bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
Authors: Huang Tiancheng , Du Maohua , Gu Xuekun , Cheng Xiao
Published: August 2022

Bone drilling is a common surgery procedure. The drill bit shape directly affects the drilling force. Excessive drilling force may cause bone damage. In this work, on the premise of analyzing and comparing several finite element method (FEM) simulation results for drill bit of 5 mm in diameter commonly used in medical practice, a combination of drilling speed and feed rates which can minimize the drilling force for drilling parameters is determined. Then, the effects of the drill bit shape parameters including helix angle, point angle and edge radius on the drilling force are simulated by using the obtained drilling parameters, and after validation the FEM analysis results show that their variation trend is the same as the experimental one. Then, the optimum bit structure parameters are obtained through the following research: (1) the prediction model of the relationship between drill edge parameters and drilling force is established based on the result of FEM of the drilling process; (2) A particle swarm optimization algorithm is used to obtain the optimal matching parameters of the bit structure; (3) The priority order of the influence of the parameters of the bit on the drilling force is analyzed. The results show that the order of the influence is: the edge radius is the largest, the point angle is the second, and the helix angle is the smallest. The optimum combination of bit structure is that point angle, helix angle and edge radius are 95°, 35°, and 0.02 mm, respectively.
Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic Bearing System
Authors: Chiu Hsin-Lin
Published: August 2022

A systematic identification approach for the rotor/radial active magnetic bearing (rotor/RAMB) system is presented in this study. First, the system identification of the controller of commercial TMP is undertaken, and the corresponding linear dynamic models are constructed. To perfectly excite the nonlinearities of the rotor/RAMB system, a parallel amplitude-modulated pseudo-random binary sequence (PAPRBS) generator, which possesses the merits of no correlation among the perturbation signals, is employed. The dynamics of the rotor/RAMB system is identified with a Hammerstein–Wiener model. To reduce the difficulty of the identified two nonlinear blocks, the output nonlinear characteristics are estimated prior to the recursive process. Two conventional nonlinear model structures, i.e., NARX and NARMAX, are employed for comparison to verify the effectiveness of the identified Hammerstein–Wiener model. The averaged fit values of the Hammerstein–Wiener model, NARX model, and NARMAX model are 93.25%, 88.36%, and 76.91%, respectively.
GLFormer: Global and Local Context Aggregation Network for Temporal Action Detection
Authors: He Yilong , Zhong Yong , Wang Lishun , Dang Jiachen
Published: August 2022

As the core component of video analysis, Temporal Action Localization (TAL) has experienced remarkable success. However, some issues are not well addressed. First, most of the existing methods process the local context individually, without explicitly exploiting the relations between features in an action instance as a whole. Second, the duration of different actions varies widely; thus, it is difficult to choose the proper temporal receptive field. To address these issues, this paper proposes a novel network, GLFormer, which can aggregate short, medium, and long temporal contexts. Our method consists of three independent branches with different ranges of attention, and these features are then concatenated along the temporal dimension to obtain richer features. One is multi-scale local convolution (MLC), which consists of multiple 1D convolutions with varying kernel sizes to capture the multi-scale context information. Another is window self-attention (WSA), which tries to explore the relationship between features within the window range. The last is global attention (GA), which is used to establish long-range dependencies across the full sequence. Moreover, we design a feature pyramid structure to be compatible with action instances of various durations. GLFormer achieves state-of-the-art performance on two challenging video benchmarks, THUMOS14 and ActivityNet 1.3. Our performance is 67.2% and 54.5% AP@0.5 on the datasets THUMOS14 and ActivityNet 1.3, respectively.
Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
Authors: Li Xudong , Li Yanjun , Cao Yuyuan , Duan Shixuan , Wang Xingye , Zhao Zejian
Published: August 2022

Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft electro hydrostatic actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by gram angle difference field (GADF) to obtain the fault feature images. Then we build a FCNN model that integrates the 1DCNN and 2DCNN, where the original 1D fault data is the input of the 1DCNN model, and the feature images obtained by GADF transformation are used as the input of 2DCNN. Multiple convolution and pooling operations are performed on each of these inputs to extract the features. Next these feature vectors are spliced in the convergence layer, and the fully connected layers and the Softmax layers are finally used to attain the classification of aircraft EHA faults. Furthermore, the multi-strategy hybrid particle swarm optimization (MSPSO) algorithm is applied to optimize the FCNN to obtain a better combination of FCNN hyperparameters; MSPSO incorporates various strategies, including an initialization strategy based on homogenization and randomization, and an adaptive inertia weighting strategy, etc. The experimental result indicates that the FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively, higher than the 1DCNN and the 2DCNN by about 16.5% and 5.7%. By comparing with LeNet-5, GoogleNet, AlexNet, and GRU, the FCNN model presents the highest diagnostic accuracy, less time in training and testing. The comprehensive performance of the proposed model is demonstrated to be much stronger. Additionally, the FCNN model improved by MSPSO has a higher accuracy rate when compared to PSO.
Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm
Authors: Jang Doyoung , Kim Jongmann , Park Yong Bae , Choo Hosung
Published: August 2022

In this paper, a method for estimating atmospheric refractivity from sea and land clutters is proposed. To estimate the atmospheric refractivity, clutter power spectrums based on an artificial tri-linear model are calculated using an Advanced Refractive Prediction System (AREPS) simulator. Then, the clutter power spectrums are again obtained based on the measured atmospheric refractivity data using the AREPS simulator. In actual operation, this spectrum from measured reflectivity can be replaced with real-time clutter spectrums collected from radars. A cost function for the genetic algorithm (GA) is then defined based on the difference between the two clutter power spectrums to predict the atmospheric refractivity using the artificial tri-linear model. The optimum variables of the tri-linear model are determined at a minimum cost in the GA process. The results demonstrate that atmospheric refractivity can be predicted using the proposed method from the clutter powers.
A Prehistorical Approach to Optimal Fluctuations for General Langevin Dynamics with Weak Gaussian White Noises
Authors: Zhao Feng , Li Yang , Liu Xianbin
Published: August 2022

The dynamics of a stochastic system that exhibits large fluctuations to a given state are almost deterministic due to weak random perturbations. Such large fluctuations occur with overwhelming probability in the vicinity of the so-called optimal path, which is a vital concept in physics, chemistry, and biology, as it uncovers the way in which a physical process escapes from the attractive domain of a metastable state and transitions between different metastable states. In this paper, we examine the prehistorical description of the optimal fluctuation with the unifying framework of Langevin dynamics by means of a quantity called prehistory probability density. We show that the optimal fluctuation has a strong connection with the time reversal of the associated diffusion process. Specifically, in the stationary or quasi-stationary cases, it is found that the prehistory probability density actually acts as the transition probability density of the reversed process. As noise intensity approaches zero, it focuses on the average dynamics of the reversed process due to the law of large numbers, which is then shown to coincide with the time reversal of the optimal path. The local dispersion of the prehistory probability density can thus be reformulated as a Gaussian distribution corresponding to the linearized part of the reversed process. In addition, as an analogue of the original definition in the stationary states, it is proven that the concept of prehistory probability density can also be extended to nonstationary cases where similar properties are still valid. Based on these theoretical results, an algorithm is designed and then successfully applied to a one-dimensional example at the end, showing its accuracy for pinpointing the location of the optimal path and its efficacy in cases where multiple optimal paths coexist.
Risk and Resilience Assessment of Lisbon’s School Buildings Based on Seismic Scenarios
Authors: Ribeiro Filipe L. , Candeias Paulo X. , Correia António A. , Carvalho Alexandra R. , Costa Alfredo Campos
Published: August 2022

The safety and resilience of school buildings against natural disasters is of paramount importance since schools represent a reference point for communities. Such significance is not only related to the direct consequences of collapse on a vulnerable part of the population, but also due to the importance of schools in the post-disaster recovery. This work is focused on the risk and resilience assessment of school buildings in Lisbon (Portugal) under seismic events. The results of this study, in which a subset of 32 schools are analyzed, are used to define a prioritization strategy to mitigate the seismic risk of the Lisbon City Council school building portfolio and to assess the overall resilience of the school network. Numerical modeling of the school buildings is performed in order to estimate losses in terms of the built-up area of the schools and recovery times associated with different seismic scenarios, which are probabilistically defined specifically for the sites of the buildings, accounting for the local soil conditions and associated amplification effects. Based on the obtained risk estimates, which are compared to reference values established on international guidelines and specialized literature, the Lisbon City Council and LNEC jointly defined a short- and medium-term risk mitigation plan, starting with a detailed inspection and assessment of the most vulnerable school buildings and continuing to the implementation of retrofitting measures.
Low-Cost Digitalization Solution through Scalable IIoT Prototypes
Authors: Vuković Marko , Jorg Oliver , Hosseinifard Mohammadamin , Fantoni Gualtiero
Published: August 2022

Industry 4.0 is fast becoming a mainstream goal, and many companies are lining up to join the Fourth Industrial Revolution. Small and medium-sized enterprises, especially in the manufacturing industry, are the most heavily challenged in adopting new technology. One of the reasons why these enterprises are lagging behind is the motivation of the key personnel, the decision-makers. The factories in question often do not have a pressing need for advancing to Industry 4.0 and are wary of the risk in doing so. The authors present a rapid, low-cost prototyping solution for the manufacturing companies with legacy machinery intending to adopt the Industry 4.0 paradigm with a low-risk initial step. The legacy machines are retrofitted through the Industrial Internet of Things, making these machines both connectable and capable of providing data, thus enabling process monitoring. The machine chosen as the digitization target was not connectable, and the retrofit was extensive. The choice was made to present the benefits of digitization to the stakeholders quickly and effectively. Indeed, the solution provides immediate results within manufacturing industrial settings, with the ultimate goal being the digital transformation of the entire factory. This work presents an implementation cycle for digitizing an industrial broaching machine, supported by state-of-the-art literature analysis. The methodology utilized in this work is based on the well-known DMAIC strategy customized for the specifics of this case study.
Machine Learning Techniques Applied to Identify the Two-Phase Flow Pattern in Porous Media Based on Signal Analysis
Authors: Li Xiangyu , Li Liangxing , Wang Wenjie , Zhao Haoxiang , Zhao Jiayuan
Published: August 2022

The development of flow pattern identification technology for gas–liquid two-phase flow in porous media is of great significance to engineering research and production. In this paper, a high accuracy identification method for two-phase flow pattern in porous media is proposed with Machine learning techniques. The gas–liquid two-phase flow patterns and corresponding differential pressure signals in porous beds with particle diameters of 1.5 mm, 3 mm, and 6 mm are obtained through visual experiments. Three time domain characteristic parameters (Mean, Standard deviation, and Range) are calculated by a statistical method, while the EMD energy spectrum of the signal is obtained by empirical mode decomposition. Based on these parameters, machine learning technology, including support vector machine (SVM) and BP neural network, are employed to identify the flow pattern. Four flow pattern identification models are trained based on SVM and BP neural network, with accuracies of 94.77%, 93.4%, 96.08%, and 91.5%. Furthermore, the three models with good performance are integrated by integrated learning technology. An integrated identification model of gas–liquid two-phase flow pattern in porous media with an overall accuracy of 98.04% is finally obtained.
Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling
Authors: Sigurpalsson Asgeir Orn , Runarsson Thomas Philip , Saemundsson Rognvaldur Johann
Published: August 2022

The stochastic high-patient-throughput surgery scheduling problem under a limited number of staffed ward beds is addressed in this paper. This work proposes a novel way to minimize the risk of last-minute cancellations by bounding the likelihood of exceeding the staffed ward beds. Given historical data, it is possible to determine an empirical distribution for the length of stay in the ward. Then, for any given combinations of patients, one can estimate the likelihood of exceeding the number of staffed ward beds using Monte Carlo sampling. As these ward patient combinations grow exponentially, an alternative, more efficient, worst-case robust ward optimization model is compared. An extensive data set was collected from the National University Hospital of Iceland for computational experiments, and the models were compared with actual scheduling data. The models proposed achieve high quality solutions in terms of overtime and risk of overflow in the ward.
Breast Geometry Characterization of Young American Females Using 3D Image Analysis
Authors: Suh Minyoung , Park Jung Hyun
Published: August 2022

The current research deals with the characterization of breast geometries in young American populations. Breast measurements using 3D image analysis tools are focused on spatial assessments, such as quadrant evaluations of angle, surface area, and volume, together with traditional linear measurements. Through the statistical analysis, different types of breast shapes and placements are clustered, and characteristic breast anthropometry was identified for each cluster. The research findings indicate that there are four shape clusters and three placement clusters. Among the American females aged 26 to 35, four different breast shapes are identified: droopy breasts (31%), small/flat breasts (19%), upward breasts (24%), and large/inward breasts (26%). Taking 36%, 44%, and 20% of the population, respectively, their breast placement characteristics are either high, medium, or low/open. Breast shapes and placement are highly associated with each other. Larger breasts are located relatively lower, while most smaller/flat breasts are positioned relatively high.
A Simple Circuit and Control Topology to Produce Bipolar Non-Inverted and Inverted Voltage Step-Down Features
Authors: Ashraf Naveed , Abbas Ghulam , Ullah Nasim , Al-Ahmadi Ahmad Aziz , Mohammad Alsharef , Farooq Umar
Published: August 2022

Single-phase AC–AC converters with a direct power conversion approach are receiving rapid development as they have the ability to produce the regulated non-inverting and inverting form of the input voltage at the output. This feature enables them to correct the line voltage profile once they are used as dynamic voltage restorers if there is an issue of voltage sag or swell in the power distribution system. The regulated non-inverting and inverting form of the output voltage may also be used to obtain the step change in the output frequency, as it is required in many industrial drive systems. However, the realization of such existing circuits needs a large number of components and semiconductor devices, especially switching transistors. The operating control (on and off) of the transistors is directly associated with the use of gate control circuits. The count of such circuits is critical as their volume and cost are much greater than the operating transistors. The number of conducting semiconductor devices in the existing converters is also a big source of high conversion losses, thus leading to lower efficiency. This article introduces a new circuit topology realized only with the use of one full bridge of four IGBTs and a full bridge of four diodes. The use of four switching transistors only requires four gate control circuits that drastically reduce the overall volume and size. All the operating modes of the proposed topology require the conduction of fewer semiconductor devices, which helps to lower the conduction losses. Detailed analysis and description were carried out to validate the attractive features of the developed circuit once compared with the existing circuit topologies. For validation purposes, the computer simulation was carried out on Simulink software. The results obtained from this environment were compared with the real results gained from a practically developed laboratory test bench. The voltage regulation characteristics of the output voltage by employing pulse width modulation (PWM) were confirmed for two values of the non-inverting and inverting outputs.
A Study on the Dynamic Behavior of a Sieve in an Industrial Sifter
Authors: Modrzewski Remigiusz , Obraniak Andrzej , Rylski Adam , Siczek Krzysztof
Published: August 2022

Various vibrating screens are often applied in various industries, e.g., mining, agriculture, and others. The complex shapes of the screen trajectories in the oscillating motion strongly affect the best processing properties of such machines. One of the possible methods for obtaining such complex shapes is the application of double-frequency vibrators on such screens. The goal of the present study was to analyze the dynamical behavior of the prototype sifter sieve elaborated. The simulation model of such a sifter sieve and the research stand for studies on its sifter trajectories were elaborated. Simulations of sifter motion were conducted, and their results were compared with those obtained from measurements on the research stand. The recommendations as to the frequency ratio of vibrators enabling obtaining a high complexity of sieve movement have been formulated and included in the paper. Particularly, the multiple of the value equal to one third for the ratio of angular velocities under their reverse synchronization for two rotary vibrators exciting the screen analyzed was the best among all analyzed values of such a ratio.
Impact of Rehabilitation on Fatigue in Post-COVID-19 Patients: A Systematic Review and Meta-Analysis
Authors: de Sire Alessandro , Moggio Lucrezia , Marotta Nicola , Agostini Francesco , Tasselli Anna , Drago Ferrante Vera , Curci Claudio , Calafiore Dario , Ferraro Francesco , Bernetti Andrea , Ozyemisci Taskiran Ozden , Ammendolia Antonio
Published: August 2022

The post-COVID-19 syndrome may affect patients after the COVID-19 post-acute phase. In particular, the 69% of patients reported persistent fatigue at the discharge. To date, no clear data are available regarding the most effective rehabilitative approaches for the treatment of this condition. Thus, this systematic review aimed to evaluate the rehabilitation treatment’s efficacy on fatigue in post-COVID-19 patients. We systematically searched PubMed, Scopus, and Web of Science databases to find longitudinal study designs presenting: post-COVID-19 patients as participants; a rehabilitative approach aimed to reduce post-COVID-19 syndrome as intervention; and fatigue intensity assessed through an evaluation tool that quantified the perceived exertion (i.e., fatigue severity scale, FSS; Borg Scale (BS); Borg Category Ratio 10, CR10; Checklist Individual Strength (CIS) fatigue scale; FACIT (Functional Assessment of Chronic Illness Therapy) fatigue scale). The present systematic review protocol was registered on PROSPERO (registration number CRD42021284058). Out of 704 articles, 6 studies were included. Nearly all patients showed COVID-19-related fatigue, and after the rehabilitation treatment, only 17% of subjects reported the persistency of symptoms. The overall effect size reported a −1.40 decrease in Borg Category Ratio 10 with a SE of 0.05 and a 95% CI between −1.50 and −1.30 (p < 0.001). The present systematic review and meta-analysis underlines the rehabilitation role in the fatigue reduction in patients affected by post-COVID-19 syndrome.
An Electrolyte Life Indicator for Plasma Electrolytic Polishing Optimization
Authors: Su Facheng , Yang Hsiharng , Wu Wenchieh , Chen Yukai
Published: August 2022

This work shows that electrolyte current-density as an indicator can assist in the optimized timing of the addition of the electrolyte to plasma electrolytic polishing (PEP) to keep it active and in operation. In this experiment, 2 wt% ammonium sulfate was used as an electrolyte to polish 1 cm × 1 cm stainless steel SUS304. The hot-bath heating method was successfully used to heat it from 60 to 90 °C, followed by suction filtration. The cathode was fixed at the beaker edge in the electrolyte and the input voltage was 340 volts. Once the gas-phase layer formed stably around the workpiece, the plasma went through the electrolyte to polish the workpiece surface. Then, the anode was slowly immersed into the electrolyte and the current-density measured. It was found that based on the current-density–temperature curve, for the timing of the addition of the electrolyte, the current-density difference could be used to decide whether it needed to be supplemented or not. When the temperature was from 75 to 80 °C and 85 to 90 °C, it was found that the 2 wt% ammonium sulfate solution should be supplemented. The result showed that the electrolyte life indicator, using the current-density, is a feasible method of practical technology for PEP.
FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble
Authors: Chen Chen , Song Yafei , Yue Shaohua , Xu Xiaodong , Zhou Lihua , Lv Qibin , Yang Lintao
Published: August 2022

As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models.
Adaptive Trust-Based Framework for Securing and Reducing Cost in Low-Cost 6LoWPAN Wireless Sensor Networks
Authors: Ahmad Rami , Wazirali Raniyah , Abu-Ain Tarik , Almohamad Tarik Adnan
Published: August 2022

Wireless Sensor Networks (WSNs) are the core of the Internet of Things (IoT) technology, as they will be used in various applications in the near future. The issue of security and power consumption is still one of the most important challenges facing this type of network. 6LoWPAN protocol was developed to meet these challenges in networks with limited power and resources. The 6LoWPAN uses a hierarchical topology and the traditional method of encryption and key management, keeping power consumption levels high. Therefore, in this paper, a technique has been developed that helps in balancing security and energy consumption by exploiting the Trust technique between low-cost WSN nodes called Trust-Cluster Head (Trust-CH). Trust between nodes is built by monitoring the behavior of packet transmission, the number of repetitions and the level of security. The Trust-CH model provides a dynamic multi-level encryption system that depends on the level of Trust between WSN nodes. It also proposes a dynamic clustering system based on the absolute-trust level in the mobile node environment to minimize power consumption. Along with a set of performance metrics (i.e., power consumption and network lifetime), the Cooja simulator was used to evaluate the Trust-CH model. The results were compared to a static symmetric encryption model together with various models from previous studies. It has been proven that the proposed model increases the network lifetime by 40% compared to previous studies, as well as saves as much as 28% power consumption in the case of using a static encryption model. While maintaining the proposed model’s resistance to many malicious attacks on the network.
Automated Detection of the Competency of Delivering Guided Self-Help for Anxiety via Speech and Language Processing
Authors: Attas Dalia , Power Niall , Smithies Jessica , Bee Charlotte , Aadahl Vikki , Kellett Stephen , Blackmore Chris , Christensen Heidi
Published: August 2022

Speech and language play an essential role in automatically assessing several psychotherapeutic qualities. These automation procedures require translating the manual rating qualities to speech and language features that accurately capture the assessed psychotherapeutic quality. Speech features can be determined by analysing recordings of psychotherapeutic conversations (acoustics), while language-based analyses rely on the transcriptions of such psychotherapeutic conversations (linguistics). Guided self-help is a psychotherapeutic intervention that mainly relay on therapeutic competency of practitioners. This paper investigates the feasibility of automatically analysing guided self-help sessions for mild-to-moderate anxiety to detect and predict practitioner competence. This analysis is performed on sessions drawn from a patient preference randomised controlled trial using actual patient-practitioner conversations manually rated using a valid and reliable measure of competency. The results show the efficacy and potential of automatically detecting practitioners’ competence using a system based on acoustic and linguistic features extracted from transcripts generated by an automatic speech recogniser. Feature extraction, feature selection and classification or regression have been implemented as blocks of the prediction model. The Lasso regression model achieved the best prediction results with an R of 0.92 and lower error rates with an MAE of 1.66 and RMSE of 2.25.
New Insight and Future Perspectives on Nutraceuticals for Improving Sports Performance of Combat Players: Focus on Natural Supplements, Importance and Advantages over Synthetic Ones
Authors: Tirla Adrian , Islam Fahadul , Islam Md. Rezaul , Ioana Vicas Simona , Cavalu Simona
Published: August 2022

Advanced nutritional interventions are one of the key components of elite sports performance in general. Combat sports require a high percentage of muscle mass with minimum body weight to generate the maximum power possible. An adequate level of nutrition knowledge, particularly with respect to identifying energy needs while avoiding confusion over dietary supplements and false perceptions of steroid requirement, which may compromise the health condition, is of crucial importance. In this context, the aim of our work is to highlight nutritional requirements/nutritional assessment, the importance of daily dietary intake in combat players, which increasingly includes a broad range of sports nutrition supplements, and the roles of vitamins, minerals and proteins, combined with antioxidants and strength training, in muscular performance. The main nutrients required in the daily diet of combat players, the mechanisms of action, the main outcomes and possible side effects are summarized. Special attention is paid to natural supplements and their importance and advantages over synthetic ones, along with future trends of development.
High-Efficiency Broadband Polarization-Independent Reflective Grating with Double-Layer Dielectric Rectangle Groove in Littrow Mounting
Authors: Jin Ge , Jia Wei , Bayanheshig  , Xie Yongfang , Zhou Changhe
Published: August 2022

The design and performance of a high-efficiency broadband and polarization-independent reflective grating is reported. The physical mechanism of the gratings can be described by the modal method. By using rigorous coupled wave analysis (RCWA) and simulated annealing (SA) algorithms, the parameters of grating were optimized. The calculated diffraction efficiencies of −1st order for TE and TM polarizations in Littrow mounting exceeded 95%, from 988 nm to 1122 nm, and by over 98% in the bandwidth ranging from 1015 nm to 1085 nm, with the value of polarization-dependent loss (PDL) lower than 0.06 dB. Moreover, the electric field distribution of the grating was simulated by the finite element method (FEM), which demonstrated that most of the energy of the incident light was diffracted to the −1st order and the electric field was distributed almost outside the grating. In addition, the great fabrication tolerances and incident angle tolerance ensured high performance of the designed grating in manufacture and application. With its properties of high efficiency, broadband, and polarization-independence, the designed grating should be of great interest for lots of practical applications, including chirped pulse amplification (CPA), interferometers, and spectrometers.
Comparative Analysis of Atterberg Limits, Liquidity Index, Flow Index and Undrained Shear Strength Behavior in Binary Clay Mixtures
Authors: Karakan Eyyüb
Published: August 2022

In geotechnical engineering applications, it is very important to obtain the undrained shear strength of remolded soils accurately and reliably. This study aims to obtain a trustworthy solution to determine the undrained shear strength of remolded clay mixtures using Atterberg limit test results in various states of consistency. An experimental study was carried out involving a wide range of clay mixtures of varying plasticity and geological origin. In the analyses, the variation in the undrained shear strength of remolded soil depending on the cone penetration depth, water content, flow index, liquidity index and log liquidity index was investigated. In the study, the highest undrained shear strength of 100% Na-montmorillonite (NaM) was obtained at 171.89 kPa and 56.60% water content, while the lowest undrained shear strength was obtained for 100% Sepiolite (S) at 9.28 kPa and 31.65% water content. The results of this study revealed that the shear strength is significantly affected by soil conditions, rather than dominant clay mineral. Moreover, it was observed that the undrained shear strength at the plastic limit was approximately 30–35 times greater than that at the liquid limit. Equations for liquid limit–flow index and plasticity index–flow index were proposed. It was concluded that the interdependence between undrained shear strength, liquidity index, log liquidity index and flow index is not unique due to the different physical and chemical properties of clays.
Dynamic Preventive Maintenance Optimization of Subway Vehicle Traction System Considering Stages
Authors: Gong Qi , Yang Li , Li Yonghua , Xue Bin
Published: August 2022

The current maintenance mode of the subway vehicle traction system does not consider the change of failure rate in the operation of components, and there is an unreasonable maintenance interval and high maintenance cost. To solve this problem, a dynamic preventive maintenance strategy for subway vehicle traction systems considering stages is proposed. Service age decreasing factor and failure rate increasing factor are introduced to build a new dynamic reliability model considering the effect of preventive maintenance on component failure rate. Non-preventive failure maintenance, preventive maintenance, preventive update maintenance and opportunistic maintenance are adopted. For the components in degradation or random failure stages, a preventive maintenance optimization model with the least maintenance cost, constrained by reliability, is established. The maintenance strategy is optimized and solved based on the enumeration method to obtain single-component irregular preventive maintenance intervals and multi-component group preventive maintenance times. The results show that the proposed maintenance models are reasonable and feasible. On the premise of ensuring the system reliability, the number of vehicle outages for maintenance and the maintenance cost are reduced. Feasible and effective maintenance models and methods are provided. They can ensure the reliability of subway vehicles and reduce maintenance costs. The effectiveness and economy of maintenance work is increased.
Optimization of the Cooling Scheme of Artificial Ground Freezing Based on Finite Element Analysis: A Case Study
Authors: Hu Jun , Li Ke , Wu Yuwei , Zeng Dongling , Wang Zhixin
Published: August 2022

The present study was envisaged to evaluate the influence of different brine cooling schemes on the freezing process in the formation of sand-cobble strata in an underground connection aisle in Hohhot, China. The brine cooling schemes were set up by modifying the starting and ending brine temperatures in the construction of an underground connection aisle. Using ADINA finite element software, the simulation of the temperature field during the freezing process of the sand and pebble strata under three different schemes was performed. It was found that the freezing process was accelerated by lowering the freezing start temperature during the cooling process when the starting and ending brine temperatures remained unchanged. Furthermore, if the initial freezing temperature was changed, keeping the same freezing time at constant soil thermophysical parameters, the final effective thickness of the frozen wall was almost identical. Considering the same location of the temperature measurement points, the measured temperature of the inner and outer holes of the freezing curtain was found to be consistent with the numerical simulation, demonstrating the rationality of the numerical model. On the basis of this study, a brine cooling plan is proposed, which could serve as a reference for future construction.
Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
Authors: Lin Jia-You , Tsai Chia-Che , Nguyen Van-Linh , Hwang Ren-Hung
Published: August 2022

Resolving traffic congestion caused by sudden events (e.g., an accident, lane closed due to construction) on the freeway has always been a problem that is challenging to address perfectly. The congestion resolution can take hours if the congestion is severe, and the vehicles must voluntarily line up to exit the congestion spots. Most state-of-the-art traffic scheduling schemes often rely on traffic signal controllers to mitigate traffic congestion in fixed areas (e.g., intersection, blocked areas). Unlike the existing studies, in this work, we introduce a novel decentralized coordinated platooning planning method, namely Coordinated Platooning Planning (CPP), for quickly resolving temporary traffic congestion in any place on multi-lane freeways heuristically. First, based on warning notifications about traffic congestion, we propose a maneuver control protocol that enables the vehicles to negotiate with surrounding vehicles to determine a consensus plan for forming platoons (who is platoon leader, the value of the distance gap, vehicle velocity, platoon size) in sequential areas. After creating the platoons, each platoon leader commands their platoon members through the maneuver protocol to urge the vehicles to move close to or merge into the same lane. Finally, the chains of platooning vehicles can safely exit the congestion using scheduled orders. The simulation results demonstrate that the proposed heuristic approach can reduce up to 22% of the delay for the last few vehicles driving through the congestion area in typical traffic density cases with the best platoon size configuration, which is a significant enhancement compared to the existing schemes.
Examining the Conceptual Model of Potential Urban Development Patch (PUDP), VOCs, and Food Culture in Urban Ecology: A Case in Chengdu, China
Authors: Shen Xiwei , Chen Mingze , Ge Mengting , Padua Mary G.
Published: August 2022

In China, traditional food is a significant element of culture that affects human behaviors. The point of interest (POI) of traditional food restaurants’ location and their volatile organic compounds (VOCs) emissions affect the urban ecology. Rather than examine potential urban development patch (PUDP) based on land use data, the perspective of this paper is to examine the PUDP, air quality, and food culture in urban ecology in Chengdu, China. Methods: First, the research identifies three types of PUDP models (open PUDP, landscape PUDP, and conflict PUDP) with the weighted overlay of land use data, then uses machine learning to examine the relationship between PUDP, POI of traditional food restaurant, and VOCs. Results: The study generates three types of PUDP which are open PUDP, landscape PUDP, and conflict PUDP. VOCs and POI of traditional restaurant have a strong correlation, and both have a significant negative correlation with open PUDP. However, the landscape PUDP and conflict PUDP do not show an obvious relationship with food POI and VOCs. Conclusion: The results indicate that the future urban ecology should consider restaurant location, VOCs from restaurants, and their relationship to urban land use data as they have a strong relationship.
Construction of Multipollutant Air Quality Health Index and Susceptibility Analysis Based on Mortality Risk in Beijing, China
Authors: Wang Yu , Ding Ding , Ji Xiaohui , Zhang Xuelei , Zhou Pengyao , Dou Yan , Dan Mo , Shu Mushui
Published: August 2022

Air pollution places a considerable disease burden on the public. Compared with the widely applied air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive measure of multiple pollutants. In this study, AQHI was constructed using environmental data, meteorological data, and the daily mortality data of Beijing residents from 2018 to 2020. Factors increasing the susceptibility of the population to the health effects of air pollution were identified to aid the construction of a specific AQHI (S-AQHI) for susceptible populations. The findings indicated that older adults, women, and people with respiratory disease are more susceptible to the short-term health effects of air pollution. The relative deviation in the AQHI and S-AQHI for changes in daily mortality percentage of various specific populations ranged from only 1.4% to 10.3%, indicating the universality of the AQHI in its capacity to predict health risks. The Spearman coefficient of correlation between the AQHI and AQI was 0.78 (p < 0.01). Each increase in the interquartile range of the AQHI and AQI results in an increase of 1.894% and 1.029% in the total daily mortality, respectively, demonstrating the stronger capacity of the AQHI to predict daily mortality compared to the AQI.
Max Fast Fourier Transform (maxFFT) Clustering Approach for Classifying Indoor Air Quality
Authors: Chu Ka-Ui , Ho Yao-Hua
Published: August 2022

Air pollution is a severe problem for the global environment. Most people spend 80% to 90% of the day indoors; therefore, indoor air pollution is as important as outdoor air pollution. The problem is more severe on school campuses. There are several ways to improve indoor air quality, such as air cleaners or ventilation. Air-quality sensors can be used to detect indoor air quality in real time to turn on air cleaner or ventilation. With an efficient and accurate clustering technique for indoor air-quality data, different ventilation strategies can be applied to achieve a better ventilation policy with accurate prediction results to improve indoor air quality. This study aims to cluster the indoor air quality data (i.e., CO2 level) collected from the school campus in Taiwan without other external information, such as geographical location or field usage. In this paper, we propose the Max Fast Fourier Transform (maxFFT) Clustering Approach to classify indoor air quality to improve the efficiency of the clustering and extract the required feature. The results show that without using geographical information or field usage, the clustering results can correctly reflect the ventilation condition of the space with low computation time.
Chemical Composition and Source Apportionment of Winter Fog in Amritsar: An Urban City of North-Western India
Authors: Asif Mohammad , Yadav Rekha , Sugha Aditi , Bhatti Manpreet Singh
Published: August 2022

Winter fog is a complex issue affecting human health and is responsible for higher numbers of traffic accidents in North India, which is further aggravated due to atmospheric pollutants. An indigenous glass-plate fog collector was used to collect fog water from December 2020 to February 2021. Thirty samples of fog water were collected from the rooftop of an academic building at Guru Nanak Dev University, Amritsar, in order to study the chemistry of fog water. The studied parameters were pH, electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), potassium (K+) sulphate (SO42−), nitrate (NO3−), chloride (Cl−), aluminum (Al), lead (Pb) and zinc (Zn). The average values were as follows: pH (4.6–7.5), EC (135 µS/cm), SO42− (77.5 ppm), Cl− (9.9 ppm), NO3− (9.3 ppm), Ca2+ (8.1 ppm), Mg2+ (2.0 ppm), K+ (2.0 ppm), Na+ (1.6 ppm), Zn (218 ppb), Al (60.8 ppb) and Pb (8.8 ppb). Cation–anion balance was used to assess the data’s reliability. The enrichment factor (EF) was utilized to distinguish between crustal and anthropogenic sources. SO42−, NO3−, Cl− and K+ originated from anthropogenic sources, whereas Mg2+ and Na+ came from crustal sources. The molar ratio of sulphate to nitrate was 10.6, which indicates a greater contribution from the combustion of fossil fuels and stack emissions. Ionic species were subjected to principal component analysis (PCA) as a dimensionality reduction approach and to group species with comparable behavior. Three principal components (PC) that together accounted for 77.5 percent of the total variance were identified by PCA. Backward trajectory analysis of air masses was performed to determine their origin, and two major clusters explained 89 percent of the contribution of air masses, primarily from the north-east and north directions. To gain a comprehensive understanding of fog water, a global perspective on pH, EC and ionic species is considered.
Application of Ion-Mobility Spectrometry to Chemical Analysis at High Concentrations
Authors: Takaya Kazunari , Shibata Nobuyuki
Published: August 2022

Ion-mobility spectrometry (IMS) can perform qualitative and quantitative analysis of multicomponent chemical mixtures in real time, which is difficult for commonly used instruments such as gas chromatography–mass spectrometry and photo-ionization detectors. IMS is commonly applied in microanalytical (ppb) sensing of toxic gases. Thus, its application to quantitative analyses of chemical substances with a high proton affinity is generally not possible at high concentrations (~1000 ppm) because multimeric complexes are generated. In a previous study, we found that calibration curves derived from shifts in nominal arrival-time spectra of chemical substances overlapping with water clusters enable quantitative analysis at high concentrations. This study investigated the applicability of high-concentration quantitative analysis using IMS, including the lower and upper limits of quantification and their chemical dependence on methyl ethyl ketone and ethanol. We found that the magnitude of the shift in the nominal arrival-time spectrum at low concentrations is different from that at high concentrations, and that the concentration of a chemical substance can be determined with high precision from the shift in the arrival-time spectrum, even at low concentrations. Proton affinity has a significant effect on spectral shift and quantification limits. Our results indicate that shifts in nominal arrival-time spectra allow accurate quantitative analysis at both low and high concentrations. Our calibration technique is derived from the shift in nominal peaks including multimeric complexes at high concentrations, which resultantly recognized the highest dynamic range ever. We could measure the dynamic range of chemical substances over three orders using this method.
Novel Fractional Grey Prediction Model with the Change-Point Detection for Overseas Talent Mobility Prediction
Authors: Jiang Peng , Wu Geng , Hu Yi-Chung , Zhang Xue , Ren Yining
Published: August 2022

Overseas students constitute the paramount talent resource for China, and, hence, overseas talent mobility prediction is crucial for the formulation of China’s talent strategy. This study proposes a new model for predicting the number of students studying abroad and returning students, based on the grey system theory, owing to the limited data and uncertainty of the influencing factors. The proposed model introduces change-point detection to determine the number of modeling time points, based on the fractional-order grey prediction model. We employed a change-point detection method to find the change points for determining the model length, based on the principle of new information priority, and used a fractional order accumulated generating operation to construct a grey prediction model. The two real data sets, the annual number of students studying abroad and returning students, were employed to verify the superiority of the proposed model. The results showed that the proposed model outperformed other benchmark models. Furthermore, the proposed model has been employed to predict the tendencies of overseas talent mobility in China by 2025. Further, certain policy recommendations for China’s talent strategy development have been proposed, based on the prediction results.