bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm
Authors: Jing Xia , Zou Qin , Yan Jumei , Dong Yingying , Li Bingyu
Published: February 2022

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.
Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine
Authors: Wei Jun , Cui Yuanlai , Luo Wanqi , Luo Yufeng
Published: February 2022

Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years; many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EFe), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture.
A Vaginitis Classification Method Based on Multi-Spectral Image Feature Fusion
Authors: Zhao Kongya , Gao Peng , Liu Sunxiangyu , Wang Ying , Li Guitao , Wang Youzheng
Published: February 2022

Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the workload of clinical laboratory staff. However, the studies only using RGB images limit the development of vaginitis diagnosis. Through multi-spectral technology, we propose a vaginitis classification algorithm based on multi-spectral image feature layer fusion. Compared with the traditional RGB image, our approach improves the classification accuracy by 11.39%, precision by 15.82%, and recall by 27.25%. Meanwhile, we prove that the level of influence of each spectrum on the disease is distinctive, and the subdivided spectral image is more conducive to the image analysis of vaginitis disease.
Constant Potential Coulometric Measurements with Ca2+-Selective Electrode: Analysis Using Calibration Plot vs. Analysis Using the Charge Curve Fitting
Authors: Bondar Anna , Mikhelson Konstantin
Published: February 2022

The possibility of analysis using charge curve fitting in constant potential coulometric mode instead of using a calibration plot is explored, for the first time. The results are compared with the analysis based on the use of a calibration plot. A Ca2+ ion-selective electrode, with and without an electronic capacitor in series, is used as a model system in pure solutions of CaCl2. Both techniques delivered good results (error within 2%) when the final and the initial concentration values differed by not more than three times. Larger differences result in 10–25% error. The presence of an electronic capacitor in the measurement circuit and in series with the electrode, allows for significantly faster response.
Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy
Authors: Jin Yongyong , Yin Yonggang , Li Chiye , Liu Hongying , Shi Junhui
Published: February 2022

For certain diseases, the continuous long-term monitoring of the physiological condition is crucial. Therefore, non-invasive monitoring methods have attracted widespread attention in health care. This review aims to discuss the non-invasive monitoring technologies for human health based on photoacoustic spectroscopy. First, the theoretical basis of photoacoustic spectroscopy and related devices are reported. Furthermore, this article introduces the monitoring methods for blood glucose, blood oxygen, lipid, and tumors, including differential continuous-wave photoacoustic spectroscopy, microscopic photoacoustic spectroscopy, mid-infrared photoacoustic detection, wavelength-modulated differential photoacoustic spectroscopy, and others. Finally, we present the limitations and prospects of photoacoustic spectroscopy.
Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
Authors: Soliman Shimaa A. M. , Eldesouki Eman M. , Attiya Ahmed M.
Published: February 2022

This paper presents the analysis and design of an X-band reflectarray. The proposed antenna can be used for a medium Earth orbit (MEO) remote sensing satellite system in the 8.5 GHz band. To obtain a nearly constant response along the coverage area of this satellite system, the proposed antenna was designed with a flat-top radiation pattern with a beam width of around 29° for the required MEO system. In addition, broadside pencil beam and tilted pencil beam reflectarrays were also investigated. The feeding element of the proposed reflectarray antennas is a Yagi–Uda array. The amplitude and phase distribution of the fields due to the feeding element on the aperture of the reflectarray antenna are obtained directly by numerical simulation without introducing any approximation. The required phase distribution along the aperture of the reflectarray to obtain the required flat-top radiation pattern is obtained using the genetic algorithm (GA) optimization method. The reflecting elements of the reflectarray are composed of stacked circular patches. This stacked configuration was found to be appropriate for obtaining a wide range of reflection phase shift, which is required to implement the required phase distribution on the reflectarray aperture. The antenna was fabricated and measured for verification.
Rapid Detection of Three Common Bacteria Based on Fluorescence Spectroscopy
Authors: Du Ranran , Yang Dingtian , Yin Xiaoqing
Published: February 2022

As an important part of environmental water quality monitoring, efficient bacterial detection has attracted widespread attention. Among them, LIF (laser-induced fluorescence) technology has the characteristics of high efficiency and sensitivity for bacterial detection. To simplify the experimental process of bacterial detection, fluorescence emission spectra of E. coli (Escherichia coli) and its deactivated controls, K. pneumoniae (Klebsiella pneumoniae) and S. aureus (Staphylococcus aureus), were analyzed with fluorescence excitation by a 266 nm laser. By analyzing the results, it was found that the dominant fluorescence peaks of bacterial solutions at 335~350 nm were contributed by tryptophan, and the subfluorescence peaks at 515.9 nm were contributed by flavin; besides, K. pneumoniae and S. aureus had their own fluoresces characteristics, such as tyrosine contributing to sub-fluorescence peaks at 300 nm. The three species of bacteria can be differentiated with whole fluorescence spectrum by statistically analysis (p < 0.05), for various concentrations of aromatic amino acids and flavin in different bacteria. The experimental results also proved that the inactivation operation did not alter the spectral properties of E. coli. The indexes of fluorescence intensity and FIR (fluorescence intensity ratio, I335~350/I515.9) can be used to retrieve the bacteria concentration as well as for bacteria differentiation using the index of slopes. The detection limit of bacteria is less than ~105 cell/mL using laser induced fluorescence methods in the paper. The study demonstrated the rapid detection capability of the LIF bacterial detection system and its great potential for rapid quantitative analysis of bacteria. This may bring new insight into the detection of common bacteria in water in situ.
Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring
Authors: Moon Hi Gyu , Jung Youngmo , Shin Beomju , Lee Donggeun , Kim Kayoung , Woo Deok Ha , Lee Seok , Kim Sooyeon , Kang Chong-Yun , Lee Taikjin , Kim Chulki
Published: February 2022

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO2, HCHO, and NH3 and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.
Topological Optimization of Circular SAW Resonators: Overcoming the Discreteness Effects
Authors: Shevchenko Sergey Yu. , Mikhailenko Denis A.
Published: February 2022

Recently, we proposed a ring-shaped surface acoustic wave (SAW) resonator sensitive element design, as well as analyzed its characteristics and suggested its optimization strategy, with major focus on their temperature stability. Here, we focus on further optimization of the design to narrow the bandwidth and improve signal detection, while taking into account typical technological limitations. Additionally, the purpose of design optimization and modeling is to check the preservation of operability in the case of lithography defects, which is the most common technological error. For that, we suggest structural alteration of the interdigital transducer (IDT) that leads to its partial fragmentation. Using COMSOL Multiphysics computer simulations, we validate several IDT options and show explicitly how it could be optimized by changing its pin geometry. Based on the results of the study, prototyping and printing of ring resonators on a substrate using photolithography will be carried out.
Analysis of Relationship between Natural Standing Behavior of Elderly People and a Class of Standing Aids in a Living Space
Authors: Miyazaki Yusuke , Hirano Kei , Kitamura Koji , Nishida Yoshifumi
Published: February 2022

As the world’s population ages, technology-based support for the elderly is becoming increasingly important. This study analyzes the relationship between natural standing behavior measured in a living space of elderly people and the classes of standing aids, as well as the physical and cognitive abilities contributing to household fall injury prevention. In total, 24 elderly standing behaviors from chairs, sofas, and nursing beds recorded in an RGB-D elderly behavior library were analyzed. The differences in standing behavior were analyzed by focusing on intrinsic and common standing aid characteristics among various seat types, including armrests of chairs or sofas and nursing bed handrails. The standing behaviors were categorized into two types: behaviors while leaning the trunk forward without using an armrest as a standing aid and those without leaning the trunk forward by using an arrest or handrail as a standing aid. The standing behavior clusters were distributed in a two-dimensional map based on the seat type rather than the physical or cognitive abilities. Therefore, to reduce the risk of falling, it would be necessary to implement a seat type that the elderly can unconsciously and naturally use as a standing aid even with impaired physical and cognitive abilities.
Component-Wise Error Correction Method for UWB-Based Localization in Target-Following Mobile Robot
Authors: Bae Kyungbin , Son Yooha , Song Young-Eun , Jung Hoeryong
Published: February 2022

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method.
Optimal Control of Air Conditioning Systems by Means of CO2 Sensors in Electric Vehicles
Authors: Muratori Luca , Peretto Lorenzo , Pulvirenti Beatrice , Di Sante Raffaella , Bottiglieri Giovanni , Coiro Federico
Published: February 2022

Considering the consistent reduction in battery range due to the operation of the Heating Ventilation and Air Conditioning (HVAC) system, this study deals with the CO2 measurement inside the cabin of an electric crane and aims to reduce the energy consumption through the control of the air recirculation. A control strategy was defined and tested through an experimental set-up where the presence of a driver was simulated as a source of CO2. The cabin was placed inside a climatic wind tunnel and the benefits of this control strategy on the HVAC system energy consumption were assessed, both in the heating and the cooling modes. In addition, we discussed the optimal position of the CO2 sensor inside the cabin by comparing the results obtained from some sensors placed around the cabin occupant with the ones logged by three sensors in the breathing zone. Finally, an investigation of the uncertainty of the indirect measurement of the leakage flow and its dependence on the number of CO2 sensors installed in the cabin was made through the Monte Carlo method.
User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
Authors: Sultana Nahar , Huq Farhana , Razzaque Md. Abdur , Rahman Md. Mustafizur
Published: February 2022

Narrowband Internet of Things (NB-IoT) is a promising technology for healthcare applications since it reduces the latency necessary in acquiring healthcare data from patients, as well as handling remote patients. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing a data transmission framework that offers differentiated Quality of Services (QoS) to the critical and non-critical data packets is challenging. The existing literature studies suffer from insufficient access scheduling considering heterogeneous data packets and relationship among them in healthcare applications. In this paper, we develop an optimal resource allocation framework for NB-IoT that maximizes a user’s utility through event prioritization, rate enhancement, and interference mitigation. The proposed Priority Aware Utility Maximization (PAUM) system also ensures weighted fair access to resources. The suggested system outperforms the state-of-the-art works significantly in terms of utility, delay, and fair resource distribution, according to the findings of the performance analysis performed in NS-3.
An Automotive Ferrofluidic Electromagnetic System for Energy Harvesting and Adaptive Damping
Authors: Lenkutis Tadas , Viržonis Darius , Čerškus Aurimas , Dzedzickis Andrius , Šešok Nikolaj , Bučinskas Vytautas
Published: February 2022

Vibration energy harvesting is receiving significant interest due to the possibility of using extra power in various machines and constructions. This paper presents an energy-harvesting system that has a structure similar to that of a linear generator but uses permanent magnets and magnetorheological fluid insets. The application of a standard vehicle example with low frequencies and amplitudes of the excitations was used for the optimization and experimental runs. The optimization for low excitation amplitudes shows that the best magnetic field change along the slider is obtained using differentially orientated radial magnets of 5 mm in width. This configuration was used for the experimental research, resulting in 1.2–3.28 W of power generated in the coils. The power conditioning system in the experimental research was replaced by loading resistors. Nevertheless, the initial idea of energy harvesting and a damping effect was confirmed by the circuit voltage output.
Analytical Review of Event-Based Camera Depth Estimation Methods and Systems
Authors: Furmonas Justas , Liobe John , Barzdenas Vaidotas
Published: February 2022

Event-based cameras have increasingly become more commonplace in the commercial space as the performance of these cameras has also continued to increase to the degree where they can exponentially outperform their frame-based counterparts in many applications. However, instantiations of event-based cameras for depth estimation are sparse. After a short introduction detailing the salient differences and features of an event-based camera compared to that of a traditional, frame-based one, this work summarizes the published event-based methods and systems known to date. An analytical review of these methods and systems is performed, justifying the conclusions drawn. This work is concluded with insights and recommendations for further development in the field of event-based camera depth estimation.
FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area
Authors: Kim Bong-Seok , Jin Youngseok , Lee Jonghun , Kim Sangdong
Published: February 2022

We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.
A Review on Planted (l, d) Motif Discovery Algorithms for Medical Diagnose
Authors: Mohanty Satarupa , Pattnaik Prasant Kumar , Al-Absi Ahmed Abdulhakim , Kang Dae-Ki
Published: February 2022

Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called “Motif”. Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient’s conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses.
Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking
Authors: Mu Weilei , Gao Yuqing , Wang Yuxue , Liu Guijie , Hu Hao
Published: February 2022

The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.
Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques
Authors: Khan Ejaz , Rehman Muhammad Zia Ur , Ahmed Fawad , Alfouzan Faisal Abdulaziz , Alzahrani Nouf M. , Ahmad Jawad
Published: February 2022

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
Singular Configuration Analysis and Singularity Avoidance with Application in an Intelligent Robotic Manipulator
Authors: Wang Helin , Zhou Ziqiang , Zhong Xianyou , Chen Qijun
Published: February 2022

Recently, robotic sensor systems have gained more attention annually in complex system sense strategies. The robotic sensors sense the information from itself and the environment, and fuse information for the use of perception, decision, planning, and control. As an important supplement to traditional industrial robots, co-bots (short for co-working robots) play an increasingly vital role in helping small and medium-sized enterprises realize intelligent manufacturing. They have high flexibility and safety so that they can assist humans to complete highly repetitive and high-precision work. In order to maintain robot safe operation in the increasing complex working environment and human–computer intelligent interactive control, this paper is concerned with the problem of applicant accuracy analysis and singularity avoidance for co-bots. Based on the dynamic model with load and torque sensors, which is used to detect the external force at the end of the robot, this paper systematically analyzes the causes of singularity phenomenon in the robot motion control. The inverse solution is obtained by analytical method and numerical method, respectively. In order to ensure the smooth and safe operation in the whole workspace, it is necessary for a robot to avoid singularity. Singularity avoidance schemes are utilized for different control tasks, including point-to-point control and continuous path control. Corresponding simulation experiments are designed to verify the effectiveness of different evasion schemes, in which the advantages and disadvantages are compared and analyzed.
Noise Immunity and Robustness Study of Image Recognition Using a Convolutional Neural Network
Authors: Ziyadinov Vadim , Tereshonok Maxim
Published: February 2022

The problem surrounding convolutional neural network robustness and noise immunity is currently of great interest. In this paper, we propose a technique that involves robustness estimation and stability improvement. We also examined the noise immunity of convolutional neural networks and estimated the influence of uncertainty in the training and testing datasets on recognition probability. For this purpose, we estimated the recognition accuracies of multiple datasets with different uncertainties; we analyzed these data and provided the dependence of recognition accuracy on the training dataset uncertainty. We hypothesized and proved the existence of an optimal (in terms of recognition accuracy) amount of uncertainty in the training data for neural networks working with undefined uncertainty data. We have shown that the determination of this optimum can be performed using statistical modeling. Adding an optimal amount of uncertainty (noise of some kind) to the training dataset can be used to improve the overall recognition quality and noise immunity of convolutional neural networks.
Influence of the Characteristics of Sports Sharing Economy Services on Intention of Use: The Mediating Effect of Consumer Attitude and Trust
Authors: Kim Sunjung , Lee Kyongmin
Published: February 2022

This study empirically investigated the effectiveness of the characteristics of sports sharing economy services that has been highlighted recently. The study used consumers’ attitude and trust as parameters of the association between the characteristics and use intention of consumers. There are three research questions in the study. The first question is whether the characteristics of sports sharing economy services affect the consumers’ use intention. The second question is whether the characteristics of sports sharing economy services affect the attitude of consumers and the trust level in sports sharing economy services. The third question is whether the attitude and trust level of consumers mediate sports sharing economy services and use intention. A survey was conducted among college students using a convenient sampling method. We found that: (1) service characteristics such as usefulness, bonding, and consistency had a positive influence on use intention, and risk factor had a negative influence; (2) the service characteristics significantly influenced consumer attitudes and trust of the sharing economy services; usefulness, bonding, and consistency had a positive influence, and risk factor had a negative influence; and (3) the attitude and trust of consumers toward sharing economy services affected use intention; usefulness, bonding, and sustainability had a positive influence, while risk factor had a negative influence. Thus, it was confirmed that the attitude and trust level of consumers mediated sports sharing economy services and use intention. This study also suggested academic and practical implications to establish a more effective sports sharing economy service marketing strategy and develop quality content in relation to the service characteristics and consumers.
Plant–Metal Interactions in the Context of Climate Change
Authors: Fernando Denise R.
Published: February 2022

Expanding fundamental understanding of the complex and far-reaching impacts of anthropogenic climate change is essential for formulating mitigation strategies. There is abundant evidence of ongoing damage and threat to plant health across both natural and cultivated ecosystems, with potentially immeasurable cost to humanity and the health of the planet. Plant–soil systems are multi-faceted, incorporating key variables that are individually and interactively affected by climatic factors such as rainfall, solar radiation, air temperature, atmospheric CO2, and pollution. This synthesis focuses on climate effects on plant–metal interactions and related plant–soil dynamics. Ecosystems native to metalliferous soils incorporate vegetation well adapted to metal oversupply, yet climate-change is known to induce the oversupply of certain immobile soil metals by altering the chemistry of non-metalliferous soils. The latter is implicated in observed stress in some non-metal-adapted forest trees growing on ‘normal’ non-metalliferous soils. Vegetation native to riverine habitats reliant on flooding is increasingly at risk under drying conditions caused by anthropogenic water removal and climate change that ultimately limit plant access to essential trace-metal nutrients from nutrient poor sandy soils. In agricultural plant systems, it is well known that environmental conditions alter soil chemistries and plant responses to drive plant metal toxicity stress. These aspects are addressed with reference to specific scenarios and studies linking climate to plant–metal interactions, with emphasis on land plants.
Propagation and Parametric Amplification in Four-Wave Mixing Processes: Intramolecular Coupling and High-Order Effects
Authors: Paz José Luis , Espinoza-Montero Patricio J. , Loroño Marcos , Torres Fernando Javier , González-Paz Lenin , Márquez Edgar , Vera-Villalobos Joan , Mora José R. , Moncada Fernando , Alvarado Ysaias J.
Published: February 2022

A strong pump-power dependence of the four-wave mixing (FWM) signal for an aqueous solution of Malachite green is reported. The characteristics of the pump-power dependence of the nonlinear signal are reproduced by a theoretical model based on the coupling between pump-probe, considering signal fields and propagation effects. The effect of the intramolecular coupling on the nonlinear intensity of the FWM signal is studied using a model molecule consisting of two-coupled harmonic curves of electronic energies with minima displaced in energy and nuclear positions. Two-vibrational states are considered while including non-adiabatic effects for the two-state model. Moreover, the coupling among the field components, as well as the propagation effects, are studied by considering a constant pump-intensity. Our calculation scheme, considering both the intramolecular coupling effects in the description of the molecular structure and the effects produced by the propagation of the FWM signal along the optical length, allows the exponential dependence of the latter, as the intensity of the pumping beam increases. Our treatments do not require the inclusion of other non-resonant processes outside the RWA approximation, due to the consideration of an adiabatic basis.
New Results on Fourth-Order Differential Subordination and Superordination for Univalent Analytic Functions Involving a Linear Operator
Authors: Mihsin Bassim Kareem , Atshan Waggas Galib , Alhily Shatha S. , Lupaş Alina Alb
Published: February 2022

We present several new results for fourth-order differential subordination and superordination in this paper by using the differential linear operator Γπ,ρ,β,μfz. Relevant connections between the new results presented here and those considered in previous works are addressed. The properties and results concerning the differential subordination theory are symmetric to the properties obtained using the differential superordination theory, and by combining them, sandwich-type theorems are obtained.
A Procedure for Tracing Chain of Custody in Digital Image Forensics: A Paradigm Based on Grey Hash and Blockchain
Authors: Ali Mohamed , Ismail Ahmed , Elgohary Hany , Darwish Saad , Mesbah Saleh
Published: February 2022

Digital evidence is critical in cybercrime investigations because it is used to connect individuals to illegal activity. Digital evidence is complicated, diffuse, volatile, and easily altered, and as such, it must be protected. The Chain of Custody (CoC) is a critical component of the digital evidence procedure. The aim of the CoC is to demonstrate that the evidence has not been tampered with at any point throughout the investigation. Because the uncertainty associated with digital evidence is not being assessed at the moment, it is impossible to determine the trustworthiness of CoC. As scientists, forensic examiners have a responsibility to reverse this tendency and officially confront the uncertainty inherent in any evidence upon which they base their judgments. To address these issues, this article proposes a new paradigm for ensuring the integrity of digital evidence (CoC documents). The new paradigm employs fuzzy hash within blockchain data structure to handle uncertainty introduced by error-prone tools when dealing with CoC documents. Traditional hashing techniques are designed to be sensitive to small input modifications and can only determine if the inputs are exactly the same or not. By comparing the similarity of two images, fuzzy hash functions can determine how different they are. With the symmetry idea at its core, the suggested framework effectively deals with random parameter probabilities, as shown in the development of the fuzzy hash segmentation function. We provide a case study for image forensics to illustrate the usefulness of this framework in introducing forensic preparedness to computer systems and enabling a more effective digital investigation procedure.
Consistency and Asymptotic Normality of Estimator for Parameters in Multiresponse Multipredictor Semiparametric Regression Model
Authors: Chamidah Nur , Lestari Budi , Budiantara I. Nyoman , Saifudin Toha , Rulaningtyas Riries , Aryati Aryati , Wardani Puspa , Aydin Dursun
Published: February 2022

A multiresponse multipredictor semiparametric regression (MMSR) model is a combination of parametric and nonparametric regressions models with more than one predictor and response variables where there is correlation between responses. Due to this correlation we need to construct a symmetric weight matrix. This is one of the things that distinguishes it from the classical method, which uses a parametric regression approach. In this study, we theoretically developed a method of determining a confidence interval for parameters in a MMSR model based on a truncated spline, and investigating asymptotic properties of estimator for parameters in a MMSR model, especially consistency and asymptotic normality. The weighted least squares method was used to estimate the MMSR model. Next, we applied a pivotal quantity method, a Cramer–Wold theorem, and a Slutsky theorem to determine the confidence interval, investigate consistency, and asymptotic normality properties of estimator for parameters in a MMSR model. The obtained results were that the estimated regression function is linear to observation. We also obtained a 1001−α% confidence interval for parameters in the MMSR model, and the estimator for parameters in MMSR model was consistent and asymptotically normally distributed. In the future, these obtained results can be used as a theoretical basis in designing a standard toddlers growth chart to assess nutritional status.
Niche Theory Analysis of Sustainable Strategic Relationships among MICE Destinations: A Case of Four Cities in Waikato and Bay of Plenty Regions of New Zealand
Authors: Lee Jiwon
Published: February 2022

This study aimed to analyze the competitive relationship among meetings, incentive, convention, and exhibition (MICE) destinations with reference to the notion of niche businesses in New Zealand, and to explore the existence of cooperative strategies between neighboring destinations. The data were collected from the Ministry of Business, Innovation, and Employment Reports on the Convention Activity Survey (CAS) in New Zealand. The study contributes towards an innovative and better understanding of the dynamics of sustainable destination competitiveness. It broadens the scope of MICE industry research by exploring new insights on the notion of destination competition and makes a theoretical connection between niche theory and the importance of coopetition. By investigating the case of MICE destinations of four central cities in New Zealand, this study provides information on the strategic significance of niche marketing for global destinations preparing for entrance into this market.
Botulinum Neurotoxin Chimeras Suppress Stimulation by Capsaicin of Rat Trigeminal Sensory Neurons In Vivo and In Vitro
Authors: Antoniazzi Caren , Belinskaia Mariia , Zurawski Tomas , Kaza Seshu Kumar , Dolly J. Oliver , Lawrence Gary W.
Published: February 2022

Chimeras of botulinum neurotoxin (BoNT) serotype A (/A) combined with /E protease might possess improved analgesic properties relative to either parent, due to inheriting the sensory neurotropism of the former with more extensive disabling of SNAP-25 from the latter. Hence, fusions of /E protease light chain (LC) to whole BoNT/A (LC/E-BoNT/A), and of the LC plus translocation domain (HN) of /E with the neuronal acceptor binding moiety (HC) of /A (BoNT/EA), created previously by gene recombination and expression in E. coli., were used. LC/E-BoNT/A (75 units/kg) injected into the whisker pad of rats seemed devoid of systemic toxicity, as reflected by an absence of weight loss, but inhibited the nocifensive behavior (grooming, freezing, and reduced mobility) induced by activating TRPV1 with capsaicin, injected at various days thereafter. No sex-related differences were observed. c-Fos expression was increased five-fold in the trigeminal nucleus caudalis ipsi-lateral to capsaicin injection, relative to the contra-lateral side and vehicle-treated controls, and this increase was virtually prevented by LC/E-BoNT/A. In vitro, LC/E-BoNT/A or /EA diminished CGRP exocytosis from rat neonate trigeminal ganglionic neurons stimulated with up to 1 µM capsaicin, whereas BoNT/A only substantially reduced the release in response to 0.1 µM or less of the stimulant, in accordance with the /E protease being known to prevent fusion of exocytotic vesicles.
DDX50 Is a Viral Restriction Factor That Enhances IRF3 Activation
Authors: Pallett Mitchell , Lu Yongxu , Smith Geoffrey
Published: February 2022

The transcription factors IRF3 and NF-κB are crucial in innate immune signalling in response to many viral and bacterial pathogens. However, mechanisms leading to their activation remain incompletely understood. Viral RNA can be detected by RLR receptors, such as RIG-I and MDA5, and the dsRNA receptor TLR3. Alternatively, the DExD-Box RNA helicases DDX1-DDX21-DHX36 activate IRF3/NF-κB in a TRIF-dependent manner independent of RIG-I, MDA5, or TLR3. Here, we describe DDX50, which shares 55.6% amino acid identity with DDX21, as a non-redundant factor that promotes activation of the IRF3 signalling pathway following its stimulation with viral RNA or infection with RNA and DNA viruses. Deletion of DDX50 in mouse and human cells impaired IRF3 phosphorylation and IRF3-dependent endogenous gene expression and cytokine/chemokine production in response to cytoplasmic dsRNA (polyIC transfection), and infection by RNA and DNA viruses. Mechanistically, whilst DDX50 co-immunoprecipitated TRIF, it acted independently to the previously described TRIF-dependent RNA sensor DDX1. Indeed, shRNA-mediated depletion of DDX1 showed DDX1 was dispensable for signalling in response to RNA virus infection. Importantly, loss of DDX50 resulted in a significant increase in replication and dissemination of virus following infection with vaccinia virus, herpes simplex virus, or Zika virus, highlighting its important role as a broad-ranging viral restriction factor.
Special Issue—Immunity to Influenza Viruses
Authors: Koutsakos Marios , Valkenburg Sophie A.
Published: February 2022

Influenza viruses remain a constant global threat with significant health and socioeconomic impact every year and have the potential to cause devastating pandemics [...]
Influence of the Delta Variant and Vaccination on the SARS-CoV-2 Viral Load
Authors: Migueres Marion , Dimeglio Chloé , Trémeaux Pauline , Raymond Stéphanie , Lhomme Sébastien , Da Silva Isabelle , Oliveira Mendes Kévin , Abravanel Florence , Félicé Marie-Pierre , Mansuy Jean-Michel , Izopet Jacques
Published: February 2022

Studies comparing SARS-CoV-2 nasopharyngeal (NP) viral load (VL) according to virus variant and host vaccination status have yielded inconsistent results. We conducted a single center prospective study between July and September 2021 at the drive-through testing center of the Toulouse University Hospital. We compared the NP VL of 3775 patients infected by the Delta (n = 3637) and Alpha (n = 138) variants, respectively. Patient’s symptoms and vaccination status (2619 unvaccinated, 636 one dose and 520 two doses) were recorded. SARS-CoV-2 RNA testing and variant screening were assessed by using Thermo Fisher® TaqPath™ COVID-19 and ID solutions® ID™ SARS-CoV-2/VOC evolution Pentaplex assays. Delta SARS-CoV-2 infections were associated with higher VL than Alpha (coef = 0.68; p ≤ 0.01) independently of patient’s vaccination status, symptoms, age and sex. This difference was higher for patients diagnosed late after symptom onset (coef = 0.88; p = 0.01) than for those diagnosed early (coef = 0.43; p = 0.03). Infections in vaccinated patients were associated with lower VL (coef = −0.18; p ≤ 0.01) independently of virus variant, symptom, age and sex. Our results suggest that Delta infections could lead to higher VL and for a longer period compared to Alpha infections. By effectively reducing the NP VL, vaccination could allow for limiting viral spread, even with the Delta variant.