Celtis sinensis is an adaptable species that is widely grown in southern China. In June 2022, a leaf blotch disease of C. sinensis was observed in Nanjing, Jiangsu, China. Based on morphological characterization, three isolates were identified as Alternaria species. Phylogenetic analysis of combined ITS, GAPDH, TEF1-α, RPB2 and Alt a 1 sequences identified the three isolates we obtained as Alternaria alternata and Alternaria koreana. Koch’s postulates were fulfilled in the greenhouse, and the pathogenicity of A. alternata and A. koreana was determined by leaf inoculation tests on C. sinensis seedlings. The symptoms of indoor inoculation were consistent with those in the field. A. alternata and A. koreana can grow at 15-35 °C, with an optimal growth temperature of 25 °C. The results of fungicide sensitivity experiments indicated that A. alternata and A. koreana were the most sensitive to prochloraz, which may be a useful strategy for the future prevention and control management of A. alternata and A. koreana. This study provides the first step for further research on A. alternata and A. koreana as pathogens of C. sinensis and provides a theoretical basis for future control strategies.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

Patients with end stage liver disease (ESLD) undergoing liver transplantation are prone to thromboses both while on the waiting list or in the perioperative period. This hypercoagulability is associated with significant endothelial dysfunction (ED) due to nitric oxide dysregulation. ED in combination with increased thrombin generation are the main factors responsible for this hypercoagulability. Sepsis itself can significantly change the coagulation profile, but in combination with ESLD, sepsis, and septic shock is responsible for very complex changes in the patients coagulation making both assessment and management of coagulation in patients with ESLD very challenging. Viscoelastic testing (VET) is a preferred method of coagulation management in patients with cirrhosis because, as with standard laboratory testing, VET can assess the entire coagulation system including the interaction between both pro- and anticoagulants and platelets.

Objective: To use the ancient and modern medical record cloud platform (V2.3.5) to organize and explore the medication patterns of the "Three Causes, Extremity, and One Disease Syndrome Formula" by Chen Wuxuan, and to summarize its therapeutic ideas. Method: Collect and organize the "San Yin Si Tian Fang" in the "San Yin Ji Yi Zheng Fang Lun", and use the modern medical record cloud platform (V2.3.5) to conduct medication frequency statistical analysis, cluster analysis, and complex network analysis. As a result, a total of 16 Chinese herbal medicines were collected in the "Three Causes, Extreme and One Disease Syndrome Formula", involving 75 traditional Chinese medicines. Statistical analysis showed that roasted licorice was the most commonly used, followed by Pinellia ternata, papaya, ginger, and Poria cocos; Medications mainly include warm drugs, sweet and pungent flavors, as well as drugs that promote lung and spleen meridians. Cluster analysis can divide the above-mentioned traditional Chinese medicine into two groups, with specific grouping as follows. Group 1: ginger, white peony, aconite, papaya, and achyranthes bidentata; Group 2: roasted licorice, poria cocos, dried ginger, pinellia ternata, and green peel. Conclusion: The characteristics of the 16 prescriptions are biased towards Xin Gan Wen, emphasizing the regulation of the spleen and stomach, which may be related to the common use of prescription by doctors in the Song Dynasty and the tendency towards warm and dry medication.

Breast cancer is a complex and heterogeneous disease that displays diverse molecular subtypes and clinical outcomes. Although it is known that the location of tumors can affect their biological behavior, the underlying mechanisms are not fully understood. In our previous study, we found a differential methylation profile and membrane potential between left (L) and right (R)-sided breast tumors. In this current study, we aimed to identify the ion channels responsible for this phenomenon and determine any associated phenotypic features. To achieve this, experiments were conducted in mammary tumors in mice, human patient samples, and with data from public datasets. The results revealed that L-sided tumors have a more depolarized state than R-ones. We identified a 6-ion channel-gene signature (CACNA1C, CACNA2D2, CACNB2, KCNJ11, SCN3A, and SCN3B) associated with side: L-tumors exhibit lower expression levels than R-ones. Additionally, in silico analyses show that the signature correlates inversely with DNA methylation writers and with key biological processes involved in cancer progression, such as proliferation and stemness. The signature also correlates inversely with patients’ survival rates. In an in vivo mouse model, we confirmed that KI67 and CD44 markers were increased in L-sided tumors and similar tendency for KI67 was found in patient L-tumors. Overall, this study provides new insights into the potential impact of anatomical location on breast cancer biology and highlights the need for further investigation into possible differential treatment options.

The anthropogenic deterioration of aquatic ecosystems affects water resources due to agricultural malpractices, pollution from domestic septic tanks, recreational activities, and poor watershed management, among others. This study examines the management of Las Curias Reservoir (176,974 m2), San Juan, Puerto Rico, post the 2016 arrival of the invasive aquatic fern Salvinia molesta. In December 2019, a community-led initiative introduced the Cyrtobagous salviniae weevil, an effective biological control agent for S. molesta, and commenced a mechanical removal campaign using an aquatic harvester. Limnological sampling (September 2019 to March 2022) and drone flights were employed to measure physicochemical and floating plant cover changes, respectively, in the reservoir. Monitoring of weevils in the reservoir demonstrated a rapid establishment and dispersal, which resulted in visible damage including browning of plants and eventually sinking of entire mats. By January 29, 2021, 71% (125,652 m2) of the reservoir surface was covered by salvinia. In 2022, the reservoir recorded an average dissolved oxygen concentration of 2.38 mg/L -1 (± 0.021, n = 144), the highest in the study period and indicative of ecosystem recovery. After three years of control efforts, dissolved oxygen, pH, and specific conductance returned to levels recorded prior to Salvinia molesta introduction. This ecosystem recovery, a first in Puerto Rico, could be attributed to an early use of mechanical control and a long-term impact of biological control.

It is extremely fascinating and astonishing that the gravitational field on the surface of a neutron star is with a relativistic mass density of 2.65*10¹⁶~5.87*10¹⁸kgm^-3 which can be larger than the mass density of the neutron star (~10¹⁷kgm^-3).Therefore, it is the author’s first intuitional imagining that this field could directly convert into mass. In so strong a gravitational field, electron and proton could be produced directly from graviton–photon collision. The gravitational field exists in everywhere in our universe. No vacuum that the region of a space is “empty” does exist. A particle is clearly always being acted on by the gravitational field. The quantum vacuum fluctuation and vacuum polarization need be re-understood with the interaction between photon and gravitational field. Therefore, the gravitational field is naturally one of the foundations of modern physics.

Excitation of the acoustic field leading to the Blaha effect affects the plasticity of the material significantly in ultrasonic vibration-assisted forming. In a micro-forming field, the effects are more significant in the deformation in surface of materials [1]-[3], in which reduction of the surface roughness based on the increasing of plastic deformation of surface asperity was effective [4]. On the other hand, the effect on deformation behavior of the bulk region indicted reduction in the yield stress of materials, and not only acoustic effect [5], but also impact effect is found to generate a large amount of dislocation and produce plastic deformation [6][7]. However, the effect on the bulk is more significant as that on the surface. Differences in the effect on the surface and the bulk are not clarified. In this study, the mechanism of the deformation in the surface of the material with ultrasonic vibration assistance is investigated and compared with that in the bulk. Forging tests using a newly developed ultrasonic vibrator were carried out on pure Cu foils with various process conditions. The longitudinal vibration frequency of the ultrasonic transducer is 60∓2kHz, and the vibration amplitude is in an adjustable range of 0~10μm. Forging test was carried out at different initial stress, specimen size and amplitude. The difference in acoustic softening and impact effects on the surface and the bulk was discussed.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

There are gravitational field and baryonic matter (star, planet, gases etc.) in our world; gravitational field is much more fundamental for that the relativistic mass of the gravitational field of a neutron star is almost 10²¹ times the mass of the neutron star. And, it is astonishing that the relativistic mass density of the gravitational field on the surface of a neutron star can be larger than the mass density of the neutron star. It is well known that, in the weak gravitational field a light line appears a straight line while in the strong field it is a curved line. It is stressed that the result measured in the strong field is more fundamental for that in a galaxy the measurable object is generally with strong field. It results in that the way that the result measured on the Earth with weak field is used as the universal standard need be revised as gravity is studied in the scale of a galaxy. It seems that a new theory of gravity need be developed for the new observations.

Appling the controlling relative permittivity and permeability in the equations for the gravitational-magnetic-electric field interaction, a very large variation of the gravitational acceleration of the Earth by electric/magnetic field could be arrived at. This conclusion may be supported by some of the experiments for the gravitational effect of superconductivity.

Studying the two famous old problems that why the moon can move around the Sun and why the orbit of the Moon around the Earth cannot be broken off by the Sun under the condition that calculating with F=GMm/R^2, the attractive force of the Sun on the Moon is almost 2.2 times that of the Earth. We found that the planet and moon are unified as one single gravitational unit which results in that the Sun cannot have the force of F=GMm/R^2 on the moon. The moon is moved by the gravitational unit orbiting around the Sun. It could indicate that the gravitational field of the moon is limited inside the unit and the gravitational fields of both the planet and moon is unified as one single field interacting with the Sun. The findings are further clarified by reestablishing Newton’s repulsive gravity.

In polymer-based dental composites, wear is a three-body wear system mainly abrasive, because of the food particles and wear products suspended in the oral cavity, which are transferred to the microcavities of the surface of the replacements. Due to this fact, the incorporation of nanodiamond as reinforcement in these polymer matrix composites, which promotes the creation of a solid lubricant tribofilm surface could be advantageous. When nanodiamond is added in contents of 0.8, 1.6 and 3.2 wt %, an increase in microhardness from 95 up to 420 %, compared to the one of neat BisGMA/TEGDMA, is observed. Additionally, the incorporation of a content of 1.6 wt % is enough to cause a diminution of ~78 % in the friction coefficient and a reduction of the specific wear rate and Archard’s coefficient of ~50 %. Nevertheless, the addition of relatively high contents reduces the effectiveness of photoinitiation and UV-curing, which is related to the scattering and absorption of UV radiation by ND.

Mathematics first is a logical system. But, the Mathematics Crisis showed that we cannot have a logical self-consistent mathematics system. Physics is a kind of applied science. It first need be valid to invent new production and understand new observation. In one theory of physics, the studied object is usually defined as an abstract concept. For example, the Sun is defined as a point particle in the Newtonian theory of gravity. In different theories of physics, the abstract concept for a same object is different. It determines that logical self-consistent is not needed for different theories of physics. Physics only can be developed from observation and experiment. But, in the past a century, physicists’ main attempt is to have a standard model, such as the Grand Unification or the Theory for Everything, from mathematics derivation. Therefore, the current attempt to develop new physics is questioned. In recent, the strong gravitational field was studied. It is found that the fundamental physical constants which are measured in the weak field shall be varied in a super strong field. For the two reasons, modern physics need be advanced greatly.

Schistosoma infection in snails can be monitored by microscopy or indirectly by sentinel mice. As both these approaches sometimes miss infections, more sensitive tests are needed, particularly in low-level transmission settings. In this study, the loop-mediated isothermal amplification (LAMP) technique, designed to detect a specific 28S ribosomal S. japonicum gene with high sensitivity, was compared to microscopy using snail samples from 51 areas endemic for schistosomiasis in five Chinese provinces. The results were compared with those by polymerase chain reaction (PCR) adding DNA sequencing as a reference when needed. The testing of pooled snail samples showed that a dilution factor of 1/50, i.e., one infected snail plus 49 non-infected ones, would still result in a positive reaction after the recommended number of amplification cycles. Testing a total of 232 pooled samples, emanating from 4,006 snail specimens, with the LAMP assay showed a 6.5% rate of infection, while traditional microscopy found only 0.04% positive samples in the same materials. Parallel PCR analysis confirmed the diagnostic accuracy of the LAMP assay, with DNA sequencing even giving LAMP a slight lead. Microscopy and the LAMP test were carried out at local schistosomiasis-control stations demonstrating that the potential of the latter assay to serve as a point-of-care (POC) test with results available within 60–90 minutes, while the more complicated PCR test had to be carried out at the National Institute of Parasitic Diseases (NIPD) in Shanghai, China. In conclusion, LAMP was found to be clearly superior to microscopy and as good as, or better, than PCR. Application of LAMP testing would be useful for surveillance and risk prediction as it requires less time than other techniques and can be used under field conditions, which improves and accelerates schistosomiasis control.

Dynamic time history analysis is a powerful and effective method to study the seismic response of tunnels, the dynamic time history to analyze the seismic response of the tunnel in the typical subway in this paper. The analysis results show that the overall levelling of the tunnel will not affect the tunnel too much, and the seismic response of the tunnel is mainly related to the relative displacement of the ground around the tunnel. The analysis results show that the internal force of the tunnel and the tunnel inclination have a good linear relationship, and the tunnel inclination can be used to measure the magnitude of the seismic response of the tunnel.

During deep-sea mining, the heavy metals pollutants can cause contamination in the marine environment. In this paper, the multiphasic coupling model is established to describe the heavy metal migration process during deep-sea mining, which takes the effects of convection-diffusion, adsorption-desorption and sedimentation-resuspension of heavy metals in the aquatic environment into full consideration. Due to the advantages of Lattice Boltzmann method, it is adopted to numerically solve the multiphasic coupling model and achieve the simulation for heavy metal migration process during deep-sea mining. In addition, taking Cadmium as an example, the concentration variations are discussed and analyzed in detail. Based on the established model and Lattice Boltzmann method, the concentration distribution of heavy metals can be accurately described to provide the reasonable bases for the evaluation of marine environmental protection.

Mitochondrial alternative oxidase 1a (AOX1a) plays an extremely important role in critical node of seed viability. However, the regulatory mechanism is still poorly understood. The study aims to identify regulatory mechanisms by comparing between OsAOX1a-RNAi and wild type (WT) rice seed during artificial aging treatment. Gain weight and P50 significantly decreased in OsAOX1a-RNAi rice seed, indicating that there might be impaired in seed development and storability. Compared to WT seeds in the 100%, 90%, 80%, and 70% germination, respectively, NADH and succinate-dependent O2 consumption, the activity mitochondrial malate dehydro-genase and ATP contents were decreased in OsAOX1a-RNAi seeds in the 100%, 90%, 80%, and 70% germination, respectively, indicating that mitochondrial status in the OsAOX1a-RNAi seeds after imbibition was weaken than the WT. In addition to, the reduced abundance of complex I N and P module subunits might showed that the capacity of mitochondrial electron transfer chain was significantly inhibited in the OsAOX1a-RNAi seed at critical node of seed viability. Above results might indicated that the ATP production was impaired in OsAOX1a-RNAi seeds during ageing. Therefore, we conclude that the activities of mitochondrial metabolism and alternative pathways were severely inhibited in OsAOX1a-RNAi seeds at critical node of viability, which was leading to accelerate the collapse of seed viability. The precise regulatory mechanism of the alternative pathway at the critical node of viability still needs to be further analyzed.

Technology has transformed aeroponics, providing numerous advantages over traditional soil-based agriculture. However, there remains a need for comprehensive information on the types of technology employed in aeroponics and their impact on the growing process. This paper presents a literature review of 47 studies published between 2012 and 2023 in peer-reviewed journals and conference proceedings. As a result, it identifies the status and tendencies in the usage of technology in aeroponics as well as the main opportunities and challenges. Furthermore, this paper introduces the Technology Adoption and Integration in Sustainable Agriculture (TAISA) model. TAISA is a model that identifies the degree of technology integration in any sustainable agriculture system to determine how technology affects production and quality. Overall, the systematic review suggests that technology can play a vital role in improving the efficiency and sustainability of aeroponic farming. However, careful consideration must be given to the costs and technical requirements associated with using these technologies. Moreover, the TAISA model reveals that technology has primarily been employed in creating new processes that are not possible to implement without the help of technology. Therefore, we conclude that technology use has taken root in aeroponics and can be promoted to improve sustainable agriculture.

The new observations, including the fastest star, the circular velocity curve of the Milky Way and the fast galaxy bar, should imply that the mass of the center of the Milky Way is almost 10¹¹ times the mass of the Sun.

While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to investigate the impact of built environment factors on ride-hailing travel demand, including their non-linear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. population density, land use mix and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.

With the daily increasing demands for higher-quality services, mobile Internet of Things (MIoT) are facing various challenges, such as communication security, availability, scalability, etc. Its changing topology features bring a lot of trouble when solving the above problems. Though the evolved cellular network is expected to bring promising solutions, some inherent problems in traditional MIoTs may keep hindering its development. Thus, to overcome above problems, we propose a software-defined MIoTs-based model providing communication security and privacy protection under emergencies. In our scheme, the control plane is responsible to compute routes for online learning devices (OLDs), and forward entries for switches. Taking use of information that OLDs and facilities collect, controller is able to coordinate the overall situation. To ensure the authenticity and reliability of messages sent by OLDs, signature and authentication should be provided. We also introduce an emergency-dealing system. It transplants the multicast technology into software-defined MIoTs to generate a Steiner Tree among impacted nodes, so that OLDs will be informed as soon as emergency happening. The security analysis proves that our scheme is able to ensure the communication security in software-defined MIoTs. The evaluation of performance indicates that our scheme outperforms other existing schemes.

The article describes the rational for inhibition of the angiotensin-converting enzyme 2 (ACE2) pathways as specific targets in patients infected by SARS-CoV-2 in order to prevent the establishment of positive feedback loops triggered by COVID-19 in some predisposed subjects. Making use of a large quantity of published reports in which human/rodent ACE2 pathway inhibitors were administered in vivo, it is hypothesized a possible therapeutic pharmacological intervention through an inhibition strategy of the zinc metalloprotease ACE2 and its downstream pathway for SARS-CoV-2 patients. Of even more interest, metal (zinc) chelators and renin inhibitors (both FDA approved drugs) may also work alone or in combination in inhibiting the positive feedback loops, initially triggered by COVID-19 and subsequently sustained by hypoxia independently on viral trigger, when both arms of renin-angiotensin system (ACE2 and ACE) are upregulated, leading to critical, advanced and untreatable stages of the disease.

The high-quality development of urban-rural integration is a vital component of China's economy, serving as a strategic response to the in-depth deployment of new urbanization and rural revitalization strategies. This integration is underscored by the promotion of entrepreneurship in agriculture, rural areas, and among farmers, which acts as a driver for fostering high-quality development in urban-rural areas through market competition. The resulting influx of resources and commodities with similar characteristics plays a crucial role in bridging the urban-rural divide. To maintain objectivity, it is important to adopt a balanced approach and refrain from using biased language. This study delves into the connotation and internal mechanism of the high-quality development of urban-rural integration, centering on the principle of 'quality'. Specifically, it examines the role of entrepreneurship in agriculture, rural areas, and among farmers, and evaluates its direct impact and transmission mechanism. The research employs balanced panel data from 41 cities in the Yangtze River Delta region spanning from 2003 to 2021. The findings reveal that entrepreneurship in agriculture, rural areas, and among farmers serves as a catalyst for promoting high-quality urban-rural development through the construction of a virtuous cycle, whereby resource factors and commodity services are exchanged. Moreover, this deep integration yields diverse effects across economic, spatial, demographic, social, and ecological domains, underscoring the necessity of continued support for entrepreneurship in agriculture, rural areas, and among farmers to facilitate a seamless urban-rural economic cycle and fully realize the benefits of high-quality development.

This article presents a combined route and path planning strategy to guide Skid-Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity, and ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrated the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

In the development of automatic control, PID control is the oldest of the basic control mode, the control algorithm is the most widely used in engineering, especially the applied research of robustness has extensive engineering practical value. In this paper, based on the principle of the internal model PID parameter setting method was studied, through different approximate processing method, derived the three different formula of PID internal model setting and simulation verify the effectiveness of the algorithm. At the same time, the robustness of the controlled process under different parameter perturbation is theoretically analyzed and simulated.Finally, the paper summarizes the whole paper and looks forward to the future development trend.

A facilitative transport system exists on the blood brain barrier (BBB) that has been tacitly assumed to be a path for glutamate entry to brain. But glutamate is a non-essential amino acid whose brain content is much greater than plasma, and studies in vivo show that glutamate does not enter brain in material quantities except in those small regions with fenestrated capillaries (circumventricular organs). The situation became understandable when luminal (blood facing) and abluminal (brain facing) membranes were isolated and studied separately. Facilitative transport of glutamate and glutamine exist only on the luminal membranes whereas Na+-dependent transport systems for glutamate, glutamine and some other amino acids are present only on the abluminal membrane. The Na+-dependent cotransporters of the abluminal membrane are in a position to actively transport amino acids from the extracellular fluid (ECF) into the endothelial cells of the BBB. These powerful secondary active transporters couple the energy of the Na+-gradient to move glutamate and glutamine into the ECF whereupon glutamate can exit to blood on the luminal facilitative glutamate transporter. Glutamine may also exit brain on a separate facilitative transport system that exists on the luminal membranes or glutamine can be hydrolyzed to glutamate within the BBB thereby releasing ammonia that is freely diffusible. The γ-glutamyl participate cycle participates indirectly by producing oxoproline (pyroglutamate) that stimulates almost all secondary active transporters yet discovered in the abluminal membranes of the BBB.

In recent, Chae found that, at weak gravitational acceleration g_N≲10⁻⁹ms⁻², the observed orbit of the wide binary stars is not accordant with current theory of binary star. Here, we present, this finding should be an observational evidence for that Newtonian theory of orbit perturbation is still valid in galactic scale.

In any region of a space, the gravitational field cannot be eliminated. The speed of light in a vacuum has never been observed and cannot be observed with current technology. Till now, only the speed of light in a gravitational field has been observed. It results in that all the observed speed of light are always being varied by the gravity. Here, it is presented that light could be dispersion in a gravitational field analogous to the dispersion of light in the Newtonian prism experiment. The relativistic mass density on the surface of a neutron star is on the level of 10¹⁷kg/m³ while on the surface of the Earth is only 10^-7kg/m³, the effect of the gravitational field acting on the speed of light by a neutron star is much larger than that by the Earth. Therefore, the dispersion of light in strong gravitational field could be generally observed from the image of a star and it should have been observed through the double-image gravitational lensing and the spectroscopic binary system. And, it should affect the observed numbers, spectrogram, size and distance of the celestial objects.

It is discovered that the gravitational field on the surface of a neutron star is with a relativistic mass density of 2.65*10¹⁶~5.87*10¹⁸kgm^-3 which can be larger than the mass density of the neutron star (~10¹⁷kgm^-3). And, the total relativistic mass of the gravitational field of the Sun is ~10⁷ times the mass of the Sun. For different stars, the relativistic mass of the gravitational field is larger as the mass density of the star is larger. In the Milky Way, the total relativistic mass of the gravitational fields is much larger than the total mass of the stars, planets and gas. And, the relativistic mass density of the observed strongest magnetic field is 2.17*10⁸kgm^-3. This discovery should be a new frame to understand the fundamental problem of physics.

(1) Background: AI technology has been deeply applied to online shopping platform to provide more accurate and personalized services for consumers. It is of great significance to study the different functional experience of AI for consumers to improve the current application status of AI technology.(2)Method: Based on the "S-O-R" model, this study divided the AI technology expe-rienced by the consumers of online shopping platform into accuracy, insight and interaction experience. Takes the perceived value as the mediating variable from the prospect of perceived utility value and perceived hedonic value. This article use empirical research method to analyze the effect of three dimensions of online shopping AI experience to research the internal influence mechanism of consumers purchase intention. (3) Results:① The accuracy, insight and interaction experience of AI marketing technology have a significant positive impact on consumers' per-ceived utility value and hedonic value respectively; ②Both of the perceived utility value and perceived hedonic value obtained by AI technology experience can promote the formation of consumers' purchase intention; ③ The perceived hedonic value was better than perceived utility value to promote the consumers' purchase intention; ④The results of multi group analysis show that some younger and less experiences consumers groups prefer the pleasure experience such as shopping desire stimulation, shopping process relaxation and pleasure that AI marketing brought. However, utilitarian value cannot promote this kind of consumers' purchase intention. (4) Con-clusions: Perceived utility value and perceived hedonic value can be the intermediary between AI technology and consumers' purchase intention.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

Today's city needs more than ever methodological devices that ensure a specific urbanism, in keeping with the complexity of relationships that occur in the urban space. Networks or systems of diverse nature must be activated to achieve a certain success of the city, even more so in its fragile central fabrics. The study is focused on the historic neighborhoods of the city of Jaén (Spain), which have a high vulnerability index. The aim is to establish a strategy to revitalize their urban and social capital and improve their articulation with the rest of the urban and territorial structure. A methodological proposal for the analysis of vulnerability factors and an urban and cartographic analysis of the public space is addressed through an interweaving of a triple network: environmental, social, and urban. For this purpose, the configuration of this overlapping network of analysis and formulation of criteria is presented, which considers the urban multiplicity and promotes new dynamics for the integrated urban regeneration of the area and for the activation of public and collective urban spaces.

Efficiency serves as a crucial metric in assessing the advancement of scientific research for universities. Reasonable inputs of human resources, finances, materials, and other resources, along with high-quality research output, are important guarantees for the research efficiency of Chinese universities. This study delves into the patterns of changes of research efficiency among universities across the 31 provinces and municipalities of China from 2012 to 2022. By scrutinizing input-output perspectives and utilizing data sourced from the China Statistical Yearbook, the analysis unveils prevailing scenarios of input redundancy and output insufficiency in research efficiency across universities. The static and dynamic trends in research efficiency were further investigated by employing a static non-parametric Data Envelopment Analysis (DEA)-BCC model and a dynamic Malmquist index. Key findings from the analysis from 2012 to 2022 include: (1) notable growth in investments in teaching, research equipment assets, and research and development funds among nationwide universities; (2) a steady increase in the mean value of technical efficiency (TE) from 0.97 to 0.99, indicating continuous enhancement in overall efficiency in research input resource allocation across the provinces; (3) the mean Malmquist productivity index of 1.285, with an average annual increase of 28.5%, signifying a consistent improvement in university research efficiency throughout the study period; and (4) the provinces experiencing varying degrees of input redundancy or output insufficiency were selected to suggest the need for tailored strategies in resource allocation. The study underscores the importance of a holistic evaluation system involving stakeholders from government, universities, and society to optimize research efficiency and promote scientific innovation and social development.

Splines and quasi-interpolation operators are important both in approximation theory and applications. In this paper, we construct a family of quasi-interpolation operators for the bivariate quintic spline spaces S₅³ (∆_mn⁽²⁾). Moreover, the properties of the proposed quasi-interpolation operators are studied, as well as its applications for solving two-dimensional Burgers’ equation and image reconstruction. Some numerical examples show that these methods, which are easy to implement, provide accurate results.

Objective: The aim of this study is to explore what inside the lipid lump obtained from skin. Try to understand the reasons for these physiological functions. In this paper, the lipid droplets or lumps was aimed to shed light on, figuring out what kind of cracks from metabolism were embedded inside. Results: A novel method was introduced for the detection of pathogens within lipid droplets extracted from acne or inflamed pores, as well as normal pores, using microscopic analysis. Through this technique, the presence of cracks from our body within these lipid droplets were identified. Methods: Observations were conducted using a microscope, with samples placed on slides and grind like blood smear. To enhance the transparency of the samples, tween-20 was utilized. This reagent was first time to be used for observing such big lipid lump of skin secretion. The evidences were recorded using the DMi8 camera from Leica.

In the present work, a predictive modelling and optimization with the adaptive network based fuzzy inference system(ANFIS) modelling of mechanical properties of laser-coated NB/SiC/Ni welds was studied based on Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties was taken as the output of the ANFIS. The microstructure of welds were analysed using scanning electron microscopy with an energy dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi's design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

The research aims to examine the extent to which the perceived organizational support (POS) predicted career related self and environmental exploration via the mediation effect of career adaptability. Multi-group comparisons based on the gender, majors and places of origin were also performed to evaluate the differences among these variables. An internet survey was conducted to collect empirical data from 611 Chinese undergraduates. Structural equation modeling (SEM) was conducted to demonstrate the proposed relations in the model and multiple group analysis. Clearly supported was the direct relations between POS and career related self and environmental exploration and the mediation effect of career adaptability. The results also showed that gender and major are the moderating variables for the proposed model, and no significance difference among the rural and urban subgroups was found. POS could be effectively delivered to students in general, marginalized social groups, such as females and those majored in the humanities and social sciences in particular. Four dimensions of career adaptability (concern, control, curiosity and confidence) could also be targeted at in order to promote students’ sustainable employment. The findings provide a better understanding of the career construction model by incorporating the contextual factor in a collective cultural environment.

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

This preprint is focused in the presence of plastics and microplastics in food. We will discuss how many we eat, and how they arrive to the food, and why. We will treat many other things, such as the waste treatment in Europe and in Spain, with updated data; how much plastic waste is generated; what are microplastics and how they are analyzed, I will tell about the experience we have at the University of Alicante (UA); how they can be removed and we will estimate how many we eat over the course of a year.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

Recurrent pregnancy loss (RPL) affects around 2 % of women of reproductive age. Primary RPL is defined by ≥2 pregnancy losses and no normal birth delivery. In secondary RPL, the losses are after a normal pregnancy and delivery. Most cases have no clear aetiology, although primary cases are the most complex. Several gene polymorphisms have been associated with RPL. Some polymorphisms are increased in women of Asian or Caucasian race. In admixed RPL populations, the information on possible genetic links is scarce and contradictory. The study aimed to assess the frequency of two SNP in different enzymes involved in medical conditions observed during pregnancy. Microsomal epoxy hydrolase (mEPH) is involved in detoxifying xenobiotics. It is present in ovaries, and it is hormonally regulated. The endothelial nitric oxide synthase forms nitric oxide from arginine and is involved in vascular tone. Two SNPs, rs1051740 and rs1799983, were assessed. The study included 26 controls and 63 primary RPL patients. Significant differences were observed in the frequencies of both SNP (p<0.05). The frequency of mutated alleles was higher in patients. Double mutated homozygotes were encountered only in RLP patients (p<0.05). Genetic polymorphisms rs1051740 and rs1799983 may be involved in primary RPL in the Venezuelan admix population. Genetic studies could provide crucial information on the aetiology of primary RPL.

The economic, social and environmental benefits generated by the use of urban public transportation infrastructure constitute a complex dynamic urban public transportation infrastructure utilization benefit system. This paper evaluates the coupling coordination among these three benefits taking four Chinese autonomous municipalities as an example. These four cities have large-scale urban public transportation infrastructures but their utilization has many serious problems. The basic function of urban public transportation infrastructure has not been fully played in these cities. Whether the different benefits of urban public transportation infrastructure have been developed in harmony or not is unclear. We analyzed the coordinated development among three benefits by constructing coupling coordination degree model and used Gini coefficient to study the difference of coordinated development among three benefits of four cities. The result shows that the levels of coordinated development among three benefits of urban public transportation infrastructure were lower in these four cities and have positive correlation with it of urban public transportation infrastructure utilization benefit. Raising the level of urban public transportation infrastructure utilization benefit is the most crucial solution of promoting the coordinated development among three benefits.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

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.

Abstract: The population issue has been increasingly urgent in China in recent years. Fertility desire is an essential component of fertility research. Studies on fertility desire mainly contain two aspects: socio-economic structure and individual family[1]. The psychological structure of individuals in individual families is the primary factor of fertility desire. Based on the parasocial relationship theory, this study analyzed the changes in real behavioral intention achieved by players through the identification and attachment to avatars while constructing parasocial relationships[2]. The results indicate that character attachment can significantly enhance the variables of game concentration and player self-identity, actively mediates parasocial emotions and parasocial cognition, and ultimately increases fertility desire. As a theoretical contribution, this study provides a comprehensive interpretation of the psychology of Chinese players in a Simulation Game and explores the theoretical connections among role attachment, role identity, and parasocial relationships in virtual game players.

A construction for the systems of natural and real numbers is presented in Zermelo-Fraenkel Set Thoery, that allows for simple proofs of the properties of these systems, and practical and mathematical applications. A practical application is discussed, in the form of a Simple and Linear Fast Adder (Patent Pending). Applications to finite group theory and analysis are also presented. A method is illustrated for finding the automorphisms of any finite group $G$, which consists of defining a canonical block form for finite groups. Examples are given, to illustrate the procedure for finding all groups of $n$ elements along with their automorphisms. The canonical block form of the symmetry group $\Delta_4$ is provided along with its automorphisms. The construction of natural numbers is naturally generalized to provide a simple and sound construction of the continuum with order and addition properties, and where a real number is an infinite set of natural numbers. A basic outline of analysis is proposed with a fast derivative algorithm. Under this representation, a countable sequence of real numbers is represented by a single real number. Furthermore, an infinite $\infty\times\infty$ real-valued matrix is represented with a single real number. A real function is represented by a set of real numbers, and a countable sequence of real functions is also represented by a set of real numbers. In general, mathematical objects can be represented using the smallest possible data type and these representations are calculable. In the last section, mathematical objects of all types are well assigned to tree structures in a proposed type hierarchy.

In the paper, the authors discover several series identities involving the Catalan numbers, the Catalan function, the Riemanian zeta function, and the alternative Hurwitz zeta function.

In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

Soil extracellular enzyme activity (EEAs) and enzymatic stoichiometry (ES) can provide a crucial indication of changes in soil ecosystem's nutrient availability and the microbial resource limitations. However, the changing characteristics of soil EEAs and ES at different stages of the native succession process and their key drivers are unclear. In order to investigate the soil EEAs, ES and driving factors of soil under vegetation at different succession stages, we adopted the "spatio-temporal substitution" method to collect the surface soil of bryophyte community, herbaceous community, shrub community and tree community in the new volcanic lava platform of Wudalianchi Volcanic Nature Reserve. We measured seven soil EEA, including carbon(C)-acquiring enzyme (β-1,4-glucosidase (BG)), N-acquiring enzymes (β-N-acetyl-glucosaminidase (NAG) and leucine aminopeptidase (LAP)) and phosphorus (P)-acquiring enzyme (acid phosphatase (AP)) activities. The length and angle of vectors defined by ratios of enzyme activities (BG/(NAG + LAP) vs. BG/AP) were used to indicate relative microbial investments in C- (length), and N- and P- (angle) acquiring enzymes. Our results showed that the contents of TC, TN, TP, MBC, DOC and NO3-N in shrub community soil were significantly higher than those in bryophyte, herb and tree communities, and increased by 441%, 246%, 137%, 5570%, 12% and 484%, respectively. The highest soil EEA of C-, N- and P-acquiring were found in shrub community, and the soil EEAs/MBC of C-, N- and P-acquiring were the highest in bryophyte community. Enzyme C:N, C:P and N:P ratios increased progressively in the order of bryophyte, herb and shrub community, but the enzyme C:N, C:P and N:P ratios of tree community were both far less than shrub community. Vector lengths increased progressively in the order of bryophyte (1.16), herb (1.27), tree (1.29) and shrub (1.40), and Vector angles decreased progressively in the order of bryophyte (49.15°), herb (45.65°), Tree (45.31°) and shurb (44.54°), suggested that as succession progresses, soil microbial nutrients transforms from P limitation (angle＞45°) to N limitation (angle＜45°). Redundancy analysis showed that TC, TN, EC and C:N were important drivers of variation in soil EEAs and ES in vegetation at different succession stages. Our findings highlight that the primary succession process cause nutrient limitation transformation. Soil ES might be a sensitive indicator mediated by soil microorganisms to the relative resource limitation at different stages of the primary succession process.

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capabilities in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibility and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering was minimally discussed in literature neither. This paper first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model into generating new game levels within two well-known game settings; as well as validating the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments were also utilized for evaluating the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and spatial results, several potential drawbacks and future enhancement of the established model were outlined, with the aim of maximizing the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

(1) Background: Ectopic pregnancy (EP) is when fertilized ovum implanted outside of uterine cavity. The incidence was 1.5% to 2.0% of all pregnancies [1, 2]. EP decreases fertility ability [3]. This study focused on the pregnancy rate after EP and various parameters influencing pregnancy. (2) Methods: The data was extracted from National Health Insurance Research Database (NHIRD) during 2000-2013. The study group enrolled women with one EP history. Those who did not have EP was put into the control group. Chi-square/Fisher exact test was performed on category variable and t-test was used on continue variables. (3) Results: The EP group had a higher cumulative rate of conceive, 41.55% than control group, 37.14% and 1.16 times higher risk in developing pregnancy (P&lt;0.001). The conceiving rate was lower than the control group in the first 5 years, but higher than the control group in the 10th to 14th years. (4) Conclusions: The fecundability after EP is lower at the beginning, but regained after long term follow.

Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.

Plant protein phosphatase 2Cs (PP2Cs) serve as negative regulators of protein kinase cascades activated in different processes and play important roles in plant development and abiotic-stress-mediated signaling pathways. In this study, a genome-wide study was conducted on the CqPP2C gene family. A total of putative 117 CqPP2C genes were identified. Comprehensive analyses of physical and chemical feature, chromosome localization and subcellular localization were conducted. According to phylogenetic analysis, CqPP2Cs were divided into 13 subfamilies. CqPP2Cs in the same subfamily had similar gene structure, conserved motif and all the CqPP2C proteins had the type 2C phosphatase domains. Gene duplication revealed that segmental duplication was the major driving force for CqPP2Cs expansion and all duplicated CqPP2Cs evolved from purifying selection. The expression of CqPP2Cs in various tissues under different abiotic stresses was analyzed using RNA-seq data. The results revealed that CqPP2C genes were involved in regulating development and stress responses of quinoa. Real-time quantitative reverse transcription PCR (qRT-PCR) analysis of six CqPP2C genes in subfamily A revealed that they were up-regulated or down-regulated under salt and drought treatments. Furthermore, the results of yeast two-hybrid assays revealed that subfamily A CqPP2Cs interacted not only with subclass III CqSnRK2s, but also with subclass II CqSnRK2s. Subfamily A CqPP2Cs could interact with CqSnRK2s in different combinations and intensities to respond to various biological processes and stresses. Overall, our results will be useful for understanding the functions of CqPP2C in regulating ABA signals and responding to abiotic stress.

The influence of instrumental variables on farmers ‘perceptions using rotation fallow as sustainable agriculture practices had been the aim of our research. By using simultaneous equations models, this study tried to understand the relationship between green manure farmer’s perceptions on using rotation-fallow and planting green manure in Guangxi Province, south of China. The results showed that subsidizing green manure farmers based on standard subsidy by the unit of green manure sown area; the training on the green manure planting technology or the demonstration preaching; the green manure farmers preference for the kinds of subsidy (funds, seeds, and mechanical services) were the mostly great drivers of the program of restoring planting green manure in the study area. These incentives measures must be enhanced as priority to restore definitely green manure planting. However, the findings of the study showed that the total income of the 2017 year, the area of an agricultural field of household and the household members (16–65 years old), providing labor in the agricultural field, have some influences on planting green manure and on farmer’s perceptions on using rotation-fallow as sustainable agriculture practices. Brief, the unobserved factors which influenced the endogenous factors play an important role to enhance widely the impact of farmers’ perceptions on planting green manure.

In this paper, we proposed a high accurate and stable Legendre transform algorithm which can reduce the potential instability for very high order at a very small increase in the computational time. The error analysis of interpolative decomposition for Legendre transform is presented. By employing block partitioning of Legendre-Vandermonde matrix and butterfly algorithm, a new Legendre transform algorithm with computational complexity O(N(logN)^2/loglogN) in theory and less than O(N(logN)^4) in practical application is obtained. Numerical results are provided to demonstrate the efficiency and numerical stability of the new algorithm.

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

Herein, the mechanical properties and carbonation durability of engineered cementitious composites (ECC) were studied. For cost-efficient utilization of ECC materials, polypropylene (PP) and hydrophilic polyvinyl alcohol (PVA) fibers were employed to cast different types of specimens. The compressive strength, Poisson’s ratio, strength-deflection curves, cracking/post-cracking strength, impact index, and tensile strain-stress curves of the two types of ECC materials, with different fiber contents of 0 vol%, 1 vol%, 1.5 vol% and 2 vol%, were investigated by conducting compressive tests, four-point bending tests, drop weight tests, and uniaxial tensile tests. In addition, the matrix microstructure and failure morphology of the fiber in the ECC materials were studied by scanning electron microscopy (SEM) analysis. Furthermore, carbonation test and steel corrosion after carbonization were employed to study durability resistance. The results indicated that for both PP fiber- and hydrophilic PVA fiber-reinforced ECCs, the compressive strength first increases and then decreases as fiber content increases from 0 vol% to 2 vol% and reaches the maximum at 1 vol% fiber content. The bending strength, deformation capacity, and impact resistance show significant improvement with increasing fiber contents. The ECC material reinforced with 2 vol% PP fiber shows superior carbonized durability with maximum carbonation depth of only 0.8 mm.

Air quality is a topic of growing relevance on the global agenda. Two key indicators of air quali-ty, nitrogen dioxide (NO2) and carbon monoxide (CO), represent significant hazards to human health and the environment putting the sustainability of natural resources at risk. Therefore, it is imperative to regularly monitor and reduce atmospheric pollutant concentrations to mitigate their harmful consequences. This study presents an analysis of NO2 total and CO emissions in Argentina, utilizing remote sensing data. The research aims to determine the spatiotemporal distribution of NO2 and CO emissions in Argentina from 2019 to 2021. Subsequently, it seeks to establish the influence of land uses and land cover on the emission of NO2 and CO through dif-ferent climatic, anthropic, and natural indicators. The study was carried out in Argentina during the period 2019-2021, where random points were placed for the different land covers with a total of 800 points surveyed. The year with the highest CO concentration (mol/m-2) was 2020. The values were highest for tree covers and herbaceous wetland coverage in the northern part of the country. For total NO2, the highest concentrations were reached during the years 2020 and 2021. Regarding its distribution, throughout the evaluated period, the highest concentrations of total NO2 were found in the built-up and cropland coverages, with the capital of the country and the northern region of the Buenos Aires province being the most affected areas. In addition, the concentration of CO was influenced by climatic variables (atmospheric pressure, wind speed, maximum environment temperature, Palmer index), natural (height, humidity, NDVI), and ur-ban variables (distances to mining extraction, airports, power plants, urban index) for the differ-ent uses and land covers. Finally, the concentrations of total NO2 were influenced by climatic variables (Palmer index and wind speed), natural (height and NDVI), and urban variables (dis-tance to airports, power plants, industries, service stations, and open dumpsites) for the differ-ent uses and land cover. This study contributes to sustainable environmental management, ena-bling the formulation of effective strategies for mitigating emissions and promoting the long-term health and well-being of communities.

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Reticulate evolution through the interchanging of genetic components across organisms can impact significantly on the fitness and adaptation of species. Bread wheat (Triticum aestivum subsp. aestivum) is one of the most important crops in the world. Allopolyploid speciation, frequent hybridization, extensive introgression, and occasional horizontal gene transfer (HGT) have been shaping a typical paradigm of reticulate evolution in bread wheat and its wild relatives, which is likely to have a substantial influence on phenotypic traits and environmental adaptability of bread wheat. In this review, we outlined the evolutionary history of bread wheat and its wild relatives with a highlight on the interspecific hybridization events, demonstrating the reticulate relationship between species/subspecies in the genera Triticum and Aegilops. Furthermore, we discussed the genetic mechanisms and evolutionary significance underlying the introgression of bread wheat and its wild relatives. An in-depth understanding of the evolutionary process of Triticum species should be beneficial to future genetic study and breeding of bread wheat.

Leasing and recycling are important issues to achieve low-carbon sustainable development of construction machinery industry, however there are some dilemmas bothering which is caused by excessive restriction of product service life. Focusing on the problem of pricing and coordination, a two-stage model of leasing closed-loop supply chain based on the Industrial Internet platform practical application was proposed. Under the centralized and decentralized decision-making scenarios, the price of leasing and selling, maintenance effort and recovery rate are calculated respectively. By using the Shapley value method to optimize the selling price, and the two-part pricing contract to calculate the compensation fee, the global profit is reasonably distributed, while the supply chain achieving Pareto optimality. The performance of different contracts on the improvement of supply chain profit was investigated. And the correlation among parameters was also explored through sensitivity analysis and numerical simulation. The findings showed that maintenance and recycling of construction machinery can be improved with the advantage of the Industrial Internet platform, while achieving the supply chain coordination by contracts. The enhancement of maintenance effort can decrease the remanufacturing cost which is positively correlated to the selling price and leasing price of product, furthermore promoting the recovery rate of used products. In the future, reasonable control of platform cost can help improve the recycling of construction machinery.

This article is about school evaluation. The objective was to explain critically the mediating relationships between educational evaluation and the production of school knowledge from the perspective of the Dialectical Historical Materialism. With this interest, a documentary analysis was made, obtaining as result four explanatory categories: production, subsumption, distribution and exchange of knowledge, which form a concrete totality with distinctions and antagonistic relationships between them, limited by their opposite. It is concluded that the evaluation as a mediating form lacks antagonistic pair and consequently of control by opposition, emerging from the school daily as autonomous and sovereign entity, oppressive of school subjects.

In response to escalating global concerns regarding climate change, nations worldwide have been undertaking structural adjustments in their energy sectors to promote diversification and decarbonization. This study conducts a comparative analysis of the policies and measures adopted by major economies, including the United States, the United Kingdom, Japan, Germany, and France, focusing on climate change, energy structural adjustments, and carbon emission reduction. By analyzing recent carbon emission and highlighting achievements in renewable energy promotion, green industry development, industrial restructuring, and energy production decarbonization, this study aims to provide valuable insights and formulate strategies for constructing an innovative energy system that ensures economic and social development while facilitating rapid growth and wide applications of low-carbon energy sources.

To explore the relevant factors that affect the efficacy of consciousness-regaining therapy (CRT)for prolonged disorder of consciousness(pDOC),a retrospective analysis was conducted on the case data of 114 patients with pDOC admitted to department of functional neurosurgery of Tianjin Huanhu Hospital from January 2019 to January 2022. Basic information on the cases,data on pDOC disease assessment, consciousness-regaining therapy methods, and efficacy evaluation were collected. A total of 114 patients were grouped and compared based on the efficacy at the end of treatment. Among them, there were 61 cases in the ineffective group and 53 cases in the effective group. There was no statistically significant difference (P&gt;0.05) between the two groups in terms of gender, age, etiology, acute cerebral herniation, emergency craniotomy surgery, emergency decompressive craniectomy, time from onset to start of consciousness-regaining therapy , and duration of consciousness-regaining therapy(P&gt;0.05). However, there were statistically significant differences (P&lt;0.05) in terms of secondary hydrocephalus, , consciousness-regaining therapy methods, CRS-R grading before treatment, and GOSE score at 6 months after treatment. Binary logistic regression analysis showed that the type of therapy (OR=0.169, 95% CI: 0.057-0.508) was a related factor affecting the efficacy of consciousness-regaining therapy (P&lt;0.05).Compared with non-invasive therapy, personalized awakening therapy using various invasive consciousness-regaining therapy methods can help improve the efficacy of therapy for pDOC.

The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

Attracting and retaining international students has been widely discussed in higher education settings. Increasing the number of international students has become an indispensable strategy for national and global competition. This study focuses on effective strategies and international students' issues regarding satisfaction in the most popular STEM (science, technology, engineering, and mathematics) programs. We designed a structural equation modeling (SEM) method to determine the effect of institutional mediation between push factors and satisfaction factors for the development of better strategies by which to attract and retain international students. Taking Taiwan as an example, this study employed a self-designed questionnaire to collect data: 485 degree-seeking international students in STEM programs were invited and successfully participated in this study during spring 2021. IBM SPSS 26 and AMOS 26 (Analysis of Moment Structure) were used to carry out the data analysis. We employed reliability, factor, and SEM analyses. This study assumed that the impact of push factors can be modified by institutional situations and result in international students’ satisfaction with their learning and environment and regarding migration policy. The results revealed that the predictors, mediation, and criteria were significant at the 0.05 or 0.01 levels. The findings suggest that push factors impact international students’ satisfaction when using institutional leadership and international strategy. The results of the bootstrap with a generalized least square method showed that the SEM model fit in 2000 bootstrap samples. The effect of institutional mediation can provide useful information for STEM programs to boost their future recruitment and retention strategies. This study provides an innovative approach to the detection of issues among international students in specific programs. These findings can enrich our knowledge regarding attracting and retaining global students in higher education settings.

Bacterial biofilms can cause widespread infection. In addition to causing urinary tract infections and pulmonary infections in patients with cystic fibrosis, biofilms can help microorganisms adhere to the surfaces of various medical devices, causing biofilm-associated infections on the surfaces of biomaterials such as venous ducts, joint prostheses, mechanical heart valves, and catheters. Biofilms provide a protective barrier for bacteria and provide resistance to antimicrobial agents, which increases the morbidity and mortality of patients. This review introduces the formation process and drug resistance mechanism of biofilms in detail, and further summarizes the main characteristics of clinical persistent infection caused by biofilms and the many methods of treating biomaterial-related biofilm. This provides ideas and directions for the development of new biofilm infection strategies related to therapeutic materials.

A plant’s early response to pathogen stress is a vital indicator of its disease resistance. In order to study the response mechanism of resistant and susceptible flax cultivars to Fusarium oxysporum f. sp. lini (Foln), we applied RNA-sequencing to analyze transcriptomes of flax with Foln 0.5, 2 and 8 hours post inoculation (hpi). We found a significant difference in the number of differential expression genes (DEGs) between resistant and susceptible flax clutivars. The number of DEGs in the Fusarium-resistant cultivar increased dramatically at 2 hpi, and a large number of DEGs participated in the Fusarium-susceptible cultivar response to Foln infection 0.5 hpi. GO enrichment analysis determined that the up-regulated DEGs of both flax cultivars were enriched such as oxidoreductase activity and oxidation-reduction process. At the same time, the genes involved in diterpenoid synthesis were up-regulated in resistant cultivar, while those involved in extracellular region, cell wall and organophosphate ester transport were down-regulated in susceptible cultivar. KEGG enrichment analysis showed the genes encoded WRKY 22 and WRKY33 which involved in MAPK signaling pathway were up-regulated expressed in S-29 and down-regulated expressed in R-7, negatively regulated the disease resistance of flax; The genes encoded Hsp 90 family which in involved in plant pathogen interaction pathway were up-regulated in R-7 and down-regulated in S-29, which positively regulated the disease resistance of flax; The genes encoded MYC2 transcription factor and TIFY proteins which involved in plant hormone signaling pathway were up-regulated in R-7, and regulated the jasmonic acid metabolism of flax and the signal transduction of plant hormones. Meanwhile seven regulatory genes with the most correlation were screened out, Among Lus10025000.g and Lus10026447.g regulated other genes expressed both in plant hormone signal transduction pathway and MAPK signal pathway. In conclusion, these findings will facilitate further studies on the function of these candidate genes in flax of response to Fusarium stress, and the breeding of disease-resistant flax cultivar.

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

China combines green energy and industrial policy in its power market reform with various policy initiatives, including price support scheme for electricity from renewable sources and subsidies in the push for broader use of greener energy. This study focuses on the impacts of power market reform on the stock price volatility of listed power companies: 1) we use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural break points in stock prices; 2) we analyze the characteristics of stock price volatility based on the GARCH model; 3) we report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. The result suggests three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies. We find that industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run. However, our study also indicates the recent policies related to renewable energy do not have a very significant impact on the power stock market.

Container crane has been widely used for port operation. However, the structure of port container crane is large, which always lead to crane damage under the vibration and strong wind. Therefore, a method for vibration reduction of container crane structure based on particle damping technology is proposed. In this investigation, the scale 1:80 crane model is built, the equivalent mechanical model is also established to preliminary verify the effect of vibration suppression. Furthermore, the dependence of vibration suppression effect on the key parameters of the damper, i.e., filling material, filling rate, particle size, and installation position of dampers are analyzed through experiments. The results indicate that the vibration peak of the crane structure displayed in Simulink oscilloscope is weakened and lags behind, the installation of the damper brings the effect of vibration suppression for the crane, the vibration suppression effect reaches 25%; the crane mode obtains the best vibration suppression effect under the condition that the material filled in the damper has a large density and elastic modulus, sufficient collision space and high collision frequency, and the dampers are installed far away from the crane center of gravity. The optimum parameters of the damper obtained from experiments, 12 mm lead beads, filling rate of 60%, installed in the distal ends within the strength range of the forearm of the crane. It is thus concluded that the particle damping technology has provided an effective way in wind and vibration reduction for container crane.

This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79 respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in-situ protection and ex-situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

Understanding the spatial distribution pattern and evolution characteristics of the elderly population in urban areas is of great significance for the development of urban planning and the implementation of public management policies in the context of rapid aging. Accurately identifying the spatial distribution and evolution characteristics of the elderly population in the city requires a comprehensive analysis of multiple indicators and large-scale data. Taking Wuhan city as an example, this article measures the spatial distribution characteristics and evolution trend of the elderly population from 2000 to 2020 at the street/township level, based on the fifth, sixth, and seventh census data, using methods such as kernel density hotspot detection, spatial clustering analysis, and standard deviation ellipse analysis. The results show that: ① there are significant differences in the aging spatial pattern between the central area and the suburban areas of Wuhan; ② overall, Wuhan's aging rate shows a typical "core-periphery" growth mode in space, while the density of the elderly population has significant spatial aggregation characteristics and shows an evolution trend of "centralized concentration, peripheral outliers, axial development, and near-field growth"; ③the center of gravity of the elderly population remains relatively stable over time.

In the context of freshwater ecosystems, turbidity and suspended solids play crucial roles, with their levels significantly influenced by anthropogenic activities. This study focuses on assessing and monitoring these parameters in Albufera de Valencia using Sentinel-2 imagery. The primary aim is to establish reliable estimation algorithms that can contribute to effective ecosystem management. The study calibrated and validated algorithms for estimating turbidity and suspended solids. The R783×R705/R490 model proved to have the best performance for estimating turbidity and total solids in the Albufera. However, R783/R490 obtained a higher coefficient of de-termination for the organic part, while the R705 band was selected for the inorganic part. However, to achieve better estimates of turbidity and inorganic matter, more research is needed in the future. The implications of excessive suspended solids are underscored, including the depletion of dissolved oxygen, and reduced primary productivity due to limited light penetration and habitat availability. Collaboration between disciplines such as limnology, optics and water chemistry are crucial to advance water quality estimation models in lakes and lagoons such as Albufera. By integrating expertise and approaches from these diverse fields, new knowledge can be gained and the basis for more effective management and conservation strategies can be laid.

Infrared and visible image fusion technologies are used to characterize the same scene by diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to the source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. And the visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.

Ammonium sulfate has been utilized in agriculture; however, there is a dearth of research on its application in maize cultivation subsequent to the implementation of nitrification inhibitors or coating. This study aims to analyze the impacts of various combinations of ammonium sulfate fertilizers on soil nutrients, plant nutrient uptake, yield, and fertilizer utilization efficiency in maize cultivation to ascertain the optimal and stabilized disposal method for ammonium sulfate. A completely randomized design was employed with five treatments (AU, the control using urea; AS, treatment using ammonium sulfate; ASN, treatment using ammonium sulfate with a nitrification inhibitor; ASG, treatment using oil-coated ammonium sulfate; ASD, treatment using oil-humic acid-coated ammonium sulfate). Results showed that: (1) Compared with AU and AS, ASN, ASG and ASD decreased the leaching rates of TN, NH4+-N and NO3-N to the 10-20 cm soil layer, and more residual N was obtained in 0-10 cm soil layer. The first-order kinetic equation Nt=N0(1-e-kt) can better fit the process of nitrogen accumulation and release, and the N release rate constant is AU>CK>AS>ASH>ASN>ASD; (2) Compared with AU and AS treatments, the plant dry weight, grain dry weight, spike width, spike length, and yields of maize increased by 8.85-11.08%, 12.98-14.15%, 2.95-3.52%, 5.50-5.65%, and 43.21-51.10% under ASG treatment respectively. The impact of effective spike number on maize yield was found to be significant, as demonstrated by the path analysis conducted in this study. Furthermore, the accumulation levels of nitrogen, phosphorus, and potassium within above-ground plants significantly increased under the ASG treatment compared to the AU and AS treatments. The partial productivity of nitrogen, phosphorus, and potassium increased by 1.43-1.51-fold under ASG treatment, while grain nitrogen balance, grain phosphorus balance, and grain potassium balance increased by 1.41-1.58-fold, 1.51-1.95-fold, and 1.15-l.96-fold respectively. The oil-coated ammonium sulfate, therefore, exhibited the optimal slow-release effect of nutrients, thereby achieving superior performance in enhancing maize production and efficiency.

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR -CDR) provides a nearly 40-year global dataset that has a potential to fill a gap in long term snow cover fractional monitoring. Our study selects the Qinghai-Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface reflectance at 0.64μm (SR1) and Surface reflectance at 0.86μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm & improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1、 ground snow-depth measurements and ERA5. Results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. Improved algorithm results in better consistency with MOD10A1 than vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2,000 meters or above 6,000 meters, and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. Results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land cover type and topographic factors are important to the algorithm. Our study lays the foundation for the global snow cover percentage product based on AVHRR-CDR，furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

The phenomenon of ordered self-assembly on solid substrates is a topic of interest in both fundamental surface science research and its applications in nanotechnology. The regulation and control of two-dimensional (2D) self-assembled supra-molecular structures on surfaces have been realized through applying external stimuli. By utilizing scanning tunneling microscopy (STM), researchers can investigate detailed phase transition process about self-assembled monolayers (SAMs), providing insight into the interplay between intermolecular weak interactions and substrate-molecule interactions, which govern the formation of molecular self-assembly. This review will discuss the structural transition of self-assembly probed by STM in response to external stimuli and provide state-of-art methods such as tip induced confinement for the alignment of SAM domains and selective chirality. Finally, we discuss the challenges and opportunities in the field of self-assembly and STM.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

Eddy current based approaches have been investigated for a wide range of inspection applications. Dodd-Deeds model and the truncated region eigenfunction expansion (TREE) method are widely applied in various occasions, mostly for the cases that the sample is relatively larger than the radius of the sensor coil. The TREE method converts the integral expressions to the summation of many terms in the truncated region. In a recent work, the impedance of the co-axial air-cored sensor due to a plate of finite radius was calculated by the modified Dodd-Deeds analytical approach proposed by authors. In this paper, combining the modified analytical solution and the TREE method, a new finite region eigenfunction expansion (FREE) method is proposed. This method involves modifying its initial summation point from the first zero of the Bessel function to a value related to the radius of the plate, therefore makes it suitable for plate with finite dimensions. Experiments and simulations have been carried out and compared for the verification of the proposed method. Further, the planar size measurements of the metallic circular plate can be achieved by utilising the measured peak frequency feature.

The pharmacological effects of the fruiting body of Ophiocordyceps sinensis (Os) exerted antioxidant, anti-virus, and immunomodulatory, whereas the anti-inflammatory effects and active components of Os mycelial submerged culture (OMSC) are not known. This study aimed to investigate the active compounds in the fermented liquid (FLOS), hot water (WEOS), and 50-95% (EEOS-50, EEOS-95) ethanol extracts of OMSC and their anti-inflammatory effects and potential mechanisms in LPS-stimulated microglial BV2 cells. The results demonstrated that all of the OMSC extracts could inhibit NO production in BV2 cells. EEOS-95 exhibited the strongest inhibitory effects (41 % inhibitory ability at 500 µg/mL), and its ergosterol, GABA, total phenolic, and total flavonoid contents were significantly higher than those of the other extracts (18.60, 18.60, 2.28, and 2.14 mg/g, p &lt; 0.05, respectively). EEOS-95 also has a strong inhibitory ability against IL-6, IL-1β and TNF-α with IC50 of 617, 277 and 507 µg/mL, respectively, which is higher than that of 1 mM melatonin. The anti-inflammatory mechanism of EEOS-95 seems to be associated with up-regulation of PPAR-γ/Nrf-2/HO-1 antioxidant-related expression and down-regulation of NF-κB/COX-2/iNOS pro-inflammatory expression signaling. Therefore, we hypothesized that EEOS-95 can prevent neurodegenerative diseases, but the effect on the application in vivo should be further investigated.

According to official sources, the numbers of children-to-parent violence (CPV) in the most advanced countries have been on an increasing trend for more than a decade, which generates great social concern. This phenomenon has also aroused enormous interest among researchers, who have identified risk and protective factors related to adolescent CPV in numerous studies. To contribute to a better explanation of CPV, the aim of the present study was to analyze the relationship between family communication problems and CPV, through psychosocial adjustment variables -psychological distress and positive attitude toward the transgression of social norms- in schooled adolescents. The sample consisted of 8115 adolescents enrolled in schools in the State of Nuevo León (Mexico) (51.5% boys, 49.5% girls), whose ages ranged from 11 to 16 years (M = 13.34, SD = 1.04). Structural equation modeling was performed using Structural Equation Modeling Software (EQS). The results showed that family communication problems have a direct and signif-icant relationship with CPV. It was also observed that there is an indirect relationship between both variables, through the relationships of psychological distress and positive attitude towards the transgression of social norms. The multigroup analysis performed showed gender differences in some of these relationships. Finally, the results and their implications in the field of family intervention are discussed.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

In this research, a novel collector cetyl trimethyl ammonium chloride (CTAC) was used to separate hematite from quartz via reverse flotation for the first time. Micro-flotation tests showed that CTAC had a strong ability to selectively collect quartz and a separation of hematite from quartz could be accomplished with a concentration of 0.00263 mmol/L CTAC. Zeta-potential measurements indicated that the positive CTAC+ species could selectively increase the surface potential of quartz, but it had rather a weak effect on the hematite surface. X-ray photoelectron spectroscopy (XPS) detection indicated that CTAC had a stronger binding affinity to oxygen sites on the surface of quartz than that of hematite, resulting in a large amount of CTAC predominantly adsorbed on quartz rather than hematite.

Initial water content significantly affects the efficiency of soil stabilization. In this study, the effects of initial water content on the compressibility, strength, microstructure and composition of a lean clay soil stabilized by compound calcium-based stabilizer were investigated by static compaction test, unconfined compression test, optical microscope observations, environment scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffraction spectroscopy. The results indicate that as the initial water content increases in the range studied, both the compaction energy and the maximum compaction force decrease linearly and there are less soil aggregates or agglomerations, and smaller proportion of large pores in the compacted mixture structure. In addition, for specimens cured with or without external water supply and under different compaction degrees, the variation law of the unconfined compressive strength with initial water content is different and the highest strength value is obtained at various initial water contents. With the increase of initial water content, the percentage of oxygen element tends to increase in the reaction products of the calcium-based stabilizer, whereas the crystalline mineral of the soil did not change obviously.

We present a new penalized method for estimation in sparse logistic regression models with group structure. Group sparsity suggests us to consider the Group Lasso penalty. Being different from penalized log-likelihood estimation, our method can be viewed as a penalized weighted score function method. Under some mild conditions, we provide non-asymptotic oracle inequalities promoting group sparsity of predictors. A modified block coordinate descent algorithm based on a weighted score function is also employed. The net advantage of our algorithm over the existing Group Lasso-type procedures is that the tuning parameter can be pre-specified. The simulations show that this algorithm is considerably faster and more stable than competing methods. Finally, we illustrate our methodology with two real data sets.

Analytical mechanics is the most basic discipline. The basic principles of analytical mechanics should also be applicable to general deformed objects. However, the virtual displacement principle proposed by analytical mechanics is only applicable to particle systems and rigid body systems, but not to general deformed objects. In this paper, the generalized virtual displacement principle of general deformed objects (such as, elastic objects, plastic objects, elasto-platic objects and flexible objects etc ) is derived by the method of analytical mechanics, which is also applicable to particle systems and rigid body systems. First of all, according to the method of analytical mechanics, the external force, internal force, constraint reaction force and elastic recovery force of the deformed object system under the equilibrium state are analyzed, and the concepts of virtual displacement, ideal constraint and virtual work are introduced, and the generalized virtual displacement principle (also called generalized virtual work principle) of deformed objects is proposed; secondly, vector form, coordinate component form and generalized coordinate form of generalized virtual displacement principle of deformed object are given; thirdly, as the application of the principle, the virtual displacement principle of deformed objects in plane polar coordinate system, space cylindrical coordinate system and spherical coordinate system is given; finally, a brief conclusion is drawn. This work unifies the virtual displacement principle of elastic object, plastic, elastoplastic etc deformed object systems and rigid object systems with the basic method of analytical mechanics. It is a basic principle for dealing with the static problems of deformed objects. This work also lays a foundation for the further study of the dynamics of deformed object systems.

In this paper, we consider the fourth-order parabolic equations with integer and fractional order time derivatives with Neumann boundary conditions. The integer order time derivatives are approximated by backward Euler difference quotients, and the fractional order time derivatives are approximated by L1 interpolation. We propose the block-centered finite difference scheme for fourth-order parabolic equations of integer and fractional order time derivatives. We prove the stability of the block-centered finite difference scheme and the second-order convergence of the discrete L2 norms of the approximate solution and its derivatives of every order. Numerical examples are given to verify the effectiveness of the block-centered finite difference scheme.

Edible insects can diversify diets, improve livelihoods, contribute to food and nutrition security, and have a smaller ecological impact. European Union has categorized insects as novel food, and recently, in 2021 and 2022, two species: Tenebrio molitor and Acheta domesticus have been authorized for commercialization. Acceptance and perception of food risk derived from insect consumption vary depending on factors impacting insect consumption acceptability, including neophobic tendencies, gender differences, familiarity, and gastronomic perceptions. The aim of this work was to evaluate the perception and acceptance of edible insects by exploring those factors. The study was carried out on higher education students from Universities from Valencia (Spain). Students recognize insects for their high nutritional value, particularly protein content, and have varying knowledge levels about specific nutritional components. In terms of labeling and marketing, removing health and sustainability benefits from packaging can improve consumer responses. Most respondents prefer clear labeling of insect derivatives, quality certification seals, and complete information about insect content. Students consider marketing and knowledge as significant influencers of insect consumption. In summary, the text highlights the multifaceted nature of insect consumption acceptability. These insights offer valuable perspectives on insect consumption dynamics.

The purpose of this longitudinal study was to determine the effects of a rowing training program on physical fitness and body composition in female breast cancer survivors (56.78 ± 6.38 years). Over six months, the participants (n=40) performed two training sessions per week for an average of 75 minutes, combining resistance and muscle strength exercises with rowing. To evaluate the impact of the program, physical fitness tests and anthropometric measurements were carried out before and after the training protocol. The results show statistically significant improvements in measures such as weight (-1.84 kg), BMI (-0.7 kg/m2), hip (-3.24 cm) and waist circumference (-2.45 cm). In addition, variables associated with physical fitness also improved including leg strength (3.04 cm), dominant handgrip strength (4.24 kgf), non-dominant handgrip strength (3.49 kgf), aerobic endurance (78.35 m) and muscle flexibility (3.96 cm). We can therefore conclude that a rowing-based exercise program is an effective strategy to improve physical fitness in this type of population, yielding improvements in both anthropometric measurements and basic physical capacity variables, essential factors for better health and quality of life.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Background: Passive drinking is prevalent in adolescents worldwide, but its prevalence and harm are understudied. Methods: Secondary students (n=5840, grades 7-12) from 23 selected schools in Hong Kong participated in the survey from 2015-16. Students reported the harm of passive drinking, perceived health status, Patient Health Questionnaire-2, Perceived Stress Scale-4, perceived happiness, family health, happiness, and harmony in the questionnaire. The associations were analyzed using multivariable logistic regression (odds ratio, OR) and linear regression (unstandardized coefficient, b), adjusted for confounders. Results: 29.1% (95% CI 27.8 to 30.5%) of students experienced passive drinking in the past 30-day. Past 30-day parental passive drinking was associated with a higher level of depressive symptoms (AOR 1.63, 95% CI 1.26 to 2.10), stress (adjusted b 0.76, 0.42 to 1.10), and lower level of perceived happiness (adjusted b -0.52, -0.72 to -0.33). Past 30-day parental passive drinking was associated with a lower level of family health (adjusted b -1.39, 95% CI -1.66 to -1.11), family happiness (adjusted b -1.36, -1.64 to -1.08), and family harmony (adjusted b -1.40, -1.70 to -1.10). Conclusion: Passive drinking was associated with poorer mental health, family wellbeing, and lower level of happiness among Hong Kong Chinese adolescents.