bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhanced electrical conductivity of anticorrosive coatings by functionalized carbon nanotubes: effect of hydrogen bonding
Authors: Lou Ding , Younes Hammad , Yang Jack , Jasthi Bharat K , Hong George , Hong Haiping , Tolle Charles , Bailey Craig , Widener Christian , Hrabe Rob 
Published: December 2021

Abstract
Carbon nanotubes and nanofibers (CNFs) are well-known nano additives to produce coating materials with high electrical and thermal conductivity and corrosion resistance. In this paper, coating materials incorporating hydrogen bonding offered significantly lower electrical resistance. The hydrogen bonding formed between functionalized carbon nanotubes and ethanol helped create a well-dispersed carbon nanotube network as the electron pathways. Electrical resistivity as low as 6.8 Ω cm has been achieved by adding 4.5 wt% functionalized multiwalled carbon nanotubes (MWNT-OH) to 75%polyurethane/25%ethanol. Moreover, the thermal conductivity of polyurethane was improved by 332% with 10 wt% addition of CNF. Electrochemical methods were used to evaluate the anti-corrosion properties of the fabricated coating materials. 75%polyurethane/25%ethanol with the addition of 3.0 wt% of MWNT-OH showed an excellent corrosion rate of 5.105 × 10–3 mm year−1, with a protection efficiency of 99.5% against corrosive environments. The adhesion properties of the coating materials were measured following ASTM standard test methods. 75%polyurethane/25%ethanol with 3.0 wt% of MWNT-OH belonged to class 5 (ASTM D3359), indicating the outstanding adhesion of the coating to the substrate. These nanocoatings with enhanced electrical, thermal, and anti-corrosion properties consist of a choice of traditional coating materials, such as polyurethane, yielding coating durability with the ability to tailor the electrical and thermal properties to fit the desired application.
Investigating the key role of carrier transport mechanism in SnSe nanoflakes with enhanced thermoelectric power factor
Authors: Mandava Srikanth , Bisht Neeta , Saini Anjali , Kumar Bairwa Mukesh , Bayikadi Khasimsaheb , Katre Ankita , Sonnathi Neeleshwar 
Published: December 2021

Abstract
A novel SnSe nanoflake system is explored for its thermoelectric properties from both experiments and ab initio study. The nanoflakes of the low temperature phase of SnSe (Pnma) are synthesized employing a fast and efficient refluxing method followed by spark plasma sintering at two different temperatures. We report an enhanced power factor (12–67 μW mK−
2 in the temperature range 300–600 K) in our p-type samples. We find that the prime reason for a high PF in our samples is a significantly improved electrical conductivity (1050–2180 S m−1 in the temperature range 300–600 K). From our ab initio band structure calculations accompanied with the models of temperature and surface dependent carrier scattering mechanisms, we reveal that an enhanced electrical conductivity is due to the reduced carrier-phonon scattering in our samples. The transport calculations are performed using the Boltzmann transport equation within relaxation time approximation. With our combined experimental and theoretical study, we demonstrate that the thermoelectric properties of p-type Pnma-SnSe could be improved by tuning the carrier scattering mechanisms with a control over the spark plasma sintering temperature.
Control Ports Authorities’ Equipment Based on RFID and SQL Security System
Authors: Amgad Mirhan , Morsi Iman , Omar Mohamed M. M. 
Published: December 2021

Abstract
Equipment surveillance or tracking is a hard-to-accomplish mission for a multi-locations company. This study examines a complete security system in Alexandria and EL-Dekheila ports of Egypt, as case study, to track equipment location. The system uses RFID (radio frequency identification) and Arduino kit as hardware to detect equipment motion, and visual studio and SQL (structured query language) as software to produce reports. The original place of equipment is saved as a reference. IR and RFID sensors are used at every department’s gate for IT access and for detecting and decoding the received equipment’s RFID card and ID data. Visual studio and SQL help in issuing the report on each equipment’s location and movement time. The system sends instant email messages and cellular phone messages to the control room.
A Cepstrum-Based Spectrum Sensing Approach for Detecting Spread Spectrum Signals
Authors: Moawad Azza , Yao Koffi-Clément , Mansour Ali , Gautier Roland 
Published: December 2021

Abstract
In this manuscript, we introduce a semi-blind spectrum sensing technique based on cepstral analysis for interweave cognitive systems. The misdetection problem of spread spectrum signals leads to erroneous sensing results, which affect the quality-of-service of a legitimate user. The simplicity and accuracy of cepstral analysis approaches make them reliable for signals detection. Therefore, we formulate the averaged autocepstrum detection technique that utilizes the strength of the autocepstral features of spread spectrum signals. The proposed technique is compared with the energy detection and eigenvalue-based detection techniques and shows reliability and efficacy in terms of detection accuracy.
Efficient Brain Tumor Detection Based on Deep Learning Models
Authors: Shoaib Mohamed R. , Elshamy Mohamed R. , Taha Taha E. , El-Fishawy Adel S. , Abd El-Samie Fathi E. 
Published: December 2021

Abstract
Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer learning model, a CNN-based model that is designed from scratch, a pre-trained inceptionresnetv2 model and a pre-trained inceptionv3 model. The performance of the four proposed models is tested using evaluation metrics including accuracy, sensitivity, specificity, precision, F1_score, Matthew’s correlation coefficient, error, kappa and false positive rate. The obtained results show that the two proposed models are very effective in achieving accuracies of 93.15% and 91.24% for the transfer learning model and BRAIN-TUMOR-net based on CNN, respectively. The inceptionresnetv2 model achieves an accuracy of 86.80% and the inceptionv3 model achieves an accuracy of 85.34%. Practical implementation of the proposed models is presented.
Autonomous Vehicle Path Planning using Q-Learning
Authors: Ibrahim Mohammed M S , Atia Mostafa Rostom , Fakhr MW 
Published: December 2021

Abstract
Path planning is vital in autonomous vehicle technology, from robots to self-driving cars and driverless trucks, it is impossible to navigate without a proper path planning algorithm, various algorithms exist Q-learning being one of them. Q-learning is used extensively in discrete applications as it is effective in finding solutions to these problems. This research investigates the possibility of using Q-learning for solving the local path planning problem with obstacle avoidance. Q-learning is split into two phases, the first being the training phase, and the second being the application phase. During training, Q-learning requires exponentially increasing training time based on the system’s state space. However, when Q-learning is applied it becomes as simple as a lookup table which allows it to run on even the simplest microcontrollers. Two simulations are conducted with varying environments. One to showcase the ability to learn the optimal path, the other to showcase the ability for learning navigation in variable environments. The first simulation was run on a static environment with one obstacle, with enough training episodes, Q-learning could solve the path planning problem with minimal movement steps. The second simulation focuses on a randomized environment, obstacles and the agent’s starting position are randomly chosen at the start of every episode. During testing, Q-learning was able to find a path to the target when a path did exist, as It was possible in certain configurations for the vehicle to be stuck in between obstacles with no feasible path or solution.
Investigating performance of transformer health index in machine learning application using dominant features
Authors: Mohmad Azlan , Ibrahim Shapiai M , Solehin Shamsudin M , Abu Mohd Azlan , Hamid Amirah Abd 
Published: December 2021

Abstract
Transformer Health Index (HI) has become a standard tool for performing transformer health evaluations. Due to economic constraints, the recently published paper focuses on developing various techniques to identify the most dominant features for transformer HI prediction. However, the fundamental problems concerning their input features remain unresolved since most suggested features contradict industry practice. In this paper, the primary objective is to investigate the performance of the transformer HI by developing and utilizing only dominant features following the industry recommendation. The investigated dominant features in this paper using 1) CO2/CO ratio and 2) the Incipient fault for detecting temperature abnormalities, and 3) the Dissipation Factor (DF) for detecting oil contamination. The performance validation is carried out using various machine learning (ML) classifiers. Also, the performance of the ML model is validated based on 10-fold type cross-validation to avoid biases in the experiment. As a result, the proposed Artificial Neural Network (ANN) network utilizing the investigated dominant features following the industry practice has produced the highest average accuracy of 80.09% than others ML techniques as a classifier. Hence, additional studies to complement the investigated dominant features may be considered for the subsequent investigation.
Battery Management System Performance Enhancement using Single Sliding Mode Based Charge Equalization Controller
Authors: Ashraf Ali , Sobaih Abdul-Azim , Nabil Essam 
Published: December 2021

Abstract
In this paper, the study aims to evolve a precision model of a Battery charge equalization controller (BCEC) that manage each cell of a lithium-ion (Li-ion) battery, monitoring a nd balancing by charging a nd discharging a series-connected Li-ion battery with several cells (n) in electric vehicle (E.V.) applications. An intelligent Sliding mode controller is evolved to activate bidirectional cell switches and regulate a chopper circuit’s direct current (DC-DC flyback converter circuit) with PWM generation. To implement a small-scale BCEC, the model consists of individual models of an E.V., cells of Li-ion battery, a fly-back converter, and a single sliding mode controller (SSMC) for charging and discharging are integrated with n series-connected cells of Li-ion-battery. The BCEC output shows that the cells are operated within a safe range, and the difference in the state of charge (SOC) is maintained to be 0.1%. Comparing the developed BCEC with the existing controllers, with the performance, control algorithm, efficiency, power loss, size, and cost.
Enhancement of the Ball Balancing on the Plate using hybrid PD/Machine learning techniques
Authors: Elshamy Mohamed R. , Nabil Essam , Sayed Amged , Abozalam Belal 
Published: December 2021

Abstract
This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.
The role of fine management techniques in relation to agricultural pollution and farmer income: the case of the fruit industry
Authors: Yuan Bin , Yue Fangzhou , Cui Yuhu , Chen Chao 
Published: December 2021

Abstract
Although agriculture is enjoying booming development it is facing increasingly serious environmental pressures. With increase in the scale of fruit planting, inorganic mineral elements are becoming one of the main sources of non-point pollution. How to achieve sustainable production in agriculture is an issue that needs urgent attention in current rural development. In this paper, based on the micro-production data of peach farmers in 18 prefecture-level provinces, we introduce fine management techniques into the production function to analyze the effects of different techniques and further explore the influence of fine management techniques on fertilizer efficiency. Our findings show that with no change in the degree of investment in fine management techniques the increase in use of chemical fertilizers and pesticides has not only made little contribution to increasing profits but has also resulted in excessive investment in fertilizers that damage the environment. Notably, fine management techniques exerting positive effects on the application efficiency of mineral elements could be an efficient and sustainable way to ease the conflict between environment and profit. However, such techniques are used rarely in practice due to the lack of economic incentives. A brief review of the main measures, such as timely updating of market information, agricultural product branding and socialized services, is offered.
Biophysical feedback of forest canopy height on land surface temperature over contiguous United States
Authors: Zhang Zhijiang , Li Xinxin , Liu Hongguang 
Published: December 2021

Abstract
Forests are considered important in the mitigation of climate change. Biophysical effects of afforestation and deforestation on land surface temperature (LST) have been extensively documented. As a fundamental variable of forest structure, however, few studies have investigated the biophysical feedback of forest canopy height (FCH) changes on LST at large scale. This study is designed to investigate the impact of FCH changes on local land LST and clarify the biophysical processes controlling LST change from 2003 to 2005 over the contiguous United States, based on satellite observations. To this end, one satellite-based FCH product is selected, and the space-for-time approach, together with the energy balance equation, is applied. Results show that for different forest types, namely evergreen forest (EF), deciduous forest (DF), and mixed forest (MF), taller forests present a greater net cooling effect (0.056–0.448 K) than shorter forests at annual scale. The increase in net radiation and sensible heat flux was less than the increase in the latent heat flux when FCH classes converted from shorter to taller, resulting in annual net cooling effects. Furthermore, the cooling effect of EF is stronger than that of DF and MF, whether for tall, medium, or short FCH classes. Multiple regression analysis reveals that the changes in biophysical components can effectively explain the LST change during the growing season. Our findings provide a new insight for forest management decision-making with the purpose of mitigating climate warming.
Modeling of critical strain for dynamic recrystallization of niobium microalloyed steels
Authors: Xing Jun , Ding Hanlin , Zhu Guohui , Li Fan , Li Junliang 
Published: December 2021

Abstract
The critical strain for dynamic recrystallization (DRX) is most important in designing rolling schedules for the refinement of grain size by boundary-induced transformation mechanisms. Modeling of the critical strain for DRX from the stress-strain curves obtained from hot compression was physically built in this paper. The stress-strain behaviour of materials during hot deformation should be a combination of work-hardening and recrystallization softening. Before DRX occurred, the stress-strain behaviours could be described by a constitutive equation in which basic strain hardening and the effect of strain rate and temperature on stress-strain behaviour are included. Once DRX was promoted, obvious deviation between the experimental and calculated stress-strain curves appeared, which denoted the critical strain for DRX. The modeling in this work could be used not only to accurately calculate the critical strain for DRX but also to analyze the dynamic softening behaviours during hot deformation. To validate the calculated results, the stress-strain database was analyzed in the H beam sample deformed at 1000 °C with a strain rate of 0.1/s, and a critical strain of 0.22 was obtained by this novel method as an example. The calculated result is in good agreement with the experimental data obtained by micrographical observations.
Bouncing dynamics of electrostatically actuated NEM switches
Authors: Bognash Mohamed , Asokanthan Samuel 
Published: December 2021

Abstract
The aim of the present research is to understand the bouncing dynamic behavior of nanoelectromechanical (NEM) switches in order to improve switch performance and reliability. It is well known that bouncing can dramatically degrade the switch performance and life; hence, in the present study, the bouncing dynamics of a cantilever-based NEM switch has been studied in detail. To this end, the repulsive van der Waals force is incorporated into a nano-switch model to capture the contact dynamics. Intermolecular forces, surface effects, and gas rarefication effects were also included in the proposed model. The Euler–Bernoulli beam theory and an approximate approach based on Galerkin’s method have been employed to predict transient dynamic responses. In the present study, performance parameters such as initial contact time, permanent contact time, major bounce height, and the number of bounces, were quantified in the presence of interactive system nonlinearities. The performance parameters were used to investigate the influence of surface effects and rarefication effects on the performance of an electrostatically actuated switch. Recommended operating conditions are suggested to avoid excessive bouncing for these types of NEM switches.
A Randomized Clinical Trial Demonstrating Feasibility and Preliminary Efficacy of a Videoconference-Delivered Physical Activity Lifestyle Intervention Among Adolescents With a Congenital Heart Defect
Authors: Jackson Jamie L , Fox Kristen R , Rausch Joseph R , Swenski Taylor N , Neville Steven P , Marousis Noelle C , Korth Christina X , Cua Clifford L , Garg Vidu , Vannatta Kathryn 
Published: December 2021

Abstract

Background
Individuals with congenital heart defects are at increased risk for developing further cardiovascular complications, which can be mitigated by increasing physical activity. Given that positive health behaviors begin declining during older adolescence, it is vital to promote lifestyle changes in this population.

Purpose
The current study aims to (a) determine the feasibility/acceptability of the Congenital Heart Disease Physical Activity Lifestyle (CHD-PAL) intervention among adolescents (ages 15–18) with moderate and complex congenital heart defects, and (b) estimate the preliminary efficacy of CHD-PAL for increasing time spent in moderate-to-vigorous physical activity (MVPA) and cardiorespiratory fitness and decreasing sedentary behavior.

Methods
Eligible participants were randomized into either CHD-PAL (eight 30-min videoconferencing sessions over 20 weeks with an interventionist + Fitbit + exercise prescription) or a comparator (Fitbit + exercise prescription).

Results
Sixty adolescents were randomized (76% recruitment rate; 94% of participants were retained from baseline to follow-up). Most adolescents (73%) and their parents/guardians (76%) reported that the trial was enjoyable. While there was no effect of arm on change in MVPA, sedentary behavior, or cardiorespiratory fitness for the entire sample, among those who engaged in &lt;21 min of MVPA on average at baseline, adolescents in the CHD-PAL intervention had an increase of 16 min/day of MVPA more than comparators (d = 0.90).

Conclusions
The CHD-PAL intervention warrants examination in a larger trial to establish efficacy among those adolescents with a congenital heart defect who engage in &lt;21 min of MVPA/day and should include follow-up assessments to examine effect durability.

Clinical trials registration
NCT03335475.
A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions
Authors: Zhou Ya-Hui , Li Guo , Zhang Yuan-Ming 
Published: December 2021

Abstract
Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.