bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Directional Shape Change Analysis of Biotensegrity Model Mimicking Human Spine Curvature
Authors: Oh Chai Lian , Choong Kok Keong , Nishimura Toku , Kim Jae-Yeol
Published: February 2022

This paper presents a numerical strategy for the shape change analysis of spine biotensegrity models in multi-directional modes. The formulation of incremental equilibrium equations and optimization problem for shape change analysis via the forced elongation of cables to achieve the target coordinates of the monitored nodes of spine biotensegrity models are presented. The distance between the monitored nodes and the target coordinates is chosen as the objective function which is minimized subject to inequality constraints on member axial forces and cable forced elongation. Three spine biotensegrity models were analyzed to validate the effectiveness of the proposed method. The deformation characteristics of the Class-1 four-stage biotensegrity models mimicking the natural curvature of the human spine were investigated. A highly successful rate in achieving the target coordinates was observed in a total of 258 analysis cases, with percentages of 99.9%, 99.9% and 98.9% for shape change analysis involving uni-, bi- and tri-directional modes, respectively. The results show that the spine biotensegrity models have more flexibility in undergoing bending in comparison with axial deformation. With the established shape change strategy, the flexibility and versatility of the movement of spine biotensegrity models can be further studied for potential application in the shape change control of deployable structures together with the use of IoT.
Dynamic Analysis of Quasi-Zero Stiffness Pneumatic Vibration Isolator
Authors: Vo Ngoc Yen Phuong , Le Thanh Danh
Published: February 2022

This paper focuses on analyzing the dynamic response of an innovated quasi-zero stiffness pneumatic vibration isolator (QZSPVI) using two mechanisms, including wedge and semicircle cam. Different from other studies relating quasi-zero stiffness isolation system, the pneumatic cylinder in this paper works as an air spring in order to easily adjust the dynamic stiffness of the proposed system according to the change of the isolated load through regulating the pressure. Firstly, the dynamic stiffness of the QZSPVI will be analyzed. Then, the condition for which the minimum dynamic stiffness is quasi-zero around the equilibrium position is also determined. The fundamental resonance response of the QZSPVI subjected to the externally harmonic force is analyzed through multi-scale method and the numerical simulations are verified. Secondly, due to exiting relative sliding frictional phenomenon between the cylinder and piston, instead of an experiment, another key content of this work is to identify the friction force model of the cylinder through virtual prototyping model. From this identified result, the complex dynamic response of the QZSPVI and coexistence of multiple solutions will be discovered by realizing the direct integration of the original dynamic equation through using the 5th-order Runge–Kutta algorithm. The analysis and simulation results clearly show the advantages of the proposed model against the equivalent pneumatic vibration isolator (EPVI), which only employs the wedge mechanism. This research will offer a useful insight into design and QZSPVI in practice.
Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks
Authors: Islam Md Kamrul , Gazder Uneb , Alam Md Shah , Shalabi Faisal I. , Arifuzzaman Md
Published: February 2022

Flexible pavement deterioration due to moisture intrusion and aging is the key concern worldwide for highway engineers. However, this damage has not been properly investigated in detail due to lack of appropriate experimental and modeling techniques. Such lacking hinders the design of long-lasting pavements, as the effects of environmental damages are unknown, especially for modified asphalt. Therefore, the current study aims at determining a better approach for modeling asphalt adhesion damage using Artificial Neural Networks (ANNs). The Atomic Force Microscopy (AFM) test was deployed to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and Antistripping Agents (ASAs). Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used in this effort. Two popular modifications, namely ensemble learning and hierarchical modeling, were also engaged to achieve convenient and accurate damage models. The analysis found that RBFNN was better suited for hierarchical models than MLP. RBFNN is preferred for aged and moisture-damaged samples which have less variation in their datasets. Hierarchical models are convenient to apply as they can be applied to any type of asphalt sample. However, they produced a small reduction in accuracy (less than 10%) as compared to other models. The accuracy of the hierarchical model was found to be satisfactory. The ensemble learning approach showed slight improvement in accuracy for all models ranging between 1–3%, i.e., 6–8 nN. This study recommends the use of hierarchical models, developed with ensemble learning, for prediction of asphalt damage. The results of the study will be helpful for researchers and practitioners working on pavement materials for developing prediction models to prepare a better mix design of polymer modified asphalt.
Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing
Authors: Kim Myungsoo , Lee Jaehyeong , Lee Chaegyu , Jeong Jongpil
Published: February 2022

Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models predict historical market demand, predict future demand, and enable systematic inventory management. However, in most small and medium enterprise (SMEs), there is no systematic management of data, and because of the lack of data and the volatility of random data, it is difficult for prediction models to work well. Since the predictive model is a core function derived from the management of the enterprise’s inventory data, the poor performance of the model causes the company’s inventory data-management system to be degraded. Companies that have poor inventory data because of this vicious cycle will continue to have difficulty introducing data-management systems. In this paper, we propose a framework that can reliably predict the inventory data of a firm by modeling the volatility of a firm stochastically. The framework makes the prediction using the point prediction model by means of LSTM(Long Short Term Memory), the 2D kernel density function, and the prediction result reflecting inventory-management cost. Through various experiments, the necessity of interval prediction in demand prediction and the validity of the cost-effective prediction model through the readjustment function were shown.
Calculation of the Dangerous Failure Rate of the Safety Function
Authors: Rástočný Karol , Ždánsky Juraj , Hrbček Jozef , Medvedík Milan
Published: February 2022

Each safety-related function must be implemented with a defined safety integrity level (SIL) if the control system implements safety-related functions (SFs) in addition to the standard control functions. The required SIL of the SF depends on the quantity of the risk associated with the failure of this one SF. The SIL against random failure can be expressed through the dangerous failure rate of the SF for an electronic safety-related control system (ESRCS) operating in a continuous mode of operation. The proof must be provided (among other things) that the SIL requirements for the individual SFs are met so the ESRCS can be accepted and implemented. The assessment of the impact of random failures on the SIL of the SF must be performed using the quantitative analysis method. This paper describes the procedure and derives equations for evaluating the impact of random failure on SIL of the SF using Markov chains with two absorption states. The achieved results are presented for SF implemented by ESRCS with dual architecture based on composite fail-safety technique.
Numerical Analysis of Newtonian Heating Convective Flow by Way of Two Different Surfaces
Authors: Rehman Khalil Ur , Shatanawi Wasfi , Ashraf Shazia , Kousar Nabeela
Published: February 2022

In this paper, we offer a numerical study on heated non-Newtonian fluid with a Newtonian heating effect towards thermally stable stretching surfaces. A comparative analysis for two stretched surfaces, namely, plate and cylinder, is offered. The Casson fluid model is considered to be a non-Newtonian fluid model. By applying the suitable set of transformations, the non-linear coupled PDEs are transformed into non-linear ODEs. It is difficult to obtain the exact solution of such non-linear differential equations; therefore, we used the shooting method along with Runge–Kutta scheme. The influence of pertinent flow variables on velocity and temperature is presented through graphs. Notably from the results, heat generation parameters, Newtonian heating, and magnetic parameters enhanced the temperature profile, whereas Casson fluid and magnetic field parameters reduced the fluid velocity. It is also observed that increases in fluid temperature were more influenced at the cylindrical surface as compared with the flat plate. Moreover, we obtained remarkable results for the heat transfer rate by imposing Newtonian heating conditions at the surface; tables are used to present variations in the skin friction coefficient and Nusselt number at the thermally stable surfaces.
Parallel fuzzy minimals on GPU
Authors: Manacero Aleardo , Guariglia Emanuel , de Souza Thiago Alexandre , Lobato Renata Spolon , Spolon Roberta
Published: February 2022

Clustering is a classification method that organizes objects into groups based on their similarity. Data clustering can extract valuable information, such as human behavior, trends, and so on, from large datasets by using either hard or fuzzy approaches. However, this is a time-consuming problem due to the increasing volumes of data collected. In this context, sequential executions are not feasible and their parallelization is mandatory to complete the process in an acceptable time. Parallelization requires redesigning algorithms to take advantage of massively parallel platforms. In this paper we propose a novel parallel implementation of the fuzzy minimals algorithm on graphics processing unit as a high-performance low-cost solution for common clustering issues. The performance of this implementation is compared with an equivalent algorithm based on the message passing interface. Numerical simulations show that the proposed solution on graphics processing unit can achieve high performances with regards to the cost-accuracy ratio.
Unique Features of Nonlocally Nonlinear Systems with Oscillatory Responses
Authors: Liang Guo , Liu Jinlong , Hu Wei , Guo Qi
Published: February 2022

We review the recent investigation of a new form of nonlocally nonlinear system with oscillatory responses. The system has various new features, such as the nonlocality-controllable transition of self-focusing and self-defocusing nonlinearities, a unique modulational instability and new forms of solitons. We also discuss the propagation of the optical beam in a nematic liquid crystal with negative dielectric anisotropy and demonstrate theoretically that propagation can be modelled by the system.
Simulation of Static Tyre–Pavement Interaction Using Two FE Models of Different Complexity
Authors: Király Tamás , Primusz Péter , Tóth Csaba
Published: February 2022

The aim of this research study was to analyse the approaches for establishing a spatial model of a radial truck tyre, based on the finite element method, in order to perform a realistic analysis of static as well as dynamic tyre–pavement interactions. A complex rubber tyre model having a large number of elements was formulated combining current state-of-the-art modelling techniques and, from that model, a simplified model having a smaller number of elements was derived. The complex model proved to be useful only for static loading, because of its high computational demand, while the simplified model proved to be also suitable for dynamic modelling. The two tyre models having different numbers of elements were compared by analysing the contact areas and stresses. Our results indicate that the basic idea of not changing material characteristics while simplifying the model, rebuilding only the carcass using composite shell elements, did not prove to be a satisfactory direction. The results given by the simplified model do not describe the behaviour of the radial tyre well but, rather, describe the behaviour of the diagonal tyre, regarding contact areas and stresses. On the contrary, when analysing stresses and strains in the road pavement structure, the two finite element models provided similar results in practice. Based on our comparison calculations, applying the average contact pressure q at analysis points at a 5–8 cm depth, the contact behaviour of the finite element tyre model can be used in any elastic-layer theory-based software.
Bogie Stability Control and Management Using Data Driven Analysis Techniques for High-Speed Trains
Authors: Shiau Jiun-Yan , Wang Sze-Tsung
Published: February 2022

The bogie is a critical component of a train set. Any failure in the bogies’ structure/mechanism transmission will result in a derailment in the revenue operation. In order to prevent derailment due to bogie failures, the Bogie Instability Detection System (BIDS) is installed on high-speed trains, which provides alarms when abnormality is detected so that appropriate action can be taken. Taking the Taiwan High Speed Rail Corporation (THSRC) as an example, there have been ongoing reports of BIDS alarms since its launch. In almost every case, intermittent warnings are generated when wheel/rail interface faults occur, but no hard fault is found in the bogie’s structure or mechanism. While these reports do not constitute hard faults, corresponding operation actions and inspections are necessary nonetheless. As a result, delays and additional maintenance costs are incurred. In order to save maintenance costs and reduce delays, a decision tree algorithmic approach is proposed to study the potential factors of wheel/rail interface concerns, discover relationships between potential factors, and set up rules for BIDS alarms. In this case study, we found that temperature is the main factor for BIDS alarms; train speed, accumulated mileage, and location are subordinate factors. The proposed decision tree approach can lead to some improvements in preventive maintenance. It also suggests embedding such an intelligent algorithm into BIDS control units for drivers to avoid BIDS alarms.
Biodegradable Solvents: A Promising Tool to Recover Proteins from Microalgae
Authors: Moldes David , Rojo Elena M. , Bolado Silvia , García-Encina Pedro A. , Comesaña-Gándara Bibiana
Published: February 2022

The world will face a significant protein demand in the next few decades, and due to the environmental concerns linked to animal protein, new sustainable protein sources must be found. In this regard, microalgae stand as an outstanding high-quality protein source. However, different steps are needed to separate the proteins from the microalgae biomass and other biocompounds. The protein recovery from the disrupted biomass is usually the bottleneck of the process, and it typically employs organic solvents or harsh conditions, which are both detrimental to protein stability and planet health. Different techniques and methods are applied for protein recovery from various matrices, such as precipitation, filtration, chromatography, electrophoresis, and solvent extraction. Those methods will be reviewed in this work, discussing their advantages, drawbacks, and applicability to the microalgae biorefinery process. Special attention will be paid to solvent extraction performed with ionic liquids (ILs) and deep eutectic solvents (DESs), which stand as promising solvents to perform efficient protein separations with reduced environmental costs compared to classical alternatives. Finally, several solvent recovery options will be analyzed to reuse the solvent employed and isolate the proteins from the solvent phase.
Food Allergies: Immunosensors and Management
Authors: Krisnawati Dyah Ika , Alimansur Moh , Atmojo Didik Susetiyanto , Rahmawati Elfi Quyumi , Rahayu Dwi , Susilowati Erna , Kuo Tsung-Rong
Published: February 2022

Food allergies (FA) are commonly depicted as immune responses. The mechanism of allergic reactions involves immunoglobulin E (IgE) and non-immunoglobulin E (non-IgE)-related responses caused by contact with specific foods. FAs can be fatal, have negative effects and have become the subject of fanaticism in recent years. In terms of food safety, allergic compounds have become a problem. The immune response to allergens is different to that from food intolerance, pharmacological reactions, and poisoning. The most important allergenic foods are soybeans, milk, eggs, groundnuts, shellfishes, tree nuts, cereals and fish, which together are known as the “Big Eight”. This review will introduce and discuss FAs in milk, peanuts, nuts, shellfishes, eggs and wheat and their detections and potential treatments will also be provided. We believe that this review may provide important information regarding food-induced allergies for children who have allergic reactions and help them avoid the allergenic food in the future.
Non-Linear Analysis of Structures Utilizing Load-Discretization of Stiffness Matrix Method with Coordinate Update
Authors: Saeed Najmadeen , Manguri Ahmed , Szczepanski Marcin , Jankowski Robert
Published: February 2022

This paper proposes a stiffness method based structural analysis algorithm for geometrically non-linear structures. In this study, the applied load on the joints has been discretized to a sequence of a few loadings applied. Each loading step produces incremental external nodal displacements, which are added to the corresponding coordinates to get a new geometrical shape of the structure. This process is iteratively repeated until the sum of the loading of all iterations matches the total initial applied loading. The size of the increments affects the technique’s accuracy, subsequently affecting the number of iterations. The configuration of non-linear geometrical structures is vital in the work; a slight change of the coordinates makes a considerable variation of nodal displacements. In this paper, three pin-jointed assemblies and a cantilever beam have been examined using the proposed technique; significantly reasonable outcomes emerged, compared to the non-linear approaches, such as Dynamic Relaxation Method (DRM) and Non-linear approach by Kwan. In a numerical sense, the dissimilarity between the results of the conventional Stiffness Matrix (SM) method and the non-linear method is about 228%, while the maximum discrepancy between the proposed approach and the non-linear methods is just above 15%.
Neuronal Constraint-Handling Technique for the Optimal Synthesis of Closed-Chain Mechanisms in Lower Limb Rehabilitation
Authors: Muñoz-Reina José Saúl , Villarreal-Cervantes Miguel Gabriel , Corona-Ramírez Leonel Germán , Valencia-Segura Luis Ernesto
Published: February 2022

The optimal methods for the synthesis of mechanisms in rehabilitation usually require solving constrained optimization problems. Metaheuristic algorithms are frequently used to solve these problems with the inclusion of Constraint-Handling Techniques (CHTs). Nevertheless, the most used CHTs in the synthesis of mechanisms, such as penalty function and feasibility rules, generally prioritize the search for feasible regions over the minimization of the objective function, and it notably influences the exploration and exploitation of the algorithm such that it could induce in the premature convergence to the local minimum and thus the solution quality could deteriorate. In this work, a Neuronal Constraint-Handling (NCH) technique is proposed and its performance is studied in the solution of mechanism synthesis for rehabilitation. The NCH technique uses a neural network to search for the fittest solutions into the feasible and the infeasible region to pass them to the next generation of the evolutionary process of the Differential Evolution (DE) algorithm and consequently improve the obtained solution quality. Two synthesis problems with four–bar and cam–linkage mechanisms are the study cases for developing lower-limb rehabilitation routines. The NCH is compared with four state-of-the-art constraint-handling techniques (penalty function, feasibility rules, stochastic ranking, ϵ-constrained method) included into four representative metaheuristic algorithms. The irace package is used for both the algorithm settings and neuronal network training to fairly and meaningfully compare through statistics to confirm the overall performance. The statistical results confirm that, despite changes in the rehabilitation trajectories, the proposal presents the best overall performance among selected algorithms in the studied synthesis problems for rehabilitation, followed by penalty function and feasibility rule.
Mechanically Tunable Extracellular Matrix of Genipin Crosslinked Collagen and Its Effect on Endothelial Function
Authors: Robinson Jacob , Russell Teal , Xu Zhigang , Yun Yeoheung
Published: February 2022

Mechanical rigidity of a matrix, to which cells adhere, plays a significant role in regulating phenotypic cellular behaviors such as spreading and junction formation because vascular cells sense and respond to changes in their mechanical environment. Controlling mechanical properties of extracellular matrix by using a crosslinker is important for cell and tissue mechanobiology. In this paper, we explored genipin, a natural plant extract, to crosslink collagen-I in order to enhance mechanical properties with low cytotoxicity. We characterized the effects of genipin concentration on the mechanical properties, color change, degradation, structure, cell viability, and endothelial function such as transendothelial electrical resistance (TEER). Through the analysis of both material properties and endothelial response, it was found that genipin-based glycation caused an increase in viscoelastic moduli in collagen hydrogels, as well as increased fiber density in their structural morphology. Endothelial cells were found to form better barriers, express higher levels of tight junction proteins, and exhibit better adhesion on stiffer matrices.
Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image
Authors: Kim Songeun , Park Soon-Yong
Published: February 2022

Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.
A Deep Learning-Based Password Security Evaluation Model
Authors: Hong Ki Hyeon , Lee Byung Mun
Published: February 2022

It is very important to consider whether a password has been leaked, because security can no longer be guaranteed for passwords exposed to attackers. However, most existing password security evaluation methods do not consider the leakage of the password. Even if leakage is considered, a process of collecting, storing, and verifying a huge number of leaked passwords is required, which is not practical in low-performance devices such as IoT devices. Therefore, we propose another approach in this paper using a deep learning model. A password list was made for the proposed model by randomly extracting 133,447 words from a total of seven dictionaries, including Wikipedia and Korean-language dictionaries. After that, a deep learning model was created by using the three pieces of feature data that were extracted from the password list, as well as a label for the leakage. After creating an evaluation model in a lightweight file, it can be stored in a low-performance device and is suitable to predict and evaluate the security strength of a password in a device. To check the performance of the model, an accuracy evaluation experiment was conducted to predict the possibility of leakage. As a result, a prediction accuracy of 95.74% was verified for the proposed model.
An Implementation of the HDBSCAN* Clustering Algorithm
Authors: Stewart  Geoffrey , Al-Khassaweneh Mahmood
Published: February 2022

An implementation of the HDBSCAN* clustering algorithm, Tribuo Hdbscan, is presented in this work. The implementation is developed as a new feature of the Java machine learning library Tribuo. This implementation leverages concurrency and achieves better performance than the reference Java implementation. Tribuo Hdbscan provides prediction functionality, which is a novel technique to make fast predictions for unseen data points using an HDBSCAN* clustering model. Tribuo Hdbscan cluster results and performance measurements are also compared with the state-of-the-art HDBSCAN* implementation, the Python module hdbscan.
Machine-Learning Model Prediction of Ionic Liquids Melting Points
Authors: Acar Zafer , Nguyen Phu , Lau Kah Chun
Published: February 2022

Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (Tm) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the Tm of ILs, a systematic study and accurate prediction of Tm of ILs is highly desirable. However, the Tm of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in Tm of ILs can be challenging. In this study, we employed a deep-learning model to predict the Tm of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an R2 score of 0.90 with RMSE of ~32 K, and the Tm of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the Tm of ILs.
Learning Human Strategies for Tuning Cavity Filters with Continuous Reinforcement Learning
Authors: Wang Zhiyang , Ou Yongsheng
Published: February 2022

Learning to master human intentions and behave more humanlike is an ultimate goal for autonomous agents. To achieve that, higher requirements for intelligence are imposed. In this work, we make an effort to study the autonomous learning mechanism to solve complicated human tasks. The tuning task of cavity filters is studied, which is a common task in the communication industry. It is not only time-consuming, but also depends on the knowledge of tuning technicians. We propose an automatic tuning framework for cavity filters based on Deep Deterministic Policy Gradient and design appropriate reward functions to accelerate training. Simulation experiments are carried out to verify the applicability of the algorithm. This method can not only automatically tune the detuned filter from random starting position to meet the design requirements under certain circumstances, but also realize the transfer of learning skills to new situations, to a certain extent.
Long Term Trends of Base Cation Budgets of Forests in the UK to Inform Sustainable Harvesting Practices
Authors: Vanguelova Elena , Benham Sue , Nisbet Tom
Published: February 2022

There is growing concern in the UK that available base cation pools in soil are declining due to the combined effects of acid deposition and forest harvesting. To help inform the issue, elemental mass balances for calcium (Ca), magnesium (Mg) and potassium (K) were calculated using more than 10-years (10–24 years) of data from the UK’s ICP Forest Intensive Monitoring Network (Level II) of plots, covering a range of soil types and three tree species—oak, Scots pine and Sitka spruce. Out of the ten sites investigated, small negative Ca balances were observed at three sites and negative K balances on two sites, all on acid geology and nutrient poor soils, which were previously heavily acidified due to acid deposition. There is sufficient Ca and K in the soil exchangeable pool to sustain forest growth on these sites, however, if the present rate of Ca and K loss continues forest health and productivity are likely to be threatened within a few forest rotations. Magnesium showed a positive balance at all but one site, partly sustained by marine deposition. Base cation budgets were significantly (p < 0.01) positively related to soil exchangeable cations and soil base saturation status. Six of the sites showed an increasingly statistically significant positive cation balance with time, attributed to a decline in leaching linked to recovery from acidification. This included the three sites with negative Ca balance, although Ca remained in deficit. One site (Alice Holt) exhibited a decreasing cation balance, driven by a continued significant decline in base cation deposition thought to be related to pollutant emission control. The results were used to simulate the impact of different forest biomass harvesting scenarios involving the removal of brown (extracted after needle drop) or green (extracted before needle drop) brash. Podzols and deep peats were found to be the most vulnerable to brash harvesting causing Ca and K imbalance, but problems also occurred on brown earths. Impacts were greatest for the extraction of green brash from higher productivity stands. Base cation balance calculations remain highly uncertain due to the restricted nature of available measurements and wide variation of some estimates, particularly inputs from mineral weathering. More data are required to check and improve model predictions to better guide forest harvesting practice and ensure sustainable forest management.
Sleep-Disordered Breathing Risk with Comorbid Insomnia Is Associated with Mild Cognitive Impairment
Authors: Agudelo Christian , Ramos Alberto , Sun Xiaoyan , Kaur Sonya , Del Papa Dylan , Kather Josefina , Wallace Douglas ,   on behalf of the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: February 2022

Introduction: Few studies have evaluated the combined association between SDB with comorbid insomnia and mild cognitive impairment (MCI). To test the hypothesis that SDB with comorbid insomnia is associated with greater odds of MCI than either sleep disorder independently, we used ADNI data to evaluate cross-sectional associations between SDB risk with comorbid insomnia status and MCI. Methods: Participants with normal cognition or MCI were included. Insomnia was defined by self-report. SDB risk was assessed by modified STOP-BANG. Logistic regression models evaluated associations between four sleep disorder subgroups (low risk for SDB alone, low risk for SDB with insomnia, high risk for SDB alone, and high risk for SDB with insomnia) and MCI. Models adjusted for age, sex, BMI, APOE4 genotype, race, ethnicity, education, marital status, hypertension, cardiovascular disease, stroke, alcohol abuse, and smoking. Results: The sample (n = 1391) had a mean age of 73.5 ± 7.0 years, 44.9% were female, 72.0% were at low risk for SDB alone, 13.8% at low risk for SDB with insomnia, 10.1% at high risk for SDB alone, and 4.1% at high risk for SDB with insomnia. Only high risk for SDB with comorbid insomnia was associated with higher odds of MCI (OR 3.22, 95% CI 1.57–6.60). Conclusion: Studies are needed to evaluate SDB with comorbid insomnia as a modifiable risk factor for MCI.
Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
Authors: Jaamoum Amine , Hiscock Thomas , Di Natale Giorgio
Published: February 2022

Cache timing attacks, i.e., a class of remote side-channel attack, have become very popular in recent years. Eviction set construction is a common step for many such attacks, and algorithms for building them are evolving rapidly. On the other hand, countermeasures are also being actively researched and developed. However, most countermeasures have been designed to secure last-level caches and few of them actually protect the entire memory hierarchy. Cache randomization is a well-known mitigation technique against cache attacks that has a low-performance overhead. In this study, we attempted to determine whether address randomization on first-level caches is worth considering from a security perspective. In this paper, we present the implementation of a noise-free cache simulation framework that enables the analysis of the behavior of eviction set construction algorithms. We show that randomization at the first level of caches (L1) brings about improvements in security but is not sufficient to mitigate all known algorithms, such as the recently developed Prime–Prune–Probe technique. Nevertheless, we show that L1 randomization can be combined with a lightweight random eviction technique in higher-level caches to mitigate known conflict-based cache attacks.
DoH Tunneling Detection System for Enterprise Network Using Deep Learning Technique
Authors: Nguyen Tuan Anh , Park Minho
Published: February 2022

In spite of protection mechanisms for Domain Name System (DNS), such as IP blacklist and DNS Firewall, DNS still has privacy issues in reality, since DNS is a plain-text protocol. Recently, to resolve this problem, an encrypted DNS, called DNS-over-HTTPS (DoH), has been developed, and is becoming more widespread. As the secured version of DNS, DoH guarantees privacy and security to prevent various attacks such as eavesdropping and manipulating DNS data by using the HTTPS protocol to encrypt the data between the DoH client and the DoH-based DNS resolver. DoH is one of the best security options for an enterprise network where more sensitive data protection is required. However, DoH may cause an unintended security breach, i.e., information leakage via malicious DoH tunneling. Since the DoH traffic is encrypted and indistinguishable from other HTTPS traffic, data hidden inside DoH packets can be easily leaked out of an enterprise network. Although some countermeasures to detect DoH tunneling attacks have been proposed, they still have limitations. Previous research used Supervised Machine Learning methods to detect DoH tunneling, which required a high volume of labeled data. In practice, collecting and labeling all of the data is an impossible task, especially in DoH, when all of the data are encrypted. Furthermore, Supervised Machine Learning methods rely heavily on human-engineered feature extraction, which makes classifying encrypted DoH traffic difficult. Furthermore, no previous research has mentioned a complete functional DoH detection applied to network infrastructure. Therefore, we propose a detection system for DoH tunneling attacks based on Transformer to detect a malicious DoH tunneling and build a fully functional DoH detection system that can be integrated with the security operation system of an enterprise network. The experiment results show a significant improvement compared with previous works.
Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions
Authors: Silva-López Mariana Sarai , Ilizaliturri Hernández César Arturo , Navarro Contreras Hugo Ricardo , Rodríguez Vázquez Ángel Gabriel , Ortiz-Dosal Alejandra , Kolosovas-Machuca Eleazar Samuel
Published: February 2022

Cervical cancer is frequent neoplasia. Currently, the diagnostic approach includes cervical cytology, colposcopy, and histopathology studies; combining detection techniques increases the sensitivity and specificity of the tests. Raman spectroscopy is a high-resolution technique that supports the diagnosis of malignancies. This study aimed to evaluate the Raman spectroscopy technique discriminating between healthy and premalignant/malignant cervical cells. We included 81 exfoliative cytology samples, 29 in the “healthy group” (negative cytology), and 52 in the “CIN group” (premalignant/malignant lesions). We obtained the nucleus and cytoplasm Raman spectra of individual cells. We tested the spectral differences between groups using Permutational Multivariate Analysis of Variance (PERMANOVA) and Canonical Analysis of Principal Coordinates (CAP). We found that Raman spectra have increased intensity in premalignant/malignant cells compared with healthy cells. The characteristic Raman bands corresponded to proteins and nucleic acids, in concordance with the increased replication and translation processes in premalignant/malignant states. We found a classification efficiency of 76.5% and 82.7% for cytoplasmic and nuclear Raman spectra, respectively; cell nucleus Raman spectra showed a sensitivity of 84.6% in identifying cervical anomalies. The classification efficiency and sensitivity obtained for nuclear spectra suggest that Raman spectroscopy could be helpful in the screening and diagnosis of premalignant lesions and cervical cancer.
Time Series Visualization and Forecasting from Australian Building and Construction Statistics
Authors: Zhang Wei Emma , Chang Ruidong , Zhu Minhao , Zuo Jian
Published: February 2022

The Australian Bureau of Statistics (ABS) regularly releases statistical information, for the whole of Australia, for public access. Building- and construction-related statistics are important to reflect the status of this pillar industry of Australia and help researchers, practitioners, and investors with decision-making. Due to complex retrieval hierarchy of ABS’s website and irregular update frequency, it is usually time-consuming to find relevant information. Moreover, browsing the raw data from ABS’s webpages could not provide the insights to the future. In this work, we applied techniques from computer science to help users in the building and construction domain to better explore the ABS statistics and forecast the future trends. Specifically, we built an integrated Web application that could help collect, sort, and visualize the ABS statistics in a user-friendly and customized way. Our Web application is publicly accessible. We further injected our insights into the Web application, based on the existing data by providing online forecasting on user’s interested information. To achieve this, we identified a series of related economic factors as features and adjusted a multi-variant, LSTM-based time series forecasting model by considering the most informative factors. We also compared our approach with the most widely used SARIMA-based forecasting model to show the effectiveness of the deep learning-based models. The forecast values are depicted at the end of the time series plots, selected by the users.
A Theoretical Model to Identify the Fundamental Frequency of Simply Supported Girders from a Passing Heavy Vehicle
Authors: Wu Qi , Han Bing , Yu Jiaping , Yan Wutong , Zhang Jinquan , Xie Huibing
Published: February 2022

This paper discusses the influence of vehicle parameters on the identification accuracy of bridge fundamental frequency from a passing vehicle and validates such influence through laboratory tests. In the process of theoretical derivation, the interaction between a vehicle and bridge is no longer ignored to uncouple the equation group and the stepwise integration method is used to solve the equation group. Key parameters such as the vehicle speed αv, vehicle-to-bridge mass ratio αmv, and frequency ratio μ1, are dimensionless. Based on the simplified vehicle-to-bridge system model, the numerical analysis method is used to analyze their influence on the identified bridge frequency. Through parameter analysis, it is found that the vehicle-to-bridge mass ratio and vehicle speed have a great impact on the patterns of identified bridge frequency; and when the vehicle frequency is greater than or less than the bridge frequency, the identified bridge frequency exhibits different patterns. The bridge frequency deviation rate can be greater than 20% when the vehicle speed and vehicle-to-bridge mass ratio increases. In order to ensure the accuracy of bridge frequency identification, it is recommended that the vehicle-bridge mass ratio should not exceed 10%. Besides, appropriately increasing the vehicle-to-bridge mass ratio will increase the probability of accurately identifying the bridge frequency under certain conditions. In addition, a novel frequency extraction method was proposed to more accurately filter out the frequency from the vehicle acceleration spectrum.
Physicochemical and Storage Characteristics of Pork Tteokgalbi Treated with Boesenbergia pandurata (Roxb.) Powder
Authors: Lee Jae-Joon , Choi Jungseok , Ha Jung-Heun
Published: February 2022

This study investigates the physicochemical and storage characteristics of Tteokgalbi treated with various concentrations of Boesenbergia pandurata (Roxb.) powder (BP). BP is constituted mainly of carbohydrates (77.9%), possesses free-radical scavenging activity due to the presence of polyphenol and flavonoids, and is slightly acidic (pH 5.99). Five Tteokgalbi samples were treated with 0 (CON), 0.5% (B1), 1.0% (B2), or 2.0% (B3) of BP or 0.05% of ascorbic acid (REF). Compared to CON, BP-treated Tteokgalbi demonstrated significantly higher carbohydrate content and water-holding capacity and decreased cooking loss (%). BP-treated Tteokgalbi had significantly altered Hunter color properties, with decreased L* and increased b* values. Additionally, BP treatment significantly changed the textural properties by increasing the hardness (B3) and chewiness (B2 and B3) and decreasing the springiness (B3) of Tteokgalbi. Owing to the increased total polyphenol and flavonoid content, BP addition significantly enhanced the DPPH and ABTS radical scavenging activities of Tteokgalbi during vacuum-packed cold storage (0–14 days) at 5 °C. BP-treated Tteokgalbi maintained a higher pH compared to CON, and BP-treatment significantly suppressed 2-thiobarbituric acid, volatile basic nitrogen, and total microbial count during the cold storage period (7 and 14 days). Therefore, BP is a natural, edible antioxidant that may be applied to Tteokgalbi.
Ground Roll Attenuation of Multicomponent Seismic Data with the Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) Method
Authors: Xiao Liying , Zhang Zhifu , Gao Jianjun
Published: February 2022

Multicomponent seismic exploration provides more wavefield information for imaging complex subsurface structures and predicting reservoirs. Ground roll is strongly coherent noise in land multicomponent seismic data and exhibits similar features, which are strong energy, low frequency, low velocity and dispersion, in each component. Ground roll attenuation is an important step in seismic data processing. In this study, we utilized multivariate empirical mode decomposition to multicomponent seismic data for attenuating ground roll. By adding extra components containing independent white noise, noise-assisted multivariate empirical mode decomposition is adopted to overcome the mode-mixing effect in standard empirical mode decomposition. This method provides a more robust analysis than the standard empirical mode decomposition EMD method performed separately for each component. Multicomponent seismic data are decomposed into different intrinsic mode functions in frequency scale. According to different frequency scales of seismic reflection wave and ground roll, intrinsic mode functions with low frequency are eliminated to suppress ground roll, and the remaining are reconstructed for seismic reflection waves. Synthetic and field data tests show that the proposed approach performs better than the traditional attenuation method.
Electromyography, Wavelet Analysis and Muscle Co-Activation as Comprehensive Tools of Movement Pattern Assessment for Injury Prevention in Wheelchair Fencing
Authors: Borysiuk Zbigniew , Błaszczyszyn Monika , Piechota Katarzyna , Cynarski Wojciech J.
Published: February 2022

The aim of the study was to determine the correct movement patterns of fencing techniques in wheelchair fencers. Through a comprehensive analysis, the key muscles in the kinematic chain exposed to potential injuries were identified. The study participants were 16 wheelchair fencers, divided into two groups representing two categories of disability: Group A (N = 7) comprising fencers with mild paraplegia, having freedom of movement of the trunk and arms; and Group B (N = 9) comprising fencers with a spinal cord injury and partial paresis of the arms. EMG and an accelerometer were used as the main research tools. The EMG electrodes were placed on the muscles of the sword arm as well as on the left and right sides of the abdomen and torso. The EMG signal was transformed using wavelet analysis, and the muscle activation time and co-activation index (CI) were determined. In Group A fencers, first the back and abdominal muscles were activated, while in Group B, it was the deltoid muscle. The wavelet coherence analysis revealed intermuscular synchronization at 8–20 Hz for Group A fencers and at 5–15 Hz for Group B fencers. In Group A fencers, the co-activation index was 50.94 for the right-side back and abdominal muscles, 50.75 for the ECR-FCR, and 47.99 for the TRI-BC pairs of upper limb muscles. In contrast, Group B fencers demonstrated higher CI values (50.54) only for the postural left-side muscle pairs. Many overload injuries of the shoulder girdle, elbow, postural muscles, spine, and neck have been found to be preventable through modification of current training programs dominated by specialist exercises. Modern wheelchair fencing training should involve neuromuscular coordination and psychomotor exercises. This will facilitate the individualization of training depending on the fencer’s degree of disability and training experience.