bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal kinetics, thermodynamics, decomposition mechanism, and thermal safety performance of typical ammonium perchlorate-based molecular perovskite energetic materials
Authors: An Erhai , Chen Shaoli , Li Xiaoxia , Tan Yingxin , Cao Xiong , Deng Peng
Published: March 2022

In this work, we report the thermal kinetics, thermodynamics, and decomposition mechanism of AP-based molecular perovskite energetic materials and estimate their thermal safety performance. Typical AP-based molecular perovskite energetic materials, (H2dabco)[NH4(ClO4)3] (DAP-4), (H2pz)[NH4(ClO4)3](PAP-4), (H2mpz)[NH4(ClO4)3](PAP-M4), and (H2hpz)[NH4(ClO4)3] (PAP-H4), were synthesized and characterized. These were studied using differential scanning calorimetry (DSC). The results show that all of the obtained AP-based molecular perovskite energetic materials have higher thermal decomposition temperatures, and the peak temperatures are more than 360 °C. All follow random nucleation and growth models. Other thermodynamic parameters, such as the reaction enthalpy (ΔH), entropy change (ΔS), and Gibbs free energy (ΔG), show that they are generally thermodynamically stable. Moreover, their adiabatic induced temperatures were obtained; TD24 of DAP-4, PAP-4, PAP-M4, and PAP-H4 were 246.6, 201.2, 194.5, and 217.5 °C, respectively. This study offers an important and in-depth understanding of the thermal decomposition characteristics of AP-based molecular perovskite energetic materials and their potential applications.
Sedimentary mercury as a proxy for redox oscillations during the Cambrian SPICE event in western Newfoundland
Authors: Hagen Amy P.I. , Jones David S. , Tosca Nicholas J. , Fike David A. , Pruss Sara B.
Published: March 2022

The Steptoean Positive Isotopic Carbon Excursion, or SPICE event, was a globally recorded +2‰ to +6‰ shift in δ13C values during the later Cambrian (ca. 500 Ma). The excursion is associated with regional sea level change and trilobite extinctions documented from multiple paleocontinents, yet the mechanism(s) for these events and the excursion itself are not fully understood. Recent studies have suggested that the SPICE is accompanied by global changes in redox conditions, which may have served as an extinction driver. Here, we present new geochemical and mineralogical data from an outcrop of the SPICE located along the Port au Port Peninsula in western Newfoundland. We focus on paired sedimentary mercury and glauconite enrichments as a potential redox indicator in an effort to evaluate local redox conditions during the SPICE in Newfoundland and assess the validity of Hg as a redox proxy. These strata record small mercury enrichments (<20 ppb) relative to SPICE background, sometimes coupled with elevated glauconite abundance. This coupling occurs after the carbon isotope peak, suggesting that the environment during the falling limb of SPICE experienced redox oscillations. Findings from this study supplement existing global proxies by adding information on redox conditions during the SPICE in a shallow shelf environment. Further, the coupling of Hg enrichments with elevated glauconite provides some support for the use of sedimentary Hg as a redox indicator when enrichments are elevated compared to background.
A Time Series Forecasting Approach Based on Nonlinear Spiking Neural Systems
Authors: Long Lifan , Liu Qian , Peng Hong , Yang Qian , Luo Xiaohui , Wang Jun , Song Xiaoxiao
Published: March 2022

Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear spiking mechanism of biological neurons. NSNP systems have a nonlinear structure and the potential to describe nonlinear dynamic systems. Based on NSNP systems, a novel time series forecasting approach is developed in this paper. During the training phase, a time series is first converted to frequency domain by using a redundant wavelet transform, and then according to the frequency data, an NSNP system is automatically constructed and adaptively trained in frequency domain. Then, the well-trained NSNP system can automatically generate sequence data for future time as the prediction results. Eight benchmark time series data sets and two real-life time series data sets are utilized to compare the proposed approach with several state-of-the-art forecasting approaches. The comparison results demonstrate availability and effectiveness of the proposed forecasting approach.
Critical Interactive Risks in Project Portfolios from the Life Cycle Perspective
Authors: Bai Libiao , Liu Jiale , Huang Ning , Zheng Kanyin , Hao Tingting
Published: March 2022

The need for enterprises to manage project portfolio risks over the life cycle has become increasingly prominent. It is essential to evaluate and manage them to achieve project portfolios and organizations’ success. Unlike project risk, project portfolio risk is more complex and uncertain due to risk interactions. Risk management is unsatisfactory in project portfolios due to the lack of awareness of risk interactions and the life cycle. The purpose of this paper is to identify the critical risks of project portfolios over the life cycle considering risk interactions. We primarily verified 20 identified risks through a questionnaire survey and an expert interview method and evaluated the interactions among them using the Delphi method. Furthermore, risk interactions were analyzed using the social network analysis (SNA) methodology to determine the important risks. Finally, a comprehensive evaluation of important risks was carried out to identify critical risks according to the evaluation principles. The results identified six critical portfolio risks, two key risk contagion paths and revealed risk characteristics of different life cycle phases. This research considerably contributes to the body of knowledge pertaining to project portfolio management that will enable organizations that implement project portfolios and similar multi projects to emphasize critical risks.
Relatively hyperbolic groups with semistable peripheral subgroups
Authors: Haulmark M. , Mihalik M.
Published: March 2022

Suppose G is a finitely presented group that is hyperbolic relative to [Formula: see text] a finite collection of finitely generated proper subgroups of G. Our main theorem states that if each [Formula: see text] has semistable fundamental group at [Formula: see text], then G has semistable fundamental group at [Formula: see text]. The problem reduces to the case when G and the members of [Formula: see text] are all one ended and finitely presented. In that case, if the boundary [Formula: see text] has no cut point, then G was already known to have semistable fundamental group at [Formula: see text]. We consider the more general situation when [Formula: see text] contains cut points.
Seismic Response Prediction of Open- and Closed-Loop Structural Control Systems Using a Multi-State-Dependent Parameter Estimation Approach
Authors: Zamani Abbas-Ali , Etedali Sadegh
Published: March 2022

In this paper, a new modified multi-state-dependent parameter (MSDP) model estimation method is proposed with the aim of application for seismic response prediction of open- and closed-loop structural control systems. In the first phase, the recursive Kalman filtering is improved and an improved recursive fixed interval-smoothing algorithm is then proposed in the second phase. The numerical studies are conducted on a seismic-excited 3-story benchmark structure in three cases of open-loop structural system, open-loop structural control with tuned mass damper (TMD) as a passive device, and closed-loop structural control with an active tendon system (ATS). Using the proposed MSDP models, the time-histories of acceleration of the floors are predicted during earthquakes and the validation of the proposed models is evaluated with those given by the finite element method (FEM) of OpenSees software. Using three evaluation criteria, the superiority of the modified MSDP over the MSDP is found. The simulation results show that the proposed models are capable of following the seismic responses of the structure well and estimating them with high accuracy in further time steps. With high accuracy and lower computational cost, the surrogate models can be easily utilized in optimal design processes of the passive device for the open-loop structural control and the active control algorithm for the closed-loop structural control and structural health monitoring (SHM) applications.
Analysis and dynamical behavior of a novel dengue model via fractional calculus
Authors: Jan Rashid , Shah Zahir , Deebani Wejdan , Alzahrani Ebraheem
Published: March 2022

The infection of dengue is an intimidating vector-borne disease caused by a pathogenic agent that affects different temperature areas and brings many losses in human health and economy. Thus, it is valuable to identify the most influential parameters in the transmission process for the control of dengue to lessen these losses and to turn down the economic burden of dengue. In this research, we formulate the transmission phenomena of dengue infection with vaccination, treatment and reinfection via Atangana–Baleanu operator to thoroughly explore the intricate system of the disease. Furthermore, to come up with more realistic, dependable and valid results through fractional derivative rather than classical order derivative. The next-generation approach has been utilized to compute the basic reproduction number for the suggested fractional model, indicated by [Formula: see text]; moreover, we conducted sensitivity test of [Formula: see text] to recognize and point out the role of parameters on [Formula: see text]. Our numerical results predict that the reproduction number of the system of dengue infection can be controlled by controlling the index of memory. The uniqueness and existence result has been proved for the solution of the system. A novel numerical method is presented to highlight the time series of dengue system. Eventually, we get numerical results for different assumptions of [Formula: see text] with specifying factors to conceptualize the effect of [Formula: see text] on the dynamics. It has been noted that the fractional-order derivative offers realistic, clear-cut and valid information about the dynamics of dengue fever. Moreover, we note through our analysis that the input parameters’ index of memory, biting rate, transmission probability and recruitment rate of mosquitos can be used as control parameter to lower the level of infection.
Enhanced bounds for rho-invariants for both general and spherical 3-manifolds
Authors: Lim Geunho
Published: March 2022

We establish enhanced bounds on Cheeger–Gromov [Formula: see text]-invariants for general [Formula: see text]-manifolds and yet stronger bounds for special classes of [Formula: see text]-manifold. As key ingredients, we construct chain null-homotopies whose complexity is linearly bounded by its boundary. This result can be regarded as an algebraic topological analogue of Gromov’s conjecture for quantitative topology. The author hopes for applications to various fields including the smooth knot concordance group, quantitative topology and complexity theory.
Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph
Authors: Liu Wenzheng , Zhang Heming , Tang Chao , Wu Shuangfei , Zhu Hongguang
Published: March 2022

Modeling and Simulation of Cyber-Physical Systems (MSCPS) is demanding in terms of immediate response to dynamic and complex changes of CPS. Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new simulation application, which avoid repeated modeling and thus reduce the redevelopment of submodels. Model composition, one of the important methods, enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems. In this paper, a real-time model integration approach for global CPS modeling is proposed, which reuses developed submodels by compositing submodel nodes. Specifically, a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching. Submodel properties, including co-simulation distance between submodel nodes, reuse benefit and simulation performance of model nodes, are quantified. Based on the properties, the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algorithm (GA). In the experiment, the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions, among which the Pareto optimal solutions are retrieved. Results show that the effectiveness of the model integration method proposed in this paper is verified.
Joint CLT for top eigenvalues of sample covariance matrices of separable high dimensional long memory processes
Authors: Tian Peng
Published: March 2022

For [Formula: see text], consider the sample covariance matrix [Formula: see text] from a data set [Formula: see text], where [Formula: see text] is a [Formula: see text] matrix having i.i.d. entries with mean zero and variance one, and [Formula: see text] are deterministic positive semi-definite Hermitian matrices of dimension [Formula: see text] and [Formula: see text], respectively. We assume that [Formula: see text] is bounded in spectral norm, and [Formula: see text] is a Toeplitz matrix with its largest eigenvalues diverging to infinity. The matrix [Formula: see text] can be viewed as a data set of an [Formula: see text]-dimensional long memory stationary process having separable dependence structure. As [Formula: see text] and [Formula: see text], we establish the asymptotics and the joint CLT for [Formula: see text], where [Formula: see text] denotes the [Formula: see text]th largest eigenvalue of [Formula: see text] and [Formula: see text] is a fixed integer. For the CLT, we first study the case where the entries of [Formula: see text] are Gaussian, and then we generalize the result to some more generic cases. This result substantially extends our previous result in Merlevède et al. 2019, where we studied [Formula: see text] in the case where [Formula: see text] and [Formula: see text] with [Formula: see text] having Gaussian entries. In order to establish this CLT, we are led to study the first order asymptotics of the largest eigenvalues and the associated eigenvectors of some deterministic Toeplitz matrices related to long memory stationary processes. We prove multiple spectral gap properties for the largest eigenvalues and a delocalization property for their associated eigenvectors.
Dynamic Response and Seismic Vulnerabilities of the Historic Tophane-i Amire: Dynamic and Seismic Analysis of a Historic Masonry Building
Authors: Aktürk F. , Aras F. , Çeribaşi S.
Published: March 2022

This study deals with the dynamic characterization of the historic Tophane-i Amire building, located in Istanbul. The domed structure was constructed in the 18th century and is known as the first industrial building in the Ottoman times. It was an arsenal building used for the production of cannons and cannon balls. An ambient vibration survey was conducted with the Enhanced Frequency Domain Decomposition method to identify the dynamic characteristics of the structure. Later, a finite element model of the structure was constructed and numerical dynamic identification was pursued. The obtained experimental and numerical results have been compared and the finite element model of the structure has been tuned to obtain a validated tool to represent the real dynamic behavior of the structure. While the tuned model of the structure is intended to contribute to understanding its structural behavior and evaluate the mechanical properties of masonry, it has also been used for the evaluation of its seismic vulnerabilities. For this purpose, the structure has been analyzed under maximum considered earthquake response spectrum, and stress concentration regions have been determined. The results also serve as data for future structural health monitoring studies related to the structure.
Achieving High In Situ Training Accuracy and Energy Efficiency with Analog Non-Volatile Synaptic Devices
Authors: Huang Shanshi , Sun Xiaoyu , Peng Xiaochen , Jiang Hongwu , Yu Shimeng
Published: March 2022

On-device embedded artificial intelligence prefers the adaptive learning capability when deployed in the field, and thus in situ training is required. The compute-in-memory approach, which exploits the analog computation within the memory array, is a promising solution for deep neural network (DNN) on-chip acceleration. Emerging non-volatile memories are of great interest, serving as analog synapses due to their multilevel programmability. However, the asymmetry and nonlinearity in the conductance tuning remain grand challenges for achieving high in situ training accuracy. In addition, analog-to-digital converters at the edge of the memory array introduce quantization errors. In this work, we present an algorithm-hardware co-optimization to overcome these challenges. We incorporate the device/circuit non-ideal effects into the DNN propagation and weight update steps. By introducing the adaptive “momentum” in the weight update rule, in situ training accuracy on CIFAR-10 could approach its software baseline even under severe asymmetry/nonlinearity and analog-to-digital converter quantization error. The hardware performance of the on-chip training architecture and the overhead for adding “momentum” are also evaluated. By optimizing the backpropagation dataflow, 23.59 TOPS/W training energy efficiency (12× improvement compared to naïve dataflow) is achieved. The circuits that handle “momentum” introduce only 4.2% energy overhead. Our results show great potential and more relaxed requirements that enable emerging non-volatile memories for DNN acceleration on the embedded artificial intelligence platforms.
Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design
Authors: Sun Qi , Chen Tinghuan , Liu Siting , Chen Jianli , Yu Hao , Yu Bei
Published: March 2022

High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing and facilitates the implementations of modern applications (e.g., deep learning models) on
Field-programmable Gate Array (FPGA)
, by using high-level languages and HLS directives. However, finding good HLS directives is challenging, due to the time-consuming design processes, the balances among different design objectives, and the diverse fidelities (accuracies of data) of the performance values between the consecutive FPGA design stages.

To find good HLS directives, a novel automatic optimization algorithm is proposed to explore the Pareto designs of the multiple objectives while making full use of the data with different fidelities from different FPGA design stages. Firstly, a non-linear Gaussian process (GP) is proposed to model the relationships among the different FPGA design stages. Secondly, for the first time, the GP model is enhanced as correlated GP (CGP) by considering the correlations between the multiple design objectives, to find better Pareto designs. Furthermore, we extend our model to be a deep version deep CGP (DCGP) by using the deep neural network to improve the kernel functions in Gaussian process models, to improve the characterization capability of the models, and learn better feature representations. We test our design method on some public benchmarks (including general matrix multiplication and sparse matrix-vector multiplication) and deep learning-based object detection model iSmart2 on FPGA. Experimental results show that our methods outperform the baselines significantly and facilitate the deep learning designs on FPGA.
TextureMe: High-Quality Textured Scene Reconstruction in Real Time
Authors: Kim Jungeon , Kim Hyomin , Nam Hyeonseo , Park Jaesik , Lee Seungyong
Published: March 2022

Three-dimensional (3D) reconstruction using an RGB-D camera has been widely adopted for realistic content creation. However, high-quality texture mapping onto the reconstructed geometry is often treated as an offline step that should run after geometric reconstruction. In this article, we propose
TextureMe
, a novel approach that jointly recovers 3D surface geometry and high-quality texture in real time. The key idea is to create triangular texture patches that correspond to zero-crossing triangles of truncated signed distance function (TSDF) progressively in a global texture atlas. Our approach integrates color details into the texture patches in parallel with the depth map integration to a TSDF. It also actively updates a pool of texture patches to adapt TSDF changes and minimizes misalignment artifacts that occur due to camera drift and image distortion. Our global texture atlas representation is fully compatible with conventional texture mapping. As a result, our approach produces high-quality textures without utilizing additional texture map optimization, mesh parameterization, or heavy post-processing. High-quality scenes produced by our real-time approach are even comparable to the results from state-of-the-art methods that run offline.
DiffusionNet: Discretization Agnostic Learning on Surfaces
Authors: Sharp Nicholas , Attaiki Souhaib , Crane Keenan , Ovsjanikov Maks
Published: March 2022

We introduce a new general-purpose approach to deep learning on three-dimensional surfaces based on the insight that a simple diffusion layer is highly effective for spatial communication. The resulting networks are automatically robust to changes in resolution and sampling of a surface—a basic property that is crucial for practical applications. Our networks can be discretized on various geometric representations, such as triangle meshes or point clouds, and can even be trained on one representation and then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes. The only other ingredients in the method are a multi-layer perceptron applied independently at each point and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces and demonstrate state-of-the-art results for a variety of tasks, including surface classification, segmentation, and non-rigid correspondence.
Tactile Materials in Practice: Understanding the Experiences of Teachers of the Visually Impaired
Authors: Phutane Mahika , Wright Julie , Castro Brenda Veronica , Shi Lei , Stern Simone R. , Lawson Holly M. , Azenkot Shiri
Published: March 2022

Teachers of the visually impaired (TVIs)
regularly present tactile materials (tactile graphics, 3D models, and real objects) to students with vision impairments. Researchers have been increasingly interested in designing tools to support the use of tactile materials, but we still lack an in-depth understanding of how tactile materials are created and used in practice today. To address this gap, we conducted interviews with 21 TVIs and a 3-week diary study with eight of them. We found that tactile materials were regularly used for academic as well as non-academic concepts like tactile literacy, motor ability, and spatial awareness. Real objects and 3D models served as “stepping stones” to tactile graphics and our participants preferred to teach with 3D models, despite finding them difficult to create, obtain, and modify. Use of certain materials also carried social implications; participants selected materials that fostered student independence and allow classroom inclusion. We contribute design considerations, encouraging future work on tactile materials to enable student and TVI co-creation, facilitate rapid prototyping, and promote movement and spatial awareness. To support future research in this area, our paper provides a fundamental understanding of current practices. We bridge these practices to established pedagogical approaches and highlight opportunities for growth regarding this important genre of educational materials.
Synthesis and Optimization of ZnMn2O4 Cathode Material for Zinc-Ion Battery by Citric Acid Sol-Gel Method
Authors: Cai Kexing , Luo Shao-hua , Cong Jun , Li Kun , Yan Sheng-xue , Hou Peng-qing , Wang Qing , Zhang Yahui , Liu Xin , Lei Xuefei
Published: March 2022

Zinc manganese spinel composite oxide ZnMn2O4 is an important inorganic material, which is rich in natural resources and environment-friendly. Herein, pure phase ZnMn2O4 was prepared by a simple sol-gel process as cathode material for zinc ion batteries. In order to improve the cycle performance of the material while maintaining the capacity, acetonitrile/aqueous co-solvent electrolyte (0.5 M Zn(CF3SO3)2/AN-H2O) was used as the electrolyte of the zinc ion battery. The influence of different experimental conditions on the electrochemical properties of ZnMn2O4 was studied by orthogonal experiment. In order to improve its electrochemical performance and cycling ability, the non-equivalent substitution of pure ZnMn2O4 was performed by adding Al3+. The non-equivalent substitution of Al3+ reduced the cell volume of ZnMn2O4 and the mixed manganese valence appeared. The specific capacity of initial discharge increases from 52.9 mAh·g−1 to 109.4 mAh·g−1 at 0.05C, and the cycle stability is also greatly improved. After 100 cycles, the capacity retention rate was 82.3%. This work shows that ZnMn2O4 is a promising cathode material for zinc ion batteries, which expands the application of spinel oxide in zinc ion batteries.
Detection of Environmentally Toxic Molecules Using Carbon Nanotubes: A First-Principles Theoretical Study
Authors: Fujimoto Yoshitaka , Saito Susumu
Published: March 2022

The adsorption process of environmentally harmful (CO2), toxic (CO, NO and NO2) and common (O2 and N2) molecules in air on boron and nitrogen-doped carbon nanotubes (CNTs) and its effects on quantum transport are studied using the first-principles density-functional calculations combined with the quantum transport method. It is found that CO, NO, NO2 and O2 molecules can strongly bind on the B-doped (10,0) CNTs while only NO and NO2 molecules are strongly adsorbed on the N-doped (10,0) CNTs in air. Quantum transport properties of (10,0) CNTs regarding the adsorption of the molecules are quantitatively investigated, and it is found that the adsorption of the molecules changes sizably the quantum conductance of the CNTs, which depends on the types of the adsorbed molecules. The origin associated with the wide variation of the quantum conductance induced by the molecular adsorption is revealed, and the possibility to selectively detect toxic CO, NO, and NO2 molecules in air is discussed.
A Data-Driven Model Framework Based on Deep Learning for Estimating the States of Lithium-Ion Batteries
Authors: Gong Qingrui , Wang Ping , Cheng Ze
Published: March 2022

The accurate estimation of state of charge (SOC) and state of health (SOH) of lithium-ion battery is crucial to ensure the safe and stable operation of the battery. In this paper, a data-driven model framework based on deep learning for estimating SOC and SOH is proposed, which mainly consists of long short-term memory (LSTM) neural network and back propagation (BP) neural network. The switch between SOC estimation model and SOH estimation model can be realized by adjusting the output mode of LSTM. When estimating SOC, the LSTM is set to have corresponding output at each input. The model takes 10 consecutive voltage sampling points as input and the estimated value of SOC at the last sampling moment as output. When estimating SOH, the LSTM is set to have a corresponding output only at the last input. The model takes the sequence of 150 sampling points on the charging voltage curve as input and the SOH value at the current cycle as output. Experiments are carried out on the Oxford battery degradation dataset, and the results show that the proposed model framework can obtain accurate and reliable estimates of SOC and SOH.
Electrochemical Determination of Paraquat Using Gold Nanoparticle Incorporated Multiwalled Carbon Nanotubes
Authors: Rajaram Rajendran , Gurusamy Tamilselvi , Ramanujam Kothandaraman , Neelakantan Lakshman
Published: March 2022

The present work focuses on electrochemical estimation of the herbicide, paraquat using Au nanoparticle incorporated multi-walled carbon nanotubes (AuNP-MWCNT). The catalyst was synthesized and characterized using physical characterization techniques like HRSEM, HRTEM, Raman spectroscopy, and XRD. The techniques reveal the successful incorporation of Au nanoparticles onto MWCNT and it was utilized for the electrochemical determination of paraquat (PQ). The AuNP-MWCNT modified glassy carbon electrode (AuNP-MWCNT/GCE) was able to detect PQ reversibly using cyclic voltammetric analysis. From the electrochemical tests, it was discovered that the electrode is an efficient catalyst for the effective electrochemical sensing of PQ. Using the cathodic stripping square wave voltammetric technique (CSSWV), the sensitivity and limit of detection of the matrix against PQ were evaluated. They were found to be 0.197 mA μM−1 and 32 nM, respectively. The electrode is able to produce the linear variation in sensitivity in the concentration range of 1.0 μM to 2.0 μM with a regression coefficient value of 0.99. Further, selectivity studies clearly demonstrated that the proposed catalyst was interference-free from other possible foreign molecules. The practical applicability of the electrode was checked in the electrolyte containing real samples and the appreciable recovery was achieved.
Bile acid detergency: permeability, inflammation, and effects of sulfation
Authors: Camilleri Michael
Published: March 2022

Bile acids are amphipathic, detergent molecules. The detergent effects of di-α-hydroxy-bile acids are relevant to several colonic diseases. The aims were to review the concentrations of bile acids reaching the human colon in health and disease, the molecular structure of bile acids that determine detergent functions and the relationship to human diseases (neuroendocrine tumors, microscopic colitis, active celiac disease, and ulcerative colitis, Crohn’s disease and ileal resection), the relationship to bacterial uptake into the mucosa, mucin depletion, and epithelial damage, the role of bile acids in mucosal inflammation and microscopic colitis, and the role of sulfation of bile salts in detoxification or prevention of the detergent effects of bile acids. The concentrations of bile acids reaching the human colon range from 2 to 10 mM; di-α-hydroxy bile acids are the only bile acids with detergent effects that include mucin depletion, mucosal damage, bacterial uptake, and microscopic inflammation that may be manifest in diseases associated with no overt inflammation of the mucosa, such as bile acid diarrhea, ileal diseases such as neuroendocrine tumors, ileal resection, and nonalcoholic steatohepatitis. Sulfation inactivates colonic secretion due to primary bile acids, but it may render secondary bile acids proinflammatory in the colon. Other evidence in preclinical models of inflammatory bowel disease (IBD) suggests reduced sulfation causes barrier dysfunction, inflammation, or carcinogenesis. These advances emphasize relevance and opportunities afforded by greater understanding of the chemistry and metabolism of bile acids, which stands to be further enhanced by research into the metabolic interactions of microbiota with bile acids.
The Effect of Position on Radiographic Angle Measurements of the Lower Extremities
Authors: Chung Jeehyeok , Lee Joonhee , Han Hyuk-Soo , Lee Myung Chul , Ro Du Hyun
Published: March 2022

Purpose. Accurately measuring an angle on a lower extremity X‐ray is essential for the diagnosis and treatment of knee osteoarthritis (KOA). However, the angle is often affected by position, especially with flexion contracture and rotation. To date, there have been no quantitative analyses examining the relationship between lower extremity angle and patient position and no studies targeting patients with deformities. The aim of this study is to quantify the effect of position on angle measurements in lower extremity X‐rays and to compare the effect in patients with different deformities. Methods. Computed tomography (CT) data of 131 patients with knee pain were retrospectively analyzed. The subjects were categorized into the following groups: neutral (hip‐knee‐ankle angle (HKAA) between 175 and 185°), varus (HKAA less than 175°), valgus (HKAA more than 185°), and flexion (flexion contracture more than 10°). CT images were digitally reconstructed to anterior‐posterior X‐ray images using an average intensity projection algorithm. The process was then repeated while rotating the reconstruction plane from internal 9° to external 9°. In this manner, X‐ray images were reconstructed in different rotational states. The following angles were measured from reconstructed X‐ray images: HKAA, lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), and femoral valgus angle (FVA). The measurements were then compared according to the degree of rotation. Results. FVA significantly differed according to rotation in all groups (P < 0.001), with a difference of 1.3° (±0.4°). HKAA significantly changed only in the flexion contracture group (P < 0.001), which showed a difference of 1.0° (±0.7°). However, HKAA in the other groups, LDFA, and MPTA did not significantly differ depending on rotation. Conclusions. Radiographic measurement of FVA is subject to change according to rotation. HKAA significantly changed only in the flexion contracture group, so more care should be taken while obtaining X‐rays of patients with flexion contracture.
On Element Selection in STAR-RIS for NOMA Transmission
Authors: Sheng Bin
Published: March 2022

Reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless propagation environment through reflecting the incident signals with a large number of low-cost passive elements. Recently, a novel concept of simultaneous transmitting and reflecting RIS (STAR-RIS) has been proposed, where incident signals can be transmitted and reflected to users located on different sides of the surface. Therefore, there are two coefficients that needed to be configured for each element of STAR-RIS. One is used to adjust the phase shifts and amplitudes of the transmitted signal; the other is configured for the reflected signal. In some harsh situations where the direct links between access points (AP) and users are blocked, STAR-RIS can be deployed to provide additional signal propagation paths. With the aid of STAR-RIS, multiple users can share the same time-frequency resources and transmit signals in a power-domain nonorthogonal multiple access (NOMA) case. However, when the users move into cell edge where they have almost the same transmit power and distance to AP, the system performance will degrade largely. To solve this problem, a novel element selection method is proposed, which can reshape the channel to a favorable propagation environment, for NOMA by only activating the appropriate elements in STAR-RIS.
Clinical Efficacy of LSC and TVT-O for Stress Urinary Incontinence Complicated with Pelvic Organ Prolapse and Factors Influencing Postoperative Urinary Function Recovery
Authors: Du Wenling , Liu Zhihu , Wang Daya
Published: March 2022

Pelvic organ prolapse is caused by various causes, which leads to the weakness of the tissue supporting the pelvic floor and then causes the downward displacement of female reproductive organs and adjacent organs. Clinical studies have shown that pop is often associated with stress urinary incontinence. This research project aimed to clarify the clinical effect of laparoscopic sacrocolpopexy (LSC) and tension-free vaginal tape obturator (TVT-O) for stress urinary incontinence (SUI) complicated with pelvic organ prolapse (POP) and the influencing factors of postoperative urinary function (UF) recovery. The clinical data of 125 patients with SUI complicated with POP treated in Wenzhou Central Hospital and Beidahuang Industry Group General Hospital between March 2018 and December 2019 were retrospectively analyzed. Patients were assigned to the following two arms based on different treatment methods: the combination group (n = 65, treated with LSC plus TVT-O) and the control group (n = 60, treated with LSC). The alterations of perioperative clinical and urodynamic indexes were analyzed. The objective and subjective response rates were observed and compared. The degree of POP before and after surgery was evaluated. According to the urinary function recovery time, the patients were divided into the fast and non-fast recovery groups, and then, the factors influencing postoperative UF recovery were analyzed between groups. The combination group showed statistically longer operation time (OT) and postoperative indwelling catheter and higher intraoperative blood loss (IBL) than the control group (all

P
<
0.05

), but the anal exhaust time and postoperative length of stay (LOS) differed insignificantly between the two arms. The combination group outperformed the control group in the objective response rate, as well as the scores of illness, quality of life (QOL), and sexual life (all

P
<
0.05

). Menopause, maximum flow rate (MFR), and preoperative residual urine volume were identified as the influencing factors for normal urination. For patients with SUI complicated with POP, the efficacy of laparoscopic sacrocolpopexy was limited, while combining with TVT-O would obtain more significant short-term and long-term efficacy, which can significantly improve patients’ urination and long-term quality of life, with higher safety and clinical promotion value.
Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes
Authors: Sun Yuxin , Zhao Zhenying , Wang Zhongyi , He Haiyang , Guo Feng , Luo Yuchen , Gao Qing , Wei Ningjing , Liu Jialin , Li Guo-Zheng , Liu Ziqing
Published: March 2022

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end‐to‐end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream.
The Relationship between Antenatal Corticosteroid Administration-to-Delivery Intervals and Neonatal Respiratory Distress Syndrome and Respiratory Support
Authors: Li Lixia , Li Haijing , Jiang Yejun , Yu Beimeng , Wang Xiuren , Zhang Wujiang
Published: March 2022

Background. Administration of antenatal corticosteroids (ACSs) is an effective strategy for managing preterm infants, which improves neonatal respiratory distress syndrome (NRDS) and attenuates the risk of neonatal mortality. However, many preterm infants are not exposed to a complete course of ACS administration, and the effects of different ACS-to-delivery intervals on NRDS and respiratory support remain unclear. Therefore, this study explored the relationship between ACS-to-birth intervals and NRDS and respiratory support in preterm infants. Methods. In this retrospective cohort study, the preterm infants born between 240/7 and 316/7 wks of gestation were recruited from January 2015 to July 2021. All participants were categorised based on the time interval from the first ACS dose to delivery: <24 h, 1-2 d, 2–7 d, and >7 d. Multivariable logistic regression analysis examined the relationships between the ACS-to-birth interval and primary or secondary outcome while adjusting for potential confounders. Results. Of the 706 eligible neonates, 264, 83, 292, and 67 received ACS-to-delivery intervals of <24 h, 1-2 d, 2–7 d, and >7 d, respectively. After adjusting these confounding factors, the multivariable logistic analysis showed a significantly increased risk of NRDS (aOR: 1.8, 95% CI: 1.2–2.7), neonatal mortality (aOR: 2.8, 95% CI: 1.1–6.8), the need for surfactant use (aOR: 2.7, 95% CI: 1.7–4.4), endotracheal intubation in the delivery room (aOR: 1.9, 95% CI: 1.0–3.7), and mechanical ventilation (aOR: 1.9, 95% CI: 1.1–3.4) in the ACS-to-delivery interval of <24 h group when compared with the ACS-to-birth interval of 2–7 d group. Conclusions. Neonatal outcomes such as NRDS, neonatal mortality, the need for surfactant use, intubation in the delivery room, and the risk of mechanical ventilation are higher when the neonates are exposed to an ACS interval for less than 24 h before delivery.
[Retracted] The Evaluation of Music Teaching in Colleges and Universities Based on Machine Learning
Authors: Xiongjun Xia , Lv Danmeng
Published: March 2022

With the implementation of the strategic policy of rejuvenating the country through science and education, many innovative and practical teaching concepts and teaching models have been comprehensively developed. This breaks the backward teaching mode of traditional teaching activities. With the development of science and technology and Internet technology, deep learning is widely used in the field of education. Music teachers in colleges and universities constantly update their teaching methods and comprehensively use a variety of methods to carry out in‐depth teaching in the classroom, and strive to stimulate students’ learning Interest and enthusiasm, and comprehensively enhance students’ music aesthetic ability. This article uses decision tree algorithms, support vector machines, Bayesian theory, and random forest four different classification techniques to evaluate the student curriculum evaluation dataset. Classification experiment: through the analysis of the experimental results, the performance of the four classifier models was compared, and the data showed the difference in accuracy, precision, recall, and F1 value of the four classifiers. At the same time, each of the classifier models was analyzed. This article verifies the effectiveness of machine learning models in curriculum evaluation and higher education mining, the importance of evaluation features.
Immunological Perspective: Helicobacter pylori Infection and Gastritis
Authors: Yang Hang , Hu Bing
Published: March 2022

Helicobacter pylori is a spiral-shaped gram-negative bacterium. Its infection is mainly transmitted via oral-oral and fecal-oral routes usually during early childhood. It can achieve persistent colonization by manipulating the host immune responses, which also causes mucosal damage and inflammation. H. pylori gastritis is an infectious disease and results in chronic gastritis of different severity in near all patients with infection. It may develop from acute/chronic inflammation, chronic atrophic gastritis, intestinal metaplasia, dysplasia, and intraepithelial neoplasia, eventually to gastric cancer. This review attempts to cover recent studies which provide important insights into how H. pylori causes chronic inflammation and what the characteristic is, which will immunologically explain H. pylori gastritis.
Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images
Authors: Geng Qiang , Yan Huifeng , Lu Xingru
Published: March 2022

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob’s decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve’s classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.
Association of Paraoxonase 1 Gene Polymorphisms with the Risk of Breast Fibroadenoma and Breast Cancer in the Females of Guangxi, China
Authors: Chen Kun , Wang Xiaogang , Li Dihang , Luo Weiping , Xue Chuang , Kong Yong
Published: March 2022

Fibroadenoma of the breast is a common cause of a benign breast lump in premenopausal women. The consensus view is that women with fibroadenomas are not at significantly increased risk of developing breast cancer. The objective of this research was to explore the association of PON1 rs662 and rs705382 with the risk of breast fibroadenoma (BF) and breast cancer (BC) in the females of Guangxi in southern China. The PON1 rs662 and rs705382 single-nucleotide polymorphisms (SNPs) were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 55 BF patients, 80 BC patients, and 98 healthy controls. Significant associations with FB and BC risks were observed for the rs662 SNP. Diagnosis is based on the combination of clinical examination, imaging, and nonsurgical tissue biopsy (the triple test) [21]. In haplotype analyses, the haplotype GA increases the risk and haploid GG decreases the risk in BF and BC. Our research indicated that the PON1 rs662 SNP might be a risk factor for BF and BC. The results of this research indicated that the locus of rs662 in PON1 is relevant to risk of developing BF and BC in the females of Guangxi. Further prospective studies are needed to support our conclusions.