites including uric acid, guanine and creatinine may also be the potential tumor markers in urine for the noninvasive lung cancer diagnosis. Such a method paves a way for the noninvasive cancer screening and it can be further modified for the detection of other molecules on medical diagnosis.

The development of effective cancer vaccines remains a significant challenge due to immune tolerance and limited clinical benefits. Oncolytic herpes simplex virus type 1 (oHSV-1) has shown promise as a cancer therapy, but efficacy is often limited in advanced cancers. In this study, we constructed and characterized a novel oHSV-1 virus (VG22401) expressing the human epidermal growth factor receptor 2 (HER2), a transmembrane glycoprotein overexpressed in many carcinomas. VG22401 exhibited efficient replication and HER2 payload expression in both human and mouse colorectal cancer cells. Mice immunized with VG22401 showed significant binding of serum anti-HER2 antibodies to HER2-expressing tumor cells, inducing antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). Furthermore, mice primed with VG22401 and intratumorally boosted with the same virus showed enhanced antitumor efficacy in a bilateral syngeneic HER2(+) tumor model, compared to HER2-null backbone virus. This effect was accompanied by the induction of anti-HER2 T cell responses. Our findings suggest that peripheral priming with HER2-expressing oHSV-1 followed by an intratumoral boost with the same virus can significantly enhance antitumor immunity and efficacy, presenting a promising strategy for cancer immunotherapy.

Abstract The key challenge in RBGT tracking is how to fuse dual‐modality information to build a robust RGB‐T tracker. Motivated by CNN structure for local features, and visual transformer structure for global representations, the authors propose a two‐stream hybrid structure, termed CMC2R, to take advantage of convolutional operations and self‐attention mechanisms to lean the enhanced representation. CMC2R fuses local features and global representations under different resolutions through the transformer layer of the encoder block, and the two modalities are collaborated to get contextual information by the spatial and channel self‐attention. The temporal association is performed with the track query, each track query models the entire track of an object, and updated frame‐by‐frame to build the long‐range temporal relation. Experimental results show the effectiveness of the proposed method, and achieve the SOTAs performance.

During the underground mining of coal resources, the rock pressure emerges acutely and the mine geological disasters occur frequently. It is of great significance to grasp the manifestation law of rock pressure in time to guide the safety production and operation in the pit. In this research, the calculation equations and concept of information entropy based on zone safety degree are primarily defined, and the synergetic theory of the maximum information entropy principle is combined to put forward the synergy prediction model of information entropy based on zone safety degree. In the meantime, the synergy prediction model of information entropy based on zone safety degree is employed to calculate and predict the first weighting step and the periodic weighting step of the main roof of the 9203 working face of Hengsheng Coal Mine in China’s Shanxi Province, as well as verifying the validity and reliability of the synergy prediction model of information entropy based on zone safety degree by the comparison of similar simulation test results, which has presented a scientific basis for the effective control of rock pressure and roof management.

It is generally acknowledged that the stability evaluation of surrounding rock denotes nonlinear complex system engineering. In order to accurately and quantitatively assess the safety states of surrounding rock and provide a scientific basis for the prevention and control of surrounding rock stability, the analysis method of the synergetic theory of information entropy using the failure approach index has been proposed. By means of deriving the general relationship between the total two-dimensional plastic shear strain and the total three-dimensional plastic shear strain and obtaining the numerical limit analysis step of the plastic shear strain, the threshold value of the ultimate plastic shear strain can be determined, which has provided the key criterion for the calculation of the information entropy based on the failure approach index. In addition, combining with the synergetic theory of the principle of maximum information entropy, the evolution equation of the excavation step and information entropy based on the failure approach index of the surrounding rock system in underground mining space are established, and the equations of the general solution and particular solution as well as the expression of the destabilizing excavation step are given. To account for this, the method is applied to analyze the failure states of the floor surrounding rock after the mining of the 71 coal seam in Xutuan Coal Mine and involve the disturbance effect and stability control method of the underlying 72 coal seam roof from the macroscopic and microscopic aspects. Consequently, the validity of the analysis method of synergetic theory of information entropy based on the failure approach index has been verified, which presents an updated approach for the stability evaluation of surrounding rock systems that is of satisfactory capability and value in engineering applications.

Abstract Changes in soil carbon (C):nitrogen (N):phosphorus (P) stoichiometry have great significance on understand regulatory mechanism and restoration of ecosystem functions. However, the responses of C, N and P stoichiometry to soil depth and different vegetation types remains elusive. To address this problem, the study aims to explore the effects of soil depth and vegetation types on soil C, N, and P stoichiometry, and their relationships with microbial biomass in low mountain and hill region of China. The results indicated that soil SOC and TN concentrations in oak forest were markedly higher than those in grassland, and the vertical distribution of SOC and TN concentration showed an inverted triangle trend as the soil deepens. However, there was no significant change in soil TP concentration among 0–20 cm, 20–40 cm, and 40–60 cm. Soil C/N among different layers (0–20, 20–40, and 40–60 cm) is narrower fluctuation margin, and its value is basically stable within a certain range (11–14.5). Both soil C/P and N/P showed significant variability in different vegetation types, and soil N/P decreased with soil layers deepen. Both the microbial biomass C (MBC) and N (MBN) showed a decreasing trend with the increase of soil depth, and three soil layers from high to low was: oak forest > pine forest > grassland. Our results will potentially provide useful information for the vegetation restoration and forest management and great significance to enrich the scientific theory of ecological stoichiometry.

A water-borne polyurethane-acrylate emulsion (WPUA) prepolymer was synthesized by using polycaprolactone diol (PCL), toluene diisocyanate (TDI), 2,2-bis (hydroxymethyl) butyric acid (DMBA), 1,4-butanediol (BDO) and methyl methacrylate (MMA) as main materials. After the coupling modified nano-SiO2 was grafted with graphene oxide, it was polymerized with polyurethane prepolymer in situ. The amine-free waterborne polyurethane-acrylate composite emulsion (SiO2-NH2-GO/WPUA) modified by functionalized graphene was prepared by self-emulsification. The amount of functionalized graphene was discussed, and its composite emulsion and film were tested and characterized. Compared with waterborne polyurethane-acrylate (WPUA), SiO2-NH2-GO/WPUA has better temperature resistance and film properties. When the mass fraction of SiO2-NH2-GO is 0.75%, compared with unmodified emulsion film, the thermal decomposition temperature of the composite film increased by 14.51oC; The tensile strength increased to 81.28 MPa; the adhesive prepared by the composite emulsion has a T-peel strength of 15.4 N in aluminum foil/PVC film. The indexes such as the easy oxide meet the standard of pharmaceutical packaging containers, indicating that the waterborne polyurethane adhesive is suitable for pharmaceutical packaging.

Abstract Part‐MOT, a one‐stage anchor‐free architecture which unifies the object identification representation and detection in one task for visual object tracking is presented. For object representation, a position relevant feature is obtained using the center‐ness information, which takes advantage of the anchor‐free ideal to encode the feature map as the instance‐aware embedding. To adapt to the object's movement, the clustering‐based method to get the global instance feature is introduced. This enables this approach more robust to make better tracking decisions. Part‐MOT achieves the state‐of‐the‐art performance on public datasets, with especially strong results for object deformation and movement changes.

Abstract Background There was no “gold standard” to assess the success or failure of thoracic paravertebral block (TPVB). Measurement of skin temperature with infrared thermography (IT) would be a reliable method to evaluate the effectiveness of regional blocks. This study aimed to explore the feasibility of using skin temperature difference (Td) determined by IT between the blocked and unblocked side to predict the spread of TPVB. Methods Sixty-one patients undergoing elective unilateral breast or thoracoscopic surgery were enrolled in this prospective observational study. TPVB was performed at T4 and T5 under real-time ultrasound guidance with 10 mL of 0.4% ropivacaine for each patient, respectively. Td between the blocked and unblocked side were measured with IT from T2 to T10 at the anterior chest wall before TPVB and 5 min, 10 min, 15 min and 20 min after TPVB. Pinprick test was performed at 20 min after TPVB. Successful TPVB was defined as no sensation to pinprick in 3 or more adjacent dermatomes corresponding to the site of injection at 20 min after TPVB. Td was compared to pinprick test for evaluating its effectiveness in predicting the success of TPVB. The sensitivity, specificity, and cut-off value of Td for predicting successful TPVB were determined by receiver operator characteristic (ROC) curve analysis. Results Compared with the baseline value before block, Td from T2 to T10 were significantly increased at each time point in successful blocks. In failed blocks, Td was not increased in any dermatome. The increase of Td at T4-T7 was more than 1 °C 20 min after successful TPVB. Fifteen minutes after block, Td increase at T4 had the greatest potential to predict block success. The area under the ROC curve was 0.960 at a cut-off value of 0.63 °C with a sensitivity of 83.3% and a specificity of 100.0%. Conclusions This study suggested that the increase of Td at T4 dermatome determined by IT between the blocked and unblocked side is an early, quantitative, and reliable predictor of successful TPVB. Trial registration Clinical trial registration: NCT04078347 .

BackgroundHemodynamic changes caused by hybrid surgery for brain arteriovenous malformations (bAVM) are usually related to long-term lesions from “blood stealing”. There are currently no viable low-cost, noninvasive procedures for assessing cerebral perfusion in the operating room. This study aims to investigate the use of intraoperative laser speckle contrast image (LSCI) software in AVM surgery.MethodsIn Zhongnan Hospital of Wuhan University, 14 patients who underwent surgery with LSCI were collected. To analyze the hemodynamic features of AVM and the influence on the peripheral cortex of AVM embolization and resection, we assessed the transit time between feeding arteries and drainage veins by intraoperative digital subtraction angiography (DSA). Meanwhile, LSCI was performed at pre-embolization, post-embolization, and after complete resection of bAVM.ResultsIn this study, the transit time of bAVM before and after embolization was compared, the transit time before embolization was significantly shorter than that after embolization (p < 0.05). We also got good visualization of relative CBF, in addition, to flow imaging in the cortical vasculature round bAVM with LSCI. The flux of post-surgery was significantly higher than pre-embolization (p < 0.01).ConclusionHemodynamic variable assessment plays an important role in the resection of AVM in the hybrid operative room and LSCI can be used to visualize and evaluate cortical cerebral blood flow to detect pathological hyperperfusion in real-time with a good spatial-temporal resolution in a sensitive and continuous, non-invasive mode.

Abstract Contact lenses have become a popular health‐monitoring wearable device due to their direct contact with the eyes. By integrating biosensors into contact lenses, real‐time and noninvasive diagnoses of various diseases can be realized. However, current contact lens sensors often require complex electronics, which may obstruct the user's vision or even damage the cornea. Moreover, most of the reported contact lens sensors can only detect one analyte. Therefore, an optical‐based dual‐functional smart contact lens sensor has been introduced to monitor intraocular pressure (IOP) and detect matrix metalloproteinase‐9 (MMP‐9), both of which are key biomarkers in many eye‐related diseases such as glaucoma. Specifically, the elevated IOP is continuously monitored by applying an antiopal structure through color changes, without any complex electronics. Together with the peptide modified gold nanobowls (AuNBs) surface‐enhanced Raman scattering (SERS) substrate, the quantitative analysis of MMP‐9 at a low nanomolar range is achieved in real tear samples. The dual‐sensing functions are thus demonstrated, providing a convenient, noninvasive, and potentially multifunctional sensing platform for monitoring health and diagnostic biomarkers in human tears.

Public policy for the preservation and development of traditional villages in China has witnessed a shift. That is from the equal distribution of finances to officially recognised traditional villages to the prioritisation of the development of more potential traditional villages that stimulate the development of surrounding villages and regions. This study takes 206 traditional villages in Hubei Province as an example and proposes a method framework for ‘Feature identification, Hierarchical classification (FIHC) that combines field research, spatial geography and spatial network methods. The results of spatial geography show that traditional villages in Hubei province are unevenly distributed, mainly along the mountain ranges in the east and west, with scattered distribution in the central plains. The kernel density result reveals “3 + 2 high-density hotspot zones” (A, B, and C are high-density areas, whereas D and E are low-density areas). Furthermore, the results of the spatial network appear that the traditional villages’ ecological situation in Zone A is better than in other areas, but their transportation accessibility and economy are poor; Zone B villages’ transportation accessibility is better than in other areas; Zone C has the best economy; and three conditions of traditional villages in the D and E zones are poor. FIHC can identify villages with more development potential and stratify these villages in a multidimensional way. It is innovative and an important contribution to policymakers and planners in developing ‘phased and focused’ public policies and rural planning.

Abstract Background Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification. Results A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8′-hydroxylase, is verified to play a negative role in plant drought tolerance. Conclusions This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

Traditional deception-based cyber defenses (DCD) often adopt the static deployment policy that places the deception resources in some fixed positions in the target network. Unfortunately, the effectiveness of these deception resources has been greatly restricted by the static deployment policy, which also causes the deployed deception resources to be easily identified and bypassed by attackers. Moreover, the existing studies on dynamic deployment policy, which make many strict assumptions and constraints, are too idealistic to be practical. To overcome this limitation, an intelligent deployment policy used to dynamically adjust the locations of these deception resources according to the network security state is developed. Starting with formulating the problem of deception resources deployment, we then model the attacker-defender scenario and the attacker's strategy. Next, the preliminary screening method that can derive the effective deployment locations of deception resources based on threat penetration graph (TPG) is proposed. Afterward, we construct the model for finding the optimal policy to deploy the deception resources using reinforcement learning and design the Q-Learning training algorithm with model-free. Finally, we use the real-world network environment for our experiments and conduct in-depth comparisons with state-of-the-art methods. Our evaluations on a large number of attacks show that our method has a high defense success probability of nearly 80%, which is more efficient than existing schemes.

In order to plan the robot path in 3D space efficiently, a modified Rapidly-exploring Random Trees based on heuristic probability bias-goal (PBG-RRT) is proposed. The algorithm combines heuristic probabilistic and bias-goal factor, which can get convergence quickly and avoid falling into a local minimum. Firstly, PBG-RRT is used to plan a path. After obtaining path points, path points are rarefied by the Douglas-Peucker algorithm while maintaining the original path characteristics. Then, a smooth trajectory suitable for the manipulator end effector is generated by Non-uniform B-spline interpolation. Finally, the effector is moving along the trajectory by inverse kinematics solving angle of joint. The above is a set of motion planning for the manipulator. Generally, 3D space obstacle avoidance simulation experiments show that the search efficiency of PBG-RRT is increased by 217%, while search time is dropped by 168% compared with P-RRT (Heuristic Probability RRT). After rarefying, the situation where the path oscillated around the obstacle is corrected effectively. And a smooth trajectory is fitted by spline interpolation. Ultimately, PBG-RRT is verified on the ROS (Robot Operating System) with the Robot-Anno manipulator. The results reveal that the validity and reliability of PBG-RRT are proofed in obstacle avoidance planning.

The coordinated development of farmland transfer (FT) and labor migration (LM) is of great efficiency significance to facilitate the development of rural economy and implement the rural revitalization strategy. The study used socioeconomic data from 30 provinces/autonomous regions/municipalities (hereafter referred to as provinces) in China to measure the coupling coordination degree (CCD) of FT and LM. It adopted the coupling coordination degree model (CCDM), exploratory spatial data analysis method (ESDA), and gray relational analysis model (GARM) to investigate the spatial differences in the CCD and its influencing factors. The results indicate the following: (1) Regional differences are evident despite the fact that the comprehensive evaluation level of FT and LM in the various provinces is relatively low and displaying a rising trend. (2) The CCD of FT and LM exhibits a fluctuating upward trend and is at the primary coupling coordination stage, with a significant difference in coupling coordination levels between regions, and a spatial distribution pattern of central region > eastern region > northeast region > western region. (3) The CCD shows a strong global spatial positive correlation with clear fluctuations, demonstrating the agglomeration dispersion development tendency over time; the local spatial agglomeration state emerges and stabilizes. According to the distribution pattern, the Western region exhibits weak agglomeration type, whereas the eastern and central regions exhibit strong agglomeration type. (4) There are significant variations between provinces in terms of the intensity of the CCD of FT and LM, as well as the level of concurrent employment business, the level of non-agricultural industry development, the level of urbanization, the level of agricultural equipment, and the land approval.

Drought is a primary environmental factor limiting maize production globally. Although transferring a single gene to maize can enhance drought resistance, maize response to water deficit requires further improvement to accommodate the steadily intensifying drought events worldwide. Here, we generated dual transgene lines simultaneously overexpressing two drought-resistant genes, ZmVPP1 (encoding a vacuolar-type H+ pyrophosphatase) and ZmNAC111 (encoding a NAM, ATAF, and CUC (NAC)-type transcription factor). Following drought stress, survival rates of the pyramided transgenic seedlings reached 62–66%, while wild-type and single transgene seedling survival rates were 23% and 37–42%, respectively. Maize seedlings co-expressing ZmVPP1 and ZmNAC111 exhibited higher photosynthesis rates, antioxidant enzyme activities, and root-shoot ratios than the wild type, and anthesis-silking intervals were shorter while grain yields were higher under water deficit conditions in field trials. Additionally, RNA-sequencing analysis confirmed that photosynthesis and stress-related metabolic processes were stimulated in the dual transgene plants under drought conditions. The findings in this work illustrate how high co-expression of different drought-related genes can reinforce drought resistance over that of individual transgene lines, providing a path for developing arid climate-adapted elite maize varieties.

The newly built shaft in the western region needs to pass through the deep Cretaceous stratum, where the pores and fissures are developed, the cementation ability is poor, and the surrounding rock is rich in water. Under the coupling effect of the stress field and seepage field, the surrounding rock is easy to deteriorate and loses stability. The hydraulic coupling test of Cretaceous red sandstone was carried out by using the TAW-2000 rock mechanics testing system, and the characteristic strength evolution law of red sandstone was analyzed; Mohr’s circle and strength envelope were obtained by the M–C criterion, and the influence mechanism seepage pressure on red sandstone was explored; and combined with the effective stress principle and M–C strength criterion, a constitutive model under hydraulic coupling was established. Confining pressure limits the development of cracks and strengthens the mechanical properties. The results revealed that red sandstone has the characteristics of low less clay, loose particles, and weak cementation capacity; under the action of water pressure, the cement between particles disintegrates and loses the cementation strength, resulting in a significant decrease in cohesion, and the loss of cementation strength is the internal reason for the softening of red sandstone. The constitutive model based on the effective principle and M–C criterion can better reflect the mechanical behavior of red sandstone under hydraulic coupling. This paper provides a research basis for understanding the microscopic characteristics and hydraulic coupling characteristics of Cretaceous weakly cemented sandstone.

Oncolytic viruses (OVs) can specifically replicate in the host and cause cancer cell lysis while inducing an antitumor immune response. The aim of this study is to investigate the impact of either pre-existing immunity against herpes simplex virus type-1 (HSV-1) or multicycle treatment with OVs on anticancer efficacy of VG161, an HSV-1 OV in phase 2 clinical trial. VG161 efficacy was tested in CT26 mouse models by comparing the efficacy and immune response in naïve mice or in mice that were immunized with VG161. Moreover, VG161 efficacy in HLA-matched CD34+ humanized intrahepatic cholangiocarcinoma (ICC) patient-derived xenograft (PDX) models was also tested in multicycle treatment and was compared to standard chemotherapy for this type of cancer (gemcitabine). The HSV-1-immunized mice significantly inhibited tumor growth in VG161-treated mice compared to control naïve treated mice. RNA expression profiling and ELISPOT analyses indicated changes in the tumor’s immune profile in the immunized and treated group compared to naïve and treated mice, as well as enhanced T cell function depicted by higher numbers of tumor specific lymphocytes, which was enhanced by immunization. In the ICC PDX model, repeated treatment of VG161 with 2 or 3 cycles seemed to increase the anticancer efficacy of VG161. In conclusion, the anticancer efficacy of VG161 can be enhanced by pre-immunization with HSV-1 and multicycle administration when the virus is given intratumorally, indicating that pre-existing antiviral immunity might enhance OV-induced antitumor immunity. Our results suggest potential clinical benefits of HSV-1-based OV therapy in HSV-1-seropositive patients and multicycle administration of VG161 for long-term maintenance treatment.

Affected by fracture distribution, sandstone shows different deformation and energy evolution characteristics under cyclic loading and unloading conditions. Therefore, uniaxial cyclic loading tests were conducted on fractured sandstone with different angles. The deformation characteristics and the evolution law of energy indexes with the peak load and crack angles were obtained under cyclic loading. Studies have shown that: The deformation modulus of sandstone first increases and then decreases, and the lateral expansion coefficient is positively correlated with the peak load. Based on the viscoelastic deformation theory, an energy analysis model considering damping energy and damage energy is established. The dissipated energy can be divided into the damping energy consumed to overcome rock viscoelasticity and damage energy causing damage by viscoelastic deformation theory. Based on this model, the relationship between elastic property, damping energy, damage energy and fracture angle is obtained, and the damage energy increases slowly first and then rapidly. The research results provide a reference for predicting the damage and failure of rock.

The longitudinal deformation profile (LDP) of the surrounding rock can intuitively and effectively reflect that the deformation of the surrounding rock of the tunnel wall is affected by the “spatial effect” at the front end of the excavation face during the tunnel excavation process, and provides a theoretical basis for the best time for the construction of the supporting structure. Taking a large-section tunnel of soft rock as an example, the LDP equation (displacement release coefficient) of the surrounding rock is derived based on Unlu and Gercek, and the optimization and improvement are proposed after comprehensively considering Poisson’s ratio, the elastic modulus, cohesion, internal friction angle, blasting parameters, etc., using FLAC3D to analyze the rationality and validity of improving the LDP equation of the surrounding rock. The results show that: (1) by adding the “expanded convergent function” in the x ≥ 0 section, the LDP equation of surrounding rock is optimized and improved, and the correlation coefficient of the data is increased from the original R-square = 0.8 to R-square = 0.95; (2) by comparing with the numerical simulation data, the improved LDP equation of the surrounding rock can better match it, which confirms that the improved LDP equation of surrounding rock is more reasonable and practical; (3) it is proposed that the displacement release coefficient value is the best time to apply support when the displacement increment of the surrounding rock has a sharp increase point, and it is concluded that when the Class-III surrounding rock is constructed by the long step method, it is best to start supporting at about x = 2.24 m from the tunnel face. When the Class-IV surrounding rock is constructed by the reserved core soil method, it is best to start supporting at about x = 1.47 m from the face of the tunnel.

The issue of information security is closely related to every aspect of daily life. For pursuing a higher level of security, much effort has been continuously invested in the development of information security technologies based on encryption and storage. Current approaches using single-dimension information can be easily cracked and imitated due to the lack of sufficient security. Multidimensional information encryption and storage are an effective way to increase the security level and can protect it from counterfeiting and illegal decryption. Since light has rich dimensions (wavelength, duration, phase, polarization, depth, and power) and synergy between different dimensions, light as the input is one of the promising candidates for improving the level of information security. In this review, based on six different dimensional features of the input light, we mainly summarize the implementation methods of multidimensional information encryption and storage including material preparation and response mechanisms. In addition, the challenges and future prospects of these information security systems are discussed.

In order to study the anchoring instability mechanism of surrounding rock in deep roadway, the failure mechanism of the bolt-anchoring agent interface was studied by simulating different strength rock mass and ground temperature environment, using C20, C40, and C60 strength concrete and steel pipe to simulate different surrounding rock strength environments. Indoor pull-out tests were carried out to study the pull-out load displacement relationship, ultimate pull-out force, residual anchoring force, the distribution law of axial stress and tangential stress along the bar, and the energy consumption value of drawing failure at 20, 50, and 70°C. The test results show that, with the decrease of surrounding rock strength or the increase of ambient temperature, the pull-out force, residual anchoring force, and energy consumption value of anchorage interface gradually decrease; under different axial forces, the axial force distribution of the rod body decreases exponentially from the anchoring end to the opposite end; and the shear stress transfers to the deep part of the anchor body with the increase of the load. According to the failure phenomenon of the specimen, the failure modes of the bolt bolt-anchorage agent interface can be divided into shear slip mode and shear expansion slip mode. The shear expansion slip formula of anchorage interface is derived. Using high-strength and temperature-resistant resin anchoring agent for comparative test, the rationality of the mechanism analysis is proved, which provides more clear guidance for the construction of anchor support.

Soil microorganisms are important components of agricultural ecosystems; they are important in agricultural soil nutrient cycle and are easily affected by soil tillage. The response of soil microbial community to tillage is very complex, and the effect of the no tillage and residue mulching method on soil microbial diversity remains unclear. In 2019, the soil was collected from an experimental field after 10 years of continuous cultivation in the black soil area of the Sanjiang Plain in Northeastern China. In this study, the diversity and composition of the soil bacterial community and their relationship with soil properties were explored via high-throughput sequencing under no tillage with four residue mulching treatments. No tillage with 60% residue mulching (NTR3) significantly increased the alpha diversity of the rhizosphere soil bacteria and changed the composition of the bacterial community-consistent with changes in soil physicochemical properties. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla in the sample soil. Soil physicochemical properties explained 80.6% of the changes in soil diversity and composition, of which soil organic carbon, soil pH, and soil temperature were the principal contributors. Our results suggest that no tillage and residue mulching is conducive to increasing soil organic carbon and soil nutrient content, which is a beneficial conservation tillage measure for black soil protection in Sanjiang Plain of Northeast China. The no tillage with residue mulching, especially 60% residue mulching, alters soil bacterial community and highlights the importance of soil physicochemical properties in shaping the diversity and composition of the soil bacterial community. Our findings contribute to a broad understanding of the effects of no tillage and residue mulching on bacterial community differences and provide a scientific basis for the optimization of no tillage measures and sustainable utilization of the black soil of the Sanjiang Plain in Northeastern China.

To explore the influence of crack angle on the mechanical properties, energy evolution, and damage evolution of sandstone, uniaxial loading tests were conducted on sandstones with different crack angles. Through the stress–strain curve, the influence of the crack angle on the mechanical properties was analyzed. Based on energy theories and principles, the influence of crack angle on the energy conversion mechanism was analyzed. Based on crack angle and dissipated energy, a damage model considering the initial damage to the fractured sandstones was established. The following conclusions were drawn: (1) The strength and elastic modulus of sandstone decrease with an increase in crack angle, and Poisson’s ratio increases with an increase in crack angle; prefabricated cracks affect the crack initiation position, and accelerate the formation of fracture surfaces. (2) The stress–strain curve was divided into compaction stage, elastic stage, yield stage, and failure stage. The larger the crack angle, the longer the yield stage and the shorter the failure stage. (3) At the peak point, the elastic energy, dissipated energy, and input energy of fractured sandstone always decrease with an increase in crack angle; the energy consumption ratio increases with an increase in crack angle; and the energy storage ratio decreases with an increase in crack angle. (4) The damage variable shows a trend of slow accumulation–steady accumulation–rapid accumulation; the crack angle affects the initial damage of the specimen, and the dissipated energy affects the variation trend of the damage variable.

Software-defined network is based on flexible and open standards,which manages network functions and services by the control layer.And it has the unique advantages of control-separation and centralized control.The moving target defense technology is dedicated to build an ever-changing environment to increase the uncertainty of the network system,which requires a flexible and customizable,centralized and controllable network architecture to implement it.Therefore,the combination of moving target defense and software-defined network have become a more valuable research hotspot.Firstly,the basic concepts of software-defined network and moving target defense were introduced,the security threats of software-defined network was summarized,and the realization model of moving target defense for SDN network was described.Secondly,the technical methods for moving target defense were summarized respectively form the data layer,control layer and application layer of the SDN.Finally,summing up the challenges of existing SDN dynamic defense,and looking forward to the development direction of moving target defense technologies for the SDN.

Abstract Background Prioritizing disease genes is trying to identify potential disease causing genes for a given phenotype, which can be applied to reveal the inherited basis of human diseases and facilitate drug development. Our motivation is inspired by label propagation algorithm and the false positive protein-protein interactions that exist in the dataset. To the best of our knowledge, the false positive protein-protein interactions have not been considered before in disease gene prioritization. Label propagation has been successfully applied to prioritize disease causing genes in previous network-based methods. These network-based methods use basic label propagation, i.e. random walk, on networks to prioritize disease genes in different ways. However, all these methods can not deal with the situation in which plenty false positive protein-protein interactions exist in the dataset, because the PPI network is used as a fixed input in previous methods. This important characteristic of data source may cause a large deviation in results. Results A novel network-based framework IDLP is proposed to prioritize candidate disease genes. IDLP effectively propagates labels throughout the PPI network and the phenotype similarity network. It avoids the method falling when few disease genes are known. Meanwhile, IDLP models the bias caused by false positive protein interactions and other potential factors by treating the PPI network matrix and the phenotype similarity matrix as the matrices to be learnt. By amending the noises in training matrices, it improves the performance results significantly. We conduct extensive experiments over OMIM datasets, and IDLP has demonstrated its effectiveness compared with eight state-of-the-art approaches. The robustness of IDLP is also validated by doing experiments with disturbed PPI network. Furthermore, We search the literatures to verify the predicted new genes got by IDLP are associated with the given diseases, the high prediction accuracy shows IDLP can be a powerful tool to help biologists discover new disease genes. Conclusions IDLP model is an effective method for disease gene prioritization, particularly for querying phenotypes without known associated genes, which would be greatly helpful for identifying disease genes for less studied phenotypes. Availability https://github.com/nkiip/IDLP

Accurate estimating of the balanced nutrition for maize is necessary for optimizing fertilizer management to prevent nutrient supply surplus or deficiency. Data from 300 field experiments in the Northeast China conducted between 2006 and 2011 were gathered to study the characteristics of maize yield, and using the QUEFTS model to estimate the balanced nutrition at different yield potential. The average grain yield was 10 427 kg/ha, and average internal efficiencies were 54.3, 251.5 and 78.2 kg grain per kg plant nitrogen (N), phosphorus (P) and potassium (K), respectively. With the harvest index values < 0.40 as outliers were excluded, the model simulated a linear-parabolic-plateau curve for the balanced N, P and K uptake when the initial yield target increased to the yield potential levels of 10 000 to 14 000 kg/ha. When the yield target reached approximately 60-70% of the yield potential, 16.7 kg N, 3.8 kg P, and 11.4 kg K were required to produce 1000 kg grain. The corresponding internal efficiencies were 60.0, 265.7 and 88.0 kg grain per kg plant N, P and K, respectively. These results contributed to improving nutrient use efficiency, and to demonstrate that the QUEFTS model could be a promising approach for estimating the balanced nutrition.

In this study, we examined the effects of different temperatures and accelerators on the gelation (gel) time and compressive strength of a Polyethylene Terephthalate (PET)-type unsaturated polyester resin anchorage agent. First, the formation temperature of 20–70°C was simulated using self-made test equipment. N,N-Dimethylaniline (DMA), N,N-dimethylp-toluidine (DMT), and hydroquinone were selected as accelerators to determine gel time and heat release peak. The gel time of an anchorage agent is strongly influenced by accelerant and temperature. When DMT, DMA, and hydroquinone were added at the same temperature, the gelation time increased; with increasing ambient temperature, the gelation time of the anchorage agent decreased. The peak exothermic value of the curing reaction was less affected by the accelerator, and the peak exothermic value of the anchoring agent increased with the increase in ambient temperature. Then, the compressive strength of the anchorage agent, maintained at 20, 50, and 80°C for 1.5, 6, 12, and 24 h, was measured. We found that the compressive strength of resin anchorage agent decreased significantly with the increase in temperature, and the addition of DMT can improve the compressive strength of resin anchorage agent slightly at the same temperature conditions. Finally, through Fourier transform infrared scanning analysis, we determined the intrinsic causes of the influence of temperature and accelerator on the gelation time and compressive strength of the anchorage agent. Through SPSS fitting analysis, an empirical formula for predicting gelation time based on ambient temperature is proposed. Our findings provide a basis for reasonable mixing time and support design optimization of anchorage support in deep stratum in high-temperature geothermal environments.

The magnetic-resonance lines of alkali atoms are broadened considerably by the spin-exchange collisions for a radio-frequency magnetometer operating at high temperature. The resonance linewidths of cesium atoms are derived by solving the relaxation equations. When spin-exchange relaxation dominates, a light narrowing effect is predicted. For the cesium atomic radio-frequency magnetometer operates at high temperature, a remarkable narrowing of the cesium magnetic-resonance lines are observed by increasing the pump power. The Cs-Cs spin-exchange relaxation is partially suppressed by light narrowing. This study helps to expand the applications of radio-frequency magnetometers in the high-sensitivity radio-frequency magnetic-field detection.

To solve the difficult problems of failure of pretensioned bolt supports under high ground pressure and temperature, a new kind of anchorage agent with excellent performance is developed. First, the selection and compounding of raw materials were conducted. The new anchorage agent was obtained by modifying the PET resin by mixing with a phenolic epoxy vinyl ester resin (FX-470 resin) and adding a KH-570 silane coupling agent. Then, the viscosity, thermal stability, compressive strength under different temperatures, and anchorage capacity of the new anchorage agent were tested. Moreover, the best proportion ratio of anchorage agent by mixing resin : coarse stone powder : fine stone powder : accelerator : curing agent : KH-570 = 100 : 275 : 275 : 1 : 32.5 : 1 is obtained. The test results showed that, with the addition of a KH-570 silane coupling agent, the viscosity decreased significantly, thereby solving the difficult technical problems of pretensioned bolt supports in full-length anchorage support. Compared with the conventional anchorage agent, the compressive strength of the new anchorage agent increased by 20.4, 82.5, 118.2, and 237.5% at 10, 50, 80, and 110°C, respectively, and the anchorage capacity increased by 4.7, 8.7, 40.2, and 62.9% at 30, 50, 80, and 110°C, respectively. Finally, the enhancement in compressive strength and heat-resistant mechanism are revealed through microanalysis.

In order to economically and reasonably solve the problem of mineshaft support in complex geological conditions, the mechanical properties of high-performance steel-fiber-reinforced concrete (HPSFRC) and its application in mineshaft lining structures were investigated in this study. Firstly, the mix proportion of HPSFRC for the mineshaft lining structure was obtained through raw material selection and preparation testing. Then, a series of mechanical property tests were conducted. The test results showed that the compressive, flexural, and tensile strengths of HPSFRC were 9%, 71%, and 53% higher than that of ordinary concrete, respectively. The fracture toughness of HPSFRC was 75% higher than that of the ordinary concrete and the fracture energy of HPSFRC was 16 times that of the ordinary concrete. Finally, the model test results of the HPSFRC shaft lining structure showed that the crack resistance, toughness, and bearing capacity of the shaft lining structure had been significantly improved under a non-uniform confining load because of the replacement of ordinary concrete with HPSFRC. HPSFRC was proved to be an ideal material for mineshaft support structures under complex geological conditions.

The balance between fertilizer application and plant nutrient demand is essential for ensuring agricultural production because it is effective to prevent nutrient deficiency and excess, especially for soybean. This study used data from 29 sites of field experiments carried out in the soybean planting area of Liaoning province, China in 2011 to 2013. We (i) study the characteristics of yield, nutrient concentration, and harvest index to (ii) valuate the balanced nutrient uptake at different potential yield levels for soybean. The grain yield ranged from 804 to 4484 kg/ha, and average N, P, and K concentrations in grains were 45.7, 5.0, and 10.1 g/kg, respectively, while those in straw were 14.1, 1.8, and 6.7 g/kg, respectively. Average harvest index values of N, P, and K were 0.69, 0.65, and 0.52 kg/kg, respectively, while approximately 69% N and 65% P of the plant were stored in soybean grain, and 48% K was stored in straw. The two boundary lines of the QUEFTS (quantitative evaluation of the fertility of tropical soils) model were aN = 10.5, dN = 20.6, aP = 65.6, dP = 289.6, aK = 30.4, and dK = 162.7 as model parameters. The QUEFTS model estimated the balanced nutrient uptake with yield targets increased following a linear−parabolic−plateau curve. A continual linear increase in grain yield with 65.5 kg N, 7.0 kg P, and 13.9 kg K was required to produce 1000 kg grain, until the yield target reached approximately 60−70% of the potential yield, and the corresponding ratio of N, P, and K was 9.35:1:1.8. Results could be used to estimate balanced nutrient uptake to prevent excessive fertilizer being applied and reduce environment risk for ensuring sustainable agricultural development.

A new instrument used for treating femoral head osteonecrosis was recently proposed: the umbrella-shaped, Ni-Ti memory femoral head support device. The device has an efficacy rate of 82.35%. Traditional radiographic study provides limited information about the mechanical behaviour of the support device during an implant operation. Thus, this study proposes a finite element analysis method, which includes a 3-step formal head model construction scheme and a unique material assignment strategy for evaluating mechanical behaviour during an implant operation. Four different scenarios with different constraints, initial positions and bone qualities are analyzed using the simulation method. The max radium of the implanted device was consistent with observation data, which confirms the accuracy of the proposed method. To ensure that the device does not unexpectedly open and puncture the femoral head, the constraint on the impact device should be strong. The initial position of sleeve should be in the middle to reduce the damage to the decompression channel. The operation may fail because of poor bone quality caused by severe osteoporosis. The proposed finite element analysis method has proven to be an accurate tool for studying the mechanical behaviour of umbrella-shaped, Ni-Ti memory alloy femoral head support device during an implant operation. The 3-step construct scheme can be implemented with any kind of bone structure meshed with multiple element types.