bluuebunny/crossref_metadata_2025 huggingface dataset:
An Evaluation of Ethograms Measuring Distinct Features of Enrichment Use by Captive Chimpanzees (Pan troglodytes)
Authors: Greeson Julia L. , Gabriel Kara I. , Mulcahy J. B. , King Hendrickson Bonnie , Lonborg Susan D. , Holloway Jay C.
Published: August 2022

Environmental enrichment provides mental stimulation and minimizes abnormal behaviors in captive animals. In captive chimpanzees, individual animals may vary in the ways in which they benefit from enrichment or use enrichment devices, so investigating nuances in enrichment use may improve the welfare of captive chimpanzees. In the current study, three ethograms measuring distinct features of enrichment use (i.e., enrichment object, manipulation behavior, and social context) were evaluated by coding videos of captive chimpanzees (Pan troglodytes) at Chimpanzee Sanctuary Northwest in Cle Elum, WA. A total of 732 min and 58 s of video footage was coded from a larger video archive (i.e., 2054 videos) of enrichment use that spanned a decade. A principal component analysis (PCA) revealed that different categories of enrichment objects were more often associated with specific manipulation behaviors and social contexts, suggesting that enrichment objects might fulfill different behavioral and social needs in captivity. Specifically, toy objects were associated with active tactile behaviors in affiliative contexts while oral behaviors were used with foraging objects in solitary contexts. Additionally, individual chimpanzees showed unique preferences for enrichment objects, indicating that caregivers of captive chimpanzees should consider individual needs instead of a “one size fits all” approach to enrichment provisions.
Factors Influencing Disease Stability and Response to Tocilizumab Therapy in Severe COVID-19: A Retrospective Cohort Study
Authors: Hafez Wael , Abdelrahman Ahmed
Published: August 2022

(1) Background: The efficacy of tocilizumab in COVID-19 has been doubted. The study aimed to investigate factors affecting disease stability and response to tocilizumab among severe COVID-19 patients. (2) Methods: This was a cohort study of 70 severe COVID-19 patients at NMC Royal Hospital, UAE, from April to June 2020. (3) Results: Elderly patients and those with cardiovascular comorbidities had a higher risk of unstable COVID-19 (p = 0.025). Regarding tocilizumab therapy timing, compared to the critical group receiving tocilizumab, the unstable severe patients receiving tocilizumab had a significantly higher rate of improvement (86%). In contrast, the late critical subgroup showed a significantly increased mortality rate (52.9%). The risk for secondary infection and adverse events following tocilizumab was higher in the late critical group than in the unstable severe and early critical groups (p = 0.024 and p = 0.006, respectively). Therapeutic doses of anticoagulation and high-dose vitamin D were correlated with better outcomes than the prophylactic dose and the treatment dose of vitamin D (p < 0.001 and p = 0.07, respectively). (4) Conclusions: elderly patients and those with cardiovascular disease developed unstable COVID-19. Tocilizumab is a potentially effective choice against severe and critical COVID-19. Early tocilizumab administration combined with therapeutic dose anticoagulation and high vitamin D doses could improve the patients’ outcomes.
In Vitro Study of the Interaction of Gentamicin with Ceftriaxone and Azithromycin against Neisseria gonorrhoeae Using Agar Dilution Method
Authors: Xu Wenqi , Zhou Qian , Liu Jingwei , Zhang Yan , Zhu Xiaoyu , Zhu Bangyong , Yin Yueping
Published: August 2022

The susceptibility to gentamicin of N. gonorrhoeae isolates collected in three Chinese provinces and the correlation among the MICs of gentamicin, azithromycin, and ceftriaxone were investigated in this study. The effects of combinations from those three antibiotics were also in the scope of this study to determine the efficacy of gentamicin as a combination therapeutic drug. The agar dilution method was used to measure the minimum inhibitory concentrations (MICs) of ceftriaxone, azithromycin and gentamicin on N. gonorrhoeae isolates. The synergy between these three antimicrobials were determined using the agar dilution checkerboard method. Subgroup studies were conducted to explore differences between azithromycin- and ceftriaxone-sensitive and resistant isolates. A total of 139 (36.60%) and 233 (61.30%) isolates demonstrated full susceptibility and intermediate susceptibility to gentamicin, respectively. The correlation analysis showed that the MICs of ceftriaxone and azithromycin weakly correlated with the value of gentamicin. The overall results of the three antibiotic combinations revealed indifferent effects. Combination therapy established a significant reduction on the MIC value. Most of the N. gonorrhoeae isolates tested in this study demonstrated a certain degree of susceptibility to gentamicin. Overall, antimicrobial combinations of gentamicin with ceftriaxone or azithromycin demonstrate indifferent effects.
Survey of the Knowledge, Attitudes and Practice towards Antibiotic Use among Prospective Antibiotic Prescribers in Serbia
Authors: Horvat Olga , Petrović Ana Tomas , Paut Kusturica Milica , Bukumirić Dragica , Jovančević Bojana , Kovačević Zorana
Published: August 2022

The complex issue of antibacterial resistance (ABR) requires actions taken with the One Health approach, involving both human and veterinarian medicine. It can spread from animals to humans through the food chain or through direct contact. Health profession students, as the future antibiotic providers, can greatly impact antibiotic-related issues in the future. The study was conducted to evaluate knowledge, attitudes and practice of future antibiotic prescribers in relation to judicious use of antibiotics. This cross-sectional, questionnaire-based study was performed on 400 students of health professions who were allowed to prescribe antibiotics of the University of Novi Sad, Serbia. Students of medicine and students of dentistry showed a significantly higher knowledge score compared to students of veterinary medicine (p = 0.001). Multivariate regression identified predictors of adequate antibiotic knowledge: being a female student (B = 0.571; p = 0.020), higher grade average (B = 1.204; p = 0.001), students of medicine (B = 0.802; p = 0.006) and dentistry (B = 0.769; p = 0.026), and students who used a complete package of antibiotics during the last infection (B = 0.974; p = 0.001) or for the period recommended by the doctor (B = 1.964; p = 0.001). Out of the total sample, self-medication was reported among 42.8% of students. The identified predictors of self-medication were: more frequent (B = 0.587; p = 0.001) and irregular (B = 0.719; p = 0.007) antibiotic use, taking antibiotics until symptoms disappeared (B = 2.142; p = 0.001) or until the bottle was finished (B = 1.010; p = 0.001) during the last infection. It seems prudent to reevaluate the educational curricula regarding antibiotic use and ABR of prospective prescribers in Serbia.
Acinetobacter baumannii Infection-Related Mortality in Hospitalized Patients: Risk Factors and Potential Targets for Clinical and Antimicrobial Stewardship Interventions
Authors: Alrahmany Diaa , Omar Ahmed F. , Alreesi Aisha , Harb Gehan , Ghazi Islam M.
Published: August 2022

Due to resistance and scarcity of treatment options, nosocomial Acinetobacter baumannii infections are associated with significant fatality rates. We investigated the factors contributing to infection-related deaths to develop tailored stewardship interventions that could reduce these high mortality rates. We reviewed the medical records of adult inpatients with A. baumannii infections over two years. Patient demographics and clinical data were collected and statistically analyzed. The study included 321 patients with positive A. baumannii microbiological cultures, with respiratory infections accounting for 58.6%, soft tissues 29.3%, bacteremia 8.6%, urine 2.1%, and others 1.4%. The study population’s median (IQR) age was 62.6 (38.9–94.9) years, and hospital stay was 20 (9.5–40) days. Statistical analysis revealed that various risk factors contribute significantly to high in-hospital all-cause mortality (44%), as well as 14-day and 28-day mortality rates. Deaths increased by a factor of 1.04 with every additional year of age (p = 0.000), admission to the critical care unit (p = 0.000, OR: 2.86), and patients admitted with an infectious diagnosis had nearly three times the mortality rate as those admitted with other diagnoses (p = 0.000, OR: 3.12). Male gender (p < 0.001, OR: 2.14), any comorbid conditions (p = 0.000, OR: 5.29), prolonged hospitalization (>7 days) (p = 0.023, OR: 1.98), and hospital acquisition of infection (p = 0.027, OR: 1.68) were among the most significant predictors of mortality. All variables were investigated for their impact on all-cause, 14-day, and 28-day mortality rates. Improving multidisciplinary infection control practices, regular disinfection of patient care equipment, and optimal intubation practice that avoids unnecessary intubation are necessary interventions to reduce infection-related mortality rates. Better antibiotic selection and de-escalation, shorter hospital stays whenever possible, prompt medical stabilization of comorbid conditions, and fewer unnecessary admissions to critical care units will all lead to improved outcomes.
Variations of Bioactive Phytochemicals and Antioxidant Capacity of Navel Orange Peel in Response to Different Drying Methods
Authors: Lai Chunling , Liang Yan , Zhang Linyan , Huang Jiangjiang , Kaliaperumal Kumaravel , Jiang Yueming , Zhang Jun
Published: August 2022

The effects of five different drying methods, namely, freeze drying (FD), shade drying (SD), hot-air oven drying at 50 °C (OD50), 70 °C (OD70), and microwave drying (MD) on the bioactive phytochemicals and antioxidant capacity of navel orange peel were assessed and comprehensively discussed in detail. Compared with other drying methods, MD-treated peel contained the lowest total phenolic content (TPC) and total flavonoid content (TFC). The peel subjected to OD70 treatment was superior in TPC relative to other treatments and the highest TFC was found in the peels treated with FD. HPLC analysis identified thirteen flavonoids involving three flavanone glycosides (FGs) and ten polymethoxyflavones (PMFs) in navel orange peel and revealed that PMFs in peel were stable under all these drying methods, whereas the three major FGs (narirutin, hesperidin, and didymin) in peel significantly degraded in response to MD treatment. The peels subjected to OD50/OD70 treatments had the most potent antioxidant capacity when compared to other drying methods. Furthermore, Pearson’s correlation analysis was performed. The results revealed here allow us to recommend the use of OD50 or OD70 for the drying of orange peel, both of which help the maintenance of bioactive compounds in the peel and improve its antioxidant capacity.
Analysis and Prediction Research for Bipropellant Thruster Mixture Ratio Based on BP-RNN Chain Method
Authors: Zhang Zhen , Yan Hao , Cai Kun , Yang Shangfeng , Wang Fengshan , Mao Xiaofang , Yu Yusong
Published: August 2022

In order to improve the mixture ratio accuracy of the bipropellant thruster and prolong the service life of a satellite on orbit, a variety of different liquid-flow tests and the final firing test are critical in the production testing process. However, after comparison with a large number of tests data, it was found that the liquid-flow test results were far away from matching the firing-test data without clear regularity, resulting in a qualified rate of mixture ratio of less than 40%. This study developed a BP-RNN (back propagation—recurrent neural network) chain method based on machine learning, which uses multi-dimensional nonlinear parameters to construct the specific dataset after data enhancement. Then, the mapping characteristic of the neural network was used to fit the historical data for the weight analysis and mixture ratio prediction, and effectively improved the qualified rate of the mixture ratio. The back propagation neural network was used to learn the association rules of the 10-dimension characteristic data and the firing test results generated in the historical process of thruster production. Then, the features with high influencing weight were extracted and sorted, so the “many-to-one” mixture ratio prediction was conducted through the subsequent recurrent neural network. The accepted prediction accuracy could reach around 75% after the test data verification. By using this method, most of bipropellant thrusters could directly reach the qualified mixture ratio in the firing test after adjusting the throttle orifice size in the liquid-flow tests. This chain method first bridges the data between the liquid-flow test and the firing test.
Response Surface Optimization of Inulin and Polyphenol Extraction from Artichoke (Cynara scolymus (L.)) Solid Wastes
Authors: Garcia-Castello Esperanza M. , Mayor Luis , Calvo-Ramirez Alejandro , Ruiz-Melero Ruben , Rodriguez-Lopez Antonio D.
Published: August 2022

Artichoke wastes after processing represent 60–70% of the raw material and are a potential source of inulin and polyphenols, bioactive compounds that can be valorized as food ingredients or nutraceutical products. The aim of this work was to assess and optimize the extraction of these compounds from artichoke wastes using water or water–ethanol mixtures as extracting agents. For simultaneous inulin and polyphenol extraction and to achieve high antioxidant activity in extracts, the best process conditions using water as an extracting agent were T = 89 °C and t = 139 min, where 80% of the inulin content, 60% of the total phenolic content (TPC) and 56% of the antioxidant activity (Aox) were obtained. For water–ethanol extractions, the best results were obtained with EtOH = 22.4%, T = 81 °C and t = 217 min, leading to extraction yields of 90% of TPC, 38% of Aox and 58% of inulin content. From these results, we recommend the use of water for the recovery of inulin and polyphenols from artichoke wastes. Although the extraction yield of polyphenols is lower in water treatments, the amount extracted is considerable and it is a greener option when compared with water–ethanol mixtures.
One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
Authors: Li Ying , Sun Shiyu , Zhang Changshe , Yang Guangsong , Ye Qiubo
Published: August 2022

Plant diseases such as drought stress and pest diseases significantly impact crops’ growth and yield levels. By detecting the surface characteristics of plant leaves, we can judge the growth state of plants and whether diseases occur. Traditional manual detection methods are limited by the professional knowledge and practical experience of operators. In recent years, a detection method based on deep learning has been applied to improve detection accuracy and reduce detection time. In this paper, we propose a disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection. Based on the one-stage plant disease network YoLov5s, the coordinate attention (CA) attention module is added, along with a key feature weight to enhance the effective information of the feature map, and the spatial pyramid pooling (SSP) module is modified by data augmentation to reduce the loss of feature information. Three experiments are conducted under complex conditions such as overlapping occlusion, sparse distribution of detection targets, and similar textures and backgrounds of disease areas. The experimental results show that the average accuracy of the MFF-CNN is higher than that of currently used methods such as YoLov5s, Faster RCNN, CenterNet, and DETR, and the detection time is also reduced. The proposed method provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases.
Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
Authors: Johnsen Joshua , Runnels Timothy , Burgess Johnathan , Jdiobe Muwanika , Rouser Kurt
Published: August 2022

This paper evaluates the performance and practical challenges associated with fabricating and flight testing an unmanned aircraft powered by a turboelectric system based on a 7 kW turbine engine. Emerging hybrid gas-electric aircraft concepts have been the subject of numerous design studies and analytical evaluations; however, there is a critical need to identify and assess practical issues associated with integrating a hybrid turboelectric power system into an aircraft. The purpose of this study, relevant to emerging hybrid-powered aircraft, is to evaluate and retrofit a prototype turboelectric power system to an existing 391 N gross take-off weight unmanned airframe. The representative 7 kW turboelectric system was installed to identify challenges and to formulate data-driven recommendations for general application to urban air mobility. This work addresses performance, power and thermal management, vibration, and acoustic emissions. Results include a weight breakdown with the turboelectric system making up 21% of the total aircraft weight, in-flight voltage and current measurements with maximum loads observed during a dive pull-out, temperature measurements, accelerometer measurements, and far field sound pressure level measurements. Practical recommendations from this study are applicable to power system reliability, electronic component selection, cooling requirements, and peak power behavior, informing the design of future hybrid gas–electric aircraft.
Characteristics of Magnetic Fields Induced by the Wake of an Underwater Vehicle
Authors: Huang Bo , Liu Zhongyan , Xu Yujing , Ding Qiaochu , Pan Mengchun , Hu Jiafei , Zhang Qi
Published: August 2022

Underwater vehicles generate hydrodynamic wakes within a large area that last for a longtime during navigation, thus generating induced magnetic fields, and these are of great significance for detecting and tracking underwater vehicles. In combination with the wakefield and magnetic field simulations, this study adopts the dynamic overlapping mesh technology to conduct a numerical simulation of the wake magnetic field during the movement of an underwater vehicle. This paper introduces the causes of formation and laws of evolution of the wake magnetic field, analyzes its spatial distribution and time-domain changes, and discusses the time-frequency domain characteristics at different monitoring points as well as the effects of navigation speed and acceleration on wake magnetic fields. Our results indicate that the wake magnetic field of an underwater vehicle belongs to a low-frequency weak signal of 0–5 Hz; as the navigation speed increases, the barycenter frequency of the wake magnetic field decreases and the half-energy bandwidth increases. The increase in acceleration of the underwater vehicle will cause a higher growth rate of the wake magnetic field. This paper provides a theoretical reference for the detection of underwater vehicles based on wake magnetic fields.
Smart Interactive Technologies in the Human-Centric Factory 5.0: A Survey
Authors: Brunetti Davide , Gena Cristina , Vernero Fabiana
Published: August 2022

In this survey paper, we focus on smart interactive technologies and providing a picture of the current state of the art, exploring the way new discoveries and recent technologies changed workers’ operations and activities on the factory floor. We focus in particular on the Industry 4.0 and 5.0 visions, wherein smart interactive technologies can bring benefits to the intelligent behavior machines can expose in a human-centric AI perspective. We consider smart technologies wherein the intelligence may be in and/or behind the user interfaces, and for both groups we try to highlight the importance of designing them with a human-centric approach, framed in the smart factory context. We review relevant work in the field with the aim of highlighting the pros and cons of each technology and its adoption in the industry. Furthermore, we try to collect guidelines for the human-centric integration of smart interactive technologies in the smart factory. In this wa y, we hope to provide the future designers and adopters of such technologies with concrete help in choosing among different options and implementing them in a user-centric manner. To this aim, surveyed works have been also classified based on the supported task(s) and production process phases/activities: access to knowledge, logistics, maintenance, planning, production, security, workers’ wellbeing, and warehousing.
Informative Language Encoding by Variational Autoencoders Using Transformer
Authors: Ok Changwon , Lee Geonseok , Lee Kichun
Published: August 2022

In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-art level in numerous NLP tasks such as language modeling, summarization, and classification. Moreover, a variational autoencoder (VAE) is an efficient generative model in representation learning, combining deep learning with statistical inference in encoded representations. However, the use of VAE in natural language processing often brings forth practical difficulties such as a posterior collapse, also known as Kullback–Leibler (KL) vanishing. To mitigate this problem, while taking advantage of the parallelization of language data processing, we propose a new language representation model as the integration of two seemingly different deep learning models, which is a Transformer model solely coupled with a variational autoencoder. We compare the proposed model with previous works, such as a VAE connected with a recurrent neural network (RNN). Our experiments with four real-life datasets show that implementation with KL annealing mitigates posterior collapses. The results also show that the proposed Transformer model outperforms RNN-based models in reconstruction and representation learning, and that the encoded representations of the proposed model are more informative than other tested models.
Robot Bionic Vision Technologies: A Review
Authors: Zhang Hongxin , Lee Suan
Published: August 2022

The visual organ is important for animals to obtain information and understand the outside world; however, robots cannot do so without a visual system. At present, the vision technology of artificial intelligence has achieved automation and relatively simple intelligence; however, bionic vision equipment is not as dexterous and intelligent as the human eye. At present, robots can function as smartly as human beings; however, existing reviews of robot bionic vision are still limited. Robot bionic vision has been explored in view of humans and animals’ visual principles and motion characteristics. In this study, the development history of robot bionic vision equipment and related technologies are discussed, the most representative binocular bionic and multi-eye compound eye bionic vision technologies are selected, and the existing technologies are reviewed; their prospects are discussed from the perspective of visual bionic control. This comprehensive study will serve as the most up-to-date source of information regarding developments in the field of robot bionic vision technology.
Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders
Authors: Peng Ching-Tung , Chan Yung-Kuan , Yu Shyr-Shen
Published: August 2022

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network.
High Brightness Diode Lasers Based on Beam Splitting and Polarization Combining
Authors: Zhao Yufei , Tong Cunzhu , Wang Lijie , Wang Yanjing , Lu Huanyu , Zhang Xin , Wei Zhipeng , Wang Lijun
Published: August 2022

A new method to improve the brightness of diode lasers based on beam-waist splitting and polarization combining was proposed and demonstrated. The beam waist was split by a precisely cut prism into two parts and combined with a polarization beam combiner. The advantages of simple setup, high efficiency, brightness, and universality were presented. The slow axis M2 factor of a broad-area diode laser with a ridge beam width of 180 μm was reduced from 18.5 to 9.8, and a brightness of 42.4 MW cm−2 sr−1 was realized; this brightness was a 84.21% improvement of the same emitter at 8 A. The slow axis M2 factor of a commercial broad-area diode laser array combined by spectral beam combining was reduced from 9.08 to 4.78, and 80.6% improvement of brightness was realized on the same commercial broad-area diode laser array. A brightness of 195.8 MW cm−2 sr−1 was realized at 36 A by this diode laser array. This method can be applied in a highly polarized light source to improve the beam quality and brightness.
Correction: Garniwa et al. Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea. Appl. Sci. 2021, 11, 3445
Authors: Garniwa Pranda M. P. , Ramadhan Raden A. A. , Lee Hyun-Jin
Published: August 2022

The authors wish to make the following corrections to this paper [...]
Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
Authors: Herman Emilia , Zsido Kinga-Emese , Fenyves Veronika
Published: August 2022

Nowadays there is a large amount of information at our disposal, which is increasing day by day, and right now the question is not whether we have a method to process it, but which method is most effective, faster and best. When processing large databases, with different data, the formation of homogeneous groups is recommended. This paper presents the financial performance of Hungarian and Romanian food retail companies by using two well-known cluster analyzing methods (K-Mean and K-Medoid) based on ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) financial ratios. The research is based on two complete databases, including the financial statements for five years of all retail food companies from one Hungarian and one Romanian county. The hypothesis of the research is: in the case of large databases with variable quantitative data, cluster analysis is necessary in order to obtain accurate results and the method chosen can bring different results. It is justified to think carefully about choosing a method depending on the available data and the research aim. The aim of this study is to highlight the differences between the results of these two grouping procedures. Using the two methods we reached different results, which means a different evaluation of financial performance. The results demonstrate that the method chosen for grouping may influence the assessment of the financial performance of companies: the K-Mean method produces a greater variety of groups and the range of results obtained after grouping is larger; whereas, the group distribution and the results obtained by the K-Medoid method are more balanced.
An Experimental Analysis of Gas Reduction in Multiphase Flow with a KMS Helical Static Mixer
Authors: Vasquez-Santacruz Jose Alejandro , Portillo-Velez Rogelio de Jesus , Garcia-Ramirez Pedro Javier , Marín-Urías Luis Felipe , Sánchez-Montero Diego , Porrágas-Beltrán Luis Hector
Published: August 2022

The separation dynamics of a multiphase fluid mixture is studied with the aim to validate the gas reduction from the mixture by means of a KMS helical static mixer device through a laboratory prototype based on the jet-pump artificial lift system for oil extraction. The study is focused on the cyclone phenomenon, produced by the KMS, to experimentally verify the reduction of foaming in a reservoir behaving as a horizontal gravity separator of an oil–gas–water mixture under specific dynamics conditions of velocity and pressure of an incoming streamline, as well as some physical mounting configurations of the device. The results were numerical and experimentally validated, projecting the real performance in three-phase separators in an oil extraction field to improve efficiency in pumping machines regarding the negative effects of the gas. Concerning the gas reduction, a KMS of two elements located at 100 mm from the entrance of the horizontal reservoir and 20 mm from a gas casing tube successfully improves the gas isolation from the mixture, from 29.2% to practically 0% over 200 min in the reservoir, to let oil and water become separated by sedimentation. This gas reduction is indirectly assessed by the emulsion that is generated, which affects the time of oil separation in the counterbalance.
Facial Emotion Recognition Analysis Based on Age-Biased Data
Authors: Park Hyungjoo , Shin Youngha , Song Kyu , Yun Channyeong , Jang Dongyoung
Published: August 2022

This paper aims to analyze the importance of age-biased data in recognizing six emotions using facial expressions. For this purpose, a custom dataset (adults, kids, mixed) was constructed using images that separated the existing datasets (FER2013 and MMA FACILE EXPRESSION) into adults (≥14) and kids (≤13). The convolutional Neural Networks (CNN) algorithm was used to calculate emotion recognition accuracy. Additionally, this study investigated the effect of the characteristics of CNN architecture on emotion recognition accuracy. Based on the variables of Accuracy and FLOP, three types of CNN architectures (MobileNet-V2, SE-ResNeXt50 (32 × 4 d), and ResNeXt-101 (64 × 4 d)) were adopted. As for the experimental result, SE-ResNeXt50 (32 × 4 d) showed the highest accuracy at 79.42%, and the model that learned by age obtained 22.24% higher accuracy than the model that did not learn by age. In the results, the difference in expression between adults and kids was greatest for fear and neutral emotions. This study presented valuable results on age-biased learning data and algorithm type effect on emotion recognition accuracy.
Technology Commercialization Activation Model Using Imagification of Variables
Authors: Kim Youngho , Park Sangsung , Kang Jiho
Published: August 2022

Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that the data augmentation technique can be used when there are insufficient data for technology transfer.
Advances in Information Security and Privacy
Authors: Lax Gianluca , Russo Antonia
Published: August 2022

Due to the recent pandemic crisis, many people are spending their days smart working and have increased their use of digital resources for both work and entertainment [...]
Development of Fire Consequence Prediction Model in Fuel Gas Supply System Room with Changes in Operating Conditions during Liquefied Natural Gas Bunkering
Authors: Park Byeong-cheol , Lim Chaeog , Oh Sang-jin , Lee Jeong-eun , Jung Min-jae , Shin Sung-chul
Published: August 2022

Recently, various dynamic risk analysis methods have been suggested for estimating the risk index by predicting the possibility of accidents and damage in real time. It is necessary to quickly estimate the risk of an accident by predicting the probability and consequences of accidents, which are quantitative criteria for ship risk assessment. This study aimed to develop an algorithm for predicting the consequences of accidents in real time to perform a dynamic risk assessment and, using this algorithm, formulate a ship accident–response procedure that can be used in fire accidents during LNG bunkering. The risk of fire was estimated by predicting the hole size due to changes in the liquefied natural gas (LNG) transport conditions and the consequence based on the fire accidents scenario in LNG-fueled ships. The prediction model was trained with the hole prediction data using Ansys CFX and with the consequence data using DNV Phast, and the consequences of fire were compared and evaluated by applying the trained results. A method for estimating the size of the fire based on the predicted consequence is proposed, which supports fast decision making in fire accidents during LNG bunkering by identifying the potential size of the fire at the beginning of an accident.
Enhancement of Wheat Flour and Dough Properties by Non-Thermal Plasma Treatment of Wheat Flour
Authors: Khan Muhammad Jehanzaib , Jovicic Vojislav , Zbogar-Rasic Ana , Delgado Antonio
Published: August 2022

Demand to improve food quality attributes without the use of chemicals has risen exponentially in the past few years. Non-thermal plasma (NTP) (also called ‘cold plasma’) is becoming increasingly popular for this purpose due to its unique low-temperature and non-chemical nature. In the present research, the concept of in situ dielectric barrier discharge (DBD) plasma treatment inside a rotational reactor for the direct treatment of wheat flour was experimentally analyzed. The primary research goal was to determine the effects of short-period NTP treatment of DBD type on flour and dough properties. For this purpose, the influence of different operating parameters was tested, i.e., treatment time, the amount of flour placed in the reactor and the environmental (air) temperature. Changes in the structural attributes of the most commonly used flours (type 550 and 1050) and their respective doughs were studied using a set of analytical techniques. Rheological analysis demonstrated the ability of NTP to significantly intensify the visco-elastic properties of dough produced from wheat flour type 550 that was treated for less than 180 s. This indicated that plasma treatment enhanced intermolecular disulphide bonds in gluten proteins, which resulted in stronger protein–starch network formations. However, longer treatment times did not result in a significant increase in the visco-elastic properties of wheat dough. The obtained results showed a 6–7% increase in flour hydration due to NTP treatment, which also makes a contribution to hydrogen bonding due to changes in the bonded and free water phase. Experimental findings further confirmed the dependence of NTP treatment efficiency on environmental air temperature.
Experimental Study and Numerical Analysis of Flexural Strength of BFRP Bar Concrete Beams Reinforced with Bamboo Fiber and Steel Wire Mesh
Authors: Chen Wei , Ma Haohan , Zhang Ke , Zou Zuyin , Zeng Yusheng , Zhu Zichong
Published: August 2022

To explore the effect of adding bamboo fiber and steel wire mesh to BFRP bar-reinforced concrete beams on their flexural performance, we designed and fabricated six test beams with different parameters. Through flexural tests and numerical simulations, the effects of bamboo fiber length and steel wire mesh layout on the crack resistance, deformation capacity, and flexural ultimate bearing capacity of BFRP bar concrete beams were assessed. The feasibility of the simulation and the calculation method of flexural capacity of such specimens were considered. The research results showed that the addition of bamboo fiber can increase the cracking load and global stiffness and reduce the deformation of the test beam, but this had little effect on the ultimate flexural bearing capacity of the test beam. The cracking load, global stiffness, and ultimate flexural bearing capacity of the test beams were improved by the addition of steel wire mesh, and the stiffness and ultimate flexural bearing capacity of the test specimens with steel wire mesh in the whole beam were improved. Although the composite effect of bamboo fiber and steel wire mesh showed some improvement in these indicators, the improvement effect was not ideal.
Maximizing the Productivity of Photolithography Equipment by Machine Learning Based on Time Analytics
Authors: Jung Juyoung , Hong Jin-Hwan , Suk Jeewoong , Park Hyunsoon , Choi Byoungdeog
Published: August 2022

Maximizing productivity is one of the most critical factors for competitiveness in the manufacturing industry. Needless to say, the semiconductor industry, in which the automation rate is relatively high and the manufacturing process continues 24 h a day, requires high productivity to be maintained. This paper is about a model that analyzes the cause of an increase in time needed for the whole photolithography process and automatically classifies it in real-time by machine learning. The time analytics model based on a k-means algorithm divides the processing time into four hundred detailed time steps and classifies causes through normalizing and clustering processes. Further, true/false measures of performance were employed based on the confusion matrix. To increase the accuracy of the model, the classified cause becomes a source for creating a new algorithm that can detect problems quickly and accurately. A small number of wafers that the system has failed to classify has accumulated in the database to increase the frequency of occurrence. As a result of evaluating the time analytics model in the photolithography extreme ultraviolet (EUV) equipment, the model has classified 98.6% of the wafers that exceed the limitation. Continuous updates of new phenomena that will be generated from advanced technologies will be more important than the current classification ability. We are accumulating unclassified data for a sustainable system and will continue to classify by synthesizing new phenomena. Data classified in real-time with high accuracy become a steppingstone for maintaining high productivity. Production equipment and processes are developed to enhance individual characteristics. Nevertheless, a data mining method that divides the process time can also be widely used in manufacturing processes of other fields.
CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
Authors: Park Jangkyoung , Hassan Ammar Ul , Choi Jaeyoung
Published: August 2022

Font generation using deep learning has made considerable progress using image style transfer, but the automatic conversion/generation of Chinese characters still remains a difficult task owing to the complex character shape and large number of Chinese characters. Most known Chinese character generation models use the image conversion method of the Chinese character shape itself; however, it is difficult to reproduce complex Chinese characters. Recent methods have utilized character compositionality by separating up to three or four components to improve the quality of generated characters, but it is still difficult to generate high-quality results for complex Chinese characters with many components. In this study, we proposed the CCFont model (component-based Chinese font generation model using generative adversarial networks (GANs)) that automatically generates all Chinese characters using Chinese character components (up to 17 components). The CCFont model generates all Chinese characters in various styles using the components of Chinese characters based on conditional GAN. By acquiring local style information from the components, the information is more accurate and there is less information loss than when global information is obtained from the image of the entire character, reducing the failure of style conversion and improving quality to produce high-quality results. Additionally, the CCFont model generates high-quality results without any additional training (zero-shot font generation without any additional training) for the first-seen characters and styles. For example, the CCFont model, which was trained with only traditional Chinese (TC) characters, generates high-quality results for languages that can be divided into components, such as Korean and Thai, as well as simplified Chinese (SC) characters that are only seen during inference. CCFont can be adopted as a multi-lingual font-generation model that can be applied to all languages, which can be divided into components. To the best of our knowledge, the proposed method is the first to generate a zero-shot multilingual generation model using components. Qualitative and quantitative experiments were conducted to demonstrate the effectiveness of the proposed method.
Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
Authors: Li Hongjue , Li Hailang , Hou Zhixiong , Song Haoran , Liu Junbo , Dai Peng
Published: August 2022

Image-based rail defect detection could be conceptually defined as an object detection task in computer vision. However, unlike academic object detection tasks, this practical industrial application suffers from two unique challenges, including object ambiguity and insufficient annotations. To overcome these challenges, we introduce the pixel-wise attention mechanism to fully exploit features of annotated defects, and develop a feature augmentation framework to tackle the defect detection problem. The pixel-wise attention is conducted through a learnable pixel-level similarity between input and support features to obtain augmented features. These augmented features contain co-existing information from input images and multi-class support defects. The final output features are augmented and refined by support features, thus endowing the model to distinguish between ambiguous defect patterns based on insufficient annotated samples. Experiments on the rail defect dataset demonstrate that feature augmentation can help balance the sensitivity and robustness of the model. On our collected dataset with eight defected classes, our algorithm achieves 11.32% higher mAP@.5 compared with original YOLOv5 and 4.27% higher mAP@.5 compared with Faster R-CNN.
An Optimized CNN Model for Engagement Recognition in an E-Learning Environment
Authors: Hu Yan , Jiang Zeting , Zhu Kaicheng
Published: August 2022

In the wake of the restrictions imposed on social interactions due to the COVID-19 pandemic, traditional classroom education was replaced by distance education in many universities. Under the changed circumstances, students are required to learn more independently. The challenge for teachers has been to duly ascertain students’ learning efficiency and engagement during online lectures. This paper proposes an optimized lightweight convolutional neural network (CNN) model for engagement recognition within a distance-learning setup through facial expressions. The ShuffleNet v2 architecture was selected, as this model can easily adapt to mobile platforms and deliver outstanding performance compared to other lightweight models. The proposed model was trained, tested, evaluated and compared with other CNN models. The results of our experiment showed that an optimized model based on the ShuffleNet v2 architecture with a change of activation function and the introduction of an attention mechanism provides the best performance concerning engagement recognition. Further, our proposed model outperforms many existing works in engagement recognition on the same database. Finally, this model is suitable for student engagement recognition for distance learning on mobile platforms.
Orthodontic Surgical Treatment of Impacted Mandibular Canines: Systematic Review and Case Report
Authors: Inchingolo Alessio Danilo , Carpentiere Vincenzo , Piras Fabio , Netti Anna , Ferrara Irene , Campanelli Mariagrazia , Latini Giulia , Viapiano Fabio , Costa Stefania , Malcangi Giuseppina , Patano Assunta , Ceci Sabino , Mancini Antonio , Lucia Carlo , Scarano Antonio , Lorusso Felice , Palermo Andrea , Ciocia Anna Maria , Inchingolo Francesco , Rapone Biagio , Inchingolo Angelo Michele , Di Venere Daniela , Dipalma Gianna
Published: August 2022

Background: In the literature, there are few studies on included mandibular canines and possible therapeutic solutions for arch restoration. The aim of this review was to evaluate how the recovery of included mandibular canines is performed in adolescent patients with mixed or permanent dentition. The clinical case reported is that of successful orthodontic surgical treatment in an 11-year-old mixed-dentition patient with an included mandibular right canine. Methods: A literature search was performed using the PubMed/Medline, Scopus, Web of Science, and Cochrane Library databases with the following Boolean indicators: canine, mandibular, impacted. The inclusion criteria were year of publication between 2017 and 2022, orthodontic canine surgical approach included, age between 9 and 18 years, free full text, and English language. Results: A total of 536 articles was identified through the electronic search, and 11 articles published between 2017 and 2022 were ultimately included in the qualitative analysis. Conclusions: In the majority of cases, combined surgical–orthodontic therapy, as reported in the studies that we reviewed, is successful in correcting the eruption defect, restoring the canine to occlusion and the physiological arch perimeter, and maintaining the health of the periodontal supporting tissues.