bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Phonation Types by Clustering of Multiple Metrics
Authors: Cai Huanchen , Ternström Sten
Published: November 2022

For voice analysis, much work has been undertaken with a multitude of acoustic and electroglottographic metrics. However, few of these have proven to be robustly correlated with physical and physiological phenomena. In particular, all metrics are affected by the fundamental frequency and sound level, making voice assessment sensitive to the recording protocol. It was investigated whether combinations of metrics, acquired over voice maps rather than with individual sustained vowels, can offer a more functional and comprehensive interpretation. For this descriptive, retrospective study, 13 men, 13 women, and 22 children were instructed to phonate on /a/ over their full voice range. Six acoustic and EGG signal features were obtained for every phonatory cycle. An unsupervised voice classification model created feature clusters, which were then displayed on voice maps. It was found that the feature clusters may be readily interpreted in terms of phonation types. For example, the typical intense voice has a high peak EGG derivative, a relatively high contact quotient, low EGG cycle-rate entropy, and a high cepstral peak prominence in the voice signal, all represented by one cluster centroid that is mapped to a given color. In a transition region between the non-contacting and contacting of the vocal folds, the combination of metrics shows a low contact quotient and relatively high entropy, which can be mapped to a different color. Based on this data set, male phonation types could be clustered into up to six categories and female and child types into four. Combining acoustic and EGG metrics resolved more categories than either kind on their own. The inter- and intra-participant distributional features are discussed.
Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
Authors: Solatidehkordi Zahra , Zualkernan Imran
Published: November 2022

Training machine learning and deep learning models for medical image classification is a challenging task due to a lack of large, high-quality labeled datasets. As the labeling of medical images requires considerable time and effort from medical experts, models need to be specifically designed to train on low amounts of labeled data. Therefore, an application of semi-supervised learning (SSL) methods provides one potential solution. SSL methods use a combination of a small number of labeled datasets with a much larger number of unlabeled datasets to achieve successful predictions by leveraging the information gained through unsupervised learning to improve the supervised model. This paper provides a comprehensive survey of the latest SSL methods proposed for medical image classification tasks.
Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
Authors: Wang Shuang , Li Kewu , Han Xie , Wang Zhibin
Published: November 2022

To develop fast and integrated spectrometry, we present a new method of compressed sensing spectrometry based on electro-optical transmittance coding. A LiNbO3 electro-optic modulator was applied to a directly compressed measurement code in the spectral dimension. Subsequently, a sequential forward floating selection algorithm was employed to select the coding measurement matrix, and sparsity adaptive matching pursuit was used as a solver algorithm. The principle was analyzed, and the measurement system was built for verification experiments. The experimental results reveal that the spectral reconstruction relative error is in the order of 10−2, and the full width at half maximum of the spectral measurement is as high as 1.2 nm. The spectral resolution can reach approximately 0.4 nm in the visible light band range (0.38–0.78 μm) with 1024 spectral channels. The compressed ratio of the compressed sensing spectrometry is up to 1:29. Furthermore, the spectral signal measurement time is only 0.25 ms. The study demonstrates a novel method, which exhibits high precision, speed, high compressed ratio and hyperspectral resolution.
An Orthodontic Treatment Case of a TMD Patient with Maxillary Posterior Intrusion Using TSADs
Authors: Lee Gye-Hyeong , Park Jae Hyun , Lee Sang-Mi , Kim Eun-Jeong , Lim Seung-Weon , Moon Danal
Published: November 2022

The orthodontic treatment of patients having temporomandibular disorders are some of the most complicated cases to treat. The positions of the mandibular condyles are often unstable, which means clinicians find it difficult to have definite criteria for making an accurate and reliable orthodontic diagnosis. This article reports the orthodontic treatment of a patient showing skeletal Class II and temporomandibular disorders with condylar resorption. To stabilize her condylar position and to relieve her symptoms in the temporomandibular joint, a stabilization splint was used before orthodontic tooth movement. After the splint therapy, the patient exhibited significantly increased open bite and a more severe Class II occlusal relationship as her mandibular condyles were seated anteriorly and superiorly in the articular fossae. The occlusion and facial esthetics of the patient were improved by orthodontic camouflage treatment with the proper use of temporary skeletal anchorage devices and treatment mechanics.
Experimental and Computational Vibration Analysis for Diagnosing the Defects in High Performance Composite Structures Using Machine Learning Approach
Authors: Jakkamputi Lakshmipathi , Devaraj Saravanakumar , Marikkannan Senthilkumar , Gnanasekaran Sakthivel , Ramasamy Sivakumar , Rakkiyannan Jegadeeshwaran , Xu Yigeng
Published: November 2022

Delamination in laminated structures is a concern in high-performance structural applications, which challenges the latest non-destructive testing techniques. This study assesses the delamination damage in the glass fiber-reinforced laminated composite structures using structural health monitoring techniques. Glass fiber-reinforced rectangular laminate composite plates with and without delamination were considered to obtain the forced vibration response using an in-house developed finite element model. The damage was diagnosed in the laminated composite using machine learning algorithms through statistical information extracted from the forced vibration response. Using an attribute evaluator, the features that made the greatest contribution were identified from the extracted features. The selected features were further classified using machine learning algorithms, such as decision tree, random forest, naive Bayes, and Bayes net algorithms, to diagnose the damage in the laminated structure. The decision tree method was found to be a computationally effective model in diagnosing the delamination of the composite structure. The effectiveness of the finite element model was further validated with the experimental results, obtained from modal analysis using fabricated laminated and delaminated composite plates. Our proposed model showed 98.5% accuracy in diagnosing the damage in the fabricated composite structure. Hence, this research work motivates the development of online prognostic and health monitoring modules for detecting early damage to prevent catastrophic failures of structures.
Assessment of Parameters to Apply Osmotic Dehydration as Pretreatment for Improving the Efficiency of Convective Drying of Cassava Bagasse
Authors: Serpa-Fajardo José , Narváez-Guerrero Diego , Serpa-Padilla Carolina , Hernández-Ramos Elvis , Fernández-Lambert Gregorio
Published: November 2022

This manuscript corresponds to exploratory research carried out to evaluate and define the operating parameters for applying osmotic dehydration (OD) as pretreatment for efficiency improvement in the convective drying of cassava bagasse, providing a methodology to apply these combined techniques, not only for cassava bagasse but also to serve as a reference for its use in other high-moisture agro-industrial residues. Operating factors significant for moisture loss during OD were determined through sieving analysis. Adequate levels and operating conditions were determined for OD and subsequent drying (osmotic agent concentration, solution-to-sample ratio, agitation speed, immersion time, required draining mesh, draining time, drying temperature, and drying air speed). It was established that an osmotic treatment was able to reduce the moisture of cassava bagasse from 87.7% to 28.94% and subsequent drying time by approximately 38.8%. These results allow the use of these combined techniques for drying cassava bagasse, which in turn, open a research agenda for its application in other agro-industrial residues, contributing to its handling, revaluation, and development of a sustainable agro-industry.
Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
Authors: Liu Qinglin , Wang Yali , Sun Panxu , Wang Dongwei
Published: November 2022

A damping model is one of the key factors in dynamic analysis. Viscous damping and hysteretic damping models are commonly used in structural damping models. In this study, transient and steady responses are analyzed for a single degree of freedom system based on the two damping models. The attenuation coefficient and damped natural frequency are important parameters of the transient response. In addition, the vibration amplitude is an important parameter of the steady response. When the relative errors of the parameters for the two damping models are less than 10%, the threshold of the damping ratio is selected as 0.1736 and the threshold of the loss factor is 0.3472. The numerical examples show that the dynamic responses based on the viscous damping model are approximately equal to those based on the hysteretic damping model in small damping cases. With the increase in the damping ratio, the difference between the dynamic responses calculated by the two damping models gradually increases. In large damping cases, the two damping models must be distinguished, and the choice of the damping model depends on the characteristic of dissipate energy.
RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks
Authors: Xu Huan , Liu Shuxian , Wang Wei , Deng Le
Published: November 2022

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.
Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation
Authors: Chen Yang , Shu Yifei , Hu Mian , Zhao Xingang
Published: November 2022

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion.
A Theoretical Model of Roof Self-Stability in Solid Backfilling Mining and Its Engineering Verification
Authors: Zhang Qiang , Yang Kang , Zhang Jixiong , Wang Qi , Yuan Longfeng , Shi Zengzhu , Xu Xiling
Published: November 2022

Roof self-stability in backfilling mining was proposed to explore its connotation and characteristics after a comparative analysis of roof structures under long-wall caving and backfilling mining. The mechanical analysis models of roof self-stability along strike and dip were established. After that, the mechanical equations for cooperative roof control were constructed to analyze the elastic foundation coefficients of the backfill, support peak load, unsupported-roof distance, and drilling effect of the working face along strike, the size of the working face, and the section pillar effect along dip. Research showed that the roof self-stability was greatly impacted by the elastic foundation coefficient of backfill, and it was less impacted by the support peak load along strike. The unsupported-roof distance had no obvious effect on roof self-stability. Roof self-stability was significantly affected by the working face and coal-pillar length along the dip. Therefore, the engineering control method of roof self-stability was proposed. The backfilling engineering practice in Xinjulong Coal Mine showed that the maximum roof subsidence was 438 mm, and the backfill ratio was 86.3% when the supporting intensity of backfilling hydraulic support was 0.94 MPa; the advanced distance of the working face was greater than 638 m; the foundation coefficient of backfilling material was 4.16 × 108 Nm−3. The roof formed the self-stability structure, which satisfied safe coal mining under buildings, water bodies, and railways.
Investigation of the Static Characteristics of a Geogrid-Reinforced Embankment
Authors: Duan Yanfu , Cheng Jianjun , Liu Jie
Published: November 2022

The research object of this paper is a geogrid-reinforced embankment. Through numerical simulation and data monitoring, the characteristics of vertical displacement, horizontal displacement, vertical earth pressure, horizontal earth pressure, and internal force in a geogrid-reinforced embankment are studied. The results show that: (1) According to the analysis of monitoring data, the cumulative horizontal displacement decreases and the cumulative vertical displacement increases with time. Additionally, the cumulative vertical displacement is greater than the cumulative horizontal displacement. (2) The vertical pressure of the fill and tensile stress of the geogrid are largest at the center of the structure section, and the horizontal earth pressure is also largest on both sides of the structure. (3) The numerical simulation value and actual monitoring value of the project are compared with the design value. It is found that the tensile force, horizontal pressure, vertical pressure, horizontal displacement, and vertical displacement of the geogrid are less than the design value. The simulated characteristic value is greater than the actual monitored characteristic value. The research results provide a reference for the design of similar geogrid-reinforced embankments.
Investigation of the Fatigue Life of Bottom-Up Cracking in Asphalt Concrete Pavements
Authors: Cheng Gang , Zheng Yong , Yu Jie , Liu Jun , Hu Xinhe
Published: November 2022

Traditionally, fatigue cracking in asphalt pavement means fatigue failure, which is the basis for controlling the design thickness of asphalt pavements. In fact, the fatigue failure of asphalt pavements includes three stages: fatigue cracking, crack expansion, and structural failure. Therefore, this paper aims to investigate the fatigue life of the bottom-up cracking of asphalt concrete (AC) pavements considering the different stages of fatigue failure. The dynamic modulus of AC of different grades was experimentally determined. The tensile stresses at the bottom of the AC layer were evaluated by embedding the tested dynamic modulus into a numerical simulation, which can be used to calculate the fatigue cracking life. Then, overlay tests (OTs) at different temperatures were conducted to obtain the fracture parameters A and n from the asphalt mixture. The crack propagation life was calculated via the Paris formula based on the fracture parameters A and n. The analysis results showed that an increase in AC thickness could effectively improve the fatigue crack life of the pavement structure, and the proportion of crack propagation life to fatigue crack life at different temperatures varied significantly. Therefore, when analyzing and calculating the fatigue life of pavement structures, besides the fatigue cracking life, the crack propagation life after cracking should also be considered, which is very important for accurately calculating the entire fatigue life of asphalt pavement structures. This will offer guidance for asphalt pavement thickness design.
An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
Authors: Lu Manhuai , Leng Yi , Chen Chin-Ling , Tang Qiting
Published: November 2022

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.
Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
Authors: Dada Emmanuel Gbenga , Oyewola David Opeoluwa , Joseph Stephen Bassi , Emebo Onyeka , Oluwagbemi Olugbenga Oluseun
Published: November 2022

Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international concern” (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions.
Perceptual Characteristics of Voice Identification in Noisy Environments
Authors: Zhou Yinghui , Liu Yali , Niu Huan
Published: November 2022

Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (−8 dB), the voice information contained in the high-frequency band of 2930~6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human’s auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science.
Dietary Supplemented Anthocyanin Reduced Serum Amyloid Beta Oligomers and Improved Cognitive Dysfunction Scores in Elderly Dogs
Authors: Lee Mi-Jin , Park Miey , Lee Hae-Jeung
Published: November 2022

Like humans, the accumulation of amyloid-beta oligomers in the brains of aged dogs leads to cognitive dysfunction. Our study investigated the effects of dietary flavonoids in pet foods on cognitive dysfunction. All nine dogs (six species) recruited were older than seven years, and cognitive function was measured using a questionnaire before and after applying pet food containing cyanidin-3-O-glucoside, the main component of honeyberries. Physical examination, blood tests, cognitive dysfunction scores, and serum amyloid-beta oligomers were measured. After 90 days of pet food administration, a physical examination revealed no abnormalities in weight, body temperature, heart rate, or respiratory rate. However, the cognitive dysfunction score and serum amyloid-beta oligomers (AβO) marker levels were significantly reduced after 90 days. Inflammation and antioxidant levels were slightly, but not significantly, changed. Our results suggest that pet food containing anthocyanins effectively improves cognitive dysfunction scores and decreases serum AβO levels.
Improvement of Thermochemical Processes of Laser-Matter Interaction and Optical Systems for Wavefront Shaping
Authors: Murzin Serguei P.
Published: November 2022

Laser thermochemical processes of metal surface oxidation are promising for creating new advanced technologies to meet the growing needs of opto- and micro-electronics, photonics, catalysis, sensorics and other high-tech industries. The features of thermochemical processes of laser-matter interaction occurring in matter under exposure to intense light flows and optical systems for controlling the irradiance and wavefront spatial distribution were reviewed. The laser beam offers the possibility of good focusing, which allows us to conduct chemical reactions, including the heterogeneous oxidation of metals, locally, with high spatial resolution. In this case, the absorption mechanisms of the laser beam vary for metals and for oxides, resulting from a thermochemical reaction and represent semiconductors. For semiconductors, the intrinsic, intraband, impurity, or lattice absorption takes place. The morphology of a metal surface also influences its optical absorption capacity. The improvement of beam shaping systems with elements of computer optics, namely diffractive freeform optics, provides an opportunity for an efficient control of chemical processes by achieving the desired redistribution of the laser beam power density. Laser thermochemical processes of the formation of quasi-one-dimensional nanostructured metal oxides are of great interest for advanced research and for a wide range of applications. A special feature of these processes is that, in the case of a frequency-modulated laser beam the synergy between the heat associated effects of the laser pulses and the laser-induced vibrations allows for a significant increase in the diffusion coefficient, which is stimulated by the non-stationary stress-strain state of the material. Ensuring the means of control over the thermochemical reaction in local sections of the laser exposure zone is an issue that can be solved by adapting the shape of the laser beam by the diffractive freeform optics. The gained knowledge contributes as a foundation for new photonic technologies oriented on the formation of nanostructured metal oxides, involving control over the morphology of the synthesized structures.
Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
Authors: Fakhar Shariqa , Baber Junaid , Bazai Sibghat Ullah , Marjan Shah , Jasinski Michal , Jasinska Elzbieta , Chaudhry Muhammad Umar , Leonowicz Zbigniew , Hussain Shumaila
Published: November 2022

Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning.
Comparison of “Orthodontic First” and “Surgery First” Approaches to Quality of Life in Orthognathic Surgery Patients: A Prospective Cohort Study
Authors: Thitiyuk Chonakan , Patchanee Siripatra , Klaisiri Awiruth , Chaiprakit Narissaporn
Published: November 2022

This prospective cohort study aimed to assess changes in quality of life (QoL) for “orthodontic first” approach (OFA) and “surgery first” approach (SFA) patients. Sixty patients who underwent orthognathic surgery via either the OFA (n = 30) or the SFA (n = 30) provided self-administered questionnaires (SAQs) about their orthognathic quality of life (Thai version OQLQ). Data were collected at four time points: before surgery (T1), and postoperatively, at 1 (T2), 3 (T3) and 6 months (T4). Intragroup and intergroup comparisons were performed by the Friedman test and the Mann–Whitney U test, respectively. Both the OFA and the SFA showed that QoL scores gradually improved in each domain for 6 months after surgery. The pattern of improvement after surgery in the SFA group was: facial esthetics (E) at 1 month; awareness of facial deformities (A) and social aspects of deformity (S) at 3 months; oral function (F) at 6 months. The pattern of improvement after surgery in the OFA group was: facial esthetics (E) and social aspects of deformity (S) at 1 month; awareness of facial deformities (A) at 3 months; oral function (F) at 6 months. The most concerning domain for Thai patients in our center OQLQ was the facial esthetic domain.
The Effect of Acaricide Control of the Two-Spotted Spider Mite Tetranychus urticae Koch on the Cultivation of Sugar Beet (Beta vulgaris L.) and on the Size and Quality of the Yield
Authors: Bocianowski Jan , Jakubowska Magdalena , Zawada Daniel , Dobosz Renata
Published: November 2022

Field experiments (in the 2019–2021) were carried out at the Department of Field Experimentation of the Institute of Plant Protection—National Research Institute in Winna Góra, the purpose of which was to test the insecticidal and acaricidal effectiveness of sugar beet cultivation protection against Tetranychus urticae and to assess its impact on the size and quality of the sugar beet crop. In the experiment, the following acaricides were used: spirodiclofen—240 g—22.11%, mixture of hexythiazox—250 g—23.15% and fenpyroximate—51.2 g—5.02% and insecto-acaricide paraffin oil—770 g L−1 (89.6%) and abamectine—18 g—1.88%. The controls were plants left without chemical protection. The plants were sprayed when ten mobile individuals/two spotted spider mites appeared on the leaves. Chemical treatments were carried out in the full growing season in the phase of leaf rosette formation (July–August). In the second half of October, the plant density (PD) in the field was estimated. Parameters characterizing the size and quality of the crop were calculated: sugar beet yield (SBY), biological sugar yield (BSY), pure sugar yield (PSY), sugar content (SC), refined of sugar content (RSC), the yield of preferential sugar (YPS), recoverable sugar (RS), potassium molasses (PM), sodium molasses (SM), α-amino nitrogen (α-AN), alkalinity factor (AF) and standard molasses losses (SML). The years were statistically significantly different for all 13 traits. Statistical differences were observed in the mean values of the observed parameters in these years, except for α-amino nitrogen (α-AN) and alkalinity factor (AF). The mean values of SBY, biological sugar yield (BSY), pure sugar yield (PSY) and sodium molasses (SM) differed depending on the type of protection applied. Positive correlations were observed for 28 pairs of traits, but negative statistically significant relationships were observed between 11 pairs of traits. The first two canonical variates accounted for 85.49% of the total variability between the individual combinations. The significant positive relationship with the first canonical variate was found for PD, BSY, PSY, SC, RSC, YPS, but negative for SM. The CV2 was negatively correlated with: SBY, BSY, PSY, RS, PM, SM, α-AN and SML. The greatest variation in terms of all the 13 traits jointly was found for Vertigo 018 EC in 2020 and Vertigo 018 EC in 2021. The greatest similarity was found between control in 2019 and Ortus 05 SC in 2019.
The Efficacy, Tolerance and Acceptance of a New Fixed-Dose Salmeterol and Fluticasone Propionate Dry-Powder Inhaler—Salflumix Easyhaler® in COPD Patients in the Daily Clinical Practice
Authors: Doniec Zbigniew , Olszanecka-Glinianowicz Magdalena , Hantulik Piotr , Almgren-Rachtan Agnieszka , Chudek Jerzy
Published: November 2022

The efficacy of the fix-dose salmeterol/fluticasone propionate combination in chronic obstructive pulmonary disease (COPD) was only shown for the original product. This investigator-initiated study aimed to prove the efficacy and safety of Salflumix Easyhaler®, a second-entry product (dry-powder inhaler) in a real-life setting. The efficacy of the therapy was assessed in 440 COPD outpatients (36.1% classified as C&D groups according to GOLD) using the COPD assessment test (CAT) and the modified Medical Research Council (mMRC). During 86 ± 30 days, the frequency of COPD with a big and very big impact on life (CAT > 20 pts); and high scores of dyspnea (mMRC ≥ 2) decreased from 60.7% and 57.5% at I visit to 15.2% and 22.6% at III visits, respectively (p < 0.001). There was a greater improvement in newly diagnosed patients than those who switched from other devices due to insufficient disease control or patient dissatisfaction with the used inhaler. Patients’ satisfaction was scored 3.2–3.5 in a 4 pts scale. Physicians scored the burden related to the use of Salflumix Easyhaler® as very low. Adherence exceeded 90%. This study supports effectiveness, satisfaction, and convenience with the use of this new product in COPD, and shows that ICS-containing DPI therapy is still improperly prescribed for patients with a low risk of COPD exacerbation in real-life settings.
High-Throughput DNA Analysis Platform Based on an Optofluidic Ring Resonator Laser
Authors: Jun Chan Seok , Lee Wonsuk
Published: November 2022

We demonstrate an optofluidic laser using DNA microdroplets, an intercalating dye, and a glass capillary ring resonator. Only the target DNA emits the laser signal, while the non-target DNA, including those with single-base mismatches, exhibits zero optical signals. Subsequently, a single laser emission detected within a few milliseconds can distinguish the target DNA from others, allowing for truly digital DNA sequence analysis from an optical point of view. In addition, the microdroplets could be individually investigated using our optofluidic system. These advances in laser-based DNA analysis make our proposed scheme a promising, rapid, and high-throughput DNA sequence analysis platform.
Study of the Evolution Characteristics of Microseismic Events during the Excavation of Underground Caverns under High Geostress
Authors: Lin Xin , Li Biao , Li Peng , Ding Quanfu , Huang Mengting
Published: November 2022

A microseismic (MS) monitoring system was established, and numerical modeling was performed using Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D) to examine the evolution characteristics of MS events during the excavation of underground caverns under high geostress. Specifically, the spatial and temporal damage characteristics of the rock mass, the dynamic relationship between the evolution of MS events, the site construction conditions, and the geological structures under high geostress were also analyzed. In addition, a three-dimensional numerical model of underground caverns was built to demonstrate the deformation characteristics of the rock mass. As a result, the characteristics of a large number of MS events, multiple concentration zones of MS events, and long periods of stress adjustment were discovered in underground caverns under high geostress. It was found that the primary cause of MS events was site blasting construction. In particular, the distribution of the MS events was strongly correlated with the location of the site blasting construction, and the frequency of the MS events was positively correlated with it. The structural plane is a discontinuous plane with very low or no tensile strength, and its presence also increased the number of MS events and raised the possibility of the surrounding rock becoming unstable. Moreover, the MS monitoring data agreed with the numerical modeling results, which can be useful in estimating damage to underground caverns under high geostress and predicting disasters.
Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
Authors: Min Kyung-Eun , Jang Jae-Won , Kim Jun-Ki , Yi Sung , Kim Cheolhee
Published: November 2022

Electronic packaging has been developed with high resolution and fine interconnection pitches. Non-conductive adhesives (NCAs) have been growing with the increase of I/O pad count and density, along with fine pad bond pitch interconnections. Prediction and optimization of NCA characteristics are inherently complicated due to various and extensive materials composing NCAs. In this study, a framework predicting the curing time and shear strength of an NCA is established by a neural network model. NCA formulations with 4 resins, 3 hardeners, 8 catalysts, and a coupling agent were selected from in-house experiments, and an artificial neural network (ANN) with one dense layer with 3 nodes was trained using 65 data points. Model accuracy was improved by 28.9–35.2% compared with the reference, and the trained model was also verified through third-party reference data. Prediction of NCA properties and optimization of NCA formulations for mass production were demonstrated by using the trained ANN model. This paper provides a framework for ANN-based NCA design and confirmed the feasibility of ANN modeling, even with a small dataset.
Research on the Effect of Karst on Foundation Pit Blasting and the Stiffness of Optimal Rock-Breaking Cement Mortar
Authors: Duan Ya , Zhang Xuemin , Zhou Xianshun , Chen Xinlei , Zhou Hao
Published: November 2022

The existence of karst cavities has an important impact on the safety of foundation pit excavation projects. It is of engineering guiding value to study the influence of karst cavities on the blasting process of foundation pits and how to optimize the stiffness of cement mortar to improve the blasting effect. Based on the karst foundation pit bench blasting project of Shenzhen Dayun Foundation Pit Project, this paper adopts the SPH-FEM coupling calculation method to study the influence of karst cavities, cavity-filling water and cavity-filling silt clay on the rock-blasting process of bench blasting. We analyzed the development process of blasting damage of rock when the stiffness of karst cavity grouting filling changes under the conditions of slightly weathered, moderately weathered and strongly weathered limestone. The calculation results show that the karst cavity near the blasthole changes the direction of the minimum resistance line, which leads to the release of blasting energy; the rock breaking effect is improved when the karst cavity is filled with water medium and clay medium. Under the three limestone conditions, after the karst cavity is pretreated by cement grouting, the increase in the stiffness of the cement mortar makes the rock damage area first increase and then decrease after the karst cavity implosion, and There is a critical cement mortar stiffness that makes the best rock breaking effect. The critical cement stiffness of micro-, medium- and strongly weathered limestone is 2.2%, 6.1% and 27% of the blasted rock mass, respectively, which makes the karst cavity wall stress reach the peak value, and the rock-breaking effect is the best at this time.
A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
Authors: Badi Mame Antoine , Tapamo Jules-Raymond
Published: November 2022

Facial Expression Recognition (FER) is a growing area of research due to its numerous applications in market research, video gaming, healthcare, security, e-learning, and robotics. One of the most common frameworks for recognizing facial expressions is by extracting facial features from an image and classifying them as one of several prototypic expressions. Despite the recent advances, it is still a challenging task to develop robust facial expression descriptors. This study aimed to analyze the performances of various local descriptors and classifiers in the FER problem. Several experiments were conducted under different settings, such as varied extraction parameters, different numbers of expressions, and two datasets, to discover the best combinations of local descriptors and classifiers. Of all the considered descriptors, HOG (Histogram of Oriented Gradients) and ALDP (Angled Local Directional Patterns) were some of the most promising, while SVM (Support Vector Machines) and MLP (Multi-Layer Perceptron) were the best among the considered classifiers. The results obtained signify that conventional FER approaches are still comparable to state-of-the-art methods based on deep learning.
Hybrid TOA/AOA Virtual Station Localization Based on Scattering Signal Identification for GNSS-Denied Urban or Indoor NLOS Environments
Authors: Luo Rui , Yan Lili , Deng Ping , Kuang Yin
Published: November 2022

Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy.
An Efficient Attention-Based Convolutional Neural Network That Reduces the Effects of Spectral Variability for Hyperspectral Unmixing
Authors: Jin Baohua , Zhu Yunfei , Huang Wei , Chen Qiqiang , Li Sijia
Published: November 2022

The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI). Due to the existence of spectral variabilities (SVs), it is difficult to obtain accurate spectral features. At the same time, the performance of unmixing is not only affected by SVs but also depends on the effective spectral and spatial information. To solve these problems, this study proposed an efficient attention-based convolutional neural network (EACNN) and an efficient convolution block attention module (ECBAM). The EACNN is a two-stream network, which is learned from nearly pure endmembers through an additional network, and the aggregated spectral and spatial information can be obtained effectively with the help of the ECBAM, which can reduce the influence of SVs and improve the performance. The unmixing network helps the whole network to pay attention to meaningful feature information by using efficient channel attention (ECA) and guides the unmixing process by sharing parameters. Experimental results on three HSI datasets showed that the method proposed in this study outperformed other unmixing methods.
Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
Authors: Meftah Ali H. , Alotaibi Yousef A. , Selouani Sid-Ahmed
Published: November 2022

The goal of emotional voice conversion (EVC) is to convert the emotion of a speaker’s voice from one state to another while maintaining the original speaker’s identity and the linguistic substance of the message. Research on EVC in the Arabic language is well behind that conducted on languages with a wider distribution, such as English. The primary objective of this study is to determine whether Arabic emotions may be converted using a model trained for another language. In this work, we used an unsupervised many-to-many non-parallel generative adversarial network (GAN) voice conversion (VC) model called StarGANv2-VC to perform an Arabic EVC (A-EVC). The latter is realized by using pre-trained phoneme-level automatic speech recognition (ASR) and fundamental frequency (F0) models in the English language. The generated voice is evaluated by prosody and spectrum conversion in addition to automatic emotion recognition and speaker identification using a convolutional recurrent neural network (CRNN). The results of the evaluation indicated that male voices were scored higher than female voices and that the evaluation score for the conversion from neutral to other emotions was higher than the evaluation scores for the conversion of other emotions.