Objective: This research was aimed to estimate the anti-fatigue effects of arecoline and elucidate its underlying mechanisms using a murine model of central fatigue precipitated by sleep deprivation (SD). Methods: Seventy-two male C57BL/6 mice were randomly allocated into six groups: a control group, a SD-induced fatigue model group, a group receiving Rhodiola Rosea capsules (2.5 mg/kg), and arecoline groups administered low, medium, and high doses (10, 20, and 40 mg/kg, respectively). Following 28 days of continuous administrations, the effects of arecoline on the mouse fatigue-related behaviors was assessed by the behavioral tests, including grip strength, rotarod performance, and weight-bearing swimming endurance. Enzyme-linked immunosorbent assays (ELISAs) were utilized to measure the levels of the relevant biochemical markers. Western blotting was employed to quantify the expression of nuclear factor erythroid 2-related factor (Nrf2), Kelch-like ECH-associated protein 1 (Keap1), heme oxygenase 1 (HO-1), sequestosome-1 (p62), and NADPH quinone oxidoreductase 1 (NQO1) in the gastrocnemius muscle. Results: Arecoline administration notably enhanced grip strength, delayed the onset of fatigue as evidenced by extended latencies in rotarod tests, and increased the duration of weight-bearing swimming in mice. In the elevated plus maze, arecoline significantly reduced both the number of entries and the total distance traveled in the open arms. Arecoline markedly decreased serum levels of creatine kinase, blood urea nitrogen, lactate dehydrogenase, triglycerides, and cholesterol, while elevated total testosterone, lactate dehydrogenase, and immunoglobulin G levels. Furthermore, it significantly increased superoxide dismutase(SOD), catalase(CAT), and glutathione peroxidase activity in the gastrocnemius muscle, reduced malondialdehyde levels, augmented hippocampal SOD and CAT activity, and elevated glycogen stores in both liver and muscle tissues. Neurotransmitter levels were significantly increased, cytokine levels were markedly reduced, and the expression of Nrf2, Keap1, NQO1, p62, and HO-1 in brain tissue was significantly upregulated. Conclusion: The current study for the first time demonstrated that arecoline has significant anti-fatigue activity, and its mechanism of action is related to elevate the levels of glucose and lipid metabolism, relieve oxidative stress damage, inhibit neuroinflammatory response, regulate neurotransmitter levels and Keap1/Nrf2/HO-1 signaling pathway. The current study provides a new insight into the potential of arecoline in prevention and improvement of fatigue.

With the rapid development of tumor immunotherapy, nanoparticle vaccines have attracted much attention as a potential therapeutic strategy. The potential role of the mannose-modified lipid calcium phosphate nanoparticle vaccine in enhancing the anti-tumor immune response was investigated. The aim of this study was to investigate the effect of mannose modification on the immune response of nanoparticles in regulating the tumor microenvironment through systematic review and analysis and to explore its potential clinical application in tumor therapy. Currently, despite the potential advantages of nanoparticle vaccines in immunotherapy, achieving an effective immune response in the tumor microenvironment remains a challenge. Tumor immune escape and overexpression of immunosuppressive factors limit its clinical application. Therefore, this study will explore how to intervene in the immunosuppressive mechanism in the tumor microenvironment through mannose-modified lipid calcium phosphate nanoparticle vaccines so as to improve the immunotherapy effect of tumor patients and provide new ideas and strategies for the field of tumor therapy.

In China's mountainous coastal terrain, the storm badly damaged low-rise buildings. At present, it is not clear how the relative position of buildings and mountains affects the surface of a low-rise building’s fluctuating coefficient. The study compared these results with the wind pressure distribution without the surrounding environment. The primary objective of this study is to examine the fluctuating coefficient variation rule based on the spacing between hillsides and buildings under 0° wind angle. Additionally, the study analyzes the fluctuating wind pressure coefficient across wind angles between 0°~90°. The distribution of wind pressure in different common hillside landforms was examined through a wind tunnel experiment, which also compared with the distribution in an open environment. The study examined the fluctuating coefficient as the distance between the building and the hillside changed, specifically for wind blowing at a 0° angle. Next, an analysis is conducted on the changing pattern of the fluctuating coefficient for low-rise buildings ranging from a wind angle of 0° to 90°. Simultaneously, the investigation examined the power spectrum and wind pressure’s probability distribution, while considering the proximity of the building to an adjacent hill. The findings indicated that the changing wind pressure factor on the surface of the structure differed based on the relative location of the hillside and the building. The roof's wind pressure coefficient fluctuated and gradually increased until it reached its peak, unaffected by the surroundings. The low-frequency energy decreased in windward eaves, while the high-frequency energy progressively rose as the distance between the hillside and the building increased. The wind pressure on the leeward side exhibited Gaussian characteristics in its probability distribution.

Abstract: The chemical mechanical polishing (CMP) has been widely used for surface modification of critical materials and components and provided global planarization of topography with a low post-planarization slope high quality and efficiency. The colloidal silica slurries play an important role in the finishing processes of optical components. Monodisperse mesoporous silica nanospheres were synthesized with different surfactant by Stöber method. Their architectural features and texture parameters were characterized by XRD, FTIR, N2 adsorption–desorption isotherms, SEM, XPS, and TGA techniques. The spherical SiO2 products presented with the controllable 50-150 nm particle size and distribution. The AFM micrographs reveal that flat and smooth surfaces without distinct scratches residual particles are achieved by using the as-obtained composite particles as abrasives[The particle size of SiO2 slurry plays a critical role in the chemical-mechanical polishing process.

Developing advanced carbon materials by utilizing biomass waste has attracted much attention. Herein, a N-doped carbon material (RSM-0.33-550) was prepared by directly pyrolyzing reed straw and melamine. The as-prepared RSM-0.33-550 possessed a N content of 6.02% and a specific surface area of 547.1 m2 g-1. As the anode for supercapacitors (SCs), in 6 M KOH, such RSM-0.33-550 exhibited a capacitance of 202.8 F g-1 at a current density of 1 A g-1. At a high current density of 20 A g-1, it still remained a capacitance of 158 F g-1. Notably, it delivered an excellent stability with capacity retention of 96.3% at 20 A g-1 after 5000 cycles. This work not only offer a new electrode material for SCs, but also gives a new insight for rationally utilizing the biomass waste for energy storage.

Patients under immunosuppressive therapy for kidney diseases or on maintenance hemodialysis are more susceptible to infection than the general population since loss of renal function per se was an immunocompromised condition. Of relevance, CT imaging plays a crucial role in the detection and management of pulmonary infectious diseases. We hence presented diverse CT findings of pulmonary infections in the above said patients collected during our arduous work against a wide range of pathogens including klebsiella pneumoniae, staphylococcus aureus, candida parapsilosis, aspergillus, cryptococcus, mucor, pneumocystis carinii, cytomegalovirus, mycobacterium and nocardia. Notably, the pulmonary pathological changes were either primary pneumonia or secondary to the catheter-associated bloodstream infection. For a descriptive purpose, pulmonary manifestations of Wegener’s granuloma, lung cancer and diffuse alveolar hemorrhage/infection in vasculitis were also examined. As such, we retrospectively elaborated most likely CT features of each individual pathogen and briefly covered the differential diagnosis as well. Arguably, combination of pattern recognition with knowledge of the clinical setting could make a presumptive diagnosis and early treatment even more convenient. From the experience of first-line nephrologists, our work could make a substantial contribution to the expeditious and efficacious management of pulmonary infections in the pertinent patient population.

Due to production space and operating environment requirements, mine production equipment often breaks down, which seriously affects the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the number of the equipment, while and the ore quality was stable. Moreover, the improved optimization algorithm converged fast and was characterized by high robustness.

Klebsiella pneumonia (K. pneumoniae) is a Gram-negative bacterium that causes nosocomial infections in the lung, bloodstream, and urinary tract. Therefore, detecting K. pneumoniae in early time is important in preventing severe infections. However, clinical detection of K. pneumoniae requires a long time of agar plate culture. Nucleic acid detection like qPCR is precise but requires expensive equipment. Recent research reveals that collateral cleavage activity of CRISPR-LbCas12a has been applied in nucleic acid detection. In this study, PCR combined with CRISPR-LbCas12a targeting the K. pneumoniae system was established. This system showed excellent detection specificity and sensitivity in both bench work and clinical samples. Due to its advantages, its application can meet different detection requirements in health centers where qPCR is not accessible.

The space gravitational wave detection and drag free control requires the micro-thruster to have very low thrust noise within 0.1mHz~100mHz, which poses a great challenge to the ground thrust test. The evaluation and decoupling of thermal noise are the difficulties in the application of torsion balance for most thrusters dissipate heat in the experiment. The research has adopted a calibration scheme of micro-Newton thrust torsion balance. On the basis of Lisa Pathfinder's former research and using ideas inspired from PID control and multi time scale (MTS) for reference, the paper proposes to expand the state space of temperature to be applied on thrust prediction based on fine tree regression (FTR), to subtract the thermal noise filtered by transfer function fitted with z-domain vector fitting (ZDVF). The results show that the thrust amplitude thrust density in diurnal temperature fluctuation is decoupled from 24μN/Hz1/2 to 4.9μN/Hz1/2 at 0.11mHz. And the 1μN square wave modulations of electrostatic fins (ESF) is extracted from the simultaneously ambiguous background of temperature for PTC's heating and cooling. The FTR method is well demonstrated in thermal noise decoupling and can guide the design of thermal control and be extended to other physical quantities for noise decoupling.

Knee Osteoarthritis (KOA) is a chronic disease characterized by progressive disability and joint pain. Meniscus chondrocytes apoptosis is the main cause of reduced chondrocyte number and self-repair function. The purpose of this study was to investigate the role of miR-27b-3p in KOA.In this study, we found that the expression of miR-27b-3p was downregulated in cultured IL-1β treated chondrocyte and cartilage tissues in KOA. KOA overexpression evidently reduced IL-1β induced chondrocyte apoptosis and caspase-3 and caspase-9 expression.The upregulated iNOS and COX-2 mRNA and proteins expression was also inhibited by miR-27b-3p mimics. The expression of nitric oxide, PGE2, TNF-α and IL-6 was also inhibited by miR-27b-3p mimics. The target gene of miR-27b-3p was confirmed to be BDNF. TrkB/CREB pathway was proved to be the downstream pathway of miR-27b-3p/BDNF axis.The apoptotic cell percentage and nitric oxide, PGE2, TNF-α and IL-6 expression was induced by BDNF+IL-1β. This induction was inhibited by miR-27b-3p mimics. The cartilage tissues stained with safranin O results showed miR-27b-3p greatly decreased KOA induced cartilage degradation. The expression of BDNF、TrkB and p-CREB was inhibited by len-miR-27b-3p. MiR-27b-3p also reduced the expression of TNF-α、IL-6 and Bax, and increased Bcl-2 expression. These results indicated miR-27b-3p could applied to inhibit the development of KOA and miR-27b-3p/BDNF/TrkB/CREB pathway could serve as novel treatment target to handle KOA.

This study aims to explore the effect of real-time visual feedback (VF) information of the pres-sure of center (COP) provided by intelligent insoles on balance training in a one leg stance (OLS) and tandem stance (TS) posture. Thirty healthy female college students were randomly assigned to the visual feedback balance training group (VFT), non-visual feedback balance training group (NVFT), and control group (CG). The balance training includes: OLS, tandem Stance (dominant leg behind, TSDL), tandem stance (non-dominant leg behind, TSNDL). The training lasted 4 weeks, the training lasts 30 minutes at an interval of 1 days. There was a sig-nificant difference in the interaction effect between Groups*Times of the COP parameters (p<0.05) for OLS. There was no significant difference in the interaction effect between Groups*Times of the COP parameters (p>0.05) for TS. The main effect of the COP parameters was a significant difference in Times (p<0.05). The COP displacement, velocity, radius, and area in VFT significantly decreased after training (p < 0.05). Therefore, the visual feedback technology of intelligent auxiliary equipment during balance training can enhance the benefit of training. The use of smart wearable devices in OLS balance training may improve the visual and physical balance integration ability.

Ulcerative colitis (UC) is a chronic inflammatory disease, the incidence of which is increasing worldwide. However, the etiology and pathogenesis of UC remains unclear. The n-butanol extract of pulsatilla decoction (BEPD) of the traditional Chinese medicine herbal formula has been shown to be effective in treating UC. We aimed to explore the molecular mechanism underlying the effects of BEPD on UC, in particular its effects on neutrophil extracellular trap (NET) formation by neutrophils. High-performance liquid chromatography was used to determine the principal compounds of BEPD. UC was induced in mice using dextran sodium sulfate, and mice were treated with 20, 40, or 80 mg/kg BEPD daily for seven days. Colonic inflammation was determined by assessing the disease activity index, histopathology, colonic mucosal damage index, colonic mucosal permeability, and pro- and anti-inflammatory cytokine levels. The infiltration and activation status of neutrophils in the colon were determined by analyzing the levels of chemokine (C-X-C motif) ligand (CXCL)1 and CXCL2, reactive oxygen species, Ly6G, and numerous NET proteins. Our results showed that BEPD improved the disease activity index, histopathology, and colonic mucosal damage index scores of mice with UC, and restored colonic mucosal permeability compared with untreated mice. The expression levels of the pro-inflammatory cytokines interleukin-1β, interleukin-6, and tumor necrosis factor-α in colon tissues were significantly decreased, while the expression levels of anti-inflammatory cytokines in colon tissues were significantly increased, exceeding those of control mice. In addition, BEPD reduce the expression of the neutrophil chemokines CXCL1 and CXCL2 in the colon tissue of mice with UC, reduced neutrophil infiltration, reduced reactive oxygen species levels, and significantly reduced the expression of NET proteins. BEPD also significantly reduced NET formation. The results of this study suggest that BEPD exerts therapeutic effects in a murine model of UC by inhibiting neutrophil infiltration and activation in the colon, as well as by inhibiting the expression of key proteins involved in NET formation and reducing NET formation, thereby alleviating local tissue damage and disease manifestations.

Abstract: Steamed ginseng water (SGW), the by-product generated from the heated processing of red ginseng, with high content of active substances, better skincare activities, and large amount output ineffective utilization, resulted in environmental pollution and resource waste. In current study, UHPLC-Q-Exactive-MS/MS liquid chromatography-mass spectrometry and multivariate statistical analysis were conducted to characterize the compositional features of the repeated thermal-treated SGW. The total sugar content, total sugar content, total reducing sugar content, total non-reducing sugar content, DPPH, ABTS+, Fe3+ reducing capacity, and the hydroxyl radical scavenging abilities were employed to evaluate the gross contents variation connect with anti-oxidant activities during heat processes. The result revealed the SGW content and antioxidant activity were highly correlated, the 7th SGW sample showed the highest content and the strongest ability on anti-aging capacities. 38 significant common differential markers were screened, by the four compared SGW groups (1- and 3-, 1- and 5-, 1- and 7-, and 1- and 9) through OPLS-DA analysis. The current study showed that the thermal proceeding times could be a vital index value during the recycling operation quality control of SGW, which has a huge potential in anti-aging cosmetic raw materials, In the meanwhile, our results provided a green, sustainable, low-cost thinking of rational application on ginseng resource.

Cordycepin is an extract from the insect fungus Cordyceps. militaris, which is a traditional medicine with various biological function. In previous studies, cordycepin had been reported with excellent anti-obesity effect, but the mechanism is unclear. A large quantity of evidences showed that prolactin plays an important part in body weight regulation, hyperprolactinemia can promote appetite and accelerate fat deposition. In this study, we explored the molecular mechanism of the anti-obesity effect of cordycepin by reducing prolactin release via an adenosine A₁ receptor. In vivo, obese rats model was induced by high fat diet for 5 weeks, the serum and liver lipids coupling with serum prolactin were reduced by treatment of cordycepin, the results suggested that cordycepin is a potential drug for therapying obesity which could be related with prolactin. In vitro, cordycepin could inhibit prolactin secretion in GH₃ cells via upregulating the expression of adenosine A₁ receptor, the inhibition effect could be blocked by an antagonist of adenosine receptor A1 DPDPX, prolactin induced the upregulation of lipogenesis genes PRLR, and P-JAK2 in 3T3-L1 cells. Intriguingly, cordycepin would down-regulate the expression of prolactin receptor (PRLR). Thus, we concluded that cordycepin modulate body weight by reducing prolactin release via an adenosine A₁ receptor.

Previous studies on the water-energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China were calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu→El in Zhejiang, El in Zhejiang→El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

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.

To enhance the accuracy and efficiency of address parsing in traffic accident handling, this study designs a deep address parsing model based on BERT, named ASSPM. This model effectively addresses the limitations of existing deep learning models in Chinese address parsing tasks by integrating address spatial semantics. First, a lexicon-enhancement method based on hybrid representation is employed, transforming the spatial semantics extracted by the Address Spatial Semantics Extractor (ASSE) into a symbolic semantic space that can be integrated with the feature vector space of the deep model. This achieves effective fusion of spatial semantics and the deep model at the model level. Second, a lexicon adapter enhanced with spatial semantics is utilized to integrate spatial semantics into the Transformer layers of the BERT model, achieving semantic fusion at the BERT level. Finally, comparative experiments on a custom address dataset and a public Chinese address dataset validate the proposed method’s superior performance over other baseline methods. This research provides robust support for the application of deep address parsing models integrating address spatial semantics in traffic accident handling, promising to improve the efficiency and accuracy of traffic accident management.

Background: Though ancient Greece preserves many pictures of combat sports, there is research in terms of biomechanics on the principles of human motion. This research aimed to investigate Pankration postures of ancient Greek athletics, expecting to bridge the gap between historical sports practices and contemporary biomechanical applications. Methods: This study employed computational tools to analyze two images from ancient Greek Pankration, constructing a static multi-segmental model. Anthropometric models simulated postures analogous to those in historical depictions. Analysis estimated joint forces and moments, along with weight distribution and ground reaction forces for these postures. Implications for training and strength development were deduced through integration with modern scientific insights. Moreover, strategic elements of these postures were assessed using game theory to explore their correspondence with contemporary theoretical constructs. Results: The findings indicated that static postures facilitate the development of tendon stiffness, joint stability, and neuromuscular control. In the "readiness" posture, 68% of the athlete's centre of gravity (CoG) was distributed to the hind leg, enhancing stability and power generation, while the front leg promoted agility. Analysis of the kick posture revealed delivery with a dorsiflexed ankle and pronounced heel protrusion.Conclusion: The application of the proposed model suggests that ancient athletes optimized their performance using biomechanical principles, thereby gaining a competitive advantage. This study highlights the value of integrating ancient biomechanical strategies with contemporary athletic training and injury prevention, advocating for a synthesis of historical practices and modern biomechanical analysis in combat sports.

This study addresses the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

Based on the two-flavors Polyakov quark-meson model, we study the bubble dynamics of a first-order phase transition in chiral phase diagram at high density but low temperature in the framework of the homogeneous thermal nucleation theory. Under the mean-field approximation, after obtaining the effective potential with inclusion of the fermionic vacuum term, we build a geometric approach to probe two possibly existing minima, which can actually tunnel through a bounce interpolated between a metastable vacuum to an adjacent true vacuum. By separating our discussions into a weak first-order hadron quark phase transition and strong ones, the bubble profiles, the surface tension, the typical radius of the bounce and the saddle point action in the presence of a nucleation bubble as a function of temperature are numerically calculated in detail as the chemical potentials are fixed at $\mu=306 \mathrm{MeV}$ and $\mu=310 \mathrm{MeV}$. our results show that the surface tension remains a small value even when the density is very high and phase boundaries for hadron and quark matter should be resized properly according to the saddle point action evaluated on the bounce. It is also noticed that the inclusion of the deconfinement phase transition in term of the Polyakov loop does not change chiral phase transition dramatically for light quarks.

This study is based on the BP neural network regression model optimised by genetic algorithm and dedicated to predicting the amount of car purchase, this study is important for all parties in the automotive industry. By plotting the violins of six indicators, including gender, age, annual salary, credit card debt, net worth, and car purchase amount, we found that, except for gender, the other data showed a trend of dense in the middle gradually decreasing to the upper and lower sides, with an overall normal distribution. The correlation analysis shows that the amount of car purchase has a high positive correlation with age (0.63), followed by annual income (0.62), the correlation between net worth and the amount of car purchase is 0.49, while the correlation between credit card debt and the amount of car purchase is low (0.03). In the experimental part, we analyse the predicted-actual values of the training and test sets in comparison and find that the model predicted values are very close to the actual values. The RMSE of the training set is 11.559 and the RMSE of the test set is 11.4835, which is not much different from each other, indicating that the model has good generalisation ability. In addition, the R2 of both the training and test sets is 1, indicating that the model fits well. The MAE of the training set is 8.2512 and the MAE of the test set is 9.578; the MBE of the training set is -4.3035 and the MBE of the test set is -2.4547.These metrics indicate that the model performs well in predicting the amount of car purchase. In conclusion, this study provides an effective and reliable method to predict the amount of car purchase, creating a useful bridge between car manufacturers, dealers and consumers.

Land use land cover change(LULCC) plays a significant role in understanding the global change. Particularly in regions with high population growth and climate change, anthropogenic activities have drastically altered land use and land cover (LULC). The geographic information system (GIS) and remote sensing are prominent tools for monitoring LULC changes. The land use land cover(LULC) analysis and carbon loss assessment were carried out in Ayubia national park. The area was classified into five major LULC classes, namely grassland, built-up areas, mixed forests, conifer forests and bare land. Landsat temporal satellite data of Ayubia national park acquired on 1992,2002,2012 and 2022 were analysed to access land use change and Markov’s Chain Model was used to predict the land use land cover changes for 2040. The accuracy of the images were assessed using Kappa matrix. Our results showed that a total of 371.94 ha forest land has been converted into other land uses from 1992 to 2022 and Indicating the increasing scale of anthropogenic pressure on forest resources of the Park. The carbon pool was assessed in two forest types including temperate coniferous forest and Mixed forest. The estimated carbon value recorded in TCF is 135.19 ±9.74 MgC ha-1, whereas it is in 86.43 ±8.25 MgC ha-1 in MF. Using satellite data, a thorough examination of the forest inventory was conducted to determine the LULCC, the rate of biomass carbon loss, and the factors that influenced these trends.

On the H-type group G⊂Rm×Rn, we study the existence of solutions for the Brezis-Nirenberg problem

A class of semi-linear elliptic equations with critical Hardy-Sobolev exponent has been considered. This model is widely used in hydrodynamics and glaciology, gas combustion in thermodynamics, quantum field theory and statistical mechanics, as well as gravity balance problems in galaxies. The $\left( PS\right) _{c}$ sequence of energy functional has been investigated, and then the mountain pass lemma was used to prove the existence of at least one nontrivial solution. Also a multiplicity result has been obtained. Some known results have been generalized.

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

Energy metabolism regulates the proliferation of stem cells by regulating energy production and the production of substrates through biosynthetic pathways. It is an important target for cell regulation and stem cell applications, and is crucial for determining the pluripotency and cell fate of stem cells. In this context, this article focuses on reviewing the latest research progress on the association between glucose metabolism, amino acid metabolism, fatty acid metabolism, and mitophagy with stem cell function, elucidating how key energy metabolism pathways affect stem cell function by regulating signal transduction and epigenetic modifications. In this review, we discuss the key factors affecting energy metabolism and their association with stem cell function. The integration of energy metabolism and stem cell fate regulation will open up a new path for future regenerative medicine practices.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Climate change poses a serious threat to the ancient Magnoliaceae family, particularly for the endangered Yulania trees. Until now, little is known about the suitable geographical range of Yulania zenii, and about how it responds to past and future climate scenarios. Here, we first employed ten models from Biomod2 to preliminarily simulate its potential distribution in China and selected MaxEnt for final modeling. The results showed that among the key environmental factors influencing its distribution, the top three factors were Bio2 (Mean diurnal range), Bio15 (Precipitation seasonality of variation coefficient) and Elevation. Its current suitable distribution were primarily concentrated in southern Anhui, central Hubei, eastern Hunan, southern Jiangsu, northern Jiangxi, and northern Zhejiang. The total suitable area of Yulania zenii was 14.68 × 104 km2, only taking 1.53% of China's total territory, which is larger than known. During the Last Interglacial and Middle Holocene, its suitable habitats were larger than currently, exhibiting a relatively continuous distribution. Under various future climate scenarios, its suitable habitats may averagely decrease by 20.26% compared with the current, and these habitats become more fragmented. Collectively, the centroid of Yulania zenii is expected to migrate towards the southeast in the future. Therefore, our findings demonstrate, for the first time, that climate change takes an adverse effect on this species in distribution from the past to current till future. Our study can contribute to the conservation and management of Yulania zenii in China, and can provide reference for other endangered Yulania species of this country under the condition of climate change.

The microstructure evolution during the cold rolling and subsequent annealing of Alloy 800H was investigated. Two distinct rolling methods, unidirectional rolling and cross-rolling, were intro-duced. Results show that better ductility was observed in the unidirectional rolling than in cross-rolling, whilst a higher volume fraction of large deformed grains was observed in the cross-rolled plates than in the unidirectionally rolled plates. Recrystallization occurred faster in plates from unidirectional rolling than from cross-rolling.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

Let a,b,c be positive integers such that a²+b²=c², 2|b,gcd(a,b)=1. In 1956, Jesmanowicz conjectured that for any positive integer w, the only solution of (aw)^x+(bw)^y=(cw)^z in positive integers is (x, y, z) = (2, 2, 2). In this paper, based on Gaussian integer ring, we show that Je$\acute{s}$manowicz' conjecture is true for any positive integer

In this paper, a class of semi-linear elliptic equations involving Hardy-Sobolev critical exponents has been investigated. This problem comes from the consideration of standing waves in the anisotropic Schr\"{o}dinger equation, also it is very important in the field of hydrodynamics, glaciology, quantum field theory and statistical mechanics. By a detailed estimation for the extremum function and using Mountain Pass Lemma with $\left( PS\right) _{c}$ conditions, the existence of positive solutions has been obtained. On the other hand, by establishing Pohozaev-type identity and using the properties of Bessel function, the nonexistence of positive solution also has been obtained. These results are extensions of E. Jannelli's research (\cite[Theorem 1.A-1.C]{EJ}).

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets, and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears, and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

Water sustainability has become one of the most severe issues in the 21st Century due to dramatic urban population growth and climate change. This paper reviews some of the critical key water issues that need to be considered/incorporated in the quest for water sustainability for the upcoming decades. The purpose is to recognize the critical circumstances for maintaining water sustainability and recognize that many regions in the world have passed the ‘tipping point’ of balancing their water sustainability and failing to realize that restoring sustainability will be extremely difficult. From the water quantity perspective, several examples are used to demonstrate situations which, in hindsight, have been initially shown to be effective but highly problematic in the long term. The review considers, amongst others, the 1960s India example shows that an agricultural ‘success’ starting in the 1960s has subsequently become an environmental disaster. Additional issues, including the impacts of dietary adjustments, upstream diversions raising downstream shortfalls, and transfers of water from agriculture to urban areas, are examples that demonstrate that to achieve water sustainability, lessons must be learned from the past, and adaptive measures must be adopted to help humanity avoid adverse and irreversible environmental tragedies. Government authorities can learn from this critical review of some approaches and realize their responsibility to proactively promote better water resource management strategies (domestic and international collaborations) and strictly regulate water use practices to better manage water sustainability.

The detection and analysis of small molecules, typically defined as molecules under 1000 Da, is of growing interest ranging from the development of small molecule drugs and inhibitors to the sensing of toxins and biomarkers. However, due to challenges such as their small size and low mass, many biosensing technologies struggle to have the sensitivity and selectivity for the detection of small molecules. Notably, their small size limits the usage of labeled techniques that can change the properties of small molecule analytes. Furthermore, the capability of real-time detection is highly desired for small molecule biosensors’ application in diagnostics or screening. This review highlights recent advances in label-free real-time biosensing technologies utilizing different types of transducers to meet the growing demand for small molecule detection.

In this study, Cu/Ni and Cu/Al multilayer thin films with individual layer thickness varying from 25 nm to 200 nm were deposited at room temperature and further annealed up to 300 ℃. The mechanical and microstructural properties of the multilayers were characterized by nanoindentation, x-ray diffraction, and scanning electron microscopy. Both systems exhibited an increase in hardness with increasing annealing temperature. However, the Cu/Ni system showed a gradual and moderate (up to 30%) hardness increase from room temperature to 300 ℃, while the Cu/Al system showed a sharp increase (~150%) between 125℃ and 200℃. This distinct strengthening behavior is attributed to solid solution formation in Cu/Ni and diffusion-activated intermetallic formation in Cu/Al.

The key to building a strong bridge country lies in talent, and the foundation lies in education. The cultivation of bridge engineering talents is related to the future and destiny of building a strong bridge country. This course adopts the educational philosophy of "holographic teaching method" centered on the comprehensive development of students, cultivating "engineering thinking" guided by engineering education certification, enhancing "abilities and qualities" through professional knowledge, reconstructing the course knowledge content system through engineering philosophy thinking, and implementing the ideological and political construction of the course in a multi-level unified manner of "explicit + implicit". Based on restructuring the course content system, we will strengthen the construction of diversified course resources, integrate theory with practice throughout the entire process of the course, practice the concept of "bridge architects + bridge engineers" parallel development, and cultivate cross-border and compound talents with comprehensive qualities of "engineering + art + humanities".

As one of the main projects of facility agriculture promotion, the photovoltaic greenhouse has the problems of photovoltaic power generation competing for light with crop production, strong indoor chiaroscuro, and uneven light distribution. The internal light uniformity is tested by a zigzag greenhouse model to compare the light transmission effects of different light-transmitting materials applied to photovoltaic greenhouses. 20 line\inch 3mm and 30 line\inch 3mm, 40 line\inch 2mm, 25 line\inch 4mm grating plates, 2mm, and 3mm thick ordinary glass were used as light transmitting components, and the light intensity and light uniformity in the greenhouse were the measurement indicators. The results show that the use of grating plates as covering material can improve the light intensity at the intersection of light and dark, but the overall light transmittance is not as good as glass, because it is plastic which is easy to age with low light transmittance. It can also improve the use of land under the shade of photovoltaic modules to provide a better growth environment for crops. Test results show that the use of a 2mm thick 40-line grating plate can maximize the light intensity of the greenhouse, and the peak value can reach 69336 Lx. In sunny weather, the light intensity from point G to point I in the greenhouse is greater than 20,000 Lx, and the light environment in other areas is between 5,000 Lx and 20,000 Lx, which is suitable for planting shade-loving crops.

From the mid-19th century, Collegiate Gothic architecture gained prominence among American universities. Subsequently, through the efforts of American missionaries, the Collegiate Gothic style spread to Christian universities in Asia. Specifically, this study concentrates on the nuanced relationship between the transnational missionary routes and adopting the Collegiate Gothic style, primarily focusing on Anderson Hall at Soochow University, China. The investigation meticulously dissects the multifaceted factors influencing the choice of architectural styles, considering the perspectives of missionaries, architects, and other key stakeholders. Through an intricate analysis of Anderson Hall, this research illuminates the unique characteristics of Collegiate Gothic architecture. By unraveling the complex layers of East Asian campus heritage, this study enriches our comprehension of its origins. It establishes a robust foundation for guiding the preservation and revitalization of architectural styles within university campuses. This scholarly inquiry holds particular significance for experts in academic heritage conservation.

A single Cancer immunotherapy has ushered in a transformative era in oncology, bringing forth unprecedented promise and opportunities. However, even with its significant breakthroughs, the domain faces the continual issue of treatment resistance. Such resistance not only restricts the broad effectiveness of these pioneering treatments but also highlights the imperative need for further research. Delving into the complex world of cancer immunotherapy resistance, we identify a range of mechanisms, from primary and secondary resistance to the notable effects of genetic and epigenetic variables, and the pivotal importance of the tumor microenvironment (TME). Additionally, we emphasize the need for innovative strategies to mitigate this resistance, including combination treatments, customized immune checkpoints, and instantaneous monitoring. By advocating for these cutting-edge methods, we foresee a direction that incorporates personalized healthcare, enhanced treatment options, and a steadfast dedication to the welfare of patients. Through a comprehensive and diverse approach, we aim to address the hurdles posed by resistance, elevating cancer immunotherapy as a symbol of hope for patients worldwide.

The characteristics of exhaled aerosols outside the human respiratory airway are of significant importance in understanding virus transmission, yet they remain poorly understood. In order to effectively prevent and control the spread of respiratory infectious diseases, this study numerically investigates the exhaling characteristics of aerosols exhaled from the bronchus or larynx of a human upper airway model. This is achieved using the Euler-Lagrange method and considering various aerosol diameters (dp=0.1, 0.3, 0.5, and 1-20 μm) as well as five expiratory flow intensities (Q=15, 30, 60, 90, and 120 L/min). The important findings of this study are as follows: (1) Expiratory airflow exhibits complex flow phenomena, including jet-flow, flow separations, and vortex structures, with their characteristics being influenced by the expiratory flow intensities. (2) The exhaling characteristics of aerosols vary depending on the combined effects of expiratory flow intensities, virus aerosol diameters, and initial exhaled locations from either the bronchus or larynx. (3) A critical diameter (dc) is identified to represent the size at which aerosols can effectively exit the respiratory airway and potentially pose a transmission risk. This critical diameter is identical for aerosols exhaled from both the bronchus and larynx under the same expiratory flow intensity, but it decreases as the expiratory flow intensity increases. In conclusion, expiratory flow intensity is the most critical factor in determining whether aerosols can be expelled from the respiratory airway, as well as influencing the critical diameter (dc) for aerosols initially located in/after the larynx.

With the development of the Internet, The content people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: 1) Inefficiency when modeling the intra-modality and inter-modality dynamics and 2) Inability to effectively fuse multimodal features. In this paper, we proposed the CCDA(Cross-Correlation in Dual-Attention) model, a novel method to explore dynamics between different modalities and fuse multimodal features efficiently. We capture dynamics at intra- and inter-modal levels by using two types of attention mechanisms simultaneously. Meanwhile, the cross-correlation loss is introduced to capture the correlation between attention mechanisms. Moreover, the relevant coefficient is proposed to integrate multimodal features effectively. Extensive experiments were conducted on three publicly available datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The experimental results fully confirm the effectiveness of our proposed method, and compared with the current optimal method (SOTA), our model shows obvious advantages in most of the key metrics, proving its better performance in multimodal sentiment analysis.

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

The improvement of agricultural low-carbon efficiency is an important path to promote the realization of the "double carbon" goal in the Yellow River Basin. In the context of rapid urbanization development, it is of great significance to explore whether the promotion of urbanization will affect the agricultural low-carbon efficiency. Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses super-DEA model, three-dimensional kernel density model and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural low-carbon efficiency in the Yellow River Basin, on this basis, the panel Tobit model is used to analyze the relationship between urbanization and low-carbon efficiency in agriculture. The results show that: (1) The level of agricultural low-carbon efficiency in the Yellow River Basin is low and has not reached the effective state, showing a slow downward trend in general, among which, the agricultural low-carbon efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches is the lowest. (2) The agricultural low-carbon efficiency in the Yellow River Basin has a negative trend of transferring to the low level on the whole, and tends to maintain the original state level, and it is difficult to realize the leapfrog transfer between states. Agricultural low-carbon efficiency has obvious spatial spillover effect and "club convergence" phenomenon, high efficiency area has a positive driving effect on the neighborhood area, while low efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the low-carbon efficiency of agriculture in the upper, middle and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture. Finally, based on the above research conclusions, relevant policy recommendations were put forward, in order to provide references for promoting the improvement of low-carbon agricultural efficiency and promoting high-quality agricultural development in the Yellow River Basin.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

In the realm of autonomous vehicle environment perception, the primary objective of point cloud target detection is to swiftly and accurately identify three-dimensional objects from point cloud data. To meet this requirement, the prevalent network architecture employed in the industry is the voxel-based PointPillars model. Nonetheless, this model faces challenges in maintaining detection accuracy when objects are obscured or diminutive in size. In response to this issue, we introduce AGPNet, a novel model that seamlessly integrates four key modules: Data Augmentation, Dynamic Graph CNN, Pillar Feature Net, and Detection Head (SSD). The Data Augmentation module enhances the adaptability of point cloud data to complex and ever-changing real-world environments. The Dynamic Graph CNN module endows the network structure with geometric features, which encapsulate not only the point itself but also its adjacent points. The Pillar Feature Net module translates three-dimensional point cloud data into pseudo-image data through the utilization of voxels. Subsequently, the Detection Head (SSD) module leverages this pseudo-image data to conduct target detection of three-dimensional objects. Our experiments, conducted on the KITTI dataset, demonstrate that our proposed method boosts object detection accuracy by 6-7 percentage points compared to the PointPillars model, while maintaining similar detection times.

Modular high-rise buildings frequently adopt cast-in-place concrete to ensure building integrity. To reduce the material consumption and realize mold-free casting, a 30 mm thick light concrete mold shell wall panel ( MiC wall panel) is set as part of the shear wall. But the concrete casting process is prone to cracking and rising pattern. Its force performance and damage mechanism is not clear. To this end, this paper designs three groups of MiC wall panels with different material component types. The static load test is carried out in a graded partitioned loading mode. The effects of different material components on the mechanical properties of MiC wall panels are systematically investigated and parametrically analyzed by numerical simulation. The results show that the front cracks of the wall slab are mainly distributed under the truss tendons, and the back cracks are mainly distributed in the span position of the adjacent truss tendons. They are both along the span direction of the wall slab. Increasing the strength level of concrete and using steel fiber concrete can improve the mechanical properties of MiC wall panels to different degrees. Among them, increasing the concrete strength has a significant effect on improving the load-bearing capacity of wall panels, while incorporating steel fibers can significantly improve the deformation and crack resistance of wall panels. By building more finite element models, it was verified that the steel fiber admixture has limited effect on the mechanical properties of wall panels as the concrete grade increases. This study aims to summarize the types of wall panels with the best working properties. It provides a practical basis for optimizing the production and fabrication process of MiC wall panels.

Bacillus Calmette-Guérin (BCG) is the only widely used prophylactic tuberculosis (TB) vaccine that can prevent severe TB in infants. However, it provides poor protection in adults, and therefore, there in ongoing research on new TB vaccines and immunization strategies with more durable immune effects. The recombinant BCG and BCG prime-protein booster are two important vaccine strategies that have recently been developed based on BCG and could improve immune responses. In this study, three immune strategies based on four protective antigens, namely, ESAT-6, CFP-10, nPPE18, and nPstS1, were applied to construct recombinant rBCG-EPCP009, EPCP009 subunit protein, and BCG prime-EPCP009 booster vaccine candidates. The short- and long-term immune effects after vaccination in Balb/c mice were evaluated based on humoral immunity, cellular immunity, and the ability of spleen cells to inhibit in vitro mycobacterial growth. At 8 and 12 weeks after the initial immunization, splenocytes from mice inoculated with the BCG prime-EPCP009 protein booster secreted higher levels of PPD- and EPCP009-specific IFN-γ, IL-2, TNF-α, IL-17, GM-CSF, and IL-12 and had a higher IFN-γ⁺CD4⁺ TEM:IL-2⁺CD8⁺ TCM cell ratio than splenocytes from mice inoculated with the rBCG-EPCP009 and EPCP009 proteins. In addition, the EPCPE009-specific IgG2a/IgG1 ratio was slightly higher in the BCG prime-EPCP009 protein booster group than in the other two groups. The in vitro mycobacterial inhibition assay showed that the splenocytes of mice from the BCG prime-EPCP009 protein booster group exhibited stronger inhibition of Mycobacterium tuberculosis （M. tuberculosis）growth than the splenocytes of mice from the other two groups. These results indicate that the BCG prime-EPCP009 protein booster exhibited superior immunogenicity and M. tuberculosis growth inhibition to the parental BCG, rBCG-EPCP009, and EPCP009 proteins under in vitro conditions. Thus, the BCG prime-EPCP009 protein booster may be important for the development of a more effective adult TB vaccine.

When analyzing user geospatial information through deep learning methods, it is typically necessary to annotate existing geospatial data. Currently, manual annotation methods are commonly employed, suffering from issues related to low efficiency and accuracy. This design is based on the TensorFlow deep learning framework and first realizes the BiLSTM+CRF+AGG deep learning model. Among the models, AGG is the aggregation layer, which is introduced to solve the problem of solid particle size equilibrium. Secondly, based on the characteristics of original data and professional data, an automatic labeling algorithm is proposed. The algorithm first preprocesses the acquired original data set and professional data set, The most valuable unlabeled data set which can make the training model converge quickly is selected from the original data set as the target data set. Sort the target data set based on preset rules and set annotation parameters for the sorted target data set. Based on the set annotation parameters, the corpus is synthesized and used as the annotation result. Thirdly, based on the active learning strategy, a manual annotation auxiliary scheme is proposed, and an Excel generation module that is convenient for manual annotation correction is designed and developed to further improve the efficiency and quality of annotation through iterative processing. The combination of the BiLSTM+CRF+AGG deep learning model and high-quality annotation can accurately identify non-standard, incomplete, or even incorrect geographical information entities. This annotation method has found successful practical application within the context of the research project and has been granted an invention patent.

Zirconium dioxide (ZrO2) possesses numerous advantages such as high mechanical strength, low friction coefficient, excellent optical properties, and extended lifespan. Consequently, ZrO2 has a broad research foundation and practical significance in functional films and wear-resistant coatings. However, it suffers from brittleness and low ductility when used as a bio-coating material. In this study, a Ta/ZrO2 film was fabricated on Si (100) and titanium alloy substrates using a magnetron sputtering system, with tantalum (Ta) serving solely as a transition layer. Subsequently, Zr and Nb ions were implanted into the film at varying doses but with consistent energy levels. The analysis focused on the film's microstructure, mechanical properties, hydrophilicity, and corrosion resistance. The results demonstrated a significant improvement in the hydrophilicity and corrosion resistance of the ZrO2 film following the implantation of Zr and Nb ions. First-principles calculations based on density functional theory (DFT) principles indicated that, with increasing doping concentrations of Zr and Nb in the ZrO2 model, the stability of the model increased gradually, thereby enhancing its corrosion resistance.

Thin-film lithium niobate has gained significant attention in photonics due to its broad optical transparency, high refractive index, nonlinear coefficient, and substantial electro-optic coefficient. It holds great promise for developing electro-optic modulators with low loss, a compact size, and wide bandwidth. This study focuses on a meticulous investigation utilizing microwave photonics to establish a high-performance electro-optic modulator. Specifically, we analyze the optical mode field distribution and traveling wave electrode structure of lithium niobate thin-film materials. The proposed modulator achieves an impressive half-wave voltage-length product of 1.69 V·cm, a negligible metal loss of 0.01 dB/cm, and a substantial 3dB electro-optic bandwidth of 50 GHz. This research successfully realizes low-loss, high-efficiency LNOI electro-optical modulators, providing a strong foundation for large-scale integrated optoelectronic systems.

CuO/ZnO/Al2O3 catalyst is a commonly used catalyst for rea methanol steam reforming reaction. Oxalic acid was as precipitant in preparing oxalate precursor of CuO/ZnO/Al2O3 catalyst by co-precipitation, deionized water and ethanol were as solvents, microwave irradiation and water bath were as aging heating manner respectively. It indicated that ethanol selected crystal phase composition of oxalate precursors and restricted their growth. Microwave irradiation prompted the isomorphous substitution between Cu2+ of CuC2O4 and Zn2+ of ZnC2O4 in mother liquid, forming the master phase (Cu,Zn)C2O4 in precursor, the solid solution Cu-O-Zn formed after calcination, which exhibited nano-fibriform morphology. It possessed small CuO grains, large surface area and strong synergy between CuO and ZnO, which is beneficial to improve the catalytic performance of methanol steam reforming, the STY of H2 reached 516.7 mL·g-1·h-1, and the selectivity of CO was only 0.29%.

Photosystem II (PSII) is a quinone-utilising photosynthetic system that converts light energy into chemical energy and catalyses the splitting of water. PsbA (D1) and PsbD (D2) are the core subunits of the reaction centre that provide most of the ligands to the redox-active cofactors and exhibit photooxidoreductase activities that convert quinone and water into quinol and oxygen. This analysis explored the putative uncoupled electron transfer pathways surrounding P680+ induced by far-red light (FRL) based on photosystem II (PSII) complexes containing substituted D1 subunits in Halomicronema hongdechloris. Chlorophyll f-synthase (ChlF) is a D1 protein paralog. Modelling PSII-ChlF complexes determined several key protein motifs of ChlF. The PSII complexes showed a dysfunctional Mn4CaO5 cluster if ChlF replaced the D1 protein. We propose the mechanism of chlorophyll f synthesis from chlorophyll a via free radical chemistry in an oxygenated environment created by over-excited pheophytin a and an inactive water splitting reaction due to an uncoupled Mn4CaO5 cluster in PSII-ChlF complexes. The role of ChlF in the formation of an inactive PSII reaction centre is under debate and the putative mechanisms of chlorophyll biosynthesis are discussed.

ccurate recording of growth indicators for aquaculture species at different stages is essential for evaluating aquaculture production effectiveness and the rationality of feed formulations. Due to their convenience and non-invasiveness, weight, length, and condition factor are commonly used to assess the growth of aquaculture species . However, fish growth indicators, can vary significantly with age structure and feed components(nutrition), and relying on a single indicator may lead to misjudgments . In this study we investigated the growth indicators of gibel carp (Carassius auratus gibelio var. CAS V) at different growth stages (juvenile and adult) and different feed formulations. Meanwhile, fish weight-length relationship () was used to assess the growth indicators. The results showed that the weight-length relationship of gibel carp varied significantly with age and feed formulations. Additionally, the condition factor calculated depended on the weight-length relationship was more consistent with weight/length changes than measured. Weight analysis indicated that both weight, length, height, back thickness, and carcass ration had higher weights in analyzing fish growth. Therefore, during aquaculture production, fish growth evaluating requires considering diverse indicators such as weight, length,body height, back thickness, carcass ration,as well as condition factor to avoid misjudging the actual growth situation. Meanwhile, the use of the condition factor should consider the sufficient amount of data and whether the assumptions (such as being in a isometric growth period) are met.

Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.

Transition metal alloys have emerged as promising electrocatalysts due to their ability to modulate key parameters such as d-band electron filling, Fermi level energy, and interatomic spacing, thereby influencing their affinity towards reaction intermediates. However, the structural stability of alloy electrocatalysts during alkaline hydrogen evolution reaction (HER) remains a subject of debate. In this study, we systematically investigate the structural evolution and catalytic activity of the c-Co/Co3Mo electrocatalyst under alkaline HER conditions. We reveal that the instability of the Co3Mo alloy and H0.9MoO3 during alkaline HER leads to the destruction of the crystal structure, resulting in the formation of hexagonal cobalt (h-Co) from hexagonal Co(OH)2 as an intermediate species. Furthermore, we explore the re-adsorption and surface coordination of the Mo element, which enhances the catalytic activity for alkaline HER. This work provides valuable insights into the dynamic behavior of alloy-based electrocatalysts, shedding light on their structural stability and catalytic activity during electrochemical reduction processes

Ditching-rotary tiller was widely used in tillage and ditching furrow during rapeseed sowing. As its ditching furrow system (consist of four identical ditching plows) would bear great drag resistance from the soil during operation, the ditching plow was optimized based on differential geometry analysis and EDEM simulation. Resistance-velocity simulation indicated the plow with parabolic type guide curve had better reduce-drag characteristics. Differential geometry analysis indicated decay rate of E and growth rate of G could represent the variable regularity of drag resistance with the increase of velocity. The L value could represent the magnitude of drag resistance. The smaller L value, decay rate of E and growth rate of G, the better drag-reduce characteristics. Quadratic rotation orthogonal combination design shown the optimal structural parameter values were: Δθ=15°, θmin=45°, h=250mm, l=100mm. The minimum average drag resistance was 230.1N. The differential geometry analysis of optimal ditching plow shown |dE/dκ| and |dG/dκ| could obtain minimum value, indicating growth rate of drag resistance for optimal plow was the lowest within the experimental level. Field experiment indicated the operation effect of optimized ditching furrow system could met the requirements of rapeseed sowing. The research provided in-depth theoretical guidance for Drag-reduce design of plow.

Market uncertainty has a significant impact on market performance. Previous studies put much efforts in investigations on market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides quantity of transactions and market efficiency. In this paper, we put forward the concept of transaction entropy to measure the market uncertainty and see how it changes with price. The definition of transaction entropy is the ratio of total information entropy of all traders to quantity of transactions, reflecting the level of uncertainty in making transactions. And the transaction entropy is the lowest at equilibrium, it will be decreasing in a shortage market, and increasing in a surplus market. Additionally, we make a comparison of total entropy of the centralized market with that of the decentralized market, revealing that the price filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

Salvia hispanica (chia) is a highly nutritious food source and has gained popularity due to its high omega-3 fatty acid content. Red spider mites are a serious problem in the production of S. hispanica. However, no study has been conducted to analyse the defensive response to the infestation of red spider mites in S. hispanica. To elucidate the molecular mechanisms of the defensive response of S. hispanica to red spider mites, we performed a transcriptomic analysis of S. hispanica when infested by red spider mites. In the comparative assessment of leaf transcriptomes, a total of 1743 differentially expressed genes (DEGs) were identified between control and mite-infested S. hispanica. From these, 1208 (69%) transcripts were up-regulated and 535 (31%) were down-regulated. The DEGs included transcription factors, defense hormones, and secondary metabolites that were either suppressed or activated in response to spider mite herbivory. Gene Ontology (GO) enrichment analysis revealed that plant secondary metabolites such as glucosinolates, and signalling pathways, including the jasmonic acid signalling pathway, may play an important role in the defence against red spider mites. This study provides novel insights into the defence response of S. hispanica in response to insect herbivory and could be a resource for the improvement of pest resistance in the chia.

Snow cover plays a critical role in the global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions in addition to its weather and climate significance. The massive high mountain topography of the TP is main conditions for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how mountain topography controls snow cover distribution on the TP is largely remain unclear and the relationship is not well quantified. Here, the spatial distribution of snow cover and topographic controls on snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v5) and digital elevation model (DEM). The results show that snow cover on the TP is spatially unevenly distributed and is characterized by rich snow and high SCF on the interior and surrounding high mountain ranges, and less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence with the higher the altitude, the higher the SCF, the longer the snow cover duration and the more stable the intra-annual variation. Annual mean SCF below 3000 m above sea level (masl) is less than 4% and it reaches 77% above 6000 masl. The intra-annual snow cover variation below 4000 masl features a unimodal distribution, while above 4000 masl it presents a bimodal distribution. The mean minimum SCF below 6000 masl occurs in summer, while above 6000 masl it occurs in winter. Because of differences in solar radiation and water vapor sources, mean SCF generally increases with mountain slopes and it is the highest on the north-facing aspect, whereas the lowest is observed on the south-facing aspect.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

This study reports fractal analysis of one-year radon in groundwater disturbances from five stations in China amid the catastrophic Wenchuan (Mw=7.9) earthquake of May 12, 2008 (day 133). Five techniques are used (DFA, fractal dimensions with Higuchi, Katz, Sevcik methods, power-law analysis) in segmented portions glided throughout each signal. Noteworthy fractal areas are outlined in the KDS, GS, MSS station data, while the portions were non-significant for PZHS and SPS. Up to day 133 critical epoch DFA-exponents have 1.5<=a<2.0 with several above 1.8. Fractal dimensions exhibit Katz’s D around 1.0-1.2, Higuchi’s D between 1.5-2.0 and Sevcik’s D between 1.0-1.5. Several power-law exponents are above 1.7 and numerous above 2.0. All fractal results of KDS-GS-MSS stations are further analysed using a novel computerised methodology which locates the exact out-of-threshold fractal areas and combines the outcomes of different methods per five, four, three, and two (maximum 13 combinations) versus nineteen Mw<=5.5 earthquakes of the area. Most different-technique coincidences are before the great Wenchuan earthquake and its post-earthquakes. This is not only with one method, but with 13 different methods. Other interpretations are also discussed.

The portal website of a university or a college usually represents the cultural characteristics and teaching advantages and plays a vital role in higher education, showing the style of the university or college to society and improving the admission rate. Mississippi State (MS) is bounded by the Mississippi River in the west and the Gulf of Mexico in the south, with a population of 3.1 million. It has a rich cultural heritage, and many universities, especially historically black colleges and universities (HBCUS), have strong scientific research strength in agriculture, medicine, petrochemicals and papermaking. Therefore, this article will analyze the design of the portal websites of four HBCUs in Mississippi from two aspects: user experience (UE) and functional architecture (FA), determine their advantages, disadvantages, similarities and differences, and propose some optimization suggestions.

Powerful emitters of the ultraviolet C (UVC) light in the wavelength range of 230-280nm are necessary for the development of effective and safe optical disinfection technologies, high-sensitive optical spectroscopy and non-line-of-sight optical communication. This review considers such UVC-emitters with electron-beam pumping of heterostructures with quantum wells in the (Al,Ga)N material system. The important advantages of these emitters include the absence of the critical problem of p-type doping and the possibility of achieving record (up to several tens of watts for peak values) output optical power values in the UVC range. The review consistently considers about a decade of world experience in the implementation of various UV emitters with various types of thermionic, field-emission, and plasma-cathode electron guns (sources) used to excite various designs of active (light-emitting) regions in heterostructures with quantum wells AlxGa1-xN/AlyGa1-yN (x = 0 − 0.5, y = 0.6 − 1) fabricated either by metal-organic chemical vapor deposition or by plasma-activated molecular beam epitaxy. Special attention is paid to the production of heterostructures with multiple quantum wells/two-dimensional(2D) quantum disks GaN/AlN with a monolayer (1ML ~ 0.25nm) thickness, which ensures a high internal quantum efficiency of radiative recombination in the UVC range, low elastic stresses in heterostructures, and a high output UVC-optical powers.

Objective: To sort out the research hotspots of the application of VR in rehabilitation medicine, analyse its research themes and research trends, and offer a reference for future related research in this field. Methods: This paper provides an in-depth analysis of the development process, research hotspots and research trends in the field of VR application in rehabilitation medicine research, taking the Web of Science core dataset as the source and using bibliometric analysis by CiteSpace. Results: The application of VR in rehabilitation medicine was composed of three stages, and the research topics were reviewed in five aspects: neurological rehabilitation, psychology treatment, pain distraction, cardiopulmonary rehabilitation, and visual spatial disorder. Limitations: The literature data only come from the Web of Science core dataset, and the data sample size was not comprehensive. Conclusion: Overcoming VR technology-induced vertigo and mental disorders from overuse of VR are both challenges to research on the application of VR in rehabilitation medicine. In addition, developing VR products with better experience, developing standardized guidelines, and conducting more high-quality clinical studies are all future research trends for the application of VR in rehabilitation medicine.

The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to examine the long-term land cover changes at post-closure mine sites. Six representative post-closure mine sites were selected for the evaluation and comparison. The Normalized Difference Vegetation Index (NDVI) analysis, Landsat image classification, post-classification change detection, and Regrowth Index (RI) analysis were conducted to assess the speed and extent of landscape and vegetation recovery at the target mine sites. A significant vegetation recovery was quantified for the mine sites that have experienced active reclamation activities. In contrast, the post-closure mine area undergoing only the passive revegetation typically demonstrated a slow and minor increase in vegetation over time. The actively revegetated mine sites can typically be restored to a level that equals or better than the pre-mining situation. This work confirms that active reclamation and revegetation at post-closure mine sites is critically important in sustainable mining. The quantified mine site reclamation behavior and the relevant sustainable practices would be useful for evidence-based sustainable resource management in Canada.

In order to solve the interference of narrowband in the ultra-wideband (UWB) system, a high isolation of multiple-input multiple-output (MIMO) antenna is investigated in this paper. The MIMO antenna consists of 2 or 4 UWB elements; and each UWB antenna element is a monopole reconstructed with 2-notched slots, a U-shaped and inverted U-shaped slots etched on the monopole patch to achieve notch characteristics in WIMAX and ITU bands. The property of a 2-element MIMO antenna is analyzed firstly, which is put into a same substrate in parallel. Two rectangular branches are located in the middle to reduce the coupling. Then a 4-element MIMO antenna is studied to meet big size space and more application scenarios. Each element is placed orthogonally on the same substrate; and the isolation is implemented by a cross-shaped branch located in the middle of elements. Both MIMO antenna samples have been tested to verify the design. Measured results show that the working bandwidth is 2.45-14.88 GHz and 2.14-14.95 GHz, the isolation is greater than 17 dB and 20 dB, the peak gain is 5.7 dB and 5.9 dB, the max-imum radiation efficiency can reach 96% and 95%, the envelope correlation coefficient is less than 0.02 and 0.05 for the 2- and 4-element UWB MIMO antenna, respectively. They both are with very good omnidirectional characteristic with notched bands such as the WIMAX and ITU, and meet the requirements of the UWB application in most cases.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

Ion current rectification (ICR) is the ratio of ion current by forward bias to by backward bias and is a critical indicator of diode performance. In previous studies, many attempts have been continued to improve the performance of this ICR, but there is the intrinsic problem for geometric changes that induce ionic rectification due to their fabrication problem. Additionally, the high ICR could be achieved in the narrow salt concentration range only. Here, we propose a multi-layered bipolar ionic diode based on an asymmetric nanochannel network membrane (NCNM), which is realized by soft lithography and self-assembly of homogenous-sized nanoparticles. Owing to the freely changeable geometry based on soft lithography, the ICR performance can be explored according to the variation of microchannel shape. Interestingly, very stable ICR performance can be achieved using the multi-layered 3D configuration for the bipolar diode in a broad range of salt concentrations (0.1mM ~ 100 mM). This demonstrates the promising potential of multi-layered NCNM for applications in highly concentrated electrolytes, such as biosensors, desalination, or energy harvesting.

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

: The stability of semiconductor catalysts is a very important property in the field of catalytic reactions. Materials necessitate resistance to mechanical abrasion and chemical erosion for better overall performance. In this work, high crystallinity BiOCl nanosheets were prepared by a simple hydrothermal method. The photocatalysts demonstrate full UV light absorption ability and excellent photocatalytic norfloxacin degradation performance. 84 % of highly toxic norfloxacin were photocatalytically eliminated within 180 min. The internal structure and surface chemical state of BiOCl were analyzed by SEM, TEM, Raman, FTIR, UV-vis, XPS, and other techniques. Such high crystallinity makes BiOCl molecules closely arranged and the molecular chains strongly connected, thus exhibiting a high level of photocatalytic stability and recyclability.

With the rise of piano teaching in recent years, many people have joined the team of piano learners. However, the expensive cost of manual instruction and the unique one-on-one teaching model have made piano learning an extravagant event. Most existing approaches based on the audio modality aim to evaluate piano players' skills. Unfortunately, these methods ignored the information contained in the video, which led to a one-sided and simplistic evaluation of the piano player's skills. More recently, multimodal-based methods are proposed to assess the skill level of piano players using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which are deficient in extracting complex spatio-temporal and time-frequency features from piano performance. Furthermore, the fingering and the pitch-rhythm information of the piano performance is contained in the spatio-temporal and time-frequency features, respectively. In the paper, we propose a ResNet-based audio-visual fusion model that combines video and audio features to assess the skill level of piano players. Firstly, ResNet18-3D is used as the backbone network for our visual branches, which can extract feature information from the video data. Then, we consider ResNet18-2D as the backbone network of the aural branch and extract the feature information from the audio data. The extracted video features are fused with the audio features to generate multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80%, outperforms the state-of-the-art methods in both performance and efficiency. Then, we also explore the impact of different layers of ResNet on model performance, and the experimental results show that the audio-visual fusion model dealing with the piano skill assessment problem can make full use of both feature information when the number of video features is close to the number of audio features.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

This study investigates the Hagen–Poiseuille pipe flow of viscoplastic fluids, focusing on analytical predictions of concrete pumping using the shear-stress-dependent parabolic model. Research highlights encompass solving the steady laminar pipe flow for viscoplastic fluids described by the parabolic model, extending analytical studies to a nonlinear rheological model with easily accessible experimental parameters, presenting detailed results for the two-fluid parabolic model, and introducing computational apps implementing theoretical findings. The parabolic model outperforms linear models, such as the Bingham model, in accuracy by accounting for the nonlinearity in the shear stress and shear rate relations. The influence of rheological parameters on these relations is analyzed, and their versatility is demonstrated by a Wolfram Mathematica-based application program. This approach is adaptable for other models with shear stress as the independent variable, offering valuable insights into viscoplastic fluid flows.

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are a significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ERA5 in studying atmospheric ducts using measured data, we analyzed the space-time distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. Using the parabolic equation model, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in both uniform and non-uniform duct environments within a typical atmospheric duct structure. The study found that the NCEP CFSv2 data accurately captures the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height obtained from the inversion of ERA5 and the measured data has a good consistency. The occurrence rate and height of evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns. Conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layer, mainly concentrated from June to August. The monsoon plays a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. Electromagnetic propagation simulations demonstrate that higher frequency and lower elevation angles of radiation sources in the trapping layer of the evaporation duct make it easier to be trapped. As the evaporation duct height decreases, the amplitude of the "sinusoidal fluctuation" of the propagation loss also decreases. Frequency changes of the radiation source in the surface duct environment have minimal impact on electromagnetic propagation loss, but the elevation angle of the radiation source is a critical factor. The frequency of the "sinusoidal fluctuation" in propagation loss is higher in a hybrid duct environment compared to a uniform surface duct. Additionally, the propagation loss increases faster with distance at the height of the evaporation duct, resulting in greater electromagnetic propagation loss.

Microfinance institutions (MFIs) play an significant role in financial inclusion and poverty alleviation activities. A critical challenge facing MFIs is how on all to build its own capacity for sustainable and healthy development. By using the data from the pilot survey of 65 microcredit companies in Zhejiang of China, this paper explores the factors affecting MFIs activities by principal component analysis and analyzes the key Influencing factors and sustainable development performance with the analysis of variance and multi-regression model.The results indicate that four key factors of operational technology, external environment, financial condition and institution size have a significant positive effect on the economic sustainability performance of MFIs, while the adjusted institution size is insignificant positive effect on operational sustainability performance,which means that key factors of MFIs sustainability have a greater impact on economic sustainability performance than operational sustainability performance.

Microbial communities inhabiting the human body play a crucial role in protecting the host against pathogens and inflammation. Disruptions to the microbial composition can lead to various health issues. Microbial transfer therapy (MTT) has emerged as a potential treatment option to address such issues. Fecal microbiota transplantation (FMT) is the most widely used form of MTT and has been successful in treating several diseases. Another form of MTT is vaginal microbiota transplantation (VMT), which involves transferring vaginal microbiota from a healthy female donor to a diseased patient's vaginal cavity with the goal of restoring normal vaginal microbial composition. However, VMT has not been extensively studied due to safety concerns and a lack of research. This paper explores the therapeutic mechanisms of VMT and discusses future perspectives. Further research is necessary to advance the clinical applications and techniques of VMT.

Abstract: Self-regulated Learning has become one of educational psychology's most critical research areas. Contemporary research indicates that student creativity is crucial to the success of arts education. However, there is limited research on creativity in arts education in Self-regulated Learning. Therefore, this study aims to define the creativity characteristics that determine the quality of students’ work. This study implemented a creative competition in painting and composition for first-year undergraduates. It evaluated the characteristics of creativity during the three main phases of the task: planning, execution, and completion. We discovered through semi-open questionnaires (n=178)，and using the content analysis method. According to the competition results, the students with the highest scores in Flow, Inspiration, and Idea Generation in the Performance phase had the best quality work. In the Forethought phase, Analogical Thinking and Idea Generation worked best for most students, while in the Performance phase, Flow Sense, Idea Generation, and Idea Manipulation worked best.Data on creativity characteristics at different work phases and final quality might adequately guide and organize educational procedures. These findings can serve as a theoretical foundation for quantifying the creativity phase in Self-regulated Learning and offer arts educators a reference for creativity instruction.

Permanent magnet brushless DC motor (BLDCM) is usually controlled by six step commutation method, and flux-weakening method is used to make the motor run higher above the base speed. For now, it is considered that the weak magneto-electric angle range is 0-pi/3, and that of deep weakening is pi/3-pi/2. In the field- weakening control, the forward shift of commutation point will form inverter circulating current in the three-phase bridge of inverter and the stator winding of the motor. In this paper, the principle of inverter circulating current formed by inverter is analyzed. Through magnetic potential analysis and Simulink simulation, it is concluded that flux-weakening control will generate inverter circulating current in the inverter and motor stator winding. The inverter circulating current affects the magnetic potential of motor, and magnetic potential will move to the rotating direction of motor rotor. When the forward shift angle of inverter commutation point is 0-pi/6 electrical angle, the movement amplitude of current circulating current of inverter is less than pi/6, which plays the role of weakening magnetic field and driving effect; when the forward moving angle is pi/6-pi/3, the movement amplitude of inverter circulating current exceeds pi/6, which plays the role of magnetic weakening and braking. When the braking effect occurs, the reverse torque will appear, resulting in the decrease of motor torque and efficiency. Therefore, the range of weak magneto-electric angle should be between 0-pi/6.

In recent years, the ecological and economic values of forest plants have been gradually recognized worldwide. However, the growing global demand for new forest plant varieties with higher wood production capacity and better stress tolerance cannot be satisfied by conventional phenotype-based breeding, marker-assisted selection, and genomic selection. In the recent past, diverse omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, have been developed rapidly, providing powerful tools for the precision genetic breeding of forest plants. Genomics lays a solid foundation for understanding complex biological regulatory networks, while other omics technologies provide different perspectives at different levels. Multi-omics integration has combined the different omics technologies, becoming a powerful tool for genome-wide functional element identification in forest plant breeding. This review summarizes the recent progress of omics technologies and their applications in the genetic studies of forest plants. It will provide forest plant breeders with an elementary knowledge of multi-omics techniques for future breeding programs.

Unsmooth reversing is one of the most common faults in the four-way reversing valve of air conditioning system, and the airflow in the valve chamber and the shape of the slider of the reversing valve are the main factors in unsmooth reversing. In order to study the influence of the airflow in the chamber and the slider shape on the reversing process, the fluid flow in the valve chamber of a four-way reversing valve is obtained using computational fluid dynamics (CFD) simulations, and the influence of slider structure on slider thrust and resistance of the four-way reversing valve is analyzed. Four-way reversing valves with no-cutting, straight-cutting and arc-cutting sliders are employed to evaluate the reversing characteristics. A pressure measuring device for a four-way reversing valve chamber is designed, which is performed to test the chamber pressure of three different types of sliders of four-way reversing valves. The maximum error of experimental tests and simulations is within 5% of engineering tolerance, so as to verify the reliability of numerical simulation. The results indicate that the chamber pressure and resistance-thrust ratio with the cutting slider are reduced, which is beneficial to the reversing of the four-way reversing valve. Compared with the straight cutting structure, the arc cutting slider four-way reversing valve has a more stable reversing process and better comprehensive reversing performance. The results can provide a theoretical basis for engineers to improve the performance of four-way reversing valves.

Machine learning (ML) is widely used in the field of crop-growing information identification based on high-resolution remote sensing images. With Baoying County in Jiangsu Province, China, as the study area, this paper used Sentinel-2 images during the winter wheat growth period to construct its spectral, textural, and topographic features during its growth period and proposes a winter wheat-growing area extraction method based on the extreme gradient boosting (XGBoost) algorithm, which was compared with traditional ML algorithms such as the support vector machine (SVM), classification and regression tree (CART), and random forest (RF) algorithms. The results indicated that (1) a winter wheat-growing area identification model based on the XGBoost algorithm was successfully constructed based on Sentinel-2 images, considering 27 spectral, textural, and topographic features; (2) the constructed model could effectively extract winter wheat in the study area with an overall accuracy of 93.43% and only a small error compared with the actual winter wheat-growing area in Baoying County, meeting the accuracy requirement for crop identification in the study area; and (3) the deep learning algorithm XGBoost outperformed the three traditional ML algorithms, among which the RF algorithm was better than the SVM and CART algorithms, both of which had poor identification performance and a large error compared with the actual growing area. This paper provides a scientific basis for the accurate extraction of winter wheat-growing areas and further research on winter wheat growth monitoring and yield estimation.

Chronic hepatitis B infection remains a significant worldwide health burden, placing persons at risk for hepatocellular cancer and hepatic fibrosis. Chronic hepatitis B virus (CHB) infection is characterized by elevated levels of immunosuppressive regulatory T cells (Tregs), which can inhibit the function of effector T cells and lead to an insufficient immune clearance response against HBV in the host. Theoretically, suppression of Treg functionality and percentage could increase anti-HBV reactivity in CHB-infected patients, although this has not yet been explored. We attempted to enhance our previously established anti-CHB protocol utilizing the GM-CSF+IFN-α+rHBVvac regimen (GMI-HBVac) by incorporating Mafosfamide (MAF), which has been utilized in anticancer therapy in the past. Intravenous administration of MAF to rAAV8-1.3HBV-infected mice resulted in a dose-dependent reduction of Tregs in the blood, rebounding to pretreatment levels 10 days later. To determine the efficacy of anti-CHB, 2ug/ml MAF was combined with the GMI-HBVac in an HBV-infected animal model as an anti-Treg treatment. When rAAV8-1.3HBV-infected mice were immunized with MAF+GMI-HBVac, peripheral blood Tregs decreased significantly, leading to dendritic cell activation, HBV-specific T cell proliferation, and upregulation of IFN-gamma producing CD8+T cells. In addition, MAF+GMI-HBVac vaccination stimulates T cell infiltration in HBV-infected liver. These conditions may contribute to an enhanced immune response and the clearance of HBV-associated antigens, including serum HBsAg, serum HBcAg, and HBcAg+ hepatocytes. Overall, this is the first indication that MAF was utilized as an adjuvant to deplete Tregs and paired with GMI-HBVac as a unique therapeutic vaccine regimen against established CHB mice to produce a functional cure, as seen by the remarkable clearance of HBsAg.

Deep learning (DL) models have been recently widely used to extract task-oriented patterns from large scale of datasets, and to improve the data image understanding and analysis accuracy in many different decision-making processes for tasks such as image classification, segmentation, detection, and so on. However, in practice, the performances of DL models are easily affected by environmental illumination conditions. Conversely, DL models can also be utilized for extracting the illumination hints from the images, and these hints are critically useful for improving the model robustness, classifying the environmental scenes, estimating scene depth information, and rendering 3D objects. In this study, an extensive and exhaustive review is carried out for DL based color constancy, indoor and outdoor illumination estimation, and image depth estimations with the considerations of strengths and weaknesses of DL models. This study also explores the different network designs and the paradoxes in parameter optimization during the model training. Current technology barriers involved in implementing these models and recommendations to overcome these barriers are also suggested in the review.

Coastal marine ecosystems worldwide, like Florida Bay, are increasingly affected by tide alteration and anthropogenic disturbances which affect water quality imbalance leading to algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impact on processes like carbon cycle and services like species presence. However, exploring eco-environmental feedback patterns of algal blooms remains challenging and poorly investigated, also due to the paucity of data. Florida Bay, taken as an epitome, has long experienced algal blooms in its central and western regions, and in 2006 an unprecedented bloom occurred in the eastern habitats. We analyzed the occurrence of blooms from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoints source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to in assess environmental impacts of ecological dysbiosis and target prevention and control of algal of blooms; and (3) the biogeochemical-spreading network topological co-evolution to quantify ecosystemic stability and ecological shift likelihood in long-term. Here, we propose Transfer Entropy (TE) to quantify dynamical interactions between spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spreading, as well as environmental effects; the Pareto principle is defined for identifying the salient eco-environmental interactions of CHLa. We quantified the spatial dynamics of algal blooms, and thus obtained areas in need for ecological monitoring and potential bloom control. Results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factor. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrients' overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently this increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable areas such as tidal flats and marshes. Algal blooms are important ecosystem regulators of nutrient cycles; however, beyond limit chlorophyll-a outbreaks cause aquatic species mortality due to their effect on water turbidity, nutrient balance (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising local environmental quality, socio-ecological services are also compromised at large scales, yet ecological assessment models like the one presented are in need of application in subtropical and tropical bays worldwide.

This paper presents the common methods and corresponding drawbacks concerning nonlinear analysis of fluxgate excitation circuits and emphasizes the importance of nonlinear analysis for these circuits. With regard to the nonlinearity of the excitation circuit, this paper proposes the use of the core-measured hysteresis curve for mathematical analysis and the use of a nonlinear model that considers the coupling effect of the core and winding and influence of the historical magnetic field on the core for simulation analysis. The feasibility of mathematical calculations and simulation for the nonlinear study of fluxgate excitation circuit is verified via experiments. The results demonstrate that, in this regard, the simulation is better than a mathematical calculation. The simulation and experimental results of the excitation current and voltage waveforms under different excitation circuit parameters and structures are essentially consistent with each other, thereby verifying the effectiveness of the nonlinear excitation analysis method.

Alternating TiB2-dcMS and Cr-HiPIMS layers are used to fabricate TiB2/Cr multilayer films. In-troducing a 5-nm-thick Cr interlayer deposited under a substrate bias of -60 V produces slight increases of both film hardness and elastic modulus. The TEM observation indicates that the Cr grains favor epitaxially growth on TiB2 interlayer, forming a coherent TiB2/Cr interface. This produces the hardness increasement. Mechanic measurement by using AFM illustrates that the coherent interface increases the elastic modulus of the Cr up to ~280 GPa, which is significantly higher than bulk material.

Existing research has focused on discussing various issues in isolation, e.g., climate change or deglobalisation, without capturing the geo-political, legal, socio-economic, and technological implications in a holistic manner, especially in China and Southeast Asia. This has hindered the derivation of clear guidelines to navigate the business environment in the next decade, thus leaving the business world with fragmented theories and concepts that are hard to amalgamate and turn into tangible business strategies. Thus, this study illustrates the key challenges that are likely to influence the stability and sustainability of the global business environment by 2030, as well as suggests the main business strategies to leverage to mitigate them. This contribution, for the first time, proposes an alternative holistic approach by highlighting the key threats to global business in the next decade, as well as suggesting clear action points to account for the main ones from geo-political, legal, socio-economic, and technological standpoints in a unified theory. Local and more tailored strategies are described and discussed as referred to various industries to help in navigating a deglobalised and more competitive market by 2030.

Previous research has either neglected to consider misbehaviour or misconduct in the virtual workplace during the current COVID-19 pandemic or highlighted some challenges encountered with remote working or work from home but in isolation, e.g., misbehaviours or ethical concerns associated with improper use of technology when accessing clients’ confidential information, without understanding their aetiology and context in a holistic manner. This has impacted the ability of existing research in providing recommendations organisations can practically implement to improve the sustainability and productivity of the virtual workplace, especially as they seek to navigate the current pandemic and prepare for the global business environment in the next decade. Instead, this study, for the first time, proposes a novel holistic methodology to identify the aetiologies of ethical and performance challenges with focus to the Information Technology consulting sector, as well as developing a set of recommendations companies can adopt to manage such behavioural and productivity challenges. Thus, this study illustrates the key issues associated with remote working implemented to respond to the current COVID-19 pandemic, which mine not only the individual performance in an organisation but also its overall productivity, as well as suggests the main performance and reward management strategies that can be leveraged to mitigate them. Tailored strategies are described and discussed as referred to this industry to help in navigating a more unstable yet competitive market to manage the virtual employees’ misbehaviours in the workplace and drive growth for individuals and organisations alike.

Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.

Directed energy deposition (DED) is an efficient method to fabricate functionally graded materials (FGMs) with gradient composition and complex structures, allowing for local tailoring of properties instead of the costly need for extraneous welds and joints. In this study, a FGM from stainless steel to Inconel alloy was successfully fabricated using the powder-based laser DED. A very refined grain structure has been observed in at the composition with 75 wt.% Inconel alloy content, which also exhibits the highest (entropy). For the first time, the post heat treatments, microstructure and aging precipitation behaviors of FGMs were systematically studied via experimental characterization and computation, to elucidate their effects on the gradient smoothing and mechanical properties. The diffusion and segregation of Ni, Nb and Ti elements underly the transformation mechanism between Laves, δ, γ’ and γ’’ phases during precipitation. Homogenization on FGMs not only eliminates the heterogeneity inherited from the AM process, but also provides a practical way to smoothen the gradient on composition and microstructure for the eventual good gradient properties. It has a direct influence on the following precipitation behaviors in the FGM, which highly relies on the diffusion degree of the elements in the matrix and grain boundaries. The high-throughput thermodynamic modeling and kinetic modeling were exploited to evaluate the experimental microstructure and address computational uncertainty using different thermodynamic conditions and databases, which enables an accelerated design through local tailoring of process-structure-property relationships to develop new functional materials.

To both survive and develop continuously, enterprises must overcome many kinds of competition and challenges. Cultivating employees' active and sustainable organizational citizenship behavior is important for enterprises to successfully cope with turbulence and uncertain events during their development. In this study, we investigated the development level of and factors influencing employees' organizational citizenship behavior in current organizations. By using the Belief in a Just World Scale, Organizational Citizenship Behavior Scale, and Interpersonal Intelligence Scale, we investigated 230 employees from 15 different enterprises. The results showed that belief in a just world, interpersonal intelligence, and organizational citizenship behavior were significantly positively correlated. Interpersonal intelligence played a moderating role between belief in a just world and organizational citizenship behavior; the organizational citizenship behavior of individuals with high interpersonal intelligence increased with the strengthening of the belief in a just world, and this increase was larger than that experienced by individuals with low interpersonal intelligence. This meant that under a certain level of belief in a just world, a high level of interpersonal intelligence was more conducive to promoting employees' sustainable organizational citizenship behavior.

Rail impedance directly affects the transmission performance of track circuit . Considering the condition of earth stratification, for the difficult to calculate the rail impedance due to the semi-infinite integration interval and the oscillation of the integrand by using the Carson formula, The truncation method is proposed to divide the impedance formula is divided into definite integral and tail integral. The integral is approximated by the spline function, and the tail integral is calculated by using the exponential integral and Euler formula. Based on it, the rail impedance calculation formula of track circuit is obtained. The electromagnetic field model of track circuit with earth stratification is simulated by finite element method, and the correctness of the method is verified. Based on the formula, the influence of current frequency, soil depth and conductivity on rail impedance is studied. The relative error between the calculated results of rail impedance and the simulation results of finite element is within 5%. It can be seen that the formula has high accuracy and correctly reflects the law of rail impedance variation with current frequency, soil depth and resistivity. It provides a reliable reference for the theoretical calculation of rail impedance of track circuit.

While the long-term effects of COVID-19 are yet to be determined, its immediate impact on crowdfunding is nonetheless significant. This study takes a computational approach to more deeply comprehend this change. Using a unique data set of all the campaigns published over the past two years on GoFundMe, we explore the factors that have led to the successful funding of a crowdfunding project. In particular, we study a corpus of crowdfunded projects, analyzing cover images and other variables commonly present on crowdfunding sites. Furthermore, we construct a classifier and a regression model to assess the significance of features based on XGBoost. In addition, we employ counterfactual analysis to investigate the causality between features and the success of crowdfunding. More importantly, sentiment analysis and the paired sample t-test are performed to examine the differences in crowdfunding campaigns before and after the COVID-19 outbreak that started in March 2020. First, we note that there is significant racial disparity in crowdfunding success. Second, we find that sad emotion expressed through the campaign's description became significant after the COVID-19 outbreak. Considering all these factors, our findings shed light on the impact of COVID-19 on crowdfunding campaigns.

Metabolites are essential intermediate products in metabolism, and metabolism dysregulation indicates different types of diseases. Previous studies have shown that cigarette smoke dysregulated metabolites; however, limited information is available with electronic cigarette (E-cig) vaping. We hypothesized that E-cig vaping and cigarette smoking altered systemic metabolites, and we propose to understand the specific metabolic signature between E-cig users and cigarette smokers. Plasma from non-smoker controls, cigarette smokers, and e-cig users were collected, and metabolites were identified by UPLC–MS (Ultraperformance liquid chromatography-mass spectrometer). Nicotine degradation was activated by e-cig vaping and cigarette smoking with increased concentrations of cotinine, cotinine N-oxide, (S)-nicotine, and (R)-6-hydroxynicotine. Additionly, we found significant decreased concentrations in metabolites associated with tricarboxylic acid (TCA) cycle pathways in e-cig users verses cigarette smokers, such as: D-glucose, (2R,3S)-2,3-dimethylmalate, (R)-2-hydroxyglutarate, O-phosphoethanolamine, malathion, D-threo-isocitrate, malic acid, and 4-acetamidobutanoic acid. Cigarette smoking significant up-regulated sphingolipid metabolites, such as D-sphingosine, ceramide, N-(octadecanoyl)-sphing-4-enine, N-(9Z-octadecenoyl)-sphing-4-enine, and N-[(13Z)-docosenoyl]sphingosine, verses e-cig vaping. Overall, e-cig vaping dysregulated TCA cycle realted metabolites while cigarette smoking altered sphingolipid metabolites. Both e-cig and cigarette smoke increased nicotinic metabolites. Therefore, specific metabolic signature altered by e-cig vaping and cigarette smoking could serve as potential systemic biomarkers for early cardiopulmonary diseases.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

Plasma and serum are the most widely used blood-derived biofluids for metabolomics and lipidomics assays, but the isolation of these products from blood may introduce additional bias as indicated by the fact that many analytes that are present at high concentrations in blood cells cannot be measured and evaluated in those samples. Of particular concern, variable hemolysis during the pre-processing of blood products could compromise accurate and reproducible quantification. Compared with plasma or serum, whole blood may be a better alternative due to simplicity of processing. In this study, we provide a comprehensive method for quantification of the whole blood sphingolipidome and the concentrations were compared with those from plasma. Combining a single-phase extraction method with liquid-chromatography high resolution mass spectrometry (R=120, 000), assisted by alkaline hydrolysis, we were able to identify and simultaneously quantify more than 150 sphingolipids. Furthermore, most of sphingolipids remained stable after a freeze/thaw cycle. Whole blood contained a higher concentration of most sphingolipids than corresponding plasma. Moreover, individual variations in the levels of sphingolipids were lower for whole blood than plasma. These findings demonstrate that whole blood could be a better alternative to plasma, and potentially guide the evaluation of sphinglipidome for biomarker discovery.