bluuebunny/crossref_metadata_2025 huggingface dataset:
Probabilistic Feasibility Design of a Laser Powder Bed Fusion Process Using Integrated First-Order Reliability and Monte Carlo Methods
Authors: Meng Lingbin , Du Xiaoping , McWilliams Brandon , Zhang Jing
Published: March 2021

Quality inconsistency due to uncertainty hinders the extensive applications of a laser powder bed fusion (L-PBF) additive manufacturing process. To address this issue, this study proposes a new and efficient probabilistic method for the reliability analysis and design of the L-PBF process. The method determines a feasible region of the design space for given design requirements at specified reliability levels. If a design point falls into the feasible region, the design requirement will be satisfied with a probability higher or equal to the specified reliability. Since the problem involves the inverse reliability analysis that requires calling the direct reliability analysis repeatedly, directly using Monte Carlo simulation (MCS) is computationally intractable, especially for a high reliability requirement. In this work, a new algorithm is developed to combine MCS and the first-order reliability method (FORM). The algorithm finds the initial feasible region quickly by FORM and then updates it with higher accuracy by MCS. The method is applied to several case studies, where the normalized enthalpy criterion is used as a design requirement. The feasible regions of the normalized enthalpy criterion are obtained as contours with respect to the laser power and laser scan speed at different reliability levels, accounting for uncertainty in seven processing and material parameters. The results show that the proposed method dramatically alleviates the computational cost while maintaining high accuracy. This work provides a guidance for the process design with required reliability.
Roads less travelled by—Pleistocene piracy in Washington’s northwestern Channeled Scabland
Authors: Waitt Richard B.
Published: March 2021

The Pleistocene Okanogan lobe of Cordilleran ice in north-central Washington State dammed Columbia River to pond glacial Lake Columbia and divert the river south across one or another low spot along a 230-km-long drainage divide. When enormous Missoula floods from the east briefly engulfed the lake, water poured across a few such divide saddles. The grandest such spillway into the Channeled Scabland became upper Grand Coulee.

By cutting headward to Columbia valley, upper Grand Coulee’s flood cataract opened a valve that then kept glacial Lake Columbia low and limited later floods into nearby Moses Coulee. Indeed few of the scores of last-glacial Missoula floods managed to reach it. Headward cutting of an inferred smaller cataract (Foster Coulee) had earlier lowered glacial Lake Columbia’s outlet. Such Scabland piracies explain a variety of field evidence assembled here: apparently successive outlets of glacial Lake Columbia, and certain megaflood features downcurrent to Wenatchee and Quincy basin.

Ice-rafted erratics and the Pangborn bar of foreset gravel near Wenatchee record late Wisconsin flood(s) down Columbia valley as deep as 320 m. Fancher bar, 45 m higher than Pangborn bar, also has tall foreset beds—but its gravel is partly rotted and capped by thick calcrete, thus pre-Wisconsin age, perhaps greatly so. In western Quincy basin foreset beds of basaltic gravel dip east from Columbia valley into the basin—gravel also partly rotted and capped by thick calcrete, also pre-Wisconsin. Yet evidence of late Wisconsin eastward flow to Quincy basin is sparse. This sequence suggests that upper Grand Coulee had largely opened before down-Columbia megaflood(s) early in late Wisconsin time.

A drift-obscured area of the Waterville Plateau near Badger Wells is the inconspicuous divide saddle between Columbia tributary Foster Creek drainage and Moses Coulee drainage. Before flood cataracts had opened upper Grand Coulee or Foster Coulee, and while Okanogan ice blocked the Columbia but not Foster Creek, glacial Lake Columbia (diverted Columbia River) drained over this saddle at about 654 m and down Moses Coulee. When glacial Lake Columbia stood at this high level so far west, Missoula floods swelling the lake could easily and deeply flood Moses Coulee.

Once eastern Foster Coulee cataract had been cut through, and especially once upper Grand Coulee’s great cataract receded to Columbia valley, glacial Lake Columbia stood lower, and Moses Coulee became harder to flood. During the late Wisconsin (marine isotope stage [MIS] 2), only when Okanogan-lobe ice blocked the Columbia near Brewster to form a high lake could Missoula floodwater from glacial Lake Missoula rise enough to overflow into Moses Coulee—and then only in a few very largest Missoula floods. Moses Coulee’s main excavation must lie with pre-Wisconsin outburst floods (MIS 6 or much earlier)—before upper Grand Coulee’s cataract had receded to Columbia valley.
Diffusion Combustion during Interaction of a Supersonic Round Microjet of Air with a Coaxial (Coflowing Ring) Jet of Hydrogen
Authors: Kozlov V. V. , Litvinenko M. V. , Litvinenko Yu. A. , Tambovzev A. S. , Shmakov A. G.
Published: March 2021

The results of experimental investigations of the features of diffusion combustion during the interaction of a round supersonic microjet of air in the center and a coaxial (coflowing ring) jet of hydrogen. Such combustion is accompanied by a number of new phenomena: the formation of a cone-shaped flame near the nozzle сutoff, the locking of the combustion region in this cone, the presence of small-scale supersonic cells in the resulting flow, and the formation of laminar sections and their turbulization.
Surprising salmonid response to water diversion at four run-of-river hydroelectric projects in British Columbia
Authors: Hocking M.D. , Faulkner S.G. , Akaoka K. , Harwood A. , Hatfield T. , Lewis F.J.A.
Published: March 2021

Run-of-river (RoR) hydroelectric power provides renewable energy with potentially less impact on fish habitat productivity than large, reservoir-storage hydroelectric projects. This is the first empirical study of resident fish response to water diversion at RoR projects in BC, where the abundance of rainbow trout (Oncorhynchus mykiss) was monitored in four small streams using a before–after control–impact design. Although flows in the diversion reaches of these projects were reduced by 63% to 70%, the total biomass of rainbow trout increased from 35% to 157% across the streams compared with the “before period” and “control reaches”. The response to water diversion differed among age classes: adult biomass increased with reduced flows in the growing season; juvenile biomass increased with reduced flows in the winter, higher stream conductivity, and increased flows in late summer; and fry biomass increased with higher stream conductivity and temperatures. One novel way to contextualize these shifts in resident salmonid productivity and differential growth and (or) survival by age class is via a size–density approach, which we use here to demonstrate how the carrying capacity of a stream for rainbow trout is affected by water diversion.
Security Assessment of Dynamically Obfuscated Scan Chain Against Oracle-guided Attacks
Authors: Rahman M Sazadur , Nahiyan Adib , Rahman Fahim , Fazzari Saverio , Plaks Kenneth , Farahmandi Farimah , Forte Domenic , Tehranipoor Mark
Published: March 2021

Logic locking has emerged as a promising solution to protect integrated circuits against piracy and tampering. However, the security provided by existing logic locking techniques is often thwarted by Boolean satisfiability (SAT)-based oracle-guided attacks. Criteria for successful SAT attacks on locked circuits include: (i) the circuit under attack is fully combinational, or (ii) the attacker has scan chain access. To address the threat posed by SAT-based attacks, we adopt the dynamically obfuscated scan chain (DOSC) architecture and illustrate its resiliency against the SAT attacks when inserted into the scan chain of an obfuscated design. We demonstrate, both mathematically and experimentally, that DOSC exponentially increases the resiliency against key extraction by SAT attack and its variants. Our results show that the mathematical estimation of attack complexity correlates to the experimental results with an accuracy of 95% or better. Along with the formal proof, we model DOSC architecture to its equivalent combinational circuit and perform SAT attack to evaluate its resiliency empirically. Our experiments demonstrate that SAT attack on DOSC-inserted benchmark circuits timeout at minimal test time overhead, and while DOSC requires less than 1% area and power overhead.
Comprehensive Utilization Pattern of the Bohai Rim Coastline Using the Restrictive Composite Index Method
Authors: Zhang Yun , Wu Tong , Ye Yuanzhi
Published: March 2021

Coastlines play an important role in human activity and economic development. Reasonably allocating shoreline resources and addressing contradictions between ecological protection and development are critical issues. In this study, positive and negative factors affecting the natural, environmental, and socioeconomic status of the coastal zone while considering land and sea effects were comprehensively analyzed using ecological theories and methods, and an improved restrictive composite index model was constructed. We quantitatively analyzed the comprehensive utilization pattern of the Bohai Rim coastline, China, in terms of the coastline utilization type and spatial agglomeration characteristics. The comprehensive utilization pattern of the Bohai Rim coastline is as follows: ecological areas are present in the north and south, and industrial areas are present in the east and west. Industrial production areas along the coastline are mainly distributed in the East Liaodong and Bohai bays, and ecological protection areas are located in the estuaries of the Liaohe and Yellow River. The improved restricted comprehensive index method model weakens the interaction among variables and makes the calculation results closer to the real situation. The results of the comprehensive utilization pattern of Bohai Rim coastline obtained by quantitative evaluation are of great significance for the coordinated development of coastline ecological protection and development and utilization.
Millimetre-Wave Metamaterial-Based Sensor for Characterisation of Cooking Oils
Authors: Qureshi Suhail Asghar , Zainal Abidin Zuhairiah , Isa Ashyap Adel Yahya , Majid Huda A. , Kamarudin Muhammad Ramlee , Yue Ma , Zulkipli Mohd Syis , Nebhen Jamel
Published: March 2021

The characterisation of the cooking oils presents a significant challenge due to minor changes in their dielectric behaviour. In this paper, a new metamaterial-based sensor incorporating a split-ring resonator (SRR) with a microstrip transmission line is presented to characterise cooking oils. The design demonstrates metamaterial characteristics of negative permittivity and permeability simultaneously at the resonance frequency. Furthermore, its operation in the range of millimetre-wave frequencies can further enhance its sensitivity, especially for liquid materials. The sensor’s novelty is the operation at millimetre-wave frequencies that offers a high shift in the transmission coefficient while operating at 30 GHz. The sensor’s performance analysis is undertaken by using six MUTs with dielectric constants ranging from 0.126 to 4.47. The presented structure designed on 12 × 8 mm2 Rogers substrate offers a sensitivity of 1.12 GHz per unit change in dielectric constant. The phase's shift demonstrates a lower percentage error than the amplitude and linearly moves towards higher frequencies with the increase in dielectric constant and tangent loss of MUT. The designed sensor can be prominently useful for detecting liquids' chemical characteristics in chemistry and medicine fields.
Vessel Track Prediction Based on Fractional Gradient Recurrent Neural Network with Maneuvering Behavior Identification
Authors: Xue Han , Chai Tian
Published: March 2021

To improve the accuracy of ship track prediction, a fractional-order gradient descent method is adopted into a recurrent neural network (RNN). The convergence of the proposed algorithm is proved. Identification of ship maneuvering behavior, atmospheric information, and oceanographic information is considered in vessel tack prediction. The ship track of Xiamen Port is predicted using the new algorithm. Error analysis is made with different factional orders and traffic busy degrees. Results show that the testing and training error differs with different fractional orders. The predicted track results can not only improve the efficiency of marine traffic management but also prevent and warn the safety accidents, so as to avoid accidents.
Research on Data Optimization of CT System for Patients with Severe Pneumonia Based on Filtered Back Projection and Internet of Things
Authors: Qin Shaojie , Wang Wentao
Published: March 2021

CT technology can clearly and intuitively display the internal structure, composition, material, and defect status of the detected object under the condition of no damage to the detected object. This paper is based on the filtering back projection and the Internet of Things technology to carry out the data optimization of the CT system of patients with severe pneumonia. First of all, the Internet of Things technology is used in the structural design and operation process of the CT system for patients with severe pneumonia, which improves the advancement and convenience of the original system. Secondly, the system data is optimized through the filtering back projection algorithm, which improves the data accuracy and transmission efficiency. Finally, simulation tests have verified the effectiveness of the established CT system data optimization method for patients with severe pneumonia, which is helpful for medical comparison, analysis, and diagnosis.
Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network
Authors: Chen Ying , Zhang Ruirui
Published: March 2021

Aiming at the problem that the credit card default data of a financial institution is unbalanced, which leads to unsatisfactory prediction results, this paper proposes a prediction model based on k-means SMOTE and BP neural network. In this model, k-means SMOTE algorithm is used to change the data distribution, and then the importance of data features is calculated by using random forest, and then it is substituted into the initial weights of BP neural network for prediction. The model effectively solves the problem of sample data imbalance. At the same time, this paper constructs five common machine learning models, KNN, logistics, SVM, random forest, and tree, and compares the classification performance of these six prediction models. The experimental results show that the proposed algorithm can greatly improve the prediction performance of the model, making its AUC value from 0.765 to 0.929. Moreover, when the importance of features is taken as the initial weight of BP neural network, the accuracy of model prediction is also slightly improved. In addition, compared with the other five prediction models, the comprehensive prediction effect of BP neural network is better.
Utilizing EEG to Explore Design Fixation during Creative Idea Generation
Authors: Cao Juan , Zhao Wu , Guo Xin
Published: March 2021

Design fixation is related to the broad phenomenon of unconscious cognition bias that hinders the generation of creative solutions during the conceptual design process. While numerous research studies have gone into the study of design fixation, the experimental methods used were external to the cognitive process of designers; thus, there are some limitations. To address these limitations, the present study utilized electroencephalography (EEG) to explore the differences in neural activities between designers with different degrees of design fixation during creative idea generation. Fluency, flexibility, and the degree of copying were used to evaluate the design performance and fixation degrees of all participants; for the follow‐up analyses on brain activity patterns, participants were then divided into the Higher Fixation Group and the Lower Fixation Group according to the evaluation of the degrees of copying. Next, participants in each group were contrasted separately against the task‐related alpha power changes during creative idea generation. The comparison results revealed that participants with lower design fixation demonstrated stronger alpha synchronization in frontal, parietotemporal, and occipital regions during creative idea generation, while participants with higher design fixation showed stronger task‐related alpha desynchronization in frontal, centroparietal, and parietotemporal regions. Such findings suggested that participants with higher fixation showed lower solution flexibility because of the inability to inhibit the solutions generated overrelying on intuition. These results could contribute to a deeper understanding of design fixation from the neuroscience perspective and provide essential theoretical supports for the subsequent defixation methods and tool development.
Corticosteroid Pulses for Hospitalized Patients with COVID-19: Effects on Mortality
Authors: Cusacovich Ivan , Aparisi Álvaro , Marcos Miguel , Ybarra-Falcón Cristina , Iglesias-Echevarria Carolina , Lopez-Veloso Maria , Barraza-Vengoechea Julio , Dueñas Carlos , Juarros Martínez Santiago Antonio , Rodríguez-Alonso Beatriz , Martín-Oterino José-Ángel , Montero-Baladia Miguel , Moralejo Leticia , Andaluz-Ojeda David , Gonzalez-Fuentes Roberto
Published: March 2021

Objectives. To assess the influence of corticosteroid pulses on 60-day mortality in hospitalized patients with severe COVID-19. Methods. We designed a multicenter retrospective cohort study in three teaching hospitals of Castilla y León, Spain (865,096 people). We selected patients with confirmed COVID-19 and lung involvement with a pO2/FiO2<300, excluding those exposed to immunosuppressors before or during hospitalization, patients terminally ill at admission, or those who died in the first 24 hours. We performed a propensity score matching (PSM) adjusting covariates that modify the probability of being treated. Then, we used a Cox regression model in the PSM group to consider factors affecting mortality. Results. From 2933 patients, 257 fulfilled the inclusion and exclusion criteria. 124 patients were on corticosteroid pulses (250 mg of methylprednisolone for three days), and 133 were not. 30.3% (37/122) of patients died in the corticosteroid pulse group and 42.9% (57/133) in the nonexposed cohort. These differences (12.6%, 95% CI [8·54-16.65]) were statically significant (log-rank 4.72,p=0,03). We performed PSM using the exact method. Mortality differences remained in the PSM group (log-rank 5.31,p=0.021) and were still significant after a Cox regression model (HR for corticosteroid pulses 0.561;p=0.039). Conclusions. This study provides evidence about treatment with corticosteroid pulses in severe COVID-19 that might significantly reduce mortality. Strict inclusion and exclusion criteria with that selection process set a reliable frame to compare mortality in both the exposed and nonexposed groups.
Representativeness-Based Instance Selection for Intrusion Detection
Authors: Zhao Fei , Xin Yang , Zhang Kai , Niu Xinxin
Published: March 2021

With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situations, some selected instances are redundant and some important instances are removed, increasing storage space and reducing efficiency. Therefore, a new representativeness of instance is proposed and considers not only the influence of all instances of the same class on the selected instance but also the influence of instances of different classes on the selected instance. Moreover, it considers the influence of instances of different classes as an advantageous factor. Based on this representativeness, two instance selection algorithms are proposed to handle balanced and imbalanced data problems for intrusion detection. One is a representative-based instance selection for balanced data, which is named RBIS and selects the same proportion of instances from each class. The other is a representative-based instance selection for imbalanced data, which is named RBIS-IM and selects important majority instances according to the number of instances of the minority class. Compared with other algorithms on the benchmark data sets of intrusion detection, experimental results verify the effectiveness of the proposed RBIS and RBIS-IM algorithms and demonstrate that the proposed algorithms can achieve a better balance between accuracy and reduction rate or between balanced accuracy and reduction rate.
Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies
Authors: Magha Alice Mufur , Azinwi Tamfuh Primus , Mamdem Lionelle Estelle , Shey Yefon Marie Christy , Kenzong Bertrand , Bitom Dieudonné
Published: March 2021

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.
Fluent Numerical Study of Lifting Resistance and Wave Impact Height of a Cross‐Sea Bridge Based on Cloud Computing
Authors: Hu Jun , Yu Jiayu
Published: March 2021

Due to the relationship between the elevation of the roads on both sides of the strait and the adjustment with the surrounding landscape, the elevation of the deck of the bridge is restricted when the bridge is designed. When the water level rises below the superstructure of the sea‐crossing bridge, the main guardrail of the bridge may be partially or completely submerged in the water, and the superstructure of the bridge is affected by huge waves, which threatens the safety of the bridge. Based on cloud computing technology, this paper establishes a digital model of the crossing bridge and monitors the specific state of the bridge through the simulation of the bridge, collects various data of the bridge, analyzes the dataset, and analyzes the crossing bridge. This paper analyzes the correlation between the magnitude of the wave force and the height of the wave impact on the bridge superstructure and studies the relationship between the lift resistance coefficient and the impact height. The analysis of the lift coefficient and drag coefficient of different water depths in the case of 20‐year operation shows that the height of the main beam where the large wave force acts has two values, 2.0 and 3.0 m in the case of 20‐year operation; in case of 50‐year working conditions, it is 2.5 and 3.0 m; in case of 100‐year working conditions, the lift coefficient is 1.5 and 3.5 m, and the resistance coefficient is 2.0 and 3.5 m. Under the three water depth wave conditions, the fluctuation range of the lifting resistance curve is large when the water level depth is 10 m. Once a large wave occurs, the wave will not spread well due to the shallow current, which will cause a large wave force impact on the bridge. Therefore, when choosing a bridge, it is not recommended to build under a water depth of about 10 m. Optimizing the design of the main beam according to the analysis results of this paper will help to improve the ability of the bridge main beam to resist wave loading and provide a reference for seeking effective measures to reduce the impact of waves.
On-Line Interpretation and Real-Time Diagnosis of Rocket’s Single Equipment
Authors: Xu Erbao , Li Yan , Peng Lining , Li Yuxi , Yang Mingshun
Published: March 2021

The work state of a launch vehicle is generally interpreted automatically on software. However, the sheer number of target parameters makes it difficult to realize real-time interpretation, and abnormal interpretation result does not necessarily mean that the vehicle is in abnormal state. This paper introduces the edge computing to achieve on-line interpretation and real-time diagnosis of a single launch vehicle. Firstly, the parameters to be interpreted were subjected to thresholding, leaving only those with high interpretation value. Next, the interpretation server layer of the real-time diagnosis model was built based on the attribute and value reduction algorithm of variable precision rough set (VPRS). Moreover, the higher-grade criteria were written in criterion modeling language (CML) and used to interpret the various higher-grade interpretation data pushed by the edge layer in real time. On this basis, the outputs of the edge layer and interpretation server layer were integrated to achieve the real-time diagnosis of single vehicle faults. Finally, the proposed model was proved feasible through the application in a launch vehicle.
The Endocannabinoid, Anandamide, Acts as a Novel Inhibitor of LPS-Induced Inflammasome Activation in Human Gastric Cancer AGS Cell Line: Involvement of CB1 and TRPV1 Receptors
Authors: Sedeighzadeh Sahar Sadat , Galehdari Hamid , Tabandeh Mohammad Reza , Shamsara Mehdi , Roohbakhsh Ali
Published: March 2021

Inflammasome activation is a pivotal step for the maturation of IL-1β, which is involved in the development and progression of gastric cancer (GC). Endocannabinoids, such as anandamide (AEA), are emerging as new anticancer therapeutic agents; however, their effects on inflammasome components and underlying mechanisms have not been well elucidated. This study was designed to investigate the effects of AEA on the expression of inflammasome components in lipopolysaccharide- (LPS-) stimulated AGS cells. Moreover, we explored the involvement of cannabinoid receptors (CRs), including CB1R and TRPV1R, in the observed effects of AEA. Our results showed that inflammation was induced by LPS (10 μg/ml) in AGS cells, and inflammasome components (NLRP3, MLRC4, ASC, IL-18, and IL-1β) were overexpressed. Exposure to AEA (10 μM, 24 h) before or after inflammation induction downregulated the expression of inflammasome components and attenuated inflammasome activation as demonstrated by cleavage of caspase 1 and matured IL-1β secretion, although AEA pretreatment showed more reducing effects on the inflammasome activation. In addition, blocking of CB1R and TRPV1R by application of AM-251 and AMG-9810 antagonists remarkably reversed the observed effects of AEA and revealed that NLRP3, NLRC4, and IL-1β genes were mainly regulated via CB1R, while TRPV1R could only regulate the expression of IL-1β and IL-18 genes. In conclusion, our results would indicate a novel anticancer effect of anandamide by attenuation of inflammasome activation and consequently reducing IL-1β production in human AGS cancer cell line via CB1R and TRPV1R.
Acute Stress and Gender Effects in Sensory Gating of the Auditory Evoked Potential in Healthy Subjects
Authors: Xin Zengyou , Gu Simeng , Wang Wei , Lei Yi , Li Hong
Published: March 2021

Sensory gating is a neurophysiological measure of inhibition that is characterized by a reduction in the P50, N100, and P200 event-related potentials to a repeated identical stimulus. It was proposed that abnormal sensory gating is involved in the neural pathological basis of some severe mental disorders. Since then, the prevailing application of sensory gating measures has been in the study of neuropathology associated with schizophrenia and so on. However, sensory gating is not only trait-like but can be also state-like, and measures of sensory gating seemed to be affected by several factors in healthy subjects. The objective of this work was to clarify the roles of acute stress and gender in sensory gating. Data showed acute stress impaired inhibition of P50 to the second click in the paired-click paradigm without effects on sensory registration leading to worse P50 sensory gating and disrupted attention allocation reflected by attenuated P200 responses than control condition, without gender effects. As for N100 and P200 gating, women showed slightly better than men without effects of acute stress. Data also showed slightly larger N100 amplitudes across clicks and significant larger P200 amplitude to the first click for women, suggesting that women might be more alert than men.
Nontypeable Haemophilus influenzae Otitis Media: Mastoiditis and Meningitis Complicated with Central Venous Thrombosis in an Immunocompetent Child
Authors: Gönüllü Erdem , Özkan Nesrin , Soysal Ahmet , Acıoğlu Engin , Tavil Emine Betül , Ötgün Selin Nar , Karaböcüoğlu Metin
Published: March 2021

Implementation of the Haemophilus influenzae type B (Hib) conjugate vaccine brought about a reduction in the number of cases and morbidity from type B but an increase in nontypeable strain infections. Nontypeable Haemophilus influenzae (NTHi) commonly colonizes children’s upper respiratory tract and causes otitis media, sinusitis, and bronchitis. Invasive NTHi diseases, such as meningitis and septicemia, have rarely been reported. Herein, we discuss a previously healthy, fully immunized 3-year-old girl presented with otitis media and mastoiditis leading to meningitis caused by NTHi complicated with central venous thrombosis. She was treated with antibiotics, mastoidectomy and ventilation tube insertion, and anticoagulation therapy and recovered uneventfully. Through this case, we wish to share our unique clinical experience that NTHi should be born in mind as a potential pathogen that can cause meningitis in previously healthy children, which may be helpful in future cases.
Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique
Authors: Zhang Peng , Chen Xinyuan
Published: March 2021

Internal leakage diagnosis in a hydraulic cylinder is a key technique for the maintenance of hydraulic systems. However, it is difficult to diagnose the internal leakage under different low loads. To solve this problem, a novel fault diagnosis method based on the optimization deep belief network (DBN) combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is proposed to treat the collected AE signals. The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. The reconstructed signals are regarded as the input of the optimization DBN, and the particle swarm optimization simulated annealing (PSOSA) algorithm is adopted to identify the four internal leakage levels. The experimental results show that the proposed method exhibits a higher classification accuracy than other methods under different low loads. This result validates the effectiveness and superiority of the proposed approach to realize internal leakage diagnoses under different low loads.
Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications
Authors: Ali Elmustafa Sayed , Hasan Mohammad Kamrul , Hassan Rosilah , Saeed Rashid A. , Hassan Mona Bakri , Islam Shayla , Nafi Nazmus Shaker , Bevinakoppa Savitri
Published: March 2021

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.
Identifying Novel Cell Glycolysis‐Related Gene Signature Predictive of Overall Survival in Gastric Cancer
Authors: Zhao Xin , Zou Jiaxuan , Wang Ziwei , Li Ge , Lei Yi
Published: March 2021

Background. Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients’ prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods. In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan‐Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high‐risk and low‐risk groups. Results. We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients’ OS. The high‐risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion. The signature consisting of four glycolysis‐related genes could be used to forecast the GC patients’ prognosis.
Investigating teachers’ attitude toward digital literacy in EFL classroom
Authors: Pratolo Bambang Widi , Solikhati Hana Amri
Published: March 2021

Digital literacy has been a major concern for people involved in education sectors including students, teachers and policy makers. Thus, to make the education in this era more effective to help reach the goals, English teachers should be digitally more literate. The aims of this study are; 1) to find out how the digital literacy was implemented; 2) to determine teachers’ attitude in implementing the digital literacy; 3) to scrutinize the challenges; and 4) the actions they took to cope with them. Employing a qualitative research method, this study used a semi-structured interview and classroom observation to collect the data from two EFL teachers at a junior high school in Temanggung, Central Java, Indonesia as the subjects. The data were then identified, categorized, organized, coded, described and reported following the thematic analysis. The findings showed that they used computer and smartphone to search for digital information. The teachers exhibited positive attitudes in the use of digital literacy for EFL teaching. They used syllabus as a major consideration, understood their position as teachers, developed effective teaching, implemented multiple literacy and improved the four language skills. However, issues related to lack of technology, students’ background, lack of time and limited budget were acknowledged as hindrances in digital literacy implementation. As a strategy, teachers have therefore developed an early planning and support plan to deal with these challenges. Finally, this study suggests the need to develop teachers' technical facilities, technological pedagogy, and policy makers to give digital literacy more attention.
Utilisation of internet-enabled devices for academic and religious purposes among Muslim undergraduates
Authors: Sebutu Abdulganiyu Salman , Adeyemi Olaofe Musa , AbdulGaniyy Abdullahi
Published: March 2021

This study investigated utilisation of internet-enabled devices for academic and religious purposes among Muslim undergraduates in University of Ilorin, Nigeria. All Muslim students in the University constituted the population of the study, while only those in the Faculty of Arts were the target population. Meanwhile, 50 Muslim students were randomly selected from each of the seven departments which totaled 350 undergraduate Muslim students. The instrument used to collect data was researcher-designed questionnaire. Two research hypotheses were formulated and tested using inferential statistics of Pearson Product Moment Correlation Statistics, at 0.05 level of significance. It was revealed from the findings of the study that utilisation of internet-enabled devices significantly related to Muslim students' religious practices and academic performance. Therefore, it was concluded that students should always ensure moderation in chit-chatting and use their internet-enabled devices for the purposes which would boost their academic performance and improve them spiritually.
Managing human resource development of educators in inclusion-based elementary school
Authors: Prestiana Kartika Eva , Sugito Sugito
Published: March 2021

This study was a descriptive qualitative research. The respondents of were headmasters, human resource (HR) coordinators, teachers and special guide teachers. Data were collected through observation, interview, and documentation. Data were validated using source and method triangulations. Data were then analyzed. The results are as follows: 1) HR planning started from needs preparation, recruitment, selection, placement, orientation and adaptation to the new workplaces. The difference is found in SDIT Al Irsyad Al Islamiyyah 2 Purwokerto compared to the other two elementary schools. The recruitment of its new employees at all levels is carried out by the Committee of Education and Teaching from recruitment to orientation stages; 2) HR development is basically the same, i.e. through education or training. The difference is also found in SDIT Al Irsyad Al Islamiyyah 2 Purwokerto, where it provides scholarships; 3) HR evaluation is implemented by the headmasters regularly every semester in the form of classroom supervision and administration; 4) Compensation and other benefits received by employees depend on the employees’ work term, typesof employees (job training employees, non-permanent employees, permanent employees, and school headmasters).
The job satisfaction of university teachers
Authors: Tentama Fatwa , Merdiaty Netty , Subardjo Subardjo
Published: March 2021

Employee job satisfaction is an important factor that can determine organizational productivity, and organizations need to pay attention to this pivotal aspect. This study aimed to empirically examine the role of transformational leadership styles and employability on employee job satisfaction. The participants of this study are 49 university teachers at the University of X Yogyakarta. The sampling is randomly chosen using a simple random sampling technique. In addition, data are collected using the scale of job satisfaction, the scale of transformational leadership style, and scale of employability. The data are then analyzed using multiple linear regression techniques. The results showed that 1) Simultaneously, transformational leadership style and employability provide a very significant role in influencing job satisfaction with p=0.000 (p0.01); 2) Partially transformational leadership style provide a significant role on job satisfaction with p=0.019 (p0.05); 3) Partially there was a very significant role of employability on job satisfaction with p=0.000 (p0.01). Transformational leadership style and employability contribute 52.5% to job satisfaction. Employability contributed more dominantly to job satisfaction (35.8%) than the transformational leadership style (16.7%).
Metacognitive ability and autonomous learning strategy in improving learning outcomes
Authors: Marantika Juliaans Eliezer Rulland
Published: March 2021

The purpose of this study is to analyze the relationship between metacognitive ability and learning autonomy as a strategy for improving student learning outcomes. Metacognition has a major contribution to the success of learners because it is mainly concerned with the process of thinking. For this reason, the classroom environment must be designed to allow learners the opportunity to autonomously determine their learning strategies. Autonomous learning itself has a close relationship with the ability of learners to express, be more creative, have self-esteem, and understand conceptual learning that is more challenging. The method used in this research is the descriptive correlational method, with a sample of 30 students from the Department of Language and Arts. The research sample was students from the first semester of 2018/2019, who were chosen randomly. Data were collected through questionnaires and tests. The results showed that there was a significant correlation between metacognitive ability, learner’s autonomy, and learning outcomes in Indonesian language courses. These results indicate that there is an encouragement for students to increase their learning capacity for themselves. Also, they are encouraged to develop their learning strategies and have the opportunity to make decisions, especially in developing ideas.
Exploring the internationalisation strategy of higher education: A case of Muhammadiyah Higher Institution
Authors: Siregar Dede Iskandar , Binangkit Intan Diane , Wibowo Agung Prasetyo
Published: March 2021

This article aimed to identify the inhibiting factors and the internationalisation strategy of higher education in Indonesia. The study deployed a qualitative based on the case study at Muhammadiyah Higher Education Institutions. Data collected through in-depth interviews with key respondents include the leader of Higher Education, Research and Development Council of Muhammadiyah, university chancellors, and International Affairs Office head. Findings illustrate the factors hindering the internationalisation which are generally not much different from those described by previous research but here found one factor which has not been explained before, the geographical location of the university, which mostly located in small urban areas that depicted a condition in which cooperation with external parties, foreign institutions, is challenging. The finding also suggests that the internationalisation strategies formed in a very structured manner, involving internal and external university parties. Therefore, this is significant as it explores inhibiting factors and internationalisation strategy of higher education in Indonesia, where it still prioritises religious and cultural values in their educational systems. Practically, this result expected to be a useful reference for university leaders in formulating internationalisation strategies so that the quality of graduates produced can be further improved.
The use of social media in the creation of personal learning environment during the #studyfromhome period
Authors: Agusintadewi Ni Ketut , Mahastuti Ni Made Mitha , Darma Kadek Agus Surya , Aritama Anak Agung Ngurah
Published: March 2021

Due to the Covid-19 outbreak in Indonesia the Government urged students and lecturers to conduct the learning process from home through an online system called #studyfromhome. The architecture undergraduate students in this study were millennial students who used digital technology on a daily basis. This study was conducted to determine the role of social media, as well as millennial students’ preferences and feedback on the use of social media as learning tools to create a personal learning environment (PLE). Questionnaires were distributed online to students of Class of 2017 and 2018, out of whom 115 respondents provided their feedback. The results of the study showed that the students of the Architecture Study Program at Udayana University in Bali were fond of using social media especially audio-visual applications for learning activities. In addition, very positive feedback was also provided in terms of knowledge sharing and creativity, acquisition of information, and submission of assignments. Social media were considered more student-friendly. This condition was relevant to the characteristics of the millennial students who were independent learners, and facilitated the creation of PLE. Dealing with the new approach, the students hoped that social media could be used in a better manner as architecture learning platforms.