bluuebunny/crossref_metadata_2025 huggingface dataset:
Malleability of the cortical hand map following a finger nerve block
Authors: Wesselink Daan B. , Sanders Zeena-Britt , Edmondson Laura R. , Dempsey-Jones Harriet , Kieliba Paulina , Kikkert Sanne , Themistocleous Andreas C. , Emir Uzay , Diedrichsen Jörn , Saal Hannes P. , Makin Tamar R.
Published: April 2022

Electrophysiological studies in monkeys show that finger amputation triggers local remapping within the deprived primary somatosensory cortex (S1). Human neuroimaging research, however, shows persistent S1 representation of the missing hand’s fingers, even decades after amputation. Here, we explore whether this apparent contradiction stems from underestimating the distributed peripheral and central representation of fingers in the hand map. Using pharmacological single-finger nerve block and 7-tesla neuroimaging, we first replicated previous accounts (electrophysiological and other) of local S1 remapping. Local blocking also triggered activity changes to nonblocked fingers across the entire hand area. Using methods exploiting interfinger representational overlap, however, we also show that the blocked finger representation remained persistent despite input loss. Computational modeling suggests that both local stability and global reorganization are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings reveal complex interfinger representational features that play a key role in brain (re)organization, beyond (re)mapping.
Spatially distributed computation in cortical circuits
Authors: Gepshtein Sergei , Pawar Ambarish S. , Kwon Sunwoo , Savel’ev Sergey , Albright Thomas D.
Published: April 2022

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron’s response to one dimension strongly depends on other dimensions. Here, we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Results of this process depend on interaction between stimulus dimensions. Comparison of modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.
Adoption of beneficial management practices to improve soil health
Authors: Shah Ananka , Weersink Alfons , Vyn Richard
Published: April 2022

Healthy soils are fundamental to building prosperous and resilient farms and to efforts to reduce greenhouse gas emissions and enhance overall environmental impacts from agriculture. Understanding the adoption of beneficial management practices (BMPs) that promote soil health is necessary for these benefits to be obtained. Drawing from a survey of Ontario farmers ( n = 247) with 60% being crop producers and 22% livestock farmers, we explore the variation in adoption for six soil health BMPs: cover crops, crop rotations, no-till, soil testing, conservation buffers, and organic amendments. Soil testing had the highest rate of adoption, while conservation buffers had the least. The majority of farmers (73%) implemented four or more BMPs as the use of practices such as a rotation with winter wheat, cover cropping, and no-till tend to be positively correlated. Adopters of the BMPs tend to operate larger farms both in the area operated and farm cash receipts than non-adopters. Improving soil health was the most widely selected motivation for adoption across all six BMPs. The most effective interventions to enhance adoption among non-adopters include financial incentives, easily accessible information and advice, and farmer-to-farmer learning. Our results suggest that farmers that adopt BMPs do so primarily to enhance soil health rather than solely for economic considerations. Encouraging use among non-adopters may require monitoring and promoting the benefits of soil health. The results should aid in the development of strategic frameworks that facilitate innovations in policy to enhance soil health.
A traffic flow-forecasting model based on multi-head spatio–temporal attention and adaptive graph convolutional networks
Authors: Zhang Hong , Kan Sunan , Cao Jie , Chen Linlong , Zhao Tianxin
Published: April 2022

Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but it is a challenging task due to the complex spatial-temporal dependence and uncertainty of traffic flow. In most existing approaches, spatial correlation is captured using graph convolution networks in a pre-determined graph structure. However, some nodes in such a graph structure have spatial correlations between them but are missing a connection, so the hidden spatial correlations between these nodes cannot be captured. Traffic flow has dynamic characteristics, showing different characteristics over time. Most methods ignore the dynamics of traffic flow when modeling the spatio–temporal correlation of traffic flow. We proposed a new network model (MSTA-GCN) to solve the above problem. The model presents a gated adaptive graph convolutional network that captures the hidden spatial correlations between graph nodes from the adaptive. In addition, the model introduces a multi-head spatial-temporal attention mechanism to pay attention to spatial-temporal information of different historical moments and different spatial dimensions to effectively capture the dynamics of spatial-temporal correlation of traffic flow. Extensive experiments were conducted on four datasets of PEMS. The experimental results show that the MSTA-GCN model has better forecasting performance compared with the baseline methods.
Security versus Compliance: An Empirical Study of the Impact of Industry Standards Compliance on Application Security
Authors: Stewart Harrison
Published: April 2022

The integration of security aspects into software development is an open topic, especially in highly regulated industries where standards are accompanied by a high degree of complexity. The research question of this paper relates to the misconception of industry standards compliance and security in the field of software development. Cyber attackers are constantly inventing new tools to penetrate systems and exploit even the most minor flaws, and adherence to an industry standard is not a solution. In this study, an empirical investigation is conducted over a six-month period to observe various customer relationship management (CRM) systems. To analyze and anticipate the vulnerabilities of various CRMs, penetration testing methodologies and cross-project prediction approaches are employed. Classification using multiple machine learning approaches is utilized in the study to increase the discovery of vulnerable components in each CRM. The Student [Formula: see text]-test is also used to assess if the mean values of the two CRM datasets are substantially different from each other in order to evaluate the efficacy of overall security and its features. The results show that security best practices during application development have a significant influence on applications created in regulated environments. The action research approach used to validate this study provided positive results and its feasibility in practice to optimize security throughout the application development. This study adds to the literature on information security management systems (ISMS) and best practices in application development in terms of creating and implementing opportunities based on broader information security management measures.
OPTIMAL CONTROL AND COST-EFFECTIVE ANALYSIS OF A SCABIES MODEL WITH DIRECT AND INDIRECT TRANSMISSIONS
Authors: MHLANGA A. , MUPEDZA T. V. , MAZIKANA T. M.
Published: April 2022

Scabies is caused by sarcoptes scabiei var. hominis, which is also referred to as itch mice. The disease is transmitted through direct contact with an infected person, or from contact with infested bedding or clothing. In this paper, a mathematical model for the spread of scabies was proposed and analyzed. Sensitivity analysis of the model parameters was carried out. Optimal control theory was applied to our proposed model, with the controls representing treatment and vaccination. Our aim was to minimize cumulative infectious cases and susceptible individuals through treatment and vaccination, respectively. Pontryagin’s maximum principle was utilized to characterize the optimal levels of the two controls. The resulting optimality system was then solved numerically. The optimal control result was further highlighted by applying the results realized from the cost objective functional, the IAR, and the ICER.
A TUMOR-IMMUNE MODEL WITH MIXED IMMUNOTHERAPY AND CHEMOTHERAPY: QUALITATIVE ANALYSIS AND OPTIMAL CONTROl
Authors: ZHANG ZHIBO , LI SHENG , SI PENG , LI XUEFANG , HE XIONGXIONG
Published: April 2022

We develop a mathematical model of tumor-immune interactions, including six populations (tumor cells, CD8[Formula: see text]T cells, natural killer (NK) cells, dendritic cells, helper T cells, cytokine interleukin-12 (IL-12)) and three potential treatments (chemotherapy, Tumor-infiltrating lymphocyte (TIL) therapy and IL-12 therapy). We characterize the dynamics of our model without treatment through stability and sensitivity analysis, which provides a broad understanding of the long-term qualitative behavior. To find the best combination of the chemo-immunotherapy regimens to eliminate tumors, we formulate an optimal control problem with path constraints of total drug dose and solve it numerically with the optimal control software Pyomo. We also simulate the scenarios of traditional treatment protocols as a comparison and find that our optimal treatment strategies have a better therapeutic effect. In addition, numerical simulation results show that IL-12 therapy is a good adjunctive therapy and has a high potential for inhibiting a large tumor in combination with other therapy. In most cases, combination therapy is more effective than a single treatment.
Tax Avoidance and Corporate Investments
Authors: Hossain Mahmud , Lobo Gerald J. , Mitra Santanu
Published: April 2022

This study examines the association between firms’ tax avoidance and long-term investments. We find that tax avoidance firms make greater investment than nontax avoidance firms, and that the positive association between tax avoidance and investments holds both for firms that are financially constrained, and therefore ex-ante likely to underinvest, and for financially unconstrained firms that are ex-ante more likely to overinvest. Our results further show that CEO equity incentives and governance strength exert an incremental effect on investment decisions of tax avoidance firms. In additional analyses, we demonstrate that tax avoidance firms’ investments are associated with improved future firm performance especially when those firms are ex-ante more likely to underinvest. For tax avoidance firms that are ex-ante more likely to overinvest, current investments are associated with declined future firm performance. Tax avoidance firms have higher (lower) investment efficiency in terms of improved (declined) operating profitability in the post-investment period when they have lower (higher) CEO equity incentives and stronger (weaker) governance. Overall, our results shed light on efficiency in utilizing the available cash through tax avoidance in long-term investments that might create shareholder value for a group of financially constrained firms but diminish shareholder value for another group of financially unconstrained firms.
DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning
Authors: Du XiuQuan , Zhao XiuJuan , Zhang YanPing
Published: April 2022

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a [Formula: see text]-BtoD encoding is designed, which takes into account the composition of [Formula: see text]-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local [Formula: see text]-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/ .
Determination of Assumed Strain Hardening Relationship from Shear–Compression Model and Data Analysis
Authors: Partovi Amir , Shahzamanian Mohammad Mehdi , Wu Peidong
Published: April 2022

A new method for obtaining equivalent stress–strain curves of bilinear and Swift law material models from the numerical results of shear–compression test (SCT) is developed. This task is conducted on the basis of the approximate analytical relations for the compression and shear conditions at the gauge section and by the application of correction factors. Logarithmic correction models are developed and used to generalize the geometry of an optimum shear–compression specimen (SCS) that is calibrated by one-factor-at-time method for a reference material. Furthermore, a dataset created from 125 finite element simulations is analyzed by data analysis techniques, and a universal nonlinear strain hardening relationship is determined. This model is used to predict the stress–strain curves directly from the force–displacement curve of the SCS. The input and output stress–strain curves are in good agreement with an average error of approximately 3%. The numerical findings of this study provide a foundation to develop a general quantitative relationship to study the behavior of materials by SCT.
Honeycomb Enhanced Self-Locked Structure for Energy Absorption
Authors: Liu Xiaobo , Zhu Shaowei , Chen Liming , Li Weiguo , Liu Houchang , Pan Xin
Published: April 2022

The self-locked structures are expandable, detachable, and easy to transport. When under impact loading, their self-locked effects can prevent the lateral splashing of themselves without any outer constraints, thus such structures have great potential for energy absorbing and protection in some emergencies. However, the low specific energy absorption (EA) of the existing self-locked structures has limited their application. In this work, self-locked structures with honeycombs fillings are proposed to improve their EA capacity. The deformed configurations and EA performance of self-locked structures with and without honeycombs fillings were investigated by experimental and numerical methods. It is found that self-locked structures with honeycombs fillings not only inherit the self-locked effect of existing self-locked structures, but also their load-carrying capacity is greatly improved, and the special EA capacity of the proposed structures is higher than any of the existing self-locked structures. This work provides a guideline for the design of the self-locked structure with both lightweight and high mechanical performance, and it is instructive for the design of other structures with both functional and mechanical properties.
Thermal Entrainment in the Core Regions of Transitional Plane Fountains in Linearly Stratified Ambient Fluids
Authors: Inam M. I. , Lin Wenxian , Armfield S. W.
Published: April 2022

Thermal entrainment is an integral feature of free-shear flows involving temperature changes, including fountains (negatively buoyant jets). The understanding of thermal entrainment is essential for applications involving thermal stratification such as that in a solar water storage tank. In this study, thermal entrainment in the core regions of transitional plane fountains in linearly-stratified ambient fluids is studied numerically over a range of the Froude number, Reynolds number, and dimensionless thermal stratification parameter. The results show that thermal entrainment has a negligible effect on the inner core region, but is significant in the outer core region, particularly at the interfaces among the upflow, downflow, and ambient fluid, and the thermal entrainment parameter, which quantifies the extent of thermal entrainment, decreases with height or in a stronger stratification, but increases with increasing Froude number or Reynolds number. Several numerical relations are obtained with the numerical results for the thermal entrainment parameter.
Wide Bandwidth Wind-Induced Vibration Energy Harvester with an Angle Section Head
Authors: Chen Yu , Yang Zhichun , Zhou Shengxi
Published: April 2022

A high-performance wind-induced vibration energy harvester with an angle section head is designed. Experimental results show that the harvester has a wide frequency-locked/operating wind speed range. Experiments are carried out for the harvester with different angles and lengths of the angle section head. It is found that the reasonable design of the angle section head can benefit wind energy harvesting. In this study, the widest operating wind speed range of 13.4 m/s (from 6.6 m/s to 20 m/s) is experimentally demonstrated, and the maximum output power is 214.6 μW.
The large sieve with power moduli in imaginary quadratic number fields
Authors: Gao Peng , Zhao Liangyi
Published: April 2022

In this paper, we establish large sieve inequalities for power moduli in imaginary quadratic number fields, extending earlier work of Baier and Bansal [S. Baier and A. Bansal, The large sieve with power moduli for [Formula: see text], Int. J. Number Theory 14 (10) (2018) 2737–2756; Large sieve with sparse sets of moduli for [Formula: see text], Acta Arith. 196 (1) (2020) 17–34] for the Gaussian field.
kth powers in Piatetski-Shapiro sequences
Authors: Qi Jinyun , Guo Victor Zhenyu , Xu Zhefeng
Published: April 2022

The Piatetski-Shapiro sequences are sequences of the form [Formula: see text] with [Formula: see text] and [Formula: see text]. We investigate the distribution of [Formula: see text]th powers in Piatetski-Shapiro sequences. Moreover, we study the solution of a more general equation [Formula: see text] with [Formula: see text] for an integer [Formula: see text]. We obtain bounds for different values of [Formula: see text] and an average bound over [Formula: see text]-free numbers [Formula: see text] in an interval. In the end, we also study the solution of the equation [Formula: see text] for which [Formula: see text] is a prime number.
Morphology-Controllable Graphene-Modified Cu-Benzene-1,3,5 Tricarboxylic Acid Composites for Volatile Organic Compounds and CO2 Adsorption Property
Authors: Tang Bo , Liu Ke , Liu Jiayuan , Wang Zhengwei
Published: April 2022

Volatile organic compounds (VOCs) are the one kind of pollution, which lead to the severe atmospheric contamination problem. Metal organic frameworks (MOFs) and graphene are promising candidates to collect VOCs. In this study, composites which consist of graphene sheets and Cu-BTC (benzene-1,3,5 tricarboxylic acid) are prepared by the hydrothermal method to adsorb VOCs and CO. Moreover, the morphology of the resulting graphene material in the composites can be controlled by adjusting the pH value, reaction time and surfactant during the hydrothermal process, which further determines the adsorption ability for different gas molecules. According to the pore distribution features of various composites, the morphology of graphene exerts a fatal influence on the observed adsorption performances. Furthermore, the polarity of the as-prepared composites is another important determinant to selective adsorption for various VOCs. The stability of these composites under high temperature is also tested, and the results demonstrate that the adsorbents can be used in a wide temperature range. Moreover, the large BET areas and high stability of the composites in the water endow them a potential application in the sewage clarification field. After the corresponding optimization, the adsorption amounts of methanol and ethanol reach 13.6[Formula: see text]mmol[Formula: see text]g[Formula: see text] and 9.68[Formula: see text]mmol[Formula: see text]g[Formula: see text], respectively.
Modeling the Evolution and Runup of Breaking Solitary and Solitary-Like Waves on Straight and Composite Slopes
Authors: Wu Yun-Ta
Published: April 2022

Understanding the runup and inundation of long waves on coasts is of great importance for coastal community as flooding hazards are closely related to safety issues. For many years, solitary and solitary-like waves are frequently considered as a surrogate of extremely long waves for estimating runup and inundation. Since scaling issues are of concern when extending to real-world conditions, large-scale experiments for solitary waves on uniform beaches are reviewed and additional experiments for solitary waves on composite slopes are performed in this study. As such, those experimental data obtained from large-scale physical modeling can be used to validate numerical models and then to extend the range of parameters in terms of wave conditions and slope geometries which cannot be straightforwardly achieved in large-scale experimental works. Considering the computational efficiency, an open-source non-hydrostatic wave-flow model SWASH is used herein. Detailed model-data comparisons in terms of free surface elevation time series and maximum runup heights are carried out for long waves running up and down on beaches with different slope gradients to ensure the accuracy of the SWASH model for such applications. Finally, a simple method for estimating maximum shoreline excursion for solitary waves on a particularly designed composite slope is provided.
Seismic Ground Motion Amplification in a 3D Sedimentary Basin: Source Mechanism and Intensity Measures
Authors: Parla Rajesh , Somala Surendra Nadh
Published: April 2022

Ground motion amplification has been observed in most earthquakes triggered near the sediment-filled basins such as 2015 Nepal and 1999 Chi–Chi events. Many numerical studies pointed out the effect of various parameters such as material properties and geometric features of the basin on ground motion amplification but the effect of the complete range of source mechanism parameters needs to be addressed. This paper presents the effect of source mechanism parameters, such as strike, dip, rake angle of fault and moment magnitude ([Formula: see text] on amplification of ground motion intensity measures (IMs) due to sediment-filled basins. It is found out that the maximum spectral amplification, when subjected to the source of lateral strike mechanism, is 45, whereas for thrust fault mechanism, it is 30. Within the lateral strike mechanism, if the dip angle is increased from 0∘to 90∘, the spectral amplification increases from 10 to 30 times. The results also suggested that it is important to consider detailed focal parameters to study basin amplification. The amplification of IMs in the basin is more sensitive to variation of dip angle than the other source parameters and the magnitude of earthquake has an insignificant role in basin amplification.
Hybrid Broadband Ground Motion Simulation for 2015 Mw 7.9 Nepal Earthquake
Authors: Sreejaya K. P. , Raghukanth S. T. G.
Published: April 2022

The present study aims at simulating broadband ground motions in the epicentral region of the 2015[Formula: see text]Mw 7.9 Nepal event, using hybrid broadband technique. The spectral element method is used to simulate the low-frequency ground motion. The three-dimensional material property variation and the basin geometry at the Kathmandu basin are incorporated in the spectral element model. High frequency synthetics are simulated using scattering Green’s function approach by consistently using the source and medium model of low frequency simulation. The simulated Low Frequency (LF) (0–0.3[Formula: see text]Hz) results are combined with high-frequency scatterograms to generate broadband ground motions (0–10[Formula: see text]Hz). The scattering parameters for broadband ground motion simulation are estimated from the recorded data of Nepal mainshock. These parameters are used further for simulating the ground motions over a grid of stations at 2[Formula: see text]km [Formula: see text] 2[Formula: see text]km spacing at the epicentral region. The simulated results are shown as peak ground acceleration (PGA), peak ground velocity, and spectral acceleration contours plots. The maximum PGA in the horizontal and vertical directions are 0.35[Formula: see text]g and 0.32[Formula: see text]g in the epicentral region. Also, the acceleration time histories and corresponding response spectra are presented for some of the selected cities in the region where no records are available. These simulated outcomes are used for analyzing the validity of Indian seismic codal provisions at the near-field of large Himalayan earthquakes. The results show the significant underestimation of near-field hazard by the codal provisions at the Himalayan region.
Complex dynamics of Leslie–Gower prey–predator model with fear, refuge and additional food under multiple delays
Authors: Gupta Ashvini , Kumar Ankit , Dubey Balram
Published: April 2022

In this paper, we analyze a system of delay differential equations incorporating prey’s refuge, fear, fear-response delay, extra food for predators and their gestation lag. First, we examined the system without delay. The persistence, stability (local and global) and various bifurcations are discussed. We provide detailed analysis for transcritical and Hopf-bifurcation. The existence of positive equilibria and the stability of prey-free equilibrium are interrelated. It is shown that (i) fear can stabilize or destabilize the system, (ii) prey refuge in a specific limit can be advantageous for both species, (iii) at a lower energy level (gained from extra food), the system undergoes a supercritical Hopf-bifurcation and (iv) when the predator gains high energy from extra food, it can survive through a homoclinic bifurcation, and prey may become extinct. The possible occurrence of bi-stability with or without delay is discussed. We observed switching of stability thrice via subcritical Hopf-bifurcation for fear-response delay. On changing some parametric values, the system undergoes a supercritical Hopf-bifurcation for both delay parameters. The delayed system undergoes the Hopf-bifurcation, so we can say that both delay parameters play a vital role in regulating the system’s dynamics. The analytical results obtained are verified with the numerical simulation.
An Improving Approach for Top-Rank-k Frequent Pattern Mining
Authors: Nguyen Ham
Published: April 2022

Mining frequent patterns (FPs) received a lot of research interest which is a fundamental problem in the field of data mining. It has been studied on many database types with various applications in intelligent systems. However, the traditional methods using a given threshold to mine FPsterns suffer from a crucial drawback, that is, the number of resulting patterns is uncontrollable, sometimes very large. Mining top-rank-[Formula: see text] FPs is one of the current research directions to solve the aforementioned disadvantage of mining FPs. Many methods have been proposed to mine effectively top-rank-[Formula: see text] frequent patterns. However, these methods also have many limitations regarding memory consumption and suboptimal mining time. Therefore, this study proposes a new method based on [Formula: see text]-list structure using the Priority Queue, the Hash Table structure, and some fast pruning strategies for efficient mining top-rank-[Formula: see text] FPs from binary transaction databases. The experimental results on many datasets demonstrate that the proposed method is more effective than the previous methods in the terms of runtime and memory usage.
Federated Multi-task Graph Learning
Authors: Liu Yijing , Han Dongming , Zhang Jianwei , Zhu Haiyang , Xu Mingliang , Chen Wei
Published: April 2022

Distributed processing and analysis of large-scale graph data remain challenging because of the high-level discrepancy among graphs. This study investigates a novel subproblem: the distributed multi-task learning on the graph, which jointly learns multiple analysis tasks from decentralized graphs. We propose a
federated multi-task graph learning (FMTGL)
framework to solve the problem within a privacy-preserving and scalable scheme. Its core is an innovative data-fusion mechanism and a low-latency distributed optimization method. The former captures multi-source data relatedness and generates universal task representation for local task analysis. The latter enables the quick update of our framework with gradients sparsification and tree-based aggregation. As a theoretical result, the proposed optimization method has a convergence rate interpolates between

\( \mathcal {O}(1/T) \)

and

\( \mathcal {O}(1/\sqrt {T}) \)

, up to logarithmic terms. Unlike previous studies, our work analyzes the convergence behavior with adaptive stepsize selection and non-convex assumption. Experimental results on three graph datasets verify the effectiveness and scalability of FMTGL.
Two-Level Optimization to Reduce Waiting Time at Locks in Inland Waterway Transportation
Authors: Hammedi Wided , Senouci Sidi Mohammed , Brunet Philippe , Ramirez-Martinez Metzli
Published: April 2022

Inland vessels often have to cross numerous locks before reaching their final destination, which leads to a significant delay and sometimes represents as much as half of the total travel time. The delay affects shipment costs and can affect other parts of the transport chain, adversely impacting this transportation mode’s growth. Therefore, this work presents a two-level solution to ensure a shorter waiting time at locks and improve inland waterway transport. On the one hand, the first level focuses on making infrastructural modifications by proposing an efficient
Lock Automation Decision Making (Lock-ADM)
method. The problem modeling consists of using a three-stage algorithm. Firstly, we calculate the optimal number of locks while minimizing the investment costs using the exact solver, CPLEX. Secondly, we measure the importance of locks in the network, and finally, we select the best locks to automate using the
Genetic Algorithm (GA)
metaheuristic. Based on real data, we achieved an average reduction of 33.7% in overall lock waiting time at a low cost. On the other hand, the second level proposes a
Dynamic Lock Scheduling (Lock-DS)
to efficiently manage vessels scheduling at locks by minimizing their waiting time and optimizing their speed. We achieve an average reduction of 69.9% in vessel waiting time and a reduction of 48.03% in total fuel consumption compared to existing scheduling methods. Automating the most important locks with Lock-ADM and managing their crossing with Lock-DS ensure shorter vessels’ waiting time and represent a significant first step towards the automation of inland navigation.
COVID-19 Vaccination Certificates in the Darkweb
Authors: Georgoulias Dimitrios , Pedersen Jens Myrup , Falch Morten , Vasilomanolakis Emmanouil
Published: April 2022

COVID-19 vaccines have been rolled out in many countries and with them a number of vaccination certificates. For instance, the EU is utilizing a digital certificate in the form of a QR-code that is digitally signed and can be easily validated throughout all EU countries. In this article, we document the current state of the COVID-19 vaccination certificate market in the darkweb with a focus on the EU
Digital Green Certificate (DGC)
. We investigate 17 marketplaces and 10 vendor shops that include vaccination certificates in their listings, and discover that a multitude of sellers in both types of platforms are advertising forging capabilities. According to their claims, it is possible to buy fake vaccination certificates issued in many countries worldwide. We demonstrate some examples of such sellers, including how they advertise their services, and we develop a taxonomy of EU COVID-19 certificate forging capabilities, describing the potential methods that the vendors are utilizing to generate certificates. We highlight two particular cases of vendor shops, with one of them showing an elevated degree of professionalism, showcasing forged valid certificates, the validity of which we verify using two different national mobile COVID-19 applications.
Fabrication of High Performing and Durable Nickel-Based Catalyst Coated Diaphragms for Alkaline Water Electrolyzers
Authors: Karacan Cinar , Lohmann-Richters Felix P. , Shviro Meital , Keeley Gareth P. , Müller Martin , Carmo Marcelo , Stolten Detlef
Published: April 2022

In this work, a catalyst coated-diaphragm (CCD) for classical alkaline electrolysis was prepared by the blade-coating method, using Raney nickel as HER catalyst and a Zirfon® (AGFA Perl UTP 500) diaphragm. Our best CCD reduced the overvoltage in an alkaline single cell by 270 mV at 300 mA cm−2 compared to the benchmark, mainly due to the higher catalytic activity and surface area of the Raney nickel electrode. The new electrode system also showed a low degradation rate of 22 μA cm−2 h−1 after 1000 h at a cell voltage of 2 V. The gas purity tests showed that the CCD has hydrogen in oxygen contamination well below the lower explosion limit, even at low current densities. Therefore, we propose the use of our novel CCD architecture for atmospheric alkaline electrolyzers, which have a partly separated electrolyte cycle.
Research on VI Design of Corporate Image Based on Computer Multimedia Technology
Authors: Zhang Shizhuo
Published: April 2022

Core concepts such as corporate culture and management structure are displayed through VI (visual identity) visual design. The key effects of VI visual design include the image design of the entire enterprise and the image design of the main brand of the enterprise. In addition, the overall brand of a business will also be affected. VI visual design also plays an important role in the entire brand promotion. Through VI visual design, you can quickly understand and analyze an enterprise. The development of the Internet has changed people’s living habits and consumption patterns. In order to meet people’s consumption needs, more and more Internet companies have become the mainstream of the industry. Different from the VI image of traditional enterprises, the brand image of the Internet has huge differences in design methods, expression methods, and communication channels. This article objectively analyzes the brand characteristics of current Internet companies, as well as the consumer psychology and needs of consumers in the “Internet +” era, so as to illustrate the importance of the same complete brand image for companies. Next, we will analyze the problems existing in the traditional VI image in the Internet era and design a set of overall Internet brand image design methods. Finally, combined with social development and social hotspots, predictions are made about the changes in brand image design.
Comprehensive Evaluation on Teachers’ Knowledge Sharing Behavior Based on the Improved TOPSIS Method
Authors: Yu Xiaojuan , Hu Dianshun , Li Na , Xiao Yan
Published: April 2022

Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers’ knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher’s knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers’ knowledge sharing behavior evaluation system and method.
A Novel Deep Learning Model for Analyzing Psychological Stress in College Students
Authors: Zhang Hongning
Published: April 2022

Psychological stress refers to the load or oppression that people’s thoughts, feelings, and other inner processes bear, as well as the emotional shifts brought on by the school, work, society, everyday life, interpersonal connections, and other things. It can trigger people’s worry and other negative feelings, making them mentally dejected and frustrated, as well as raise people’s spirits to cheer up and meet stimuli and difficulties. College students are in their 20s, and they are energetic, have extreme mood swings, and are prone to mental problems. As a distinct group in the social development trend, college students are influenced by the learning and growth environment, and their understanding of the world, values, and outlook on life is maintained at the theoretical level, lacking practical thinking and experience, making it difficult to adapt in a short period of time. Excessive psychological strain is caused by new events and new contradicting conditions, which interfere with normal living and learning. This study employs a deep learning model to test and assess the psychological stress in college students, with the goal of addressing the varied psychological stresses that college students are prone to. The deep learning model employed in this paper is based on the classic ResNet50 network model, which compresses its network structure, lowering the computational cost of ResNet50 network model training and increasing the network’s efficiency. To boost processing performance and save storage and computational resources, we trained a network with few parameters, a small model, and high precision. The findings of the investigation can help college officials prevent and recognize problems in students early on. It actively builds a good home-school cooperation mechanism and enhances the students’ ability to cope with and solve stress through enhancing the students’ behavioral experience, so that students can form a good psychological stress coping thinking and behavior, while attaching importance to the cultivation of college students’ psychological quality.
PFF-CB: Multiscale Occlusion Pedestrian Detection Method Based on PFF and CBAM
Authors: Yang Guiyi , Wang Zhengyou , Zhuang Shanna , Wang Hui
Published: April 2022

Occlusion pedestrian detection is an important and difficult task in pedestrian detection. At present, the main method to deal with occlusion pedestrian detection usually adopts pedestrian parts or human body relationship methods. However, in the scene of crowd occlusion or severe pedestrian occlusion, only small parts of the body can be used for detection. Pedestrian parts or human body relationship methods cannot effectively address these issues. In view of the above problems, this paper abandoned the occlusion processing method of pedestrian parts or human body relationship. Considering that it is difficult to establish the relationship between parts and key points. The scale of visible parts of the occlusion pedestrian is small, and the scale of no occlusion pedestrian and occlusion pedestrian in the same picture is different. A multiscale feature attention fusion network named parallel feature fusion with CBAM (PFF-CB) is proposed for occlusion pedestrian detection. Feature information of different scales can be integrated effectively in the PFF-CB module. PFF-CB module uses a convolutional block attention module (CBAM) to enhance the important feature information in space and channel. A parallel feature fusion module based on FPN is used to enhance key features. The performance of the proposed module was tested on two common data sets of occlusion pedestrians with different occlusion types. The results show that the PFF-CB module makes a good performance in occlusion pedestrian detection tasks.
Factors of Parents-Reported Readiness for Hospital Discharge in Children with Acute Leukemia: A Cross-Sectional Study
Authors: Cai Wei , Zheng Xiaofen , Wang Runping , Zhu Huifen , Xu Xinxin , Shen Xiaowen , Zhang Chunmei
Published: April 2022

Aim. The aim of this study is to investigate the existing status and to explore the influencing factors of parents-reported readiness for hospital discharge in children with acute leukemia (AL) in China and to propose optimizing pathways and recommendations of discharge readiness for clinical reference. Methods. A cross-sectional survey was conducted for the 122 children with AL who were discharged from the Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University; their parents were investigated by using the modified Chinese version of Readiness for Hospital Discharge Scale (RHDS) and Quality of Discharge Teaching Scale (QDTS). Data were collected between September 2020 and May 2021.Univariate analysis and multivariate logistic regression analysis were performed to explore the influencing factors of readiness for hospital discharge. Results. The 122 children with AL included 52 females and 70 males with mean age 6.08 years. The total RHDS score was 7.7 ± 1.2, and 68.9% of the participants had high readiness for hospital discharge (RHDS score >7). The total QDTS score was 7.6 ± 2.0. Parent marital status (OR = 4.86, 95% CI: 1.31–18.05), education status (OR = 3.86, 95% CI: 1.18–12.55), family per capita monthly income (OR = 1.08, 95% CI: 1.01–2.99), and high QDTS (OR = 1.56, 95% CI: 1.11–2.68) were risk factors for high RHDS. Conclusions. Our data suggest parents of children with AL had high readiness for hospital discharge and had the ability to take care of their children after discharge. Parental marital status, education status, QDTS score, and family per capita monthly income were independently associated with high RHDS.
Intelligent Transmission Algorithm of College Physical Training Course Information Based on Big Data
Authors: Yu Zhigang
Published: April 2022

Physical education is an important part of higher education. Carrying out physical education teaching in accordance with national standards and carrying out physical fitness tests on a regular basis are the key tasks of physical education teaching. The existing problems are that the college sports curriculum is single, the evaluation standard of the sports curriculum is not unified, the timeliness and guiding significance of the physical education management system are low, and so on. The above problems make it difficult for students to correctly measure their physical health, and teachers have a heavy workload to guide students scientifically through complex data. This paper attempts to design and implement a more timely and readable college students’ physical fitness analysis system. And through data mining technology, we can mine the relevant information hidden in the data to help teachers provide more scientific and effective guidance and suggestions for students. Based on the design of the intelligent control model of athlete training plan based on big data analysis, this paper conducts simulation experiments by using environmental simulation. A training plan to solve the problem of large data fluctuations in the data classification trend is presented.
The Limitation Analysis of Two Dimensional Magnetic Field Configuration in Annular Ion Thruster
Authors: Xia Guangqing , Wang Ziping , Lu Chang , Sun Bin
Published: April 2022

In this paper, the limitations of two-dimensional magnetic field configuration for an annular ion thruster are analyzed theoretically and numerically. The two-dimensional magnetic field, which is commonly used in traditional ion thrusters, has been proved to be a very effective way for the confinement of primary electrons and ions. Hence, the confinement characteristics of primary electrons and ions in an annular ion thruster with the two-dimensional magnetic field configuration are first analyzed theoretically. Then, according to the analysis, a two-dimensional magnetic field for an annular ion thruster is designed. On this basis, the limitations of the two-dimensional magnetic field configuration are analyzed and discussed by a three-dimensional (3D) Particle-in-cell Monte Carlo collision (PIC-MCC) model. The results show that if the collision process is not considered, a uniform distribution of primary electrons can be achieved theoretically. However, when considering the collision process, the atom forms a significant impedance to the circumferential motion of primary electrons, thus greatly increasing the loss rate of primary electrons. Finally, it is found that, since primary electrons are not guided and constrained in the circumferential direction under the two-dimensional magnetic field, the discharge uniformity cannot be improved only by increasing the magnetic field intensity.
Optimization on the Mechanical Properties of Aluminium 8079 Composite Materials Reinforced with PSA
Authors: Refaai Mohamad Reda A. , Reddy R. Meenakshi , Venugopal Jayaprakash , Rao M. Venkata , Vaidhegi K. , Yishak Simon
Published: April 2022

AA 8079 with variable percentages of peanut shell ash (PSA) was created by stir casting (2.5 percent, 5 percent, 7.5 percent, 10 percent, 12.5 percent, and 15 percent). Compared some of the physical and mechanical properties of the composite material were to the alloy matrix alloy (density; porosity percentage; hardness; wear index; tensile strength; and impact strength). Peanut shell ash reinforcements were found to be distributed uniformly throughout the Aluminum Matrix, with pockets of agglomerated reinforcement particles. The hardness, wear index, and density of the composite were enhanced by adding PSA particles. Compared to the matrix alloy without reinforcement, the composites had lower tensile and impact strengths. Mixture design with Design-Expert (Stat EASY) software package indicated the appropriate matrix and reinforcing combination proportions and how they influenced composites’ studied properties. Composites’ physical and mechanical properties were predicted and optimized using regression models created and the ideal mixture of matrix and reinforcement wt. According to optimization findings, the components for optimal composite characteristics’ responses are 93.48 and 6.52 percent of matrix and reinforcement wt% particles. They can utilize PSA-reinforced AA8079 core materials to make automotive components that need lighter, stackable, and wear-resistant.