Oscillation Caused By Impulses

AbstractThe present paper is devoted to the investigation of the oscillation of a kind of very extensively studied second order nonlinear delay differential equations with impulses, some interesting results are obtained, which illustrate that impulses play a very important role in giving rise to the oscillations of equations

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

Industrial regions and relevant infrastructures are known to contribute to air pollutant emissions; thus, a detailed investigation of the air pollutant concentrations of a region based on specific land uses, with spatial reasoning, can support smart regional planning. However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited. Thus, in this study, we aimed to identify the factors that affect air-pollutant concentrations due to local spatial impacts in industrial regions across Australia. Considering the large spatial scale, the impact of a global factor can be overwhelmed by another factor due to local spatial impacts, and the phenomenon is a kind of spatial disparity. We developed a novel set of methods, including a point-of-interests-based spatial identification method and geographically weighted regression (with standardised coefficients), to: (i) identify the industrial regions in the study area, (ii) collect the remote sensing factors, and (iii) identify the factors that affect the spatial disparity of air-pollutant concentrations in industrial regions. The results indicated a significant spatial disparity in the air pollutant concentrations in the industrial region, at a continental scale. Anthropogenic factors significantly affected the spatial patterns of air pollutant concentrations in the industrial regions that were remote to cities, whereas meteorological and topographical factors had significant impacts on the air pollutant distributions in urban industrial regions. Furthermore, within the nationwide industrial lands, drives of the relatively high concentrations of ozone and sulphur dioxide, the drivers of the air pollutant concentrations were environmental factors; high concentrations of nitrogen dioxide were more associated with the topographical features of the region. The methods proposed in this study can serve as a reliable framework for analysing the air quality of industrial regions and can also, supplement future studies on emissions reduction in industrial parks

Built environment and early infection of COVID-19 in urban districts: a case study of Huangzhou

Since COVID-19 spread rapidly worldwide, many countries have experienced significant growth in the number of confirmed cases and deaths. Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as air pollution, smoking, humidity, and temperature. As there is a lack of studies at the neighborhood-level detailing the spatial settings of built environment attributes, this study explored the variations in the size of the COVID-19 confirmed case clusters across the urban district Huangzhou in the city of Huanggang. Clusters of infectious cases in the initial outbreak of COVID-19 were identified geographically through GIS methods. The hypothetic relationships between built environment attributes and clusters of COVID-19 cases have been investigated with the structural equation model. The results show the statistically significant direct and indirect influences of commercial vitality and transportation infrastructure on the number of confirmed cases in an infectious cluster. The clues ch inducing a high risk of contagions have been evidenced and provided for the decision-making practice responding to the initial stage of possible severe epidemics, indicating that the local public health authorities should implement sufficient measures and adopt effective interventions in the areas and places with a high probability of crowded residents

Occupation prediction with multimodal learning from Tweet messages and Google Street View images

Despite the development of various heuristic and machine learning models, social media user occupation predication remains challenging due to limited high-quality ground truth data and difficulties in effectively integrating multiple data sources in different modalities, which can be complementary and contribute to informing the profession or job role of an individual. In response, this study introduces a novel semi-supervised multimodal learning method for Twitter user occupation prediction with a limited number of training samples. Specifically, an unsupervised learning model is first designed to extract textual and visual embeddings from individual tweet messages (textual) and Google Street View images (visual), with the latter capturing the geographical and environmental context surrounding individuals’ residential and workplace areas. Next, these high-dimensional multimodal features are fed into a multilayer transfer learning model for individual occupation classification. The proposed occupation prediction method achieves high evaluation scores for identifying Office workers, Students, and Others or Jobless people, with the F1 score for identifying Office workers surpassing the best previously reported scores for occupation classification using social media data

Dynamic Transformation of Nano-MoS2 in a Soil-Plant System Empowers Its Multifunctionality on Soybean Growth

Molybdenum disulfide (nano-MoS2) nanomaterials have shown great potential for biomedical and catalytic applications due to their unique enzyme-mimicking properties. However, their potential agricultural applications have been largely unexplored. A key factor prior to the application of nano-MoS2 in agriculture is understanding its behavior in a complex soil-plant system, particularly in terms of its transformation. Here, we investigate the distribution and transformation of two types of nano-MoS2 (MoS2 nanoparticles and MoS2 nanosheets) in a soil-soybean system through a combination of synchrotron radiation-based X-ray absorption near-edge spectroscopy (XANES) and single-particle inductively coupled plasma mass spectrometry (SP-ICP-MS). We found that MoS2 nanoparticles (NPs) transform dynamically in soil and plant tissues, releasing molybdenum (Mo) and sulfur (S) that can be incorporated gradually into the key enzymes involved in nitrogen metabolism and the antioxidant system, while the rest remain intact and act as nanozymes. Notably, there is 247.9 mg/kg of organic Mo in the nodule, while there is only 49.9 mg/kg of MoS2 NPs. This study demonstrates that it is the transformation that leads to the multifunctionality of MoS2, which can improve the biological nitrogen fixation (BNF) and growth. Therefore, MoS2 NPs enable a 30% increase in yield compared to the traditional molybdenum fertilizer (Na2MoO4). Excessive transformation of MoS2 nanosheets (NS) leads to the overaccumulation of Mo and sulfate in the plant, which damages the nodule function and yield. The study highlights the importance of understanding the transformation of nanomaterials for agricultural applications in future studies.</p

Lipid profiles in the cerebrospinal fluid of rats with 6-hydroxydopamine-induced lesions as a model of Parkinson’s disease

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease with characteristic pathological abnormalities, including the loss of dopaminergic (DA) neurons, a dopamine-depleted striatum, and microglial activation. Lipid accumulation exhibits a close relationship with these pathologies in PD.MethodsHere, 6-hydroxydopamine (6-OHDA) was used to construct a rat model of PD, and the lipid profile in cerebrospinal fluid (CSF) obtained from model rats was analyzed using lipidomic approaches.ResultsEstablishment of this PD model was confirmed by apomorphine-induced rotation behaviors, loss of DA neurons, depletion of dopamine in the striatum, and microglial activation after 6-OHDA-induced lesion generation. Unsupervised and supervised methods were employed for lipid analysis. A total of 172 lipid species were identified in CSF and subsequently classified into 18 lipid families. Lipid families, including eicosanoids, triglyceride (TG), cholesterol ester (CE), and free fatty acid (FFA), and 11 lipid species exhibited significantly altered profiles 2 weeks after 6-OHDA administration, and significant changes in eicosanoids, TG, CE, CAR, and three lipid species were noted 5 weeks after 6-OHDA administration. During the period of 6-OHDA-induced lesion formation, the lipid families and species showed concentration fluctuations related to the recovery of behavior and nigrostriatal abnormalities. Correlation analysis showed that the levels of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) exhibited positive relationships with apomorphine-induced rotation behaviors and negative relationships with tyrosine hydroxylase (TH) expression in the midbrain.ConclusionThese results revealed that non-progressive nigrostriatal degeneration induced by 6-OHDA promotes the expression of an impairment-related lipidomic signature in CSF, and the level of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) in CSF may reveal pathological changes in the midbrain after 6-OHDA insult

Evolutionarily missing and conserved tRNA genes in human and avian

Viral infection heavily relies on host transfer RNA (tRNA) for viral RNA decoding. Counterintuitively, not all tRNA species based on anticodon are matched to all 64-triplet codons during evolution. Life solves this problem by cognate tRNA species via wobbling decoding. We found that 14 out of 64 tRNA genes in humans and the main avian species (chicken and duck) were parallelly missing, including 8 tRNA-A34NN and 6 tRNA-G34NN species. By analyzing the conservation of key motifs in tRNA genes, we found that box A and B served as intragenic tRNA promoters were evolutionally conserved among human, chicken, and duck. Thus, decoding viral RNA by similar wobbling strategies and tRNA transcripts may be

Interfacial Effects in Iron-Nickel Hydroxide–Platinum Nanoparticles Enhance Catalytic Oxidation

该研究工作是在郑南峰教授的领导下，由校内外、国内外多个课题组共同努力，历时三年完成。郑南峰、傅钢、陈明树等三个课题组紧密协作负责催化剂的合成、表征、性能测试以及催化机理研究；中国科学院物理研究所谷林研究员主要负责纳米颗粒的亚埃级球差校正高分辨透射电子显微研究；加拿大达尔豪斯大学化学系的张鹏教授课题组和台湾同步辐射研究中心李志甫研究员等参与催化剂的同步辐射X-射线吸收光谱研究。 该工作受到了国家自然科学基金委、科技部、厦门大学、固体表面物理化学国家重点实验室、能源材料化学协同创新中心以及醇醚酯化工清洁生产国家工程实验室的资助与支持。Hybrid metal nanoparticles can allow separate reaction steps to occur in close proximity at different metal sites and accelerate catalysis. We synthesized iron-nickel hydroxide–platinum (transition metal-OH-Pt) nanoparticles with diameters below 5 nanometers and showed that they are highly efficient for carbon monoxide (CO) oxidation catalysis at room temperature. We characterized the composition and structure of the transition metal–OH-Pt interface and showed that Ni2+ plays a key role in stabilizing the interface against dehydration. Density functional theory and isotope-labeling experiments revealed that the OH groups at the Fe3+-OH-Pt interfaces readily react with CO adsorbed nearby to directly yield carbon dioxide (CO2) and simultaneously produce coordinatively unsaturated Fe sites for O2 activation. The oxide-supported PtFeNi nanocatalyst rapidly and fully removed CO from humid air without decay in activity for 1 month