Search

Downward continuation (DC) of the gravity potential field is an important approach used to understand and interpret the density structure and boundary of anomalous bodies. It is widely used to delineate and highlight local and shallow anomalous sources. However, it is well known that direct DC transformation in the frequency domain is unstable and easily affected by high-frequency noise. Recent deep learning applications have led to the development of image recognition and resolution enhancement using the convolutional neural network technique. A similar deep learning architecture is also suitable for training a model for the DC problem. In this study, to solve the problems in existing DC methods, we constructed a dedicated model called DC-Net for the DC problem. We fully trained the DC-Net model on 38,400 pairs of gravity anomaly data at different altitudes using a convolutional neural network. We conducted several experiments and implemented a real-world example. The results demonstrate the following. First, several validation data subset and test data prediction results indicate that the DC-Net model was sufficiently trained. Moreover, it performed better than the traditional strategy in refining the upscaling of low-resolution images. Second, we performed tests on test datasets with changing levels of noise and demonstrated that the DC-Net model is noise-resistant and robust. Finally, we used the proposed model in a real-world example, which demonstrates that the DC-Net model is suitable for solving the DC problem and delineating the detailed gravity anomaly feature near the field source. For real data processing, noise in the gravity anomaly should be reduced in advance. Additionally, we recommend noise quantification of the gravity anomaly before network training.

In this paper, we consider the sterile neutrino portal dark matter with $Z_3$ symmetry. This model further extends the canonical type-I seesaw with a fermion singlet $\chi$ and a scalar singlet $\phi$. Under the $Z_3$ symmetry, the dark sector transforms as $\chi\to e^{i2\pi/3}\chi, \phi\to e^{i2\pi/3}\phi$, while the standard model particles and the sterile neutrino $N$ transform trivially. Besides the interactions as $y_{N} \phi \bar{\chi}N$ and $\lambda_{H\phi} (H^\dag H) (\phi^\dag \phi)$ allowed in the $Z_2$ symmetry, the $Z_3$ symmetry also introduces two new terms, i.e., $y_\chi \phi \overline{\chi^{c}} \chi$ and $\mu\phi^3/2$. These new interactions induce additional semi-annihilation processes as $\chi\chi\to N\chi$ and $\phi\phi\to h\phi$ for WIMP dark matter. We then perform a comprehensive analysis of the phenomenology of this $Z_3$ symmetric model. Viable parameter space is explored under the constraints from dark matter relic density, Higgs invisible decay, indirect and direct detection for both fermion and scalar dark matter. We find that the semi-annihilation channels $\chi\chi\to N\chi$ and $\phi\phi\to N\chi$ can lead to quite different phenomena from the $Z_2$ symmetric model, which provides a viable pathway to distinguish these two kinds of model., Comment: 23 pages, 10 figures

The terrestrial time-varying microgravity observation can be used to study the crustal mass transfer. The uncertainty of the gravity instrument is an important factor that can easily affect the data quality of gravity observations. In this paper, we focus on the time-varying gravity dataset from 2015 to 2018 in South China. Based on Bayesian analysis method, we quantify the related uncertainty sources of the gravity instruments, including the nonlinear drift of relative gravimeter and the change of scale factor of gravimeter. Furthermore, the cross-validation of the absolute gravity in-situ observation in network is applied to assess the gravity data results using this new approach. The results show that the uncertainty caused by the scale factor is larger than that caused by nonlinear drift for the relative gravimeters used in the South China survey network. The magnitude can reach up to 20×10-8 m/s2 for gravity change. Nevertheless, this uncertainty can be effectively reduced by means of the Bayesian method and absolute gravity datum control. This study can be used to assess the data quality of terrestrial gravity observation, to understand the causes of gravity change, and analysis the great earthquake preparation and mass transfer, etc.

Provided the dark sector consisted of a dark scalar $\phi$ and a dark fermion $\chi$ under an exact $Z_2$ symmetry, the sterile neutrino $N$ can act as the messenger between the dark sector and standard model via the Yukawa coupling $\lambda_{ds} \bar{\chi}\phi N$. In this paper, we focus on the specific scenario $m_N>m_\phi+m_\chi$ with $\chi$ being a FIMP dark matter. The decay width of dark scalar $\phi$ is doubly suppressed by the smallness of Yukawa coupling $\lambda_{ds}$ and mixing angle $\theta$. The delayed decay $\phi\to\chi\nu$ will have a great impact on cosmological observables such as the Big Bang Nucleosynthesis, the Cosmic Microwave Background anisotropy power spectra, the effective number of relativistic neutrino species $N_{\rm eff}$ and the energetic neutrino flux. Meanwhile, the sterile neutrino can generate displaced vertex signature at colliders when $m_N, Comment: 18 pages,8 figures

The exotic decay modes of non-Standard Model Higgses can serve as powerful search channels to explore the parameter space of extended Higgs sectors. In this Snowmass contribution, we illustrate this using the Two-Higgs Doublet Model (2HDM) as an example. We first review the current experimental constraints on the parameter space of a Type-II 2HDM arising from existing searches for the exotic Higgs decay mode $A/H\rightarrow HZ/AZ$. We then present the sensitivity of future colliders to discover addition Higgs bosons using the exotic decay channels $A\rightarrow HZ$, $A\rightarrow H^\pm W^\mp$ and $H^\pm\rightarrow H W^\pm$. We find that a 100 TeV collider can probe almost the entire region of the Type-II 2HDM parameter space that survives current theoretical and experimental constraints and would therefore be an ideal machine to search for heavier Higgses in hierarchical scalar sectors., Comment: 6 pages, 2 figures, contribution to Snowmass 2021

In this paper, we propose the sterile neutrino portal dark matter in $ν$THDM. This model can naturally generate tiny neutrino mass with the neutrinophilic scalar doublet $Φ_ν$ and sterile neutrinos $N$ around TeV scale. Charged under a $Z_2$ symmetry, one Dirac fermion singlet $χ$ and one scalar singlet $ϕ$ are further introduced in the dark sector. The sterile neutrinos $N$ are the mediators between the DM and SM. Depending on the coupling strength, the DM can be either WIMP or FIMP. For the WIMP scenario, pair annihilation of DM into $NN$ is the key channel to satisfy various bounds, which could be tested at indirect detection experiments. For the FIMP scenario, besides the direct production of DM from freeze-in mechanism, contributions from late decay of NLSP is also important. When sterile neutrinos are heavier than the dark sector, NLSP is long-lived due to tiny mixing angle between sterile and light neutrinos. Constrains from free-streaming length, CMB, BBN and neutrino experiments are considered., 27 pages, 14 figures

The exotic decay modes of non-Standard Model Higgses can serve as powerful search channels to explore the parameter space of extended Higgs sectors. In this Snowmass contribution, we illustrate this using the Two-Higgs Doublet Model (2HDM) as an example. We first review the current experimental constraints on the parameter space of a Type-II 2HDM arising from existing searches for the exotic Higgs decay mode $A/H\rightarrow HZ/AZ$. We then present the sensitivity of future colliders to discover addition Higgs bosons using the exotic decay channels $A\rightarrow HZ$, $A\rightarrow H^\pm W^\mp$ and $H^\pm\rightarrow H W^\pm$. We find that a 100 TeV collider can probe almost the entire region of the Type-II 2HDM parameter space that survives current theoretical and experimental constraints and would therefore be an ideal machine to search for heavier Higgses in hierarchical scalar sectors.

Background:AP2/ERF transcription factors perform indispensable functions in various biological processes, such as plant growth, development, biotic and abiotic stresses responses. The AP2/ERF transcription factor family has been identified in many plants, and several AP2/ERF transcription factors from Arabidopsis (Arabidopsis thaliana) have been functionally characterized. However, little research has been conducted on the AP2/ERF genes of ginger (Zingiber officinale), which is an important edible and medicinal horticultural plant. The recently published whole genome sequence of ginger allowed us to study the tissue and expression profiles of AP2/ERF genes in ginger on a genome-wide basis.Results:In this study, 163 AP2/ERF genes of ginger (ZoAP2/ERF) were identified and renamed according to the chromosomal distribution of the ZoAP2/ERF genes. According to the number conserved domains and gene structure, the AP2/ERF genes were divided into three subfamilies by phylogenetic analysis, namely, AP2 (35 members), ERF (125 members) and RAV (3 members). A total of 10 motifs were detected in ginger AP2/ERF genes, and some of the unique motifs were found to be important for the function of ZoAP2/ERF genes.Conclusion:A comprehensive analysis of AP2/ERF gene expression patterns in different tissues and rhizome development stages by transcriptom sequence and quantitative real-time PCR (qRT-PCR) showed that they played an important role in the growth and development of ginger, and genes that might regulate rhizome and flower development were preliminarily identified. This systematic analysis establishes a foundation for further studies of the functional characteristics of ZoAP2/ERF genes and improvement of ginger.

$Z^\prime$ boson as a new gauge boson has been proposed in many new physics models. The interactions of $Z^\prime$ coupling to fermions are detailed studied at the large hadron collider. A $Z^\prime$ with the mass of a few TeV has been excluded in some special models. The future lepton colliders will focus on the studies of Higgs physics which provide the advantage to investigate the interactions of Higgs boson with the new gauge bosons. We investigate the $Z^\prime ZH$ interaction via the process of $e^+e^- \to Z^\prime/Z \to ZH \to l^+l^- b \bar{b}$. The angular distribution of the final leptons decaying from the $Z$-boson is related to the mixing of $Z^\prime$-$Z$ and the mass of $Z^\prime$. The forward-backward asymmetry has been proposed as an observable to investigate the $Z^\prime$-$Z$ mixing. The angular distributions change significantly with some special beam polarization comparing to the unpolarized condition., Comment: 15 pages, 7 figures

In the framework of the perturbative Quantum Chromodynamics factorization, the cross section of the heavy meson production via the combination of a heavy quark with a light one can be factorized to be the convolution of the combination matrix element, the light quark distribution function, and the hard partonic sub-cross section of the heavy quark production. The partonic distribution and the combination matrix element are functions of a scaling variable, respectively, which is the momentum fraction of the corresponding quark with respect to the heavy meson. We studied the $D^{*\pm}$ production in jet via combination in pp collision at the LHC. Our calculation can be summed with the fragmentation contribution, and the total result is comparable with the experimental data. The combination matrix elements can be further studied in various hadron production processes., Comment: 15 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:hep-ph/0501290

Additional file 1. Supplemental Figure 1. Prevalence of dyslipidemia by sex and monthGroup A represent the first week after Spring Festival.

Shuwen Yue,1,* Ning Xue,2,* Honglei Li,3 Zhen Chen,1 Baosheng Huang,4 Xing Wang1 1Department of Pharmacology, School of Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 2Department of Acupuncture, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, People’s Republic of China; 3Department of Pharmacy, Kangda College of Nanjing Medical University, Lianyungang, People’s Republic of China; 4Department of Neurosurgery, Sir Run Run Hospital, Nanjing Medical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xing WangChina Pharmaceutical University, Nanjing, Jiangsu 211199, People’s Republic of ChinaEmail star10000@sina.comBaosheng HuangSir Run Run Hospital, Nanjing Medical University, No. 101, Longmiandadao, Jiangning District, Nanjing, Jiangsu 211166, People’s Republic of ChinaEmail labskcpu@sina.comAim: Renal injury induced by diabetes is reported to be associated with inflammation. Isomangiferin (ISO), a xanthone C-glucoside from the Cyclopia subfamily, exhibits many pharmacological properties. This study aimed to evaluate the protection of ISO against renal damage in diabetic mice.Methods: Serum glucose, insulin, uric acid, creatinine, total cholesterol (TC), triglyceride (TG), and inflammatory cytokines in serum and the kidney of db/db diabetes model mice were detected. The components of high mobility group protein B1 (HMGB1)/NACHT leucine-rich repeat- and PYD-containing 3 (NLRP3)/nuclear factor kappa-B (NF-κB) pathway in the kidney were detected by Western blot and immunohistochemical analysis.Results: ISO improved lipid profile and glucose tolerance, and inhibited the production of inflammatory cytokines in a db/db model mice. Moreover, ISO decreased biochemical indexes in the serum and inhibited the activation of HMGB1/NLRP3/NF-κB signaling in the kidney of db/db model mice.Conclusion: ISO provides protection against renal injury via inhibiting HMGB1/NLRP3/NF-κB signaling in a diabetic mouse model.Keywords: isomangiferin, renal injury, inflammation, HMGB1/NLRP3/NF-κB, diabetics

This study reviews the definition and development of Knowledge Management (KM) and discusses the differences of knowledge sharing and knowledge seeking in KM. Knowledge sharing has long been regarded as the most important factor in Knowledge Management implementations. However, this research discussed the three barriers of existing knowledge sharing in KM. Based on this discussion, it is proposed by authors that it is the knowledge seeking rather than knowledge sharing that plays the crucial role in KM.

Lithium-rich transition-metal layered oxides (LROs), such as Li1.2Mn0.6Ni0.2O2, are promising cathode materials for application in Li-ion batteries, but the low initial coulombic efficiency, severe voltage fade and poor rate performance of the LROs restrict their commercial application. Herein, a self-standing Li1.2Mn0.6Ni0.2O2/graphene membrane was synthesized as a binder-free cathode for Li-ion batteries. Integrating the graphene membrane with Li1.2Mn0.6Ni0.2O2 forming a Li1.2Mn0.6Ni0.2O2/graphene structure significantly increases the surface areas and pore volumes of the cathode, as well as the reversibility of oxygen redox during the charge–discharge process. The initial discharge capacity of the Li1.2Mn0.6Ni0.2O2/graphene membrane is ∼270 mA h g−1 (∼240 mA h g−1 for Li1.2Mn0.6Ni0.2O2) and its initial coulombic efficiency is 90% (72% for Li1.2Mn0.6Ni0.2O2) at a current density of 40 mA g−1. The capacity retention of the Li1.2Mn0.6Ni0.2O2/graphene membrane remains at 88% at 40 mA g−1 after 80 cycles, and the rate performance is largely improved compared with that of the pristine Li1.2Mn0.6Ni0.2O2. The improved performance of the Li1.2Mn0.6Ni0.2O2/graphene membrane is ascribed to the lower charge-transfer resistance and solid electrolyte interphase resistance of the Li1.2Mn0.6Ni0.2O2/graphene membrane compared to that of Li1.2Mn0.6Ni0.2O2. Moreover, the lithium ion diffusion of the Li1.2Mn0.6Ni0.2O2/graphene membrane is enhanced by three orders of magnitude compared to that of Li1.2Mn0.6Ni0.2O2. This work may provide a new avenue to improve the electrochemical performance of LROs through tuning the oxygen redox progress during cycling.

The structure, cooling principle and operation characteristics of transformer split type cooling system was different from conventional cooling systems. Such as the research work of simulation test equipment in the field was few, causing a lack of scientific basis for the equivalence of simulation test, and parameters set of split type cooling system without scientific and professional analysis. The result is that so many features and operation characteristics were not fully grasped before. Therefore, it was necessary to carry out the corresponding research work. The experimental study was carried out in this paper. This paper would research the simulation test method of split cooling system based on a 10kV distribution transformer. The test device can realize the flexible regulation for the vertical and horizontal position. According to the temperature-rise test method of transformers, performance test of split type cooling system was carried out based on the change of oil flow or radiator location. Finally, the research results of different pipe diameter, height and horizontal distribution were proposed. The study would provide more references for operation instruction of large power transformer's split type cooling devices.

Lithium-rich transition-metal layered oxides (LROs), such as Li

Governments around the world are increasingly using the internet to inform, interact and communicate with their citizens, thereby fostering e-public engagement. A significant research focus has been restricted to how governments use the internet, the policies surrounding its use, and how it impacts or benefits governance and public engagement. However, there is a need for studies to investigate–from the citizens’ perspective–factors that may influence citizens’ engagement with governments’ contents on the internet. This study, therefore, investigates citizens-content engagement using a qualitative approach. Findings indicate that there are five main factors that influence such engagement: information need, attributes of the contents, the perception of the writer, trust in government, and citizens' affinity for governments’ online platforms. With these five factors, this study formulates a citizen-content engagement (C-CE) model.

Future Higgs factories can reach impressive precision on Higgs property measurements. In this paper, instead of conventional focus of Higgs precision in certain interaction bases, we explored its sensitivity to new physics models at the electron-positron colliders. In particular, we studied two categories of new physics models, Standard Model (SM) with a real scalar singlet extension, and Two Higgs Double Model (2HDM) as examples of weakly-interacting models, Minimal Composite Higgs Model (MCHM) and three typical patterns of the more general operator counting for strong interacting models as examples of strong dynamics. We performed a global fit to various Higgs search channels to obtain the 95% C.L. constraints on the model parameter space. In the SM with a singlet extension, we obtained the limits on the singlet-doublet mixing angle sin($\theta$), as well as the more general Wilson coefficients of the induced higher dimensional operators. In the 2HDM, we analyzed tree level effects in tan($\beta$) vs. cos($\beta-\alpha$) plane, as well as the one-loop contributions from the heavy Higgs bosons in the alignment limit to obtain the constraints on heavy Higgs masses for different types of 2HDM. In strong dynamics models, we obtained lower limits on the strong dynamics scale. In addition, once deviations of Higgs couplings are observed, they can be used to distinguish different models. We also compared the sensitivity of various future Higgs factories, namely Circular Electron Positron Collider (CEPC), Future Circular Collider (FCC)-ee and International Linear Collider (ILC).

Journal of high energy physics 1712(12), 153 (2017). doi:10.1007/JHEP12(2017)153, Future Higgs factories can reach impressive precision on Higgs property measurements. In this paper, instead of conventional focus of Higgs precision in certain interaction bases, we explored its sensitivity to new physics models at the electron-positron colliders. In particular, we studied two categories of new physics models, Standard Model (SM) with a real scalar singlet extension, and Two Higgs Double Model (2HDM) as examples of weakly-interacting models, Minimal Composite Higgs Model (MCHM) and three typical patterns of the more general operator counting for strong interacting models as examples of strong dynamics. We performed a global fit to various Higgs search channels to obtain the 95% C.L. constraints on the model parameter space. In the SM with a singlet extension, we obtained the limits on the singlet-doublet mixing angle sin($\theta$), as well as the more general Wilson coefficients of the induced higher dimensional operators. In the 2HDM, we analyzed tree level effects in tan($\beta$) vs. cos($\beta-\alpha$) plane, as well as the one-loop contributions from the heavy Higgs bosons in the alignment limit to obtain the constraints on heavy Higgs masses for different types of 2HDM. In strong dynamics models, we obtained lower limits on the strong dynamics scale. In addition, once deviations of Higgs couplings are observed, they can be used to distinguish different models. We also compared the sensitivity of various future Higgs factories, namely Circular Electron Positron Collider (CEPC), Future Circular Collider (FCC)-ee and International Linear Collider (ILC)., Published by Springer, Berlin

In recent years, the mushrooming development of Online Communities (OCs) has ushered a new paradigm of research for organizational sustainability in IS. In essence, the growth and survival of an online community relies predominantly on the continuous participation of its members. Given that the emergence of OCs may decay or even diminish due to the lack of consistent involvement of members, it is of paramount importance to fathom how to retain and entice members of OCs in terms of their continuous participation in the online platform. Extant studies have focused mostly on personal belief constructs and subjective norm constructs to study users' continuous behavior. However, the important role of social situations has not been sufficiently explored and investigated in IS, particularly in the emerging context of OCs. Drawing on the Triandis model, this study proposes a research model incorporating social situations as the moderator on the continuance participation intention in OCs. We collected data from two large OCs to examine whether congruence exists between situation perceptions and situation reactions. The empirical results show that social situations play an important role in determining the strength of the relationships between affect, social factors, and perceived consequences and the continuance intention in OCs.

Based on improvement of Swift's black-box thermal-circuit model, this paper establishes a dynamic numerical model suitable for different capacity transformers. According to effect factors of the precision of thermal-circuit model, such as winding losses, oil viscosity changes, heat dissipation conditions, et al, this paper proposed ways to modify the model. The model is proved to be precise by comparing results with factory temperature rise test data.

The emerging 3D virtual worlds attract more and more people to participate in the virtual environment, creating a new market for business to sell their products. In 3D virtual worlds, members mainly interact with each other through avatars. The selling process is fulfilled through the shop avatars. How businesses sell their products successfully to the potential customers and eventually persuade the customer to purchase the product is an essential question. Trust played a key role in the selling process. In the general selling process, trust was established through the sales person. In the virtual world environment, could the trust be established between the sales avatars and customer avatars This paper aims at answering this question by examining the trust transference process in the 3D virtual world environment. An experiment was conducted to categorize the avatars into attractive and expert ones. The research result first suggest that trust formed in a 3D avatar seller could be transferred to a selling company and a product but for an expert 3D avatar seller, trust transferred to the company and product results in intent to purchase. Trust in a 3D avatar seller is transferred to trust in a product and a company and furthermore, an expert avatar can affect a consumer's intent to purchase. In the case of an attractive 3D avatar, although trust is transferred, it is only to the point of intent to purchase.

Six Sigma has long been used as an effective way to restructure a company’s management process. While there are abundant case study literatures on Six Sigma, a theoretical framework that can logically advance the previous work into a higher level is lacked. What’s more, there are few empirical studies to verify those results from previous case studies. To fill these two gaps, this study proposes a comprehensive theoretical research framework of Six Sigma-based management activities, build up a research model of its effectiveness, and further verifies its effect in improving corporate competitiveness through empirical data analysis based on valid questionnaires collected from managers in Samsung companies that have been applying Six Sigma for several years. Empirical results from Structural Equation Modeling method showed that the proposed theoretical research model of Six Sigma activities is indeed effective in refreshing process management, improving quality, and, finally, improving corporate competitiveness.

This research contributes to additional understanding of TAM’s validity in Internet banking acceptance. Although some user profile variables have been examined in technology acceptance research, there has been little research investigating the new roles of these profile variables in the dynamic technology environment. The objective of this study is to profile adopters of Internet banking system and evaluate the impacts of these profiles on technology acceptance. We are interested in exploring how sex, age and IT competency influence Internet banking system use.\ud TAM was applied to guide the evaluation of Internet banking acceptance behaviors.

This paper presents a novel technique for evaluating the performance of ventricular assist devices in vitro, innovatively combining dynamic physical testing of assist devices with a mature, numerical human cardiovascular model. Based on this technique, one self-made direct mechanical ventricular assistance (DMVA) prototype is tested. With the true representation of device performance in vivo, the real-time interactions between DMVA and the cardiovascular system are captured and studied. Hemodynamic simulations under DMVA are performed. Experimental results demonstrate that it provides a useful tool for the study of device assist impact on the cardiovascular system as well as the improvement of device structure and effectiveness of control mechanism.

A new diagnostic apparatus for online detecting gases dissolved in transformer oil is described, in which polytetrafluoroethylene membrane with microholes is used to separate H/sub 2/, CO, CH/sub 4/, C/sub 2/H/sub 2/, C/sub 2/H/sub 4/ and C/sub 2/H/sub 6/ from the oil. Mixed gases are further separated to single gas by the multiple gas chromatograph columns, then they are detected with a new sensitive hot wire gas detector. The whole online chromatograph supervising system is inexpensive and proved to be useful in practice.

This paper describes a wavelet artificial neural network (ANN) for signal classification, and applies it for transformer Fault Detection with dissolved gas analysis (DGA). The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Preliminary simulation results show wavelet ANN for DGA can get a 95% correct diagnosis rate, superior then BP ANN. Besides, precondition techniques of input data is studied, a suitable precondition algorithm play an important role in ANN.

The term “Tree of Life” was first used by Charles Darwin in 1859 as a metaphor for describing phylogenetic relationships among organisms. Over the past three decades, the recognized tree of life has improved considerably in overall size and reliability due to an increase in diversity of character resources, a dramatic growth in useable data, and the development of tree-reconstruction methods. As a bridge connecting phylogeny, evolution and related disciplines, such as molecular biology, ecology, genomics, bioinformatics and computer science, the tree of life is increasingly widely used. In this paper, we review the history and progress of tree of life studies and focus on its application in the following fields: (1) the reconstruction of phylogenetic trees at different taxonomic hierarchies to understand phylogenetic relationships among taxa; (2) investigation of the origins of taxa and biogeographic patterns based on dating estimation and biogeographic reconstruction; (3) examination of species’ diversification and its causes by integrating dated trees, ecological factors, environmental variation and key innovations; (4) the study of the origin and patterns of biodiversity, predating biodiversity dynamics, and development of conservation strategies. Finally, we evaluate the difficulties from matrix alignment, gene tree incongruence and “rogue taxa” distraction in tree reconstruction due to massive increases of useable data and in the context consider “supertree” building in the future.

Jia, Y.G.; Li, H.L.; Meng, X.M.; Liu, X.L., and Shan, H.X., 2012. Deposition-monitoring technology in an estuarial environment using an electrical-resistivity method. A new method using an electrical-resistivity probe was introduced to monitor sediment deposition. In laboratory and field experiments, a multielectrode probe was mounted in a tank and within a pit, respectively, to measure electrical resistivity with the Wenner array during sediment deposition. The observed positions of water–sediment interface were recorded synchronously. Based on the electrical-resistivity profiles, the positions of the water–sediment interface were interpreted and compared with the observed positions. Physical properties above and below the interface were analyzed as well. The results indicate that the water–sediment interface in the tank descends nonlinearly with time; the probe detects the interface well, with errors ranging from −0.4 cm to 1.7 cm and with a mean rate of 0.41 cm in the laboratory, and performs ...

The high precision measurements of the Higgs mass and couplings at the future Higgs factories are sensitive to the parameter space of the Minimal Supersymmetric Standard Model (MSSM). Focused on the dominant stop sector contributions, we study the implication of the Higgs precision measurements on MSSM using multi-variable $\chi^2$ fit. The results show nice complementarity between the indirect searches at Higgs factories and the direct searches at the current LHC program., Comment: 5 pages, 2 figures, contribution to Snowmass 2021

The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios., 424 pages

The 'economic miracle' in China during the past three decades has prompted many scholars to present a China discourse that China is no longer a country in transition but has created the China Model leading to the Chinese modernity. This article argues that the state-led rapid economic growth is not unique to China but common among many East Asian countries since the 1950s and other countries characterised as state capitalism in other parts of the world; that this model of development has generated structural problems and is not sustainable; that there is no such a thing as Chinese modernity; that the major consequence of pursuing an alternative modernity based on national culture is to create obstacles for Chinas march to modernity.

In recent years, Online Shopping plays an important role in daily life and how to improve the online shopping experience with Machine Learning and Recommender System has been discussed by a group of researchers. As a sub-field of Machine Learning, Computer vision has achieved significant developments during the last decade. The computer vision techniques can help machine to view images and extract useful information from images like human beings. However, the existing online recommender system has mostly used the labelled information and ignored the large amount of useful information extracted from images. This thesis proposed that the extracted information from images through computer vision techniques can be used in the current online recommender system for the improved online shopping experience. To do this, I firstly tackled the problem of insufficient data in the real online shopping environment. I proposed a pairwise constraint random forest algorithm with associating transfer learning strategy. This new algorithm can make use of weakly supervised labelled data which is relatively easy to collect in the real online shopping environment to train the attribute classification model. Secondly, I developed an explainable recommender system with self-identity theory. This new recommender framework is built based on the weakly learning algorithm proposed above to analyse human behaviours by self-identity theory from information system research. Compared with previous recommender system, my work concentrates on different customer behaviours distinguished by self-identity and result in an improved online shopping experience.\ud In summary, there are two major contributes for this thesis. Firstly, this thesis introduces a new weakly-supervised learning approach for semantic data classification in the online shopping environment. This new algorithm can work with noise partially labelled data to achieve better accuracy for attribute learning tasks. Secondly, by analysing the recommender system with self-identity theory, a new explainable Recommender System is proposed to improve online shopping experience. Besides, we also indicate the potential of further research in combining Computer Vision in Computer Science with online shopping experience in Information System research which can determine how Computer Vision can help to solve real world problems.

Governments around the world are increasingly investing in the publication of data and information on the internet in a bid to promote transparency and public engagement. However, studies have found that there is a poor audience and citizens’ engagement with online contents in general, and with governments’ digital data and information in particular. Studies have also shown that it is important that governments who seek to engage the citizens in State’s decisionmaking process should first engage them with their informative online contents. But the challenge is that e-public engagement research has been predominantly techno-centric.\ud Therefore, with an exploratory research design and a sequential-mixed methods approach, this study investigated the factors influencing citizens’ engagement with governments’ online contents based on the views of Nigerians. From the qualitative phase of the study, a citizen-content engagement (C-CE) model was developed. This model was then tested in the quantitative phase, and findings indicate that citizens’ engagement with governments’ online contents (CE) is directly influenced by the quality and ability of the contents in meeting the citizens’ information need (INPCQ), and by the citizens’ affinity for governments’ platforms (IVP). IVP is influenced by trust in the government (TGA), the ability to actively participate in information creation on governments platforms (CC), and the ability to interact and deliberate with other citizens and government’s officials on those platforms (IDelib). Governments’ platform-type and citizens’ level of political awareness also played a moderating role on IVP.\ud Governments’ use of social media was found to be more important than the use of websites in the influence of TGA, CC, and IDelib on IVP. Poor level of political awareness was more important than the optimal level of political awareness in the influence of IVP on CE, which\ud indicates that the more aware citizens are about the government, the less the affinity they have for their platforms. This research is important as the outcome may help governments that are interested in e-participation to shape their contents better in ways that would encourage citizencontent engagement and citizen participation.

Recommendation Systems (RSs) belong to the subclass of information filtering systems, which is to determine any given user’s “rating” of particular items. However, Cold Start Recommendations (CSR) pose significant challenges due to insufficient historical ratings of information. CSR is divided into Incomplete Cold Start Recommendation (ICSR) and Complete Cold Start Recommendation (CCSR) in historical ratings or user information; the former formulates recommendations to new users and new items, while the latter contributes to new systems. Various approaches deal with cold start challenges, including collaborative filtering, content-based fliting, the hybrid approach, increasing the diversity of data sources, context-awareness, and user profile construction. These approaches are commonly applied in the ICSR, though they are not as evident in the CCSR.\ud \ud Few previous studies have focused on solutions to CCSR; solving CCSR is significantly important due to the popularity of the recommendation system, the increasing new system, the improved recommendation performance, and the capability of reducing user input. The Peer to Peer (P2P) energy trading recommendation system (ETRS) is an example of a new system facing the complete cold start challenge. This research compares the advantages and drawbacks of different strategies to solve CSR and selects user profile construction to solve CCSR in P2P ETRS.\ud \ud First, a mixed-method data collection process was used, including semi-structured expert interviews, user-generated content analysis, and a survey to generate insights contributing to an efficient P2P energy trading user profile dataset. Second, a P2P ETRS is proposed by comparing the Decision Tree classifier and Ensemble classifier under different users’ cultural backgrounds. Different cultural backgrounds may have different viewpoints on P2P energy trading. Thus, the system is evaluated with a large body of real-world data acquired from two case-study regions, one is the western dataset, and one is the Chinese dataset. The proposed recommendation system achieves high performance by delivering personalised recommendations. In addition, previous research applied to constructing the user profile dataset, demographic information, and user personalities, neglecting the motivations behind the product selections under different cultural backgrounds. Thus, this research analysed the driving motivations of P2P energy trading with the theory of consumption values under different cultural backgrounds.\ud \ud This research has two significant contributions. First, this study identified the user profile construction strategy with a machine learning approach to solve the CCSR problem in the P2P ETRS. This strategy can effectively build the original user profile dataset, reduce the user input, and evaluate the recommended model. In addition, the research adopted ensemble learning algorithms to improve the performance of the recommended model. In terms of system performance, the accuracy of the Decision Tree (DT) model achieves an 86.1% for the Chinese dataset and a 68.5% for the western data set. In addition, ensemble learning algorithms can improve the classification of both datasets, as CatBoost performed best in the ensemble model, with an accuracy rate of 94.3% on the Chinese dataset and 79.8% on the western dataset.\ud \ud Second, this research created a bridge between machine learning and the traditional social science theory. A framework for a P2P energy trading recommendation system within the theory of consumption values was proposed to understand the collaborative consumption behaviour. Previous studies of consumption value theory had applied to collaborative consumption, including Airbnb and Uber. Unlike the previous research, P2P energy trading paid more attention to the feeling of uncertainty on the emotional value rather than enjoyment; That is to say, consumers believe the uncertainty of the supply of using renewable energy is more important than the consideration of enjoyment of the experience. Most notably, the essential driving values in the two datasets are different, although income can impact P2P product selection in both datasets. The feeling of uncertainty played the most crucial role in the Chinese dataset, while the western dataset focused on sustainability.