In order to realize rapid and nondestructive monitoring of wheat biomass in field, field experiments based on different densities, nitrogen fertilizer and variety treatments were studied. RGB images of wheat in the main growth stage were obtained by UAV, and wheat color and texture feature indices were obtained by image processing, and wheat biomass was obtained by field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the color and texture feature index suitable for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, and most of them reached a very significant correlation level. However, the correlation between image texture feature index and wheat biomass was poor, only a few indexes reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture feature in different growth stage were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the significant level, RMSE were smaller. This indicated that the estimated results by the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV image were better than the single color index models. The results would provide a new method for real-time monitoring of wheat field growth and biomass estimation.

Lithium-rich layered oxides is one of the most perspective candidates for cathode materials of lithium ion battery, because of its high discharge capacity. However, there are some disadvantages of uneven composition, voltage decay, and poor rate capacity, which are closely related to the preparation method. Here, 0.5Li2MnO3·0.5LiMn0.8Ni0.1Co0.1O2 were successfully prepared by sol-gel and oxalate co-precipitation methods. A systematic analysis of the materials shows that the 0.5Li2MnO3·0.5LiMn0.8Ni0.1Co0.1O2 prepared by the oxalic acid co-precipitation method has the most stable layered structure and the best electrochemical performance. The initial discharge specific capacity is 261.6 mAh·g-1 at 0.05 C, and the discharge specific capacity is 138 mAh·g-1 at 5 C. The voltage decay is only 210 mV, and the capacity retention is 94.2% after 100 cycles at 1 C. The suppression of voltage decay can be attributed to the high nickel content and uniform element distribution. In addition, tightly packed porous spheres help to reduce lithium ion diffusion energy and improve the stability of the layered structure, thereby improving cycle stability and rate capacity. This conclusion provides a reference for designing high energy density lithium-ion batteries.

Owing to the widespread use of bisphenol analogues (BPs) as bisphenol A (BPA) alternatives, they have been recognized to constitute a health risk for aquatic ecosystems. The occurrence and distribution of six BPs were investigated in the truly dissolved phase (< 5 kDa), colloidal phase (5 kDa to 1 µm) and suspended particulate matter (SPM > 1 µm) in a water diversion project of Nanjing, China. With the exception of bisphenol Z, all BPs were detected in at least two phases, the total concentrations of detected BPs were 161-613 ng/L, 5.19-77.2 ng/L and 47.5-353 ng/g for the truly dissolved phase, colloidal phase and SPM, respectively. Among the detected compounds, BPA still the dominant BPs in the truly dissolved phase and colloidal phase, followed by BPS, while BPAF was the major contaminant in SPM, followed by BPA. The mean contribution proportions of colloids were 1-2 orders of magnitude greater than that of SPM, suggesting that colloids play an important role in regulating the environmental behaviors of BPs. In terms of spatial distribution, although the water diversion project could reduce the pollution levels of BPs, which might further affect the ecological security of the Yangtze River.

The iridoids of H. diffusa play an important role in the anti-inflammatory process, but the specific iridoid with anti-inflammatory effect and its mechanism is lack of study. An iridoid compound named scandoside (SCA) was isolated from H. diffusa and its anti-inflammatory effect was investigated in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Its anti-inflammatory mechanism was confirmed by in intro experiment and molecular docking analysis. As results, SCA significantly decreased the productions of nitric oxide (NO), prostaglandin E₂ (PGE₂), tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) and inhibited the levels of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), TNF-α and IL-6 mRNA expression in LPS-induced RAW 264.7 cells. SCA treatment suppressed the phosphorylation of inhibitor of nuclear transcription factor kappa-B alpaha (IκB-α), p38, extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK). The docking data suggested that SCA had great binding abilities to COX-2, iNOS and IκB. Taken together, the results indicated that the anti-inflammatory effect of SCA is due to inhibition of pro-inflammatory cytokines and mediators via suppressing the nuclear transcription factor kappa-B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways, which provided useful information for its application and development.

As an active device, air source heat pumps (ASHP) are widely used for heating and cooling in buildings. However, in cold regions, their efficiency decreases, and frost formation becomes a significant issue during winter. This paper proposes a new scheme that couples air source heat pumps with passive heating. By using software simulations and mathematical models, the new scheme is compared to traditional ASHP systems to analyze its heating performance in rural homes during winter in China’s cold regions. According to the simulation and calculation analysis, on the coldest day of winter, the coupling scheme can provide approximately 99.41kWh of heat to the interior, surpassing the 86.67kWh required to maintain an indoor temperature of 20℃. The system’s power consumption is 36.96 kWh, which is 66.88% lower than that of traditional heat pump heating. The study shows that a coupled system of air source heat pumps and passive heating has better heating performance in cold regions. By incorporating phase change materials and heat storage media to address the shortage of solar energy at night, the system can meet the heating needs throughout the entire day.

Facilitating sustainable cooperation between schools and enterprises has become an important concern in many countries. However, there remains a gap in understanding the approaches taken by central and local governments to foster such cooperation through the lens of evolutionary game theory. Therefore, this paper aims to develop a quadrilateral evolutionary game model involving schools, enterprises, the central government, and local governments, to analyze the conditions under which governments can effectively promote school-enterprise cooperation. Our findings indicate that benefit is a decisive factor that affects the strategies of all parties. When the benefits of cooperation are sufficiently attractive, schools and enterprises are inclined to cooperate with each other independently of governmental intervention. While increasing incentives and penalties from the central government and local governments can stimulate school-enterprise cooperation, these measures are not sustainable in the long term. Additionally, the impact of the central government’s incentives and penalties on local governments in promoting school-enterprise cooperation is limited. Enhancing policy efficacy and the practical outcomes of school-enterprise cooperation is essential, which not only strengthens the bond between schools and enterprises but also ensures deep and enduring governmental involvement.

Determining the general-name-derived semantic relationships between geographic names often requires consulting many geographic name documents for verification. In geographic name translation and geographic name knowledge graphs, this method cannot meet the needs of large-scale extraction of general-name-derived semantic relationships between geographic entities. To solve this problem, this paper proposes a general-name derivation relation recognition method based on Prompt learning and Semantic Spatiotemporal Distribution Attention Feature Fusion (SSD-AFF). This method adopts a pipe-based relationship recognition strategy. First, the Prompt learning method is adopted to form a general-name derivation pattern with the general-name-derived geographical name category and the original geographical name category as the prompt word, and then the derivation pattern is combined with the geographical name as the input of the model so that the recognition problem of the general-name derivation of the geographical name can be transformed into a classification problem using the bidirectional language model. Then, by analyzing the characteristics of the semantic association and geographical spatio-temporal distribution between the general-name-derived geographical name and the original geographical name, the semantic spatio-temporal distribution characteristics of the general-name-derived geographical name are constructed. Finally, the network model based on the semantic spatio-temporal distribution Attention Feature Fusion (SSD-AFF) is used to distinguish the general-name-derived semantic relationships. The experimental results show that the proposed method has good performance. It can effectively mine the general-name-derived semantic relations between the vector data of geographical entities and has important application value in geographical name translation and geographical name knowledge maps.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

Crystalline silicon nanomembrane (Si NM) is promising in wearable and implantable bioelectronics due to its electrical and mechanical performances. However, the current explorations are not enough to firmly bridge ultrathin encapsulation strategies with high performance crystalline Si NM based transistors. In this work, we utilized Al2O3/alucone as the ultrathin encapsulation strategies aim to thoroughly investigate the effect of gate dielectric surface area on the electrical performance of crystalline Si NM based metal-oxide-semiconductor capacitors (MOSCAPs). The comparative analysis was implemented on the three types of diameters (Ø) MOSCAPs with and without Al2O3/alucone encapsulation under planar and concave/convex bending conditions. Benefited from the Al2O3/alucone ultrathin encapsulations, the stability of gate leakage current density, and interfacial characteristics were maintained and improved under mechanical bending deformations for MOSCAPs with predefined Ø of 160, 240, and 320 μm compared to the bare MOSCAPs. Combined with the Ø-related mechanical analysis on the maximum strain in the critical layers and the practical measurements, the encapsulated MOSCAPs with Ø 160 μm showed the most stable electro-mechanical performances. The variations for encapsulated MOSCAPs with three types of Ø emerged in the oxide layer and the interface at dielectric/Si NM that were carefully evaluated according to the bending deformations. These findings are significant for leveraging the practical applications in the ultrathin encapsulation strategies for reliable operations of crystalline Si NM based integrated circuits (ICs).

Changing the film thickness to manipulate the microstructural properties has been considered as a potential method in practical application. Here, we report that atomic-scale structural properties regulated by film thickness in NiCO2O4(NCO)/CuFe2O4(CFO) bilayer heterostructure prepared on (001)-MgAl2O4 (MAO) substrate by means of aberration-corrected scanning transmission electron microscopy (STEM). The misfit dislocations at NCO/CFO interface and antiphase boundaries (APBs) bound to dislocations within the films both are found in NCO (40 nm)/CFO (40 nm)/MAO heterostructure to contribute to relaxation of mismatch lattice strain. In addition, the non-overlapping a/4[101]-APB is found and the structural transformation of this kind of APB is resolved at atomic scale. In contrast, only the interfacial dislocations form at interface without the formation of APBs within the films in NCO (10 nm)/CFO (40 nm)/MAO heterostructure. Our results provide evidence that formation of microstructural defects can be regulated by changing film thickness to tune magnetic properties of epitaxial bilayer spinel oxide films.

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model's potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

Digital agricultural technology extension service and sustainable food production have attracted attention from academia. However, the association between digital agricultural technology extension service and sustainable food production has not yet been fully investigated. This research investigates the average and heterogeneous impacts of digital agricultural technology extension service use on eco-efficiency among 1302 maize producing farmers from Northeast China major maize producing area in 2022 to fill this knowledge gap. The slack-based measure model with undesirable outputs is applied to calculate the eco-efficiency of maize production. To obtain an unbiased estimation of the average effect, the self-selection problem generated by observable and unobservable factors is solved by the endogenous switching regression model. Quantile regression is utilized to analyze the heterogeneous effect. Notably, the mediated effects model is utilized to examine the potential mechanism between them. Our findings indicate that digital agricultural technology extension service use can increase maize production's eco-efficiency. Digital agricultural technology extension service users would reduce eco-efficiency by 0.148 (21.11%) if they had not used the digital agricultural technology extension service. Digital agricultural technology extension service nonusers would improve eco-efficiency by 0.214 (35.20%) if they had used it. The robustness check reconfirms the results. Moreover, digital agricultural technology extension service use is more helpful to maize farmers who have lower eco-efficiency than those who have higher eco-efficiency. Digital agricultural technology extension service use can improve eco-efficiency of maize production through the application of organic fertilizers, green pesticides and biodegradable agricultural films.

This research centers on the Mulberry-Dyke and Fish-Pond System in Digang Village, Huzhou, exem-plifying the traditional cyclic agricultural models of China. The study's objective is to evaluate its integral value concerning agricultural production, ecological environment, and heritage conservation. Employing the Analytical Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methodologies, we developed a comprehensive evaluation framework comprising 8 dimensions and 29 factors, aimed at assessing the provisioning, regulating, and cultural services of this ecosystem. The findings indicate a robust performance of the ecosystem services in Digang Village's Mulberry-Dyke and Fish-Pond System, with cultural services significantly standing out. In contrast, the regulating services appeared relatively weaker, pinpointing shortcomings in mulberry land management, while the provisioning services demonstrated substantial strength. These insights are pivotal for comprehending the system's ecosystem service values, highlighting areas for managerial improvements, and guiding future assessment and management strategies. This study contributes theoretical insights and empirical experiences for future evaluations of the Mulberry-Dyke and Fish-Pond System's ecosystem services.

As global warming intensifies, the development of offshore wind farms is swiftly progressing, especially deep-water floating offshore wind turbines (FOWTs) capable of energy capture in deep-sea regions, which have emerged as a focal point of both academic and industrial interest. Although numerous researchers have conducted comprehensive and multifaceted studies on various components of wind turbines, less attention has been paid to the operational stage response of FOWTs to wind, waves, and currents, and the reliability of structural components. This study primarily employs theoretical analysis to establish mathematical models under a series of reasonable assumptions, examining the possibility of collisions between FOWT transport fleets and other vessels in the passage area during the towing process. Using the model, this paper takes the Wanning Offshore Floating Wind Farm (FOWF) project, which is scheduled to be deployed in the South China Sea, as the research object and calculates the probability of collisions between FOWTs and other vessels in three time periods from the total assembly pier in Hainan Wanning to the predetermined position 22 km away. The findings of the analysis suggest that the mathematical model has the capability to efficiently determine the frequency of collisions between vessels within the domain and the transport team during the transportation process. This holds substantial relevance for future risk assessment and disaster prevention and mitigation measures in the context of FOWT transportation.

Excessive nitrogen application has become a hidden danger threatening the sustainable development of agriculture. Exploring the potential genes will help to improve the nitrogen availability of wheat through molecular breeding. We therefore conducted wheat transcriptomics and proteomics researches under complete culture medium without nitrogen (N0) and complete culture medium with nitrogen (N1). The results showed that, the nitrogen deficit decreased the fresh weight, N content and net photosynthetic rate of winter wheat. 3949 and 2675 differentially expressed genes (DEGs) were identified in the shoot and root, respectively, while 164 and 129 differentially expressed proteins (DEPs) were identified in the shoot and root respectively. 9 genes in shoot and 26 genes in root showed the same trends in RNA and protein levels. 5 genes in shoot and 4 genes in root showed the opposite trends in RNA and protein levels. Pathway enrichment of wheat transcriptome and proteome under nitrogen deficit proposed 3 candidate genes related to photosynthesis in shoot, 4 candidate genes related to carbon metabolism in shoot, and 12 candidate genes related to carbon metabolism in root. Meanwhile, the chromosomal and subcellular localization of these genes were listed, which will provide reference for improving nitrogen availability through molecular breeding.

Despite the fact that SMA restrainers exhibit a superelastic strain capacity of 7%, this capacity appears inadequate for isolated bridges due to the typically greater than 20cm relative dis-placements between girders during intense seismic events. In order to perform such a stroke, a SMA restrainer of greater than 3 metres in length might be required. In order to reduce the length of restrainers, a novel honeycomb damper constructed from superelastic shape memory alloy (SMA) is proposed. The proposed device, denoted as the superelastic SMA honeycomb damper (SHD), is comprised of steel plates to prevent the SMA plane from collapsing and superelastic SMA honeycomb to provide self-centering capability. By incorporating the large strain capacity of SMA and the geometrically nonlinear deformation of honeycomb structures, SHD has been developed to satisfy the requirements of bridge restrainers with large strokes. It is capable of functioning as a restrainer and energy dissipation device when subjected to dynamic tension and compression loads. The SHD was initially investigated from a theoretical perspective. Following this, a mul-ti-cell SHD specimen was manufactured. The specimen underwent axial tensile and compressive experiments in order to examine the mechanical properties of SHDs. Finally, experimental results were investigated through numerical simulation analyses of the SHDs using a three-dimensional high-fidelity finite element model. Additionally, a method for enhancing SHD was proposed. The findings indicate that SHD is capable of exhibiting superior self-centering capability and sta-ble hysteretic responses when subjected to earthquakes.

As an outstanding information carrier, skyrmion is increasingly widely used in devices based on complex geometries. To achieve the skyrmion-based spintronic devices, a reasonable and feasible nanotrack is essential. In this paper, we conducted a study on the current-driven skyrmion movement in a circular-ring-shaped nanotrack. Our results suggest that the asymmetry of the inside and outside boundary of the circular ring changed the stable position of the skyrmion, causing it to move like the skyrmion Hall effect when driven by currents. Moreover, the asymmetric boundaries have advantages in enhancing or weakening the skyrmion Hall effect. Additionally, we also compared the skyrmion Hall effect from the asymmetric boundary of circular ring nanotracks with that from inhomogeneous DMI. It is found that the skyrmion Hall effect caused by the circular ring is significantly greater than that caused by the inhomogeneous DMI. These results contribute to our understanding of the skyrmion dynamics in confined geometries and offer an alternative method for controlling the skyrmion Hall effect of skyrmion-based devices.

Dacomitinib is an irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor indicated for the treatment of patients with advanced non-small cell lung cancer (NSCLC) and EGFR-activating mutations. Proton pump inhibitors decreased dacomitinib exposure. This anal-ysis summarizes the effect of H2RA on dacomitinib exposure. A within-patient comparison of steady-state trough concentrations (Ctrough,ss) of dacomitinib and its active metabolite and active moiety with and without concomitant use of H2RA was conducted using a linear mixed effects model with pooled data from 11 clinical studies in patients with NSCLC. An oral absorption PBPK model was constructed and verified using clinical PK data after a single dose of dacomitinib in healthy volunteers to estimate the effect of gastric pH altered by an H2RA on dacomitinib PK. The adjusted geometric mean of dacomitinib Ctrough,ss of dacomitinib parent, metabolite and active moiety following co-administration with H2RA was approximately 86%, 104% and 100% relative to that following dacomitinib 45 mg administration without H2RA (P>0.05). The PBPK modeling showed negligible change in dacomitinib Cmax and AUC over 0-24 hours after H2RA administra-tion, when compared with those administered dacomitinib alone. Co-administration of an H2RA with dacomitinib is not expected to have any clinically relevant effect on dacomitinib exposure.

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

Recently, the triple-network convergence system (TNCS) has emerged from the deep integration of the power grid, transportation network, and information network. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the faults scale and increase faults impact. Currently, fault recovery focuses primarily on single power grids and cyber-physical systems, but there are certain shortcomings, such as ignoring uncertainties including generator start-up failures and the occurrence of new faults during recovery, energy supply-demand imbalances leading to system security issues and communication delay caused by network attacks. In this study, we propose a recovery method based on the improved TD3 algorithm, factoring in shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay and an improved Actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefit in power grid failure scenarios and demonstrates a strong resilience against communication delay caused by DoS attacks.

This paper investigates the polarization and heat generation characteristics of batteries under different ambient temperatures and discharge rates by means of using a coupled electric-thermal model. The study found that the largest percentage of polarization is ohmic polarization, followed by concentration polarization and electrochemical polarization. The values of the three types of polarization are generally small and stable under normal-temperature environments and low-discharge rates. However, they increase significantly in low-temperature environments and high-discharge rates and continue to rise during the discharge process. Additionally, ohmic heat generation and polarization generation also increase significantly under these conditions. Reversible entropy heat is less sensitive to ambient temperature but increases significantly with the increase of the discharge rate. Ohmic heat generation and polarization heat generation contribute to the total heat generation of the battery at any ambient temperature, while reversible entropy heat only contributes to the total heat generation of the battery at the end of discharge.

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).

Mitochondrial hypochlorite (ClO-) plays important and often contradictory roles in maintaining the redox balance of mitochondria. Abnormal ClO- levels can induce mitochondrial inactivation and further cause cell apoptosis. Herein. we have developed an anthracene carboxyimide-based fluorescent probe mito-ACS for imaging mitochondrial ClO- in living cells. This probe exhibits some distinctive features as excellent resistance to photobleaching, high selectivity and sensitivity, as well as good water solubility. Mito-ACS showed a noticeable fluorescence response toward ClO-with a fast response (within 6 s) and a low detection limit (23 nM). Moreover, the introduction of triphenylphosphonium makes the probe soluble in water and selectively localizes to mitochondria. Furthermore, mito-ACS was successfully applied to image mitochondria ClO- in living cells with low toxicity. Remarkably. the less used fluorophore anthracene carboxyimide exhibiting excellent photostability and desirable optical properties provides a promising application prospect in biological systems.

Kiwifruit is a perennial horticultural crop species with high nutritional and economic value. However, various pathogens stress brings a serious threat to kiwifruit. Pentatricopeptide repeat proteins (PPRs) are characterized by tandem repetitions of 35 amino acid motifs, and have been found to function in plant RNA editing. However, the roles of PPRs in plant development and disease resistance remain unclear. In this study, we performed a genome-wide identification and characterization of PPR gene family in two kiwifruit species (Actinidia chinensis (Ach), and Actinidia eriantha (Ace)) with markedly different disease resistance. A total of 497 and 499 PPRs were identified in Ach and Ace, respectively. All the kiwifruit PPRs could be phylogenetically divided into four subfamilies. There were about 40.68% PPRs predicted to be localized to mitochondria or chloroplasts. Synteny analysis showed that the expansion of kiwifruit PPRs mainly came from segmental duplication. Based on RNA-seq data from the fruit during 12 periods of development and maturity, weighted correlation network analysis suggested that two PPRs Actinidia20495.t1 and Actinidia15159.t1 were involved in the fruit development and maturation. In addition, we observed different gene expression of PPRs and chloroplast RNA editing profiles between resistant and susceptible kiwifruits after pathogen infection, indicating the roles of PPRs in stress response by modulating the editing extend of mRNA. The differentially expressed upstream transcription factors of PPRs were further identified, they may regulate resistance adaption by modulating the PPRs expression. These results provide a solid foundation for further analyses of the functions and molecular evolution of PPRs, in particular, for clarifying the resistance mechanisms in kiwifruits and breeding new cultivars with high resistance.

For deep-penetrating geochemistry, there is a frontier issue as to how to explore ore-bearing information deep in covered areas, while it is, too, important to investigate the geochemical migration of ore-forming elements for the development of exploration geochemistry. In this study, a 380-day migration column experiment was conducted with surface soil and pure Cu powder collected from the Yujiashan study area in Wuhan City, Hubei Province taken as the object of research to simulate the migration process of ore-forming elements under natural conditions. The results showed obvious vertical migration of the ore-forming elements and conversion of mobile metal forms in the process; further, according to 6 groups of simulation experiments that took 45 days to conduct on migration column samples and the surface oil and Cu powder collected from the Pulang mining area in Yunnan Province, the vertical migration of the ore-forming element was still visible, and the exogenous substance (pure Cu powder) could enhance the anomaly without changing the basic migration mechanism. This indicates that Cu might migrate in a stable manner in soil cover in the Pulang mining area, and its migration was mostly driven by the transformation of Fe-Mn oxide bound state and organic bound state; finally, a multifractal analysis was conducted, revealing that the ore-forming elements presented a nonlinear and complex structure during their upward migration, which caused anomalies in the covered area, and multiple parameters exerted a certain indicative effect on different source intensities and properties of soil. In this study, laboratory simulations were performed to summarize the active-state evolution pattern and migration mechanism of ore-forming elements with a view to providing technical support and theoretical guidance for geochemical prospecting in the covered region.

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. As lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to the unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, an blockchain based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named as proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain, theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.

The endosomal sorting complex required for transport (ESCRT) is an essential molecular machinery in eukaryotic cells that facilitates the invagination of endosomal membranes, leading to the formation of multivesicular bodies (MVBs). It participates in various cellular processes, including lipid bilayer remodeling, cytoplasmic separation, autophagy, membrane fission and remodeling, plasma membrane repair, as well as the invasion, budding, and release of certain enveloped viruses. The ESCRT complex consists of five complexes, ESCRT-0 to ESCRT-III and VPS4, along with several accessory proteins. ESCRT-0 to ESCRT-II form soluble complexes that shuttle between the cytoplasm and membranes, mainly responsible for recruiting and transporting membrane proteins and viral particles, as well as recruiting ESCRT-III for membrane neck scission. ESCRT-III, a soluble monomer, directly participates in vesicle scission and release, while VPS4 hydrolyzes ATP to provide energy for ESCRT-III complex disassembly, enabling recycling. Studies have confirmed the hijacking of ESCRT complexes by enveloped viruses to facilitate their entry, replication, and budding. Recent research has focused on the interaction between various components of the ESCRT complex and different viruses. In this review, we discuss how different viruses hijack specific ESCRT regulatory proteins to impact the viral life cycle, aiming to explore commonalities in the interaction between viruses and the ESCRT system.

The volatile fatty acids (VFAs) productions, as well as particulate organics decomposition, soluble chemical oxygen demand (SCOD) yield, and the VFAs production pathways, of mesophilic and thermophilic anaerobic fermentation were investigated. Batch experiments showed that the decomposition rate of volatile suspended solids (VSS), particulate carbohydrate (P-C) and particulate protein (P-P) followed the first-order kinetic model at different temperature. However, the intermediates， accumulated in the process of protein or carbohydrate digestion had a more significant inhibitory effect on the production of VFAs during the mesophilic anaerobic acidification process. Thermophilic fermentation led to more accumulation of VFAs compared to mesophilic fermentation. Then 13C stable isotope labeling experiment demonstrated that, acetic (HAc), propionic (HPr) and butyric (HBu) could be produced from carbohydrate and protein digestion, but valeric (HVa) was only generated from the degradation of protein. Finally, the possible pathway of HVa synthesis was proposed.

Transcranial photobiomodulation (tPBM) has been suggested as a non-invasive neuromodulation tool. Repetitive administration of light-emitting diode (LED)-based tPBM for several weeks significantly improved human cognition. To understand the electrophysiological effects of LED-tPBM on the human brain, we investigated alterations by repeated tPBM in vigilance performance and brain networks using electroencephalography (EEG) in healthy participants. Active and sham LED-based tPBM were administered to the right forehead of young participants twice a week for four weeks. The participants performed a psychomotor vigilance task (PVT) during each tPBM/sham experiment. A 64-electrode EEG system recorded electrophysiological signals from each participant during the first and last visits in the 4-week study. Topographical maps of EEG power enhanced by tPBM were statistically compared for the repeated tPBM effect. A new data-processing framework combining the group singular value decompensation (gSVD) with eLORETA was implemented to identify EEG brain networks. The reaction time of PVT in the tPBM-treated group was significantly improved over four weeks compared to that in the sham group. We observed acute increases in EEG delta and alpha powers during 10-min LED-tPBM while the participants performed the PVT task. We also found that theta, beta, and gamma EEG powers significantly increased globally after four weeks of LED-tPBM. Combining gSVD with eLORETA enabled us to identify EEG brain networks and the corresponding network power changes by repeated 4-week tPBM. This study clearly demonstrated that 4-week prefrontal LED-tPBM can neuromodulate several key EEG networks, implying a possible causal effect between modulated brain networks and improved psychomotor vigilance outcomes.

This paper focuses on a joint model to analyse longitudinal proportion and survival data. After transforming the longitudinal proportional data by a logit function, we adopt a partially linear mixed-effect model for it, in which nonlinear function of time is fitted via using B-splines technique and a centered Dirichlet Process Mixture Model (CDPMM) is specified for a general distribution of random effects. The survival data is assumed to a Cox proportional hazard model, the sharing random effects joint model is developed for the two types of data. Combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates. Simulation studies are conducted to investigate the finite sample performance of the proposed methods. An example from the MA.5 research experiment is used to illustrate the proposed methodologies.

Urea, a prevalent component found in wastewater, shows great promise as an alternative substrate to water for energy-efficient hydrogen production by electrolysis. However, the sluggish kinetics of the anodic urea oxidation reaction (UOR) significantly hampers the overall reaction rate. To address this challenge, this article presents a comprehensive study focusing on the controlled fabrication of hierarchically structured nanomaterials as potential catalysts for UOR. The prepared MnO2@NiCo-LDH hybrid catalyst demonstrates remarkable improvements in reaction kinetics, benefiting from synergistic enhancements in charge transfer and efficient mass transport facilitated by its unique three-dimensional architecture. Notably, the catalyst exhibits an exceptionally low onset potential of 1.228 V and requires only 1.326 V to achieve an impressive current density of 100 mA cm-2, representing a state-of-the-art performance in UOR. These findings highlight the tremendous potential of this innovative material-designing strategy to drive advancements in electrocatalytic processes.

Wireless sensor networks are usually applied in hostile areas where nodes are easy to be moni-tored and captured by adversary. Key distribution is an essential primitive to provide most of security mechanism. However, the characteristic of limited resources of sensors restricts the direct use of conventional key distribution schemes. In this paper, a complete security key distribution scheme based on asymmetric cryptography technology is proposed in both static and mobile scenarios. Mutual authentication is guaranteed using challenge-response mechanism. The per-formance evaluation and security analysis show that the proposed scheme with low complexity not only provides better security for wireless sensor networks, but also reduces storage overhead and key exposure risks.

Background: Numerous investigations have shed light on the intriguing interplay between gut microbiota(GM) and psoriasis (Ps) as well as psoriatic arthritis (PsA). However, the precise nature of the causal relationship between them remains an area of active investigation. Methods: For the purpose of our investigation, we meticulously curated a collection of genetic variants (P < 1 × 10−5) associated with GM (n = 18,340) derived from the MiBioGen study. To explore the intricate relationship between GM and Ps as well as PsA, we harnessed the comprehensive resources of the FinnGen database, encompassing a vast cohort of individuals, including 4,510 Ps cases and 212,242 controls, and 1,637 PsA cases and 212,242 controls. Even complementary MR methods were implemented, including an inverse variance weighting method, followed by a sensitivity analysis to verify the robustness of the results. Results: In this study, we found that certain bacterial taxa, such as Lactococcus, Ruminiclostridium 5, and Eubacterium fissicatena, were identified as risk factors. Conversely, Alloprevotella and Odoribacter demonstrated a protective effect against Ps. In the case of PsA, our results revealed a distinct set of risk factors and protective factors among the gut bacterial taxa. Lactococcus, Verrucomicrobiales, Akkermansia, Coprococcus 1, Lachnospiraceae, and Verrucomicrobiaceae were identified as risk factors for PsA. On the other hand, Odoribacter, Rikenellaceae, Clostridium innocuum, and Marvinbryantia exhibited a protective effect against the development of PsA. Conclusion: Our findings shed light on the distinctive disease characteristics and onset features between Ps and PsA. Notably, certain intestinal flora were implicated in the pathogenesis of PsA, suggesting their potential role as early diagnostic indicators. Furthermore, we identified several bacterial flora that exhibit a potential protective effect against the occurrence of Ps and PsA. These discoveries lay a solid foundation for future endeavors in the prevention and treatment of these conditions.

Based on multi-source remote sensing data, scenario analysis, and model simulation, the Pareto optimal solutions for water supply, water purification (N retention), and carbon storage and sequestration services under different scenarios were sought by adjusting its land use structure. The results showed that. In Scenario 1(S1), the water supply service needs to increase by 86.820 to 11.211 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 to 11,700 tons, and the carbon storage and sequestration service need to decrease by 2.070 to 2.487 billion tons. In Scenario 2(S2), the water supply service needs to increase by 8.243–10.666 billion cubic metres, the water purification (N retention) service needs to decrease by 11,300–1.10 million tons, and the carbon storage and sequestration service needs to decrease by 2.033 to 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 7.832–11.437 billion cubic metres, the water purification (N retention) service needs to decrease by 1.16–10,800 tons, and the carbon storage and sequestration service needs to decrease by 19.220 to 2.380 billion tons. After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. After optimising the S3 ecosystem service supply pattern, the water supply, water purification (N retention), and carbon storage and sequestration services could connect the western and eastern ecosystem service supply areas, balance the overall ecosystem service supply pattern of the study area and meet the demand for ecosystem services. The results can guide regional land planning and ecosystem service management optimisation.

Digital health has gained widespread adoption across various disciplines. Within the field of sport science, there is growing interest in digital health, leading to a surge in research activities. The objective of this study is to conduct a comprehensive bibliometric and visualization analysis, tracing the evolutionary trajectory, identifying research hotspots, and uncovering emerging trends in digital health within the domain of sport science. By doing so, this research aims to offer scholars a systematic and comprehensive understanding of the dynamic landscape in this field. To achieve this, the Web of Science core collection was chosen as the primary database. A thorough literature search was performed, focusing on digital health and sport science, resulting in the screening of 1870 English-language documents. The findings revealed a consistent upward growth trend in the annual publication count from 2010 to 2023. Notably, the United States, specifically the Commonwealth of Pennsylvania System of Higher Education, and John B. Cronin, emerged as the most prolific contributors in terms of publications. Collaborative efforts among countries, institutions, and authors were also observed. The research hotspots identified encompassed several areas, including the evaluation of digital health technologies' effectiveness, exploration of the relationship between digital health technologies and physical activity/exercise performance, and the use of digital health technologies in assessing and rehabilitating sports injuries. Among the prominent digital health technologies employed in these studies were virtual reality, social media, and wearable devices. The predominant types of studies utilized meta-analysis, randomized controlled trials, and cross-sectional studies. Looking towards the future, the study identified several key research trends. These include the exploration of telemedicine, artificial intelligence-based machine learning, therapeutic interventions, and physical therapy within the realm of digital health. These emerging areas signify promising directions for future investigation and development in the field of digital health within sport science.

Ocean circulation serve as the primary channels for transporting material and energy flows throughout the entire ocean system, which plays a crucial role in shaping Earth’s climate, weather patterns, and marine ecosystems. The ocean movements have far-reaching impacts on both the environment and human life. An effective method for semantically modeling the ocean circulation is urgently required to be established. To achieve a unified description of ocean circulation at the semantic level, this paper introduces the theory and methodology of ocean ontology, which is developed through an analysis of domain knowledge in ocean circulation. We focus on analyzing the concepts, temporal relationships, and spatial relationships of ocean circulation. By defining classes, properties, relationships, instances, and constraint conditions within the logical structure of an ontology, it is feasible to formalize the expression of conceptual elements and their relationships. Additionally, semantic inference rules are established to finalize the construction of the ocean circulation ontology. The effectiveness of ontology construction has been verified through practical examples. Furthermore, a specialized knowledge base framework has been developed upon the ontology description of ocean circulation. Some examples of knowledge base queries have been articulated and verified. The results demonstrate that this ontology can effectively represent the relevant knowledge in the domain of ocean circulation and provide a meaningful strategy for investigating semantic integration and knowledge sharing in this field.

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph regularized Lp smooth nonnegative matrix factorization (HGSNMF), by incorporating hypergraph regularization and Lp smoothing constraint terms into the standard NMF. The hypergraph regularization term can capture the intrinsic geometry structure of the high dimension space data more comprehensively than simple graph, the Lp smoothing constraint may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of HGSNMF is theoretically investigated. The experimental results on four different data sets show that the proposed method has better clustering effect than the related state-of-the-art methods in the vast majority of cases.

In this work, a series of compounds in the CaO-½Er2O3-CoOz ternary oxide system were synthesized in air at 885℃ using high temperature solid-phase synthesis method. Phase boundary of each solid solution region in the CaO-½Er2O3-CoOz system was determined by X-ray powder diffraction technique. The phase diagram of the CaO-½Er2O3-CoOz system at 885℃ includes three series of ternary oxide solid solutions : (Ca3-xErx)Co4O9-z (0≤x≤0.9), (Ca3-xErx)Co2O6-z (0≤x≤1.25) and (Er1-xCax)CoO3-z (0≤x≤0.33). Four three-phase regions and five solid solution tie-line regions were obtained. The structure of perovskite solid solution (Ca3-xErx)Co2O6-z has been analyzed by Rietveld refinements. With the increase of Er content, the cell parameters of (Ca3-xErx)Co2O6-z exhibit an decreasing trend in a and b directions and an increasing trend in c direction.

When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To tackle this problem, this study proposes a foggy weather detection method, YOLOv5s-Fog, based on the YOLOv5s framework. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer, SwinFocus. Additionally, this research incorporates decoupled head into the model and replaces the conventional non-maximum suppression method with Soft-NMS. Experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

: Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the ensemble learning prediction model EL V.1 was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model, EL V.2, was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020–2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p=0.000***) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.

The combined heat and power system is based on the principle of "cascade utilization of thermal energy" for system integration and optimization. The rational utilization of energy, as well as the rational arrangement of energy flow and the process optimization combination of the overall system are taken into account in order to achieve multifunctional goals in the field of thermal engineering. The present paper presents a detailed assessment of combined heat and power systems from above standpoints. This article is divided into two parts “CHP based on thermo-dynamic cycles” and “CHP based on non-thermodynamic cycles”. Each part is then classified based on power subsystem energy consumption efficiency (high or low), including Rankine cycle, ORC, Stirling cycle, gas turbine, reciprocating engine, PVT and fuel cell. The present paper also helps identify the research gaps in this area and provides direction on future studies on combined heat and power systems.

To address the problem that it is easy to form coverage blind areas when wireless sensor networks are randomly deployed, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed for wireless sensor networks. Firstly, the pop-ulation is initialized using tent chaotic sequence to enhance the optimization ability of the algo-rithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs com-pared with other algorithms, when it is used in the wireless sensor networks.

Urban scene point cloud pose significant challenges for registration due to its large data volume, similar scenarios and dynamic objects. In this paper, we propose PCRMLP, a model for urban scene point cloud registration that achieves comparable registration performance to prior learning-based methods. Compared to previous works which focus on extracting features and estimating correspondence, the model estimates the transformation implicitly from concrete instances. An instance-level urban scene representation method is introduced to extract instance descriptors via semantic segmentation and DBSCAN, which enable the model to obtain robust instance features, filter dynamic objects and estimate transformation in a more logical manner. Then a lightweight network consisting of MLPs is employed to obtain transformation in an encoder-decoder manner. We validate the approach on KITTI dataset. Experimental results demonstrate that PCRMLP can obtain a satisfactory coarse transformation from instance descriptors just in 0.0028s. With a subsequent ICP refinement module, the proposed method achieves higher registration accuracy and computational efficiency than prior learning-based works.

Early-season crop-type data are required for a variety of agricultural monitoring and decision-making applications. The early season in this study referred to the prophase and middle of a growth season. Early-season detection of crop types remains challenging because of limited discriminative features, especially for landscapes that are characterized by complex cropping patterns. In fact, different remote-sensing satellites can increase the frequency of data acquisition, which can provide more information in the early season. Moreover, optical and radar sensors have different degrees of sensitivity to crop parameters. Therefore, the integration and application of two types of multitemporal data are of great significance to improve the accuracy and timeliness of crop type detection.In deep learning (DL), convolutional neural network (CNN) and recurrent neural network (RNN) models have great potential for temporal feature extraction. Compared with RNNs, CNNs usually have fewer parameters and are more conducive to determining early-season detection dates of different crop types, which requires a lot of training because of the need to model on different dates. Nevertheless, revisit dates and temporal intervals of different satellites are usually different, resulting in different data acquisition time series; these data cannot be directly used as input for the same convolutional layer. To address this challenge, a Dual-1DCNN was bulit based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtained the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2). To verify this method, classical methods support vector machine (SVM) and random forest (RF) were implemented. The results demonstrated the following: (1) the Dual-1DCNN extracted discriminative features from S1A and S2 time series at the early season by producing the highest overall accuracy (OA: 87.23%); (2) for summer maize, cotton, and common yam rhizome, the Dual-1DCNN achieved F1 values of 92.39%, 87.71%, and 84.38%, respectively, at the early season (moreover, the early seasons were almost 40, 70, and 80 days before the end of the growth seasons, respectively). These findings suggested that the Dual-1DCNN is promising for the accurate and timely detection of crop types.

The number of obese people worldwide is rising dramatically, with 51% of the global population expected to be obese or overweight by 2035. And it is usually involved in a variety of chronic diseases, including diabetes, hypertension, cardiovascular and cerebrovascular diseases. In the process of obesity, adipose tissue expands indefinitely, which induces metabolic dysfunction. Here, we confirm the role of succinate in the intervention of obesity. Through adding succinate to drinking water, high-fat diet induced obese mice were associated with the lower fat mass, and improved glucose tolerance and energy expenditure. These metabolic changes are accompanied by increased expression of lipolysis genes and decreased lipogenesis genes, especially in subcutaneous adipose tissue. In summary, supplement of succinate improves lipid deposition and metabolic health in obese mice.

We introduce a novel hybrid Transformer-CNN architecture for robotic grasp detection, designed to enhance the accuracy of grasping unknown objects. Our proposed architecture has two key designs. Firstly, we develop a hierarchical transformer as the encoder, incorporating the external attention to effectively capture the correlation features across the data. Secondly, the decoder is constructed with cross-layer connections to efficiently fuse multi-scale features. Channel attention is introduced in the decoder to model the correlation between channels and to adaptively recalibrate the channel correlation feature response, thereby increasing the weight of the effective channels. Our method is evaluated on the Cornell and Jacquard public datasets, achieving an image-wise detection accuracy of 98.3% and 95.8% on each dataset, respectively. Additionally, we achieve object-wise detection accuracy of 96.9% and 92.4% on the same datasets. A physical experiment is also performed using the Elite 6Dof robot, with a grasping accuracy rate of 93.3%, demonstrating the proposed method's ability to grasp unknown objects in real-world scenarios. The results of this study show that our proposed method outperforms other state-of-the-art methods.

In the field of vision-based robot grasping, effectively leveraging RGB and depth information to accurately determine the position and pose of a target is a critical issue. To address this challenge, we propose a tri-stream cross-modal fusion architecture for 2-DoF visual grasp detection. This architecture facilitates the interaction of RGB and depth bilateral information and is designed to efficiently aggregate multiscale information. Our novel modal interaction module (MIM) with spatial-wise cross-attention algorithm adaptively captures cross-modal feature information. Meanwhile, the channel interaction modules (CIM) further enhance the aggregation of different modal streams. In addition, we efficiently aggregate global multiscale information through a hierarchical structure with skipping connections. To evaluate the performance of our proposed method, we conduct validation experiments on standard public datasets and real robot grasping experiments. We achieve the image-wise detection accuracy of 99.4% and 96.7% on Cornell and Jacquard datasets respectively. The object-wise detection accuracy reaches 97.8% and 94.6% on the same datasets. Furthermore, physical experiments using the 6-DoF Elite robot demonstrate a success rate of 94.5%. These experiments highlight the superior accuracy of our proposed method.

A system of wavelet collocation upwind schemes is constructed for solving hyperbolic conserva-tion laws based on a class of interpolation wavelets. The bias magnitude and symmetry factor are defined to depict the asymmetry of the adopted scaling basis function in wavelet theory. Effects of characteristics of the scaling functions on the schemes are explored based on numerical tests and Fourier analysis. The numerical results reveal that the stability of the constructed scheme is af-fected by the smoothness order, N, and the asymmetry of the scaling function. The dissipation analysis suggests that schemes with Neven have negative dissipation coefficients, leading to unstable behaviors. Only scaling functions with Nodd and bias magnitude of 1 can be used to construct stable upwind schemes due to the non-negative dissipation coefficients. Resolution of the wavelet scheme tends to the spectral resolution as the order of accuracy of the scheme in-creases.

Manipulation of radiation pattern control is challenging, especially at low frequencies. In this paper, we demonstrate that acoustic metamaterials as an effective array of quadrupoles can remarkably improve the directionality of acoustic radiation at low frequencies, compared with previous metamaterials as monopole and dipole structures. The directivity of the acoustic radiation can be adjusted by changing the characteristic parameter and the symmetry of the structure, which provide a flexible method of adjustable radiation di-rections. The directionality can be further improved by constructing linear array of structure. Our work opens an approach of acoustic radiation control via quadrupolar metamaterials.

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.

Pedestrian multi-target tracking technology plays an important role in artificial intelligence, driverless, virtual reality and other fields. The pedestrian multi-target tracking algorithm DeepSORT based on detection is widely used in industry. It mainly tracks multiple pedestrian targets continuously and keeps their ID unchanged. In order to improve the applicability and tracking accuracy of DeepSORT algorithm, this paper improved the IOU distance measurement in the matching process. At the same time, ResNet50 is used as the feature extraction backbone network, and combined with FPN (Feature Pyramid Network), the appearance features of multi-layer pedestrians are fused to improve the tracking accuracy of DeepSORT algorithm. The proposed algorithm is verified on the public data set MOT-16 and it’s tracking accuracy is enhanced to 4.1%.

The strong coupling between individual quantum emitters and resonant optical micro/nanocavities is beneficial to understand light and matter interactions. Here we propose a plasmonic nanoantenna placed on a metal film to achieve an ultra-high electric field enhancement in the nanogap and ultra-small optical mode volume. The strong coupling between a single quantum dot and the designed structure is investigated in detail by both numerical simulations and theoretical calculations. When a single QD is inserted into the nanogap of the silver nanoantenna, scattering spectra show remarkably large spectral splitting and typical anti-crossing behavior of the vacuum Rabi splitting, which can be realized in the scattering spectra by varying the nanoantenna thickness. Our work shows a possible way to enhance light-matter interaction at a single quantum emitter limit, which can be useful for future quantum and nanophotonic applications.

Light-addressable potentiometric sensor (LAPS) is an electrochemical sensor based on the field-effect principle of semiconductor. It is able to sense the change of Nernst potential on the sensor surface, and the measuring area can be controlled by the illumination. Due to the unique light-addressable ability of LAPS, the chemical imaging system constructed with LAPS can realize the two-dimensional image distribution detection of chemical/biomass. In this paper, the advantages of LAPS as sensing unit of microelectrochemical analysis system are summarized. Then, the greatest development of LAPS analysis system is explained and discussed. Especially, this paper focused on the research of ion diffusion, enzymatic reaction, microbial metabolism and droplet microfluidics by using LAPS analysis system. Finally, the development trends and prospects of LAPS analysis system are illustrated.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super-efficient SBM model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing of the industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, the industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: " stationary period", " recession period " and "growth period ". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

More than 110 countries including 500 cities worldwide have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, the heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision-makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution based Multi-Criteria Decision Making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the final ranking. The robustness of the proposed method is verified by 3 sensitive analyses from different aspects. Air to water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide the decision-makers apply this ranking guideline with their practical feasible situations.

COVID-19 greatly challenges human beings in the health sector and leaves behind a large amount of medical waste that poses many potential threats to the environment. In this paper, we compiled relevant data released by official agencies and the media, and conducted data supplementation based on previous studies to calculate the net value of medical waste production in Hubei Province during COVID-19 with the help of a neural network model. Then, we reviewed the data related to the environmental impact of medical waste per unit and designed four scenarios to estimate the environmental impact of new medical waste generated during the epidemic. The results showed that at a medical waste generation rate of\ 0.\ 5\ kg/(\ bed\ \cdot\ d) COVID-19 resulted in a net increase in medical waste volume of about 3366.99 tons in Hubei Province. In the four scenario assumptions, if the medical waste brought by COVID-19 is completely incinerated, it will have a large impact on the air quality. If it is disposed by distillation sterilization, it will produce a large amount of wastewater and waste residue. Based on the results of the study, three policy recommendations are proposed in this paper: strict control of medical wastewater discharge, reduction and transformation of the emitted acidic gases, and attention to the emission of metallic nickel in exhaust gas and chloride in soil. These policy recommendations provide a scientific basis for controlling medical waste pollution.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Long-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. It also has been widely used to study structural variants, phase haplotypes and more. Here, we introduce the assembler— SMARTdenovo, which is an SMS assembler that follows the overlap-layout-consensus (OLC) paradigm. SMARTdenovo (RRID: SCR_017622) was designed to be a fast assembler that did not require highly accurate raw reads for error correction, unlike other, contemporaneous SMS assemblers. It has performed well for evaluating congeneric assemblers and has been successful for a variety of assembly projects. It is compatible with Canu for assembling high-quality genomes, and several of the assembly strategies in this program have been incorporated into subsequent popular assemblers. The assembler has been in use since 2015, and here we provide information on the development of SMARTdenovo and how to implement its algorithms into current projects.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

At different heat treatment temperatures, the hardness and flexural strength of M2 high-speed steel selective laser melting (SLM) parts show mixed trends. When the heat treatment temperature is 260°C, the hardness and flexural strength of the M2 high-speed steel SLM part are decreased, but the hardness difference between the upper and lower surfaces of the M2 high-speed steel SLM part is also reduced. When the heat treatment temperature is 560°C, the hardness and flexural strength of the M2 high-speed steel SLM part are almost close to that of the original M2 high-speed steel SLM part, and the performance gradient in the sample is improved, and the overall structure is uniform. When the subsequent heat treatment temperature is 860 °C, the hardness of the SLM parts reaches a minimum, with an average value of 24 HRC. However, the flexural strength exceeds that of the original SLM parts. Moreover, the microstructure of the sample is uniform, which significantly improves the anisotropy of performance.

Jumping side volley has created breathtaking moments and cherished memories for us. Regrettably, a scientific study on the skill has not been found in literature. Relying on talent of athletes to improvise on the fly can hardly be considered a viable learning strategy. This study targets to fill the gap by quantifying factors contributing to develop its coaching method. Using 3D motion capture (12-cameras, 200Hz) and full-body biomechanical modeling, our study aimed to identify elements that govern entrainment of the skill by examining jumping, kicking and falling phases of its execution. Given the rarity of players who have acquired this skill, we found five subjects for the study. Twenty-three trials were captured and quantified. The results unveil the following key elements: 1) the control of trunk rotation during the jumping, 2) the angle between thighs upon take-off, 3) the whip-like control of the kicking leg during airborne, 4) timing between ball motion and limbs’ coordination, and 5) damping mechanism during falling. An accurate kick can only be achieved through repetitive training. This underlines the need for athletes to master a safe landing technique that minimizes risk of injury during practice. Therefore, training should begin with learning a safe falling technique.

Bread wheat (Triticum aestivum L.) is one of the most important cereal crops for food security. Of all the stresses that curtail wheat productivity, drought has the most detrimental effects. Especially terminal drought stress i.e. at the time of flowering imposes a big challenge to sustain grain production. In the current study, 339 pre-breeding lines derived from three-way crosses of exotics x elite lines were evaluated in the irrigated and drought stress environments at Obregon, Mexico for the year 2016 and 2018. Drought significantly reduced yield (Y), spike length (SL), number of grains per spikes (NGS) and thousand kernel weight (TKW) by 46.4, 19.2, 23.5 and 25.9%, respectively in comparison to irrigated conditions. Kernel abortion (KA), highly correlated with Y, increased significantly (11.6%) under drought stress environment. Population structure analysis in this panel revealed three sub-populations and a genome wide linkage disequilibrium (LD) decay was at 2.5 cM. Single marker and haplotypes-based genome wide association study (GWAS) revealed significant associations on three chromosomes; 4A (HB10.7), 2D (HB6.10) and 3B (HB8.12) with Y, SL and TKW, respectively. Likewise, associations on chromosomes 6B (HB17.1) and 3A (HB7.11) were identified for NGS and on 3A (HB7.12) for KA. Five traits i.e. normalized difference vegetation index (NDVI), canopy temperature depression (CTD) days to heading (DTH), NGS, KA were associated at chromosome 3A both under irrigated and drought conditions however, different haplotypes were estimated. Twenty-six SNPs were part of 10 haplotype blocks associated with Y, SL, TKW, NGS and KA. In silico analysis of the associated SNPs/haplotypes showed hits with candidate genes known to confer abiotic stress resistance in model species and crops. Potential candidate genes include those coding for sulfite exporter TauE/SafE family in Arabidopsis thaliana, TBC domain containing protein in Oryza sativa subsp. Japonica and heat shock proteins in Aegilops tauschii subsp. tauschii were revealed. The SNPs linked to the promising genes identified in the study can be used for marker-assisted selection.

The effects of climate change on yield and quality for different climate regions had high uncertainty. Risk assessment is an effective measure to assess the seriousness of the projected impacts for decision-makers. The modified quality model was used to simulate integrated impacts of climate change, environment and management on wheat yield and quality. Then, the Canadian Earth System Model (CanESM2) was used to forecast the daily meteorological data, and Statistical Down Scaling Model was used for downscaling. CERES-Wheat was combined with the forecasted meteorological data to simulate the future wheat yield and grain protein concentration (GPC). The risk of wheat yield and quality in three climatic regions of Shaanxi combined with two climate change scenarios of CanESM2 were assessed. Temperature increased 0.22-3.34 °C and precipitation increased 10-60 mm for RCP4.5 and RCP8.5. Elevated temperature and precipitation had positive effects on yield in all regions. The yield risk of most regions with climate change decreased 3.8%-25.1%. The GPC risk of all regions with climate change decreased 7.3%-27.2%. Irrigation decreased yield risk greatly in all regions, while had totally different effects for the three climatic regions. Yield risk with irrigation decreased 37.7%-52.1% in different climate. In contrast to previous studies, GPC risk with irrigation increased greatly 25.8%-28.9% in humid region, 3.9%-8.8% in sub-humid region, and decreased 37.7%-52.1% in semi-arid region. Climate change decreased yield risk and GPC risk together. While irrigation decreased yield risk greatly in all regions, had totally different effects for the three climatic regions.

Directional Modulation (DM) technique, as an emerging promising approach to secure wireless communications, has been attracting increasing attention for its unique characteristic in the past decade. The baseband signal is synthesized at the antenna level for maintaining the standard constellation format along the prescribed direction(s) while simultaneously distorting the signal constellations in other undesired directions. In this paper, we present a novel DM signal synthesis method based on phased array using a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which takes advantages of both GA and PSO algorithm. Then, the bit error rate of the proposed method is analyzed and simulated. Simulation results are presented to verify that the proposed DM signal have a narrower information beam-width. Therefore, it can offer a better physical layer security (PLS) performance compared with the conventional directional modulation signal.

Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

It is important to reduce the computation complexity while maintaining the accuracy of convolution neural networks. We deem it is possible to further reduce the network complexity while ensuring the accuracy. In this paper, we propose a novel feature extraction network called DSRNet which is lightweight but effective. DSRNet follows the basic ideas of stacking modules and short connection, introduces Depthwise Separable convolution and utilizes the Dilated convolution. The proposed network has fewer parameters and achieves outstanding speed. We conducted comprehensive experiments on CIFAR10, CIFAR100 and STL10 datasets, and the results showed the DSRNet has great performance improvement in terms of accuracy and speed.

Ocean wave energy conversion as one of the renewable clean energy sources is attracting the research interests of many people. This review introduces different types of power take-off technology of wave energy converters. The main focus is the linear direct drive power take-off devices as they have the advantages for ocean wave energy conversion. The designs and optimizations of power take-off systems of ocean wave energy converters have been studied from reviewing the recently published literature. Also, the simple hydrodynamics of wave energy converters have been reviewed for design optimization of the wave energy converters at specific wave sites. The novel mechanical designs of the power take-off systems have been compared and investigated in order to increase the energy harvesting efficiency.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

Improving the efficiency of the supply process in prefabricated components is challenging and requires accounting for a variety of risks involved in the management of the suppliers. The purpose of this study is to present a method to account for the systematic trade-offs between several supplier alternatives. A novel framework is presented for the whole assessment of supplier alternatives by taking advantage of the information extracted from customized building information modeling (BIM) and a database required for assessment of impacts. A data library related to assessment criteria for supply alternatives is built to facilitate the storage and sharing of information. Analytic hierarchy process (AHP) is used to select the optimal supplier that is able to provide the most satisfaction for the determined criteria. The proposed framework was also illustrated by the implementation in a mega project. The study implication is that BIM-enabled supplier selection can indeed lead to more benefits and higher values for all stakeholders.

Cost estimating based on building cost index plays an important role in project planning and management by providing accurate cost information. Recently, tremendous advances in cost estimating has been made but serious inaccuracies in it are still too frequently witnessed. This study aims to improve estimating accuracy for residential building costs in New Zealand. In this study, the New Zealand house prices index is involved in the transfer function models to produce forecasts of building costs for one-storey house, two-storey house, and town house in New Zealand. To demonstrate the effectiveness of the proposed models, this study compares the estimate results of the transfer function models with the univariate ARIMA models. The results indicate that the proposed transfer function models can achieve better outcomes than ARIMA models by considering the causality between building costs and New Zealand house prices. During the modelling process, the better cost estimation approach can be identified, and the movements of building costs are shown.

The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Polymer composites with high thermal conductivity have a great potential for applications in modern electronics due to their low cost, easy process, and stable physical and chemical properties. Nevertheless, most polymer composites commonly possess unsatisfactory thermal conductivity, primarily because of the high interfacial thermal resistance between inorganic fillers. Herein, we developed a novel method through silver functionalized graphene nanosheets (GNS) and multiwalled carbon nanotube (MWCNT) composites with excellent thermal properties to meet the requirements of thermal management. The effects of composites on interfacial structure and properties of the composites were identified, and the microstructures and properties of the composites were studied as a function of the volume fraction of fillers. An ultrahigh thermal conductivity of 12.3 W/mK for polymer matrix composites was obtained, which is an approximate enhancement of 69.1 times compared to the polyvinyl alcohol (PVA) matrix. Moreover, these composites showed more competitive thermal conductivities compared to untreated fillers/PVA composites applied to the desktop central processing unit, making these composites a high-performance alternative to be used for thermal management.

Leucine-rich repeat receptor-like kinases (LRR-RLKs) have been reported to play important roles in plant growth, development and stress responses. However, no comprehensive analysis of this family has been performed in Gossypium, which are important economic crop and suffer various stresses in growth and development. Here we conducted a comprehensive analysis of LRR-RLK family in four Gossypium species (G. arboreum, G. barbadense, G. hirsutum and G. raimondii). A total of 1641 LRR-RLK genes were identified in the four Gossypium species involved in our study. Maximum-likelihood phylogenetic tree revealed that all the LRR-RLK genes were divided into 21 subgroups. Exon-intron organization structure of LRR-RLK genes kept relative conserved in subfamilies and between Arabidopsis and Gossypium. Subfamilies XI and XII were found dramatically expanded in Gossypium. Tandem duplication acted as an important mechanism in expansion of Gossypium LRR-RLK gene family. Function analysis suggested that plant hormone signaling and plant-pathogen interaction pathway were enriched in Gossypium LRR-RLK genes. Promoters analysis and expression profiles analysis revealed that Gossypium LRR-RLK genes were extensively regulated by TFs, phytohormone and various environmental stimuli, and play key roles in stress defense and diverse development processes. Our study provided valuable information for further function study of Gossypium LRR-RLK genes.

This study investigated whether biogas reactor performance, including microbial community development, in response to a change in substrate composition is influenced by initial inoculum source. Test reactors were first started with two different inocula and operated with the same grass-manure mixture for more than 120 days. These reactors initially showed great differences depending on inoculum source, but eventually showed similar performance and overall microbial community structure. At the start of the present experiment, the substrate was complemented with milled feed wheat, added all at once or divided into two portions. The starting hypothesis was that process performance depends on initial inoculum source and microbial diversity, and thus that reactor performance is influenced by the feeding regime. In response to the substrate change, all reactors showed increases and decreases in volumetric and specific methane production, respectively. However, specific methane yield and development of the microbial community showed differences related to initial inoculum source, confirming the hypothesis. The different feeding strategies had however only minor effects on process performance and overall community structure, but still induced differences in the cellulose-degrading community and in cellulose degradation.

The electrochemical oscillation was first observed in the charging process of anolyte in VRFBs. The chronopotentiometry with current ramp results could be used to judge the appearance of electrochemical oscillation. The electrochemical oscillation could be explained in terms of the competition between the growth and the chemical dissolution of V2O5 film in the H2SO4 solution. It was possible to regular the extra power consumption resulted by the electrochemical oscillation. This work might provide new focus on the charging process of the VRFBs and guide for new methods on energy saving.

A novel starch stabilized Mg/Al LDHs material (S-LDH) was prepared in a facile approach and its fluoride ion removal performance was developed. Characterization of S-LDH was employed by using XRD, FTIR and particle size distribution. The adsorption property was studied through the assessment of adsorption isotherms, kinetic models, thermal dynamics and pH influence. The result shows that a low loading of starch of 10 mg onto LDH could obviously improve fluoride removal rate. The S-LDH had 3 times higher adsorption capacity to fluoride than that of Mg/Al LDH to fluoride. The particle size was smaller and the particle size distribution was narrower for S-LDH than that for Mg/Al LDH. Langmuir adsorption isotherm model and pseudo-second-order kinetic model fitted well with the experimental data. In thermodynamic parameters, the enthalpy (△H0) value was 35.63 kJ mol-1 and the entropy (△S0) value was 0.0806 kJ mol-1K-1. The values of △G0 were negative, implying the adsorption process is spontaneous. S-LDH reveals stable adsorption property in a wide pH range from 3 to 9. The mechanism for fluoride adsorption on S-LDH included surface adsorption and interaction ion exchange.

Species data of 249 National Nature Reserves in China was used to identify potential underlying drivers of latitudinal gradients in plant diversity. We used generalized linear models (GLMs) to assess the correlations between predictor and response variables. We also used SAM (Spatial Analysis in Macroecology) to eliminate autocorrelation along each of the 249 studied locations. We used the Akaike information criterion (AICc; Montoya et al. 2007) to select the independent variables were those included in the best models from different combinations of climate, habitat and animal variables. Variance partitioning was used to decompose the variation in plant richness across different taxonomic levels among the three groups of predictors. We found that: Total plant species, gymnosperms, angiosperms and ferns showed significant latitudinal trends in richness (p < 0.001). Water-energy and habitat variables generally explained more variation in richness across different plant groups than did animal richness. Annual precipitation was selected as the best water-energy variable across different taxonomic plants groups, soil PH and elevation range were selected as the best habitat variables across different taxonomic plant groups. The independent effects of habitatvariables were higher than that of water-energy and animal variables across different taxonomic plants groups. Water-energy, habitat heterogeneity, and animal variables explain 48.8% of the variation in total species richness, 28.2% in gymnosperm richness, 44.2% in angiosperm richness, and 38.9% in fern richness.Plants showed significant latitudinal trends in richness (p < 0.001). Water-energy and habitat variables generally explained more variation in richness across different taxonomic plants groups than did animal variables. The independent effects of habitat variables were higher than those of water-energy and animal variables across different taxonomic plants groups.

In this paper, firstly, some questionable formulas and conceptual oversights of previous reduced sigma set unscented transformation (UT) methods are revised through theoretical analysis. Then the revised UT methods based Kalman filters are used in a GPS/INS tightly-coupled system. The Kalman filter flows are the kind of square-root, since the square-root unscented Kalman filters (SRUKFs) can guarantee the stability of the system. By using the reduced sigma set SRUKFs (which contain simplex sigma set square-root unscented Kalman filter (S-SRUKF), spherical simplex sigma set square-root unscented Kalman filter (SS-SRUKF) and minimum sigma set square-root unscented Kalman filter (M-SRUKF)), the computation cost is greatly saved compared with the standard SRUKF, while the accuracy of the GPS/INS tightly-coupled system still maintained. The structure of the GPS/INS tightly-coupled system is in the form of error state, and the time updates of the state and the state covariance of SRUKFs are directly estimated without using UT, thus the computational time is also greatly saved. The pseudo-satellite is introduced to aid the system when the observation information is deficient, for example, when the GPS signal is deficient in the maneuver environment. By using the pseudo-satellite, the optimal performance of the system is guaranteed. Experiment of unmanned aerial vehicle (UAV) showed that the pseudo-satellite aided mechanism worked well.

BACKGROUND: Chronic hepatitis B infected with Hepatitis B virus remains a major health concern worldwide. Despite standard interferon-α and nucleotide analogues have been shown to reduce the deterioration of liver disease among chronic hepatitis B patients, covalently closed circular DNA was still difficult to eradicate. METHODS: A literature search of Pubmed and Web of science was performed with the following key words: ‘CRISPR’, ‘CRISPR/Cas9’, ‘hepatitis B’, ‘HBV’, ‘chronic hepatitis B’ and ‘HBV cccDNA’. The information about CRISPR/Cas9 for the treatment of HBV cccDNA or hepatitis B was reviewed. RESULTS: CRISPR/Cas9 could treat hepatitis B through suppressing or clearing HBV cccDNA with different gRNAs. CONCLUSION: With the emergence of CRISPR/Cas9 (the RNA-guided clustered regulatory interspaced short palindromic repeats, CRISPR) editing technology, clearance of hepatitis B virus and better prevention of liver carcinoma seemed to be possible.

The deformation of underground gateroads tends to be asymmetric and complex. Traditional instrumentation fails to accurately and conveniently monitor the full cross-sectional deformation of underground gateroads. Here, a full cross-sectional laser scanner was developed together with a visualization software package. The developed system used polar coordinate measuring method and the full cross-sectional measurement was realized by 360° rotation of laser sensor driven by an electrical motor. Later on, the potential impact of gateroad wall flatness, roughness and geometrical profile as well as coal dust environment on the performance of the developed laser scanner were evaluated. The studies show that a high-level flatness is favorable in application of the developed full cross-sectional deformation monitoring system. For a smooth surface of gateroad, the sensor cannot receive reflected light when the incidence angle of laser beam is large, causing data loss. Conversely, the roughness surface shows its priority as the diffuse reflection light can be received by the sensor. With regards to the coal dust in measurement environment, the fine particles of floating coal dust in the air can lead to the loss of measurement data to some certain due to scattering of laser beam.

In this paper, a land utilization efficiency evaluation model, which takes environmental loss into consideration, has been structured via taking advantage of the slack-based measure (SBM) model. Meanwhile, based on the panel data from 280 prefecture-level cities in China from 2003 to 2013, the paper thoroughly probed into, and discussed, the effect imposed by industry clustering and specialization on the utilization efficiency of urban land. Research results indicate several conclusions, as follows: (1) Taking environmental loss into account, the land utilization efficiency of prefecture-level cities in China is generally low. During the research period, the average value of the land utilization efficiency of prefecture-level cities in China is only 0.349, with, first, a declining trend, and then a rise. Geographically speaking, the land utilization efficiency presents a “depression in the center” phenomenon which means the land utilization efficiency of prefecture-level cities in the central China are relatively lower than in the east and west. Now, the difference among the urban land utilization efficiency in China significantly reflects the distinctions among Eastern, Western, and Central China. Moreover, the contribution degree of the difference of the land utilization efficiency among cities of central China to the aggregation difference shows an ascending momentum. Additionally, the relation between the population scale and land utilization efficiency in cities manifests as a U shape; (2) theoretically speaking, the relation between industry clustering and urban land utilization efficiency presents an inverted-U shape. However, this kind of relation is not significant in Western and Central China and medium-sized cities. Moreover, most of cities are still relatively far away from the inflection point or the critical value; and (3) the industry professional level has imposed a positive influence on urban land utilization efficiency. However, that influence is not significant in Eastern China and large cities. Consequently, strengthening the industry professional development of Western and Central China and small and medium-sized cities, facilitating diversified development of industries in Eastern China and large cities, and accelerating industrial clustering, all of these measures above will be conducive to improving urban land utilization efficiency in China.

Background: Citicola tubulosa (CT) is a traditional Chinese medicine that has been found to have hepatoprotective effect，but the underlying mechanisms against acute alcoholic liver injury (ALI) remain unclear. Objective：This work aims to predict the molecular mechanisms of CT in treating ALI using network pharmacology and molecular docking. Methods: Targets of CT and ALI were screened and collected from TCMSP, Genecard, OMIM and other databases. The effective ingredient target network was constructed using Cytoscape platform. The core targets were obtained by constructing protein interaction network. The biological processes and pathways of CT were analyzed using the DAVID platform. The effective components of CT were docked with core targets using AutoDock software, and visualized by PyMOL software. Results: There were seven main active components, 89 drug targets, 6291 disease targets and 32 pathways. The top three docking results were hydrogen bonding between stigmasterol and EGFR, β⁃ sitosterol and ALB, stigmasterol and BCL2. Conclusion: The effective components of CT (e.g., quercetin, stigmasterol, crocetin) may regulate core targets via multiple pathways to alleviate ALI. The use of network pharmacology and molecular docking technology can aid in revealing effective components and underlying molecular mechanisms of CT.

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

Aeromagnetic compensation plays a vital role in geomagnetic navigation and has received considerable attention throughout last few decades. Classical aeromagnetic compensation methods based on the Tolles–Lawson (T-L) model are mainly aimed at permanent, induced, and eddy-current magnetic interferences of aircraft platform, which ignores other stray magnetic field interference on the platform including the interferences caused by on-board electronic (OBE) systems. In order to cooperate with TL model, magnetometers are usually required to be installed on the extension rod outside the cabin, which is widely applied to geophysical magnetic survey. In order to ensure safety and reduce the cost of platform modification in geomagnetic navigation, it’s necessary to place magnetometers inside the cabin. It also further exacerbates the magnetic interferences and improves the difficulty of magnetic compensation. In this paper, a modified aeromagnetic compensation method is proposed, and the in-cabin OBE interferences are respectively modelled to be proportional to the currents and their temporal variations of different electronic devices. To ensure that modified model adapts to strong OBE interference in the cabin, a cut-off frequency determination method based on curvature calculation and a feature selection method based on correlation calculation are proposed. The cut-off frequency determination method helps to select passband filter which suitable for in-cabin OBE interference. The feature selection method can help to effectively select current and voltage inputs for modified model. In addition, principal component analysis (PCA) is adopted to reduce multicollinearity which is intensified by the extension of OBE interferences in coefficient-estimating. Experiments on public dataset are conducted to illustrate the effectiveness of the proposed method, and the best compensation result achieved 1.71nT of root mean square error (RMSE).

The combustion of fuel oil releases sulfur compounds, which react to form sulfur oxides that are harmful to the environment and pose a threat to human health. To mitigate these negative effects, it is crucial to pretreat fuel oil through desulfurization processes. One promising approach involves the use of deep eutectic solvents (DESs), carbon nanotubes (CNTs), and their composites (DESs/CNTs) to effectively reduce sulfur content in fuel oil.However, the conventional methods of DES-based extraction desulfurization and CNT-based adsorption desulfurization suffer from low desulfurization rates. Additionally, the composite material DESs/CNTs is in a semi-solidified form, resulting in high viscosity, complex operation procedures, and increased waste and environmental pollution. To address these challenges, this study focused on the preparation of CNTs/PEG-PVA hydrogel. This hydrogel not only maintains the high desulfurization rate observed in the DESs/CNTs composite material but also possesses several advantages. Firstly, it is convenient to use, offering ease of handling and application. Secondly, it is biodegradable, meaning it can break down naturally over time, reducing its impact on the environment. Lastly, the hydrogel is reusable, allowing for repeated use, thereby minimizing material waste. The desulfurization rate achieved with the CNTs/PEG-PVA hydrogel is remarkably high, reaching up to 99.8%. Moreover, the material utilization rate is close to 100%, meaning that almost all of the hydrogel is utilized in the desulfurization process, reducing unnecessary material consumption. In summary, the innovative CNTs/PEG-PVA hydrogel presents a highly efficient and environmentally friendly solution for desulfurization in fuel oil. Its superior desulfurization rate, alongside its convenience, biodegradability, and reusability, positions it as a promising alternative that not only ensures effective desulfurization but also contributes to sustainable practices in the energy industry.

The pandemic SARS-CoV-2 continues to evolve to give rise to variants of concern that can escape vaccine-induced immunity. As such, more effective vaccines are urgently needed. In this study, we evaluated virus-like particle (VLP) as a vaccine platform for SAS-CoV-2. The spike, envelope, and membrane proteins of the SARS-CoV-2 Wuhan strain were expressed by a single recombinant baculovirus BacMam and assembled into VLPs in cell culture. The morphology and size of the SARS-CoV-2 VLP as shown by transmission electron microscopy was found to be similar to the authentic SARS-CoV-2 virus particle. In a mouse trial, two intramuscular immunizations of the VLP BacMam with no adjuvant elicited spike-specific binding antibodies in both sera and bronchoalveolar lavage fluids. Importantly, BacMam VLP -vaccinated mouse sera showed neutralization activity against SARS-CoV-2 spike pseudotyped lentivirus. Our results indicated that the SARS-CoV-2 VLP BacMam stimulated spike-specific immune responses with neutralization activity.

Engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the mi-crostructures underneath fracture surfaces were thoroughly investigated in an AM aluminium (AlSi10Mg) alloy with horizontal and vertical building orientation endured very-high-cycle fa-tigue (VHCF) loading under stress ratios R = –1, 0 and 0.5. Two VHCF failure specimens A and B were representatively selected to further SEM examine and TEM samples preparation via FIB milling. Specimen A was horizontally printed and failed at R = –1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack ini-tiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.

This work aims at improving surface integrity and LCF life of machined Inconel 718. The correlation between the LCF life of Inconel 718 and various states of machined surface integrity is explored. In this paper, the surface integrity of Inconel 718 specimens is enhanced by low plasticity burnishing (LPB). The LCF life with different surface integrity specimens is predicted by micro-macro finite element method (FEM). Firstly, the LCF specimens with different surface integrity are machined by turning (turned specimen), polishing (matrix specimen) and LPB process (LPBed specimen). Secondly, the LCF experiment is carried out to reveal the effect of surface integrity on LCF life. Finally, the LCF micro-macro FEM model is proposed to predict the LCF of machined Inconel 718 specimens. The representative volume element (RVE) model is established based on the measured surface integrity and microstructure of Inconel 718 specimens. The effect of surface integrity on LCF life is transformed to equivalent load. The micro-macro FEM model combined with Tanaka-Mura dislocation crack initiation theory and extended finite element method (XFEM) is applied to predict the LCF life the machined specimens. The study results show that the LCF life of LPBed specimens can be improved by 90.5% and 36.1% compared with that of turned specimens and polished matrix specimens, respectively. The errors between FEM prediction results and experimental results are 13.1%, 9.2% and 12.2%, respectively. The proposed micro-macro FEM model could be utilized to predict the LCF life of Inconel 718 with different surface integrity, and further to apply the LCF life prediction in industry.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

China faces a persistent deficiency in feed protein resources. Enhancing the utilization efficiency of indigenous feed protein resources emerges as a viable strategy to alleviate the current deficit in protein feed supply. Corn gluten meal (CGM), characterized by high proportion of crude protein and glutamine, is predominantly employed in animal feed. Nonetheless, the water-insolubility of CGM protein hampers its protein bioavailability when utilized as feed material. This study aimed to augment protein bioavailability, liberate glutamine peptides from CGM, and produce gluta-mine-enriched CGM fermented feed. We executed a co-fermentation protocol using Bacillus subtilis A5, Lactobacillus 02002, and acid protease to generate the CGM fermented feed. Subsequent in vivo experiment with broilers were conducted to assess the efficacy of the fermented product. Findings revealed that the soluble protein, glutamine, small peptides, and lactic acid contents in the fer-mented feed increased by 69.1%, 700%, 47.6%, and 125.9%, respectively. Incorporating CGM fermented feed into the diet markedly enhanced the growth performance and intestinal health of broilers, positively modulated the cecal microbiota structure, and augmented the population of beneficial bacteria, specifically Lactobacillus. These results furnish both experimental and theoretical foundations for deploying CGM fermented feed as an alternative protein feed resource.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

This study examines the projected impact of climate change on the Density Altitude (DA) at Chinese airports during the summer by the end of the 21st century. Findings indicate that climate change is expected to significantly increase the DA at all Chinese airports, with an estimated rise between 300 and 800 feet. The analysis suggests temperature increases will universally contribute to a rise in DA. Pressure changes, however, are more variable. Most airports are predicted to see an increase in pressure, which could offset some temperature effects on the DA. Airports in eastern China are expected to see a decrease in pressure, amplifying the effects on DA and creating operational challenges.

This paper studies the finite-time synchronization problem of fractional-order stochastic memristive bidirectional associative memory neural networks (MBAMNNs) with discontinuous jumps. A novel criterion for finite-time synchronization is obtained by utilizing the properties of quadratic fractional-order Gronwall inequality with time delay and the comparison principle. This criterion provides a new approach to analyze the finite-time synchronization problem of neural networks with stochasticity. Finally, numerical simulations are provided to demonstrate the effectiveness and superiority of the obtained results.

Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. LASSO has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, by imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem, aiming to find a smallest set of vertices such that every edge has at least one endpoint in the set, which has extensive applications in cyber security, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain a “good” vertex cover. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: A 3-improvements (TI) framework with tiny perturbation, and an edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with two state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.