bluuebunny/crossref_metadata_2025 huggingface dataset:
The Biological Effects of Ozone Gas on Soft and Hard Dental Tissues and the Impact on Human Gingival Fibroblasts and Gingival Keratinocytes
Authors: Floare Alin Daniel , Scurtu Alexandra Denisa , Balean Octavia Iulia , Chioran Doina , Buzatu Roxana , Sava Rosianu Ruxandra , Alexa Vlad Tiberiu , Jumanca Daniela , Rusu Laura-Cristina , Racea Robert Cosmin , Coricovac Dorina , Pinzaru Iulia , Dehelean Cristina Adriana , Galuscan Atena
Published: November 2021

Ozone is an allotropic form of oxygen, so in the medical field ozone therapy has special effects. Starting from the premise that bio-oxidative ozone therapy reduces the number of bacteria, in the present study two approaches were proposed: to evaluate the biological effects of ozone gas on the tooth enamel remineralization process and to demonstrate its impact on the morphology and confluence of human primary gingival cells, namely keratinocytes (PGK) and fibroblasts (HGF). The ozone produced by HealOzone was applied in vivo to 68 M1s (first permanent molars), both maxillary and mandibular, on the occlusal surfaces at pit and fissure. The molars included in the study recorded values between 13 and 24 according to the DIAGNOdent Pen 2190 scale, this being the main inclusion/exclusion criterion for the investigated molars. Because the gas can make contact with primary gingival cells during the ozonation process, both human gingival fibroblasts and keratinocytes were exposed to different doses of ozone (20 s, 40 s, 60 s), and its effects were observed with the Olympus IX73 inverted microscope. The contact of ozone with the human primary gingival cells demonstrates cell sensitivity to the action of ozone, this being higher in fibroblasts compared to keratinocytes, but it is not considered toxic because all the changes are reversible at 48 h after exposure.
Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
Authors: Shen Lihua , Liu Hui , Chen Zhangxin
Published: November 2021

In this paper, the deterministic ensemble Kalman filter is implemented with a parallel technique of the message passing interface based on our in-house black oil simulator. The implementation is separated into two cases: (1) the ensemble size is greater than the processor number and (2) the ensemble size is smaller than or equal to the processor number. Numerical experiments for estimations of three-phase relative permeabilities represented by power-law models with both known endpoints and unknown endpoints are presented. It is shown that with known endpoints, good estimations can be obtained. With unknown endpoints, good estimations can still be obtained using more observations and a larger ensemble size. Computational time is reported to show that the run time is greatly reduced with more CPU cores. The MPI speedup is over 70% for a small ensemble size and 77% for a large ensemble size with up to 640 CPU cores.
Effects of Different Biodiesel-Diesel Blend Fuel on Combustion and Emission Characteristics of a Diesel Engine
Authors: Zhang Yanhui , Zhong Yunhao , Wang Jie , Tan Dongli , Zhang Zhiqing , Yang Dayong
Published: November 2021

In this paper, biodiesel was used as an alternative fuel to investigate the combustion and emission characteristics of a four-stroke diesel engine, in terms of cylinder pressure, heat release rate, cylinder temperature, brake thermal efficiency, brake specific fuel consumption, nitrogen oxide, soot, carbon monoxide, and hydrocarbon. Firstly, a diesel engine cylinder model was developed by AVL-Fire software coupled with CHEMKIN code to simulate the injection and combustion of biodiesel with a kinetic mechanism with 106 species and 263 reactions. Then, the simulation model was validated by experimental results under 100% and 50% load conditions and used to simulate the combustion process of a diesel engine fueled with pure diesel, biodiesel, and biodiesel–diesel blends with 10%, 20%, 30% biodiesel by volume, respectively. The results showed that the brake specific fuel consumption increased with the increase of mixed biodiesel ratio. The brake specific fuel consumptions of B10, B20 and B30 increased by 1.1%, 2.3% and 3.3%, respectively, compared with that of D100. The combustion and emission characteristics of the diesel engine are improved. Therefore, biodiesel can be used as an alternative fuel for the diesel engine. The diesel–biodiesel fuel can improve the combustion and emission characteristics of the diesel engine.
Analysis on the Leakage of the Flange Connection of the Water-Containing Hydrofluoric Acid Pipeline
Authors: Lu Kun , Dong Junhua , Zhang Aoqing , Gao Bingjun
Published: November 2021

Leakages of bolted pipe flange connections of water-containing hydrofluoric acid pipelines were frequently reported by the extraction section in the fluorine chemical industry. Water-containing hydrofluoric acid can cause severe injuries to human beings due to its strong causticity. The water-containing hydrofluoric acid pipe was a short lined pipe, so a lot of flange connections and supports were adopted in the pipeline. In this paper, the finite element models of the pipeline were established to analyze the internal force of the pipeline under conditions including internal pressure, temperature, self-weight, and so on. Based on this, the equivalent design pressure of the flange connections was determined. The results of the stress analyses of the pipeline showed that leakages were mainly caused by a large bending moment, due to the unreasonable layout of the piping supports under self-weight. When the pipeline was supported on the beam of the pipe gallery, which is not necessarily beneficial to reduce the bending moment of the pipeline, and the flange connection was close to the supporting beam at the same time, leakages frequently occurred in this flange connection. To support the pipeline reasonably, the flange connection should be placed at zero bending moment positions. Therefore, the positions with zero bending moments of the pipeline with equal and unequal spacing supports were obtained under gravity load, to provide a basis for the rational support of lining piping.
Hebrews’ High Priestly Christology: Models, Method and Aim
Authors: Regev Eyal
Published: November 2021

This article suggests a systematic analysis of the ways the author of the Letter to the Hebrews links Christ and the sacrificial system, exploring the author’s method of using the sacrificial system in his Christology. It points to the issues in which Hebrews embraces traditional Jewish cultic ideas and—building on these basics—those in which Hebrews greatly diverges and modifies high priestly features. The manner in which the author bases his Christology on the sacrificial cult shows that he acknowledges the efficacy of the high priest and sacrifices for contending with sin. Even when modifying the priestly cult to show that Christ’s atonement is superior to, and takes the place of, the Temple cult, he bases himself on the fundamentals of the high priest entering the Holy of Holies with blood. He uses the sacrificial cult as a model for Christology, like a map for navigating Christ’s doctrine of salvation. It is suggested that Hebrews’ aim is to make sense of Jesus’ death and atonement, perhaps even to shed light on Pauline Christological and cultic metaphors.
Climate Change Decreased Net Ecosystem Productivity in the Arid Region of Central Asia
Authors: Zhang Jingjing , Hao Xingming , Hao Haichao , Fan Xue , Li Yuanhang
Published: November 2021

Numerous studies have confirmed that climate change leads to a decrease in the net ecosystem productivity (NEP) of terrestrial ecosystems and alters regional carbon source/sink patterns. However, the response mechanism of NEP to climate change in the arid regions of Central Asia remains unclear. Therefore, this study combined the Carnegie–Ames–Stanford approach (CASA) and empirical models to estimate the NEP in Central Asia and quantitatively evaluate the sensitivity of the NEP to climate factors. The results show that although the net primary productivity (NPP) in Central Asia exhibits an increasing trend, it is not significant. Soil heterotrophic respiration (RH) has increased significantly, while the NEP has decreased at a rate of 6.1 g C·m−2·10 a−1. Spatially, the regional distribution of the significant increase in RH is consistent with that of the significant decrease in the NEP, which is concentrated in western and southern Central Asia. Specifically, the NPP is more sensitive to precipitation than temperature, whereas RH and NEP are more sensitive to temperature than precipitation. The annual contribution rates of temperature and precipitation to the NEP are 28.79% and 23.23%, respectively. Additionally, drought has an important impact on the carbon source/sink in Central Asia. Drought intensified from 2001 to 2008, leading to a significant expansion of the carbon source area in Central Asia. Therefore, since the start of the 21st century, climate change has damaged the NEP of the Central Asian ecosystem. Varying degrees of warming under different climate scenarios will further aggravate the expansion of carbon source areas in Central Asia. An improved understanding of climate change impacts in Central Asia is critically required for sustainable development of the regional economy and protection of its natural environment. Our results provide a scientific reference for the construction of the Silk Road Economic Belt and global emissions reduction.
Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology
Authors: Shen Yu , Zhang Xiaoyang , Wang Weile , Nemani Ramakrishna , Ye Yongchang , Wang Jianmin
Published: November 2021

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.
Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
Authors: He Yufang , Zhang Guangzong , Kaufmann Hermann , Xu Guochang
Published: November 2021

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.
Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran
Authors: Najafzadeh Faezeh , Mohammadzadeh Ali , Ghorbanian Arsalan , Jamali Sadegh
Published: November 2021

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.
Colorophone 2.0: A Wearable Color Sonification Device Generating Live Stereo-Soundscapes—Design, Implementation, and Usability Audit
Authors: Osiński Dominik , Łukowska Marta , Hjelme Dag Roar , Wierzchoń Michał
Published: November 2021

The successful development of a system realizing color sonification would enable auditory representation of the visual environment. The primary beneficiary of such a system would be people that cannot directly access visual information—the visually impaired community. Despite the plethora of sensory substitution devices, developing systems that provide intuitive color sonification remains a challenge. This paper presents design considerations, development, and the usability audit of a sensory substitution device that converts spatial color information into soundscapes. The implemented wearable system uses a dedicated color space and continuously generates natural, spatialized sounds based on the information acquired from a camera. We developed two head-mounted prototype devices and two graphical user interface (GUI) versions. The first GUI is dedicated to researchers, and the second has been designed to be easily accessible for visually impaired persons. Finally, we ran fundamental usability tests to evaluate the new spatial color sonification algorithm and to compare the two prototypes. Furthermore, we propose recommendations for the development of the next iteration of the system.
Inertial Motion Capture-Based Whole-Body Inverse Dynamics
Authors: Diraneyya Mohsen M. , Ryu JuHyeong , Abdel-Rahman Eihab , Haas Carl T.
Published: November 2021

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.
In-Flight Tests of Intruder Detection Vision System
Authors: Rzucidło Paweł , Jaromi Grzegorz , Kapuściński Tomasz , Kordos Damian , Rogalski Tomasz , Szczerba Piotr
Published: November 2021

In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.
Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
Authors: Lee Jiyoung , Jang Seunghyun , Lee Jungbin , Kim Taehan , Kim Seonghan , Seo Jongbum , Kim Ki Hean , Yang Sejung
Published: November 2021

The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments.
Performance Evaluation of GPS Auto-Surveying Techniques
Authors: Manito João , Sanguino José
Published: November 2021

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.
Analysis of a Smart Sensor Based Solution for Smart Grids Real-Time Dynamic Thermal Line Rating
Authors: Liu Yuming , Riba Jordi-Roger , Moreno-Eguilaz Manuel , Sanllehí Josep
Published: November 2021

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor’s location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.
Modelling and Validation of a Guided Acoustic Wave Temperature Monitoring System
Authors: Yule Lawrence , Zaghari Bahareh , Harris Nicholas , Hill Martyn
Published: November 2021

The computer modelling of condition monitoring sensors can aide in their development, improve their performance, and allow for the analysis of sensor impact on component operation. This article details the development of a COMSOL model for a guided wave-based temperature monitoring system, with a view to using the technology in the future for the temperature monitoring of nozzle guide vanes, found in the hot section of aeroengines. The model is based on an experimental test system that acts as a method of validation for the model. Piezoelectric wedge transducers were used to excite the S0 Lamb wave mode in an aluminium plate, which was temperature controlled using a hot plate. Time of flight measurements were carried out in MATLAB and used to calculate group velocity. The results were compared to theoretical wave velocities extracted from dispersion curves. The assembly and validation of such a model can aide in the future development of guided wave based sensor systems, and the methods provided can act as a guide for building similar COMSOL models. The results show that the model is in good agreement with the experimental equivalent, which is also in line with theoretical predictions.
Contrastive Learning for 3D Point Clouds Classification and Shape Completion
Authors: Nazir Danish , Afzal Muhammad Zeshan , Pagani Alain , Liwicki Marcus , Stricker Didier
Published: November 2021

In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other related applications. Our idea is to add contrastive learning into AutoEncoders to encourage global feature learning of the point cloud classes. It is performed by optimizing triplet loss. Furthermore, local feature representations learning of point cloud is performed by adding the Chamfer distance function. To evaluate the performance of our approach, we utilize the PointNet classifier. We also extend the number of classes for evaluation from 4 to 10 to show the generalization ability of the learned features. Based on our results, embeddings generated from the contrastive AutoEncoder enhances shape completion and classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.
The Influence of the Grid Density of Measurement Points on Damage Detection in an Isotropic Plate by the Use of Elastic Waves and Laser Scanning Doppler Vibrometry
Authors: Doliński Łukasz , Krawczuk Marek , Palacz Magdalena , Waszkowiak Wiktor , Żak Arkadiusz
Published: November 2021

Damage detection in structural components, especially in mechanical engineering, is an important element of engineering practice. There are many methods of damage detection, in which changes in various parameters caused by the presence of damage are analysed. Recently, methods based on the analysis of changes in dynamic parameters of structures, that is, frequencies or mode shapes of natural vibrations, as well as changes in propagating elastic waves, have been developed at the highest rate. Diagnostic methods based on the elastic wave propagation phenomenon are becoming more and more popular, therefore it is worth focusing on the improvement of the efficiency of these methods. Hence, a question arises about whether it is possible to shorten the required measurement time without affecting the sensitivity of the diagnostic method used. This paper discusses the results of research carried out by the authors in this regard both numerically and experimentally. The numerical analysis has been carried out by the use of the Time-domain Spectral Finite Element Method (TD-SFEM), whereas the experimental part has been based on the measurement performed by 1-D Laser Doppler Scanning Vibrometery (LDSV).
FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting
Authors: Sheu Ming-Hwa , Morsalin S. M. Salahuddin , Zheng Jia-Xiang , Hsia Shih-Chang , Lin Cheng-Jian , Chang Chuan-Yu
Published: November 2021

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The ‘FGSC’ blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The ‘FGSC’ blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.
Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
Authors: Alhumam Abdulaziz
Published: November 2021

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.
Diagnostic Accuracy of Cystic Lesions Using a Pre-Programmed Low-Dose and Standard-Dose Dental Cone-Beam Computed Tomography Protocol: An Ex Vivo Comparison Study
Authors: Al-Haj Husain Adib , Döbelin Quirin , Giacomelli-Hiestand Barbara , Wiedemeier Daniel B. , Stadlinger Bernd , Valdec Silvio
Published: November 2021

Background: This study aimed to analyze the diagnostic reliability of radiographic assessment of cystic lesions using a pre-set, manufacturer-specific, low-dose mode compared to a standard-dose dental cone-beam computed tomography (CBCT) imaging protocol. Methods: Forty pig mandible models were prepared with cystic lesions and underwent both CBCT protocols on an Orthophos SL Unit (Dentsply-Sirona, Bensheim, Germany). Qualitative and quantitative analysis of CBCT data was performed by twelve investigators independently in SIDEXIS 4 (Dentsply-Sirona) using a trial-specific digital examination software tool. Thereby, the effect of the two dose types on overall detectability rate, the visibility on a scale of 1 (very low) to 10 (very high) and the difference between measured radiographic and actual lesion size was assessed. Results: Low-dose CBCT imaging showed no significant differences considering detectability (78.8% vs. 81.6%) and visibility (9.16 vs. 9.19) of cystic lesions compared to the standard protocol. Both imaging protocols performed very similarly in lesion size assessment, with an apparent underestimation of the actual size. Conclusion: Low-dose protocols providing confidential diagnostic evaluation with an improved benefit–risk ratio according to the ALADA principle could become a promising alternative as a primary diagnostic tool as well as for radiological follow-up in the treatment of cystic lesions.
An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition
Authors: Spieker Veronika , Ganguly Amartya , Haddadin Sami , Piazza Cristina
Published: November 2021

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.
Using an Artificial Intelligence Based Chatbot to Provide Parent Training: Results from a Feasibility Study
Authors: Entenberg Guido A. , Areas Malenka , Roussos Andrés J. , Maglio Ana Laura , Thrall Jillian , Escoredo Milagros , Bunge Eduardo L.
Published: November 2021

Online parenting training programs have shown to be effective. However, no studies on parent training programs delivered through chatbots have been reported yet. Aim. This study aims to assess the feasibility of delivering parenting skills through a chatbot. Methods. A sample of 33 parents completed a pilot feasibility study. Engagement, knowledge, net-promoters score and qualitative responses were analyzed. Results. A total of 78.8% of the sample completed the intervention. On average, participants remembered 3.7 skills out of the 5 presented and reported that they would recommend the chatbot to other parents (net promoter score was 7.44; SD = 2.31 out of 10). Overall, parents sent a mean of 54.24 (SD = 13.5) messages to the chatbot, and the mean number of words per message was 3. Main themes parents discussed with the chatbot included issues regarding their child’s habits, handling disruptive behaviors, interpersonal development, and emotional difficulties. Parents generally commented on the usefulness of the intervention and suggested improvements to the chatbot’s communication style. Conclusions. Overall, users completed the intervention, engaged with the bot, and would recommend the intervention to others. This suggests parenting skills could be delivered via chatbots.
Design of Personal Trajectories for Employees’ Professional Development in the Knowledge Society under Industry 5.0
Authors: Orlova Ekaterina V.
Published: November 2021

The main feature of Industry 5.0 is “personalization”, linked not only to provide customers with personalized products, but also, in our opinion, to ensure personalization in labor relations with employees, since it increases human value through human–machine collaboration. The human capital quality determines a significant contribution not only to the labor productivity growth, but also to extend a social communication, loyalty and employees’ trust. The study proposes the new methodological approach for corporate human capital assessment and management (CHCM) over new conditions of digital transformation. The CHCM uses methods of system analysis and synthesis, expert assessments, descriptive statistical analysis and survey. The novelty of CHCM is that, firstly, it reflects all the essential features and properties of human capital under emergence of new professions; secondly, it combines and comprehensively uses both quantitative and qualitative methods for human capital assessment, reflecting the subjective and objective aspects of human capital measurement; thirdly, it allows to create warranted management decisions about individual trajectories of professional development of employees, ensuring the continuous growth of individual, corporate and social wealth. It is proved experimentally that the implementation of individual trajectories for employees’ professional development provides 2–3 years’ perspective on companies’ performance growth.
Sustainable Wellbeing Operationalization and Measurement Based on the Capabilities Approach: The Case of Latin America
Authors: Perdomo Jhoner , Phélan Casanova Mauricio , Levy-Carciente Sary
Published: November 2021

Starting from the capabilities approach, this work develops the concept of sustainable wellbeing, which highlights the importance of incorporating temporal sustainability into the analysis of wellbeing, with intergenerational justice. For its measurement, 12 dimensions are identified and defined, based on the philosophical approach of central capabilities of Martha Nussbaum. The measurement is applied to 18 Latin American countries, with 116 indicators, using a multiple correspondence analysis (MCA). The results show the viability of operationalizing the capabilities approach and its potential to support the formulation of associated policies.
Banks and Climate-Related Information: The Case of Portugal
Authors: Santos Aldina Lopes , Rodrigues Lúcia Lima
Published: November 2021

In 2014, a European Union (EU) Directive required certain large undertakings and groups to disclose non-financial information from 2017 onwards. In 2017, the EU guidelines on non-financial reporting established that reporting climate-related information is part of the non-financial information. Later, in 2019, the guidelines were reinforced to include a supplement that envisages improving climate-related information reporting. Banks can contribute to reducing climate-related risks by supporting investments in economic activities that aim to mitigate the risk of climate change. Capital needs should be reoriented towards sustainable investment. Banks shall manage financial risks arising from climate change; therefore, they must integrate climate change into their policies and procedures, assessing the potential impact of projects and financing on climate change. This study aimed to evaluate how banks in Portugal have been reporting climate-related information and whether the level of information has increased since 2017. Using content analysis, findings indicated that banks are already including climate-related information; however, they are still far from approaching what the new guidelines require. Results suggested that there is still a long way to go in this area concerning banks and regulators.
Towards Understanding the Food Consumer Behavior–Food Safety–Sustainability Triangle: A Bibliometric Approach
Authors: Lakner Zoltán , Plasek Brigitta , Kasza Gyula , Kiss Anna , Soós Sándor , Temesi Ágoston
Published: November 2021

Academic research on food consumer behavior related to food safety has developed extremely rapidly in the last decades, and a sizable amount of knowledge has been accumulated in this interdisciplinary field. This information set, as big data, lends itself to bibliometric analysis. Based on the Web of Science database and on a statistical analysis of more than 26.6 thousand articles containing more than 3.4 million bibliometric pieces of information, the current article offers a systematic analysis of these statistical data. The dynamics of relevant publications show an exponential character. The field is dominated by researchers from welfare states; however, food safety is a more important problem in developing states. There are dynamic changes in the portfolio of journals, but Bradford’s law cannot be proven. The explanatory power of Lotka’s law has been decreasing, proving the de-concentration of relevant authors. Besides traditional disciplines like consumer science, food chemistry, microbiology, and technology, new disciplines, e.g., sociology, cultural anthropology, postmodern techniques, and the real-life study of consumer behavior, going beyond the application of traditional techniques, are gaining importance. There are three key challenges for further research: (1) contribution to a deeper understanding of inherent laws governing the food-consumer-environment system; (2) quantification of results for decision-makers to enhance the efficiency of policy preparation; (3) widening the scope of research in geographical terms, better involving the developing world, and in sociological terms, focusing on the specific needs of vulnerable groups.
Achieving the Food Security Strategy by Quantifying Food Loss and Waste. A Case Study of the Chinese Economy
Authors: Durán-Sandoval Daniel , Durán-Romero Gemma , López Ana M.
Published: November 2021

Undernourished and food insecurity are recognized as two highly relevant topics. Approximately 820 million people in the world are undernourished and 2 billion people have moderate or severe food insecurity (FAO). In addition, globally roughly one-third of food is not consumed and is wasted. This article aims to provide an updated estimate of food loss and waste (FLW) in China as, in the period 2016–2018, there were still 122 million people in this country experiencing undernourishment. In this research, we use a top-down mass balance approach, discuss how it affects the achievement of SDG 2, Zero Hunger, that it is linked also to target 12.3 that “seeks to halve global food waste at retail and consumer levels, as well as to reduce food loss during production and supply” (United Nations). We point out some challenges that private and public policies still need to overcome to reduce FLW. The results of this research may contribute a more accurate baseline for the design of public policies and strategies related to FLW and the corresponding SDGs.
Towards Figurative Expression Enhancement: Effects of the SVVR-Supported Worked Example Approach on the Descriptive Writing of Highly Engaged Students
Authors: Huang Yi-Pin , Zheng Xiao-Li , Chiu Chuang-Kai , Lei Jun , Yang Gang , Kim Hoisoo , Wang Feng
Published: November 2021

During the past decades, many researchers have attempted to explore effective teaching methods for developing students’ descriptive writing performance. In this study, the worked example was implemented as an effective way of guiding students to provide step-by-step solutions to learning tasks. Moreover, a spherical video-based virtual reality (SVVR) environment was provided to place students in real-world situations which enabled them to experience the learning contexts in depth. A pretest-posttest quasi experimental study was conducted to explore the influence of the SVVR-supported worked example approach and engagement level on students’ Chinese descriptive writing performance. A total of 79 fourth-grade elementary school students participated in this study. The experimental group used SVVR with worked examples to complete Chinese writing assignments, whereas the control group used videos and worked examples. The results showed no significant effects of the SVVR-supported worked example approach compared with the conventional worked example approach regarding organization, sensory details, or creativity dimensions. As for the figurative expression dimension, students in the SVVR-supported worked example approach condition scored significantly higher. Moreover, high engagement students significantly outperformed low engagement students in all four writing performance dimensions. Additionally, a significant interaction effect between learning approach and engagement level on figurative expression was found.