bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
Authors: Keczer Bánk , Benke Márton , Marjai Tamás , Horváth Miklós , Miheller Pál , Szücs Ákos , Harsányi László , Szijártó Attila , Hritz István
Published: August 2022

Endoscopic ultrasonography (EUS) is the most accurate imaging modality for the evaluation of different types of pancreatic cystic lesions. Our aim was to analyze EUS images of pancreatic cystic lesions using an image processing software. We specified the echogenicity of the lesions by measuring the gray value of pixels inside the selected areas. The images were divided into groups (serous cystic neoplasm /SCN/, intraductal papillary mucinous neoplasms and mucinous cystic neoplasms /Non-SCN/ and Pseudocyst) according to the pathology results of the lesions. Overall, 170 images were processed by the software: 81 in Non-SCN, 30 in SCN and 59 in Pseudocyst group. The mean gray value of the entire lesion in the Non-SCN group was significantly higher than in the SCN group (27.8 vs. 18.8; p < 0.0005). The area ratio in the SCN, Non-SCN and Pseudocyst groups was 57%, 39% and 61%, respectively; significantly lower in the Non-SCN group than in the SCN or Pseudocyst groups (p < 0.0005 and p < 0.0005, respectively). The lesion density was also significantly higher in the Non-SCN group compared to the SCN or Pseudocyst groups (4186.6/mm2 vs. 2833.8/mm2 vs. 2981.6/mm2; p < 0.0005 and p < 0.0005, respectively). The EUS image analysis process may have the potential to be a diagnostic tool for the evaluation and differentiation of pancreatic cystic lesions.
Bleeding Lesion from Roux-en-Y Hepaticojejunostomy: A Successful Combined Hemostasis with Dual Emission Laser 1.9/1.5 μm
Authors: Marinoni Beatrice , Elli Luca , Tontini Gian Eugenio , Scaramella Lucia , Penagini Roberto , Vecchi Maurizio , Nandi Nicoletta
Published: August 2022

A 28-year-old woman, with a history of liver transplantation with Roux-en-Y hepaticjejunostomy, was admitted for melena and severe anemia. Bidirectional endoscopy was normal. Capsule endoscopy demonstrated fresh blood in the efferent limb downstream of the jejuno-jejunostomy. Anterograde double-balloon enteroscopy (DBE) showed an adherent clot with a visible vessel oozing next to the hepaticojejunostomy. Bleeding was treated firstly with argon plasma coagulation and endoclips and further treated with dual emission laser, achieving complete hemostasis. At the 3 months follow-up, hemoglobin was stable without evidence of re-bleeding.
Distributed Task Allocation for a Multi-UAV System with Time Window Constraints
Authors: Cui Wei , Li Ruilin , Feng Yanxiang , Yang Yikang
Published: August 2022

This paper develops a distributed method, namely, distributed allocation with time windows (DATW) for managing the multi-UAV task assignment problem (MTAP) with complex time window constraints. By aiming directly to minimize the average task completion time, the pro-posed DATW intends to achieve a conflict-free result that allocates all tasks within the validity time windows. Based on the decentralized PI (Performance Impact) framework, the proposed algorithm addresses the MTAP in a three-phase task assignment strategy, which includes task inclusion, conflict resolution, and task reallocation. The newly introduced task allocation phase achieves a noteworthy increase in an average number of allocated tasks. Unlike the traditional PI methods, the start time of each task is broadcasted among agents via communication typology, and the significance value of each task is directly related to its validity time window, such that the vast majority of tasks are able to be assigned properly without imposing any extra communication burdens. In the obtained conflict-free allocation solution by DATW, each task is allocated to a proper UAV with all given constraints satisfied. Finally, the simulation results demonstrate the effectiveness and superiority of the proposed DATW. Compared with existing (CBBA-based) solutions, results show up to an 18% increase in success rate (SR) using the proposed method.
University Rankings and Goals: A Cluster Analysis
Authors: Elbawab Roba
Published: August 2022

Nowadays, university rankings are used to assess all aspects of universities. Due to the impact of university rankings on assessing the performance of universities, this research aims to explore university rankings in depth. University rankings are considered contributors to assessing university performance. Previous literature showed different types of goals, such as output and support goals, where the literature advised to align between these two types of goals. Universities have different goals, but still, university rankings measure all universities on the same criteria. Subsequently, this research has used the most used university rankings in the literature, QS world ranking dataset. Then unsupervised machine learning was performed to cluster the universities. The results divided universities among four clusters. This study helps in allocating the university in the adequate cluster. This study helps university managers define the goals of their universities. The study recommends universities align their support goals with their output goals. The study recommends universities to develop international goals and strategies, and support the research in the universities by supporting the scholars. This study’s novelty lies in connecting the university rankings and goals using management analytics in education.
Improving Fault Tolerance and Reliability of Heterogeneous Multi-Agent IoT Systems Using Intelligence Transfer
Authors: O’Neill Vyas , Soh Ben
Published: August 2022

Driven by the ever-growing diversity of software and hardware agents available on the market, Internet-of-Things (IoT) systems, functioning as heterogeneous multi-agent systems (MASs), are increasingly required to provide a level of reliability and fault tolerance. In this paper, we develop an approach to generalized quantifiable modeling of fault-tolerant and reliable MAS. We propose a novel software architectural model, the Intelligence Transfer Model (ITM), by which intelligence can be transferred between agents in a heterogeneous MAS. In the ITM, we propose a novel mechanism, the latent acceptable state, which enables it to achieve improved levels of fault tolerance and reliability in task-based redundancy systems, as used in the ITM, in comparison with existing agent-based redundancy approaches. We demonstrate these improvements through experimental testing of the ITM using an open-source candidate implementation of the model, developed in Python, and through an open-source simulator that tested the behavior of ITM-based MASs at scale. The results of these experiments demonstrated improvements in fault tolerance and reliability across all MAS configurations we tested. Fault tolerance was observed to improve by a factor of between 1.27 and 6.34 in comparison with the control group, depending on the ITM configuration tested. Similarly, reliability was observed to improve by a factor of between 1.00 and 4.73. Our proposed model has broad applicability to various IoT applications and generally in MASs that have fault tolerance or reliability requirements, such as in cloud computing and autonomous vehicles.
Educational 5G Edge Computing: Framework and Experimental Study
Authors: Chen Qingyong , Wang Zhe , Su Yu , Fu Luwei , Wei Yuanlun
Published: August 2022

Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency.
Towards Modelica Models with Credibility Information
Authors: Otter Martin , Reiner Matthias , Tobolář Jakub , Gall Leo , Schäfer Matthias
Published: August 2022

Modeling and simulation is increasingly used in the design process for a wide span of applications. Rising demands and the complexity of modern products also increase the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. The latter is also called credible simulation process. In an article at the International Modelica Conference 2021, we summarized the state of the art and best practice from the viewpoint of a Modelica language user, based on the experience gained in projects in which Modelica models were utilized in the design process. Furthermore, missing features and gaps in the used processes were identified. In this article, new proposals are presented to improve the quality of Modelica models, in particular by adding traceability, uncertainty, and calibration information of the parameters in a standardized way to Modelica models. Furthermore, the new open-source Modelica library Credibility is discussed together with examples to support the implementation of credible Modelica models.
An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion
Authors: Yin Lifeng , Li Menglin , Chen Huayue , Deng Wu
Published: August 2022

Aiming to resolve the problems of the traditional hierarchical clustering algorithm that cannot find clusters with uneven density, requires a large amount of calculation, and has low efficiency, this paper proposes an improved hierarchical clustering algorithm (referred to as PRI-MFC) based on the idea of population reproduction and fusion. It is divided into two stages: fuzzy pre-clustering and Jaccard fusion clustering. In the fuzzy pre-clustering stage, it determines the center point, uses the product of the neighborhood radius eps and the dispersion degree fog as the benchmark to divide the data, uses the Euclidean distance to determine the similarity of the two data points, and uses the membership grade to record the information of the common points in each cluster. In the Jaccard fusion clustering stage, the clusters with common points are the clusters to be fused, and the clusters whose Jaccard similarity coefficient between the clusters to be fused is greater than the fusion parameter jac are fused. The common points of the clusters whose Jaccard similarity coefficient between clusters is less than the fusion parameter jac are divided into the cluster with the largest membership grade. A variety of experiments are designed from multiple perspectives on artificial datasets and real datasets to demonstrate the superiority of the PRI-MFC algorithm in terms of clustering effect, clustering quality, and time consumption. Experiments are carried out on Chinese household financial survey data, and the clustering results that conform to the actual situation of Chinese households are obtained, which shows the practicability of this algorithm.
Deep LSTM Model for Diabetes Prediction with Class Balancing by SMOTE
Authors: Alex Suja A. , Jhanjhi NZ , Humayun Mamoona , Ibrahim Ashraf Osman , Abulfaraj Anas W.
Published: August 2022

Diabetes is an acute disease that happens when the pancreas cannot produce enough insulin. It can be fatal if undiagnosed and untreated. If diabetes is revealed early enough, it is possible, with adequate treatment, to live a healthy life. Recently, researchers have applied artificial intelligence techniques to the forecasting of diabetes. As a result, a new SMOTE-based deep LSTM system was developed to detect diabetes early. This strategy handles class imbalance in the diabetes dataset, and its prediction accuracy is measured. This article details investigations of CNN, CNN-LSTM, ConvLSTM, and deep 1D-convolutional neural network (DCNN) techniques and proposed a SMOTE-based deep LSTM method for diabetes prediction. Furthermore, the suggested model is analyzed towards machine-learning, and deep-learning approaches. The proposed model’s accuracy was measured against the diabetes dataset and the proposed method achieved the highest prediction accuracy of 99.64%. These results suggest that, based on classification accuracy, this method outperforms other methods. The recommendation is to use this classifier for diabetic patients’ clinical analysis.
Forward Modeling of Natural Fractures within Carbonate Rock Formations with Continuum Damage Mechanics and Its Application in Fuman Oilfield
Authors: Yi Ming , Shen Xinpu , Jin Lixin , Wang Jianliang , Huang Zhiqiang , Shen Guoyang
Published: August 2022

Accurate information about the distribution of natural fractures is a key factor for the success of the exploration and development of oil and gas in carbonate rock formations. Forward modeling of natural fractures generated by tectonic movement within carbonate rock formations was investigated by jointly using the continuum damage model and finite element numerical technology. Geological analysis of natural fractures was used as the basis of the geomechanical finite element calculation. A workflow of numerical calculations for natural fractures was proposed. These achievements were applied to investigate natural fractures’ distribution within Ordovician carbonate rock formations of the Fuman Oilfield, Xinjiang, in the west of China. Finite element sub-modeling technology was used to further investigate natural fractures within key target reservoir formations with a finer mesh. The contour of natural fractures represented by the localization band of continuum damage variables was obtained. A comparison of the numerical results of the natural fractures’ distribution represented by continuum damage variables with those of natural fractures interpreted from seismic data shows that: (1) the numerical solution of natural fractures matches the measured data, and their orientations are in good accordance; (2) their distribution and locations are basically the same, with some small differences in local details; (3) the numerical results indicate that the maximum value of the damage variable SDEG within the zones of natural fractures is 0.2686, and the widths of the bands of natural fractures/faults are in the range of 500 m to 1000 m. Validation of the results of the distribution of natural fractures was performed indirectly via the distribution of the minimum horizontal stress gradient ShG.
Hybrid Deep Reinforcement Learning Considering Discrete-Continuous Action Spaces for Real-Time Energy Management in More Electric Aircraft
Authors: Liu Bing , Xu Bowen , He Tong , Yu Wei , Guo Fanghong
Published: August 2022

The increasing number and functional complexity of power electronics in more electric aircraft (MEA) power systems have led to a high degree of complexity in modelling and computation, making real-time energy management a formidable challenge, and the discrete-continuous action space of the MEA system under consideration also poses a challenge to existing DRL algorithms. Therefore, this paper proposes an optimisation strategy for real-time energy management based on hybrid deep reinforcement learning (HDRL). An energy management model of the MEA power system is constructed for the analysis of generators, buses, loads and energy storage system (ESS) characteristics, and the problem is described as a multi-objective optimisation problem with integer and continuous variables. The problem is solved by combining a duelling double deep Q network (D3QN) algorithm with a deep deterministic policy gradient (DDPG) algorithm, where the D3QN algorithm deals with the discrete action space and the DDPG algorithm with the continuous action space. These two algorithms are alternately trained and interact with each other to maximize the long-term payoff of MEA. Finally, the simulation results show that the effectiveness of the method is verified under different generator operating conditions. For different time lengths T, the method always obtains smaller objective function values compared to previous DRL algorithms, is several orders of magnitude faster than commercial solvers, and is always less than 0.2 s, despite a slight shortfall in solution accuracy. In addition, the method has been validated on a hardware-in-the-loop simulation platform.
Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin
Authors: Kim Changhyun , Dinh Minh-Chau , Sung Hae-Jin , Kim Kyong-Hwan , Choi Jeong-Ho , Graber Lukas , Yu In-Keun , Park Minwon
Published: August 2022

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.
Identification of a Mathematical Model for the Transformation of Images for Stereo Correspondence Measurements of Mining Equipment
Authors: Cheluszka Piotr , Jagieła-Zając Amadeus
Published: August 2022

The stereometry of the working units of mining machines is optimized at the design stage, in terms of selected criteria based on computer simulations of the mining process. The recovered bodies of cutting heads or drums used in manufacturing are regenerated in the overhaul process. Ensuring that their dimensions comply with the nominal ones is labor-intensive and raises production costs. However, deviations of these components from the nominal shape can make it difficult to position the pick holders (which can cause collisions) or make welding them impossible (which results from too large a distance between the pick holders’ base and the side surface of the working unit). This applies especially to robotic technologies. By utilizing automatic (online) measurements of the distribution of the actual distances of the pick holders’ bases from the side surface of the working unit (taken during their positioning using a robot), it is possible to correct their positions without changing the setting of the pick itself. This measurement can be carried out using the non-contact stereovision method. This paper presents a method of matching raster images obtained from a stereovision system installed on an experimental robotic station. The results are presented of the numerical research carried out to select a mathematical model of the geometric transformation of images, for ensuring the effectiveness of the developed method.
Using Mathematical Modeling to Evaluate the Performance of a Passenger Car When Operating on Various Fuels
Authors: Manko Ivan , Matijošius Jonas , Shuba Yevheniy , Rimkus Alfredas , Gutarevych Serhiy , Slavin Viktor
Published: August 2022

This article presents the results of experimental and computational studies of the performance of a car with a gasoline engine with spark ignition when retrofitted with a system of liquefied petroleum gas (LPG). To analyze the impact of the use of LPG instead of gasoline, the mathematical model of the car’s movement according to the modes of the European driving cycle has been refined. It was established that when using LPG for a car in the driving cycle, fuel consumption in energy units decreases by 10.38%. Gas consumption, determined in mass units, is lower than gasoline consumption. In load modes, this difference is about 8% and in idle mode, it reaches 16–18%. The adequacy of the mathematical model is confirmed by experimental studies of the car on modern equipment with the measurement of fuel efficiency and environmental performance in motion, according to the specified cycle. The deviation of the calculated fuel consumption from the results of experimental studies does not exceed 0.86%. The values of environmental indicators obtained by calculations on the mathematical model differ from the experimental ones by an average of 15%. The conducted studies showed the expediency of using LPG by retrofitting a car with a gasoline engine with an LPG power supply system and the possibility of using mathematical modeling to assess the feasibility of such retrofitting.
A Case Study on the Energy Capacity of a Flexible Rockfall Barrier in Resisting Landslide Debris
Authors: Zhao Lei , Zhang Lijun , Yu Zhixiang , Qi Xin , Xu Hu , Zhang Yifan
Published: August 2022

Landslides frequently occur in forest areas with a steep hillside, especially when severely disturbed by human activities. After sustained heavy rainfall, a landslide occurred near the Tianwan tunnel entrance of the Chongqing-Huaihua railway in China. Fortunately, the landslide debris was successfully intercepted by a flexible barrier originally installed to stop rockfalls, which is, to date, the first publicly reported case of landslide debris having been successfully intercepted by a flexible barrier without any damage, in mainland of China. A field investigation was first conducted, and then a back analysis of the landslide mobility and the interaction between the landslide and the flexible barrier was carried out. The back analysis showed that the impact energy was three-times larger than the rated energy capacity of the flexible barrier. It also showed that the elongation of the brake rings and the deflection of the flexible barrier from the numerical simulation was comparable to that from the field measurements. The fact that these brake rings were not elongated to their limit indicated that the capacity of the flexible barrier still had a surplus. Finally, to investigate the maximum energy capacity of a flexible rockfall barrier in resisting landslide debris, parametric analyses of a flexible barrier impacted by landslide debris with different impact energies and velocities were carried out using a coupled ALE-FEM modeling technique. The results showed that the flexible barrier dissipated less than 40% of the total energy of the landslide debris. With an increase of impact energy, the energy dissipation ratio of the flexible barrier decreased linearly. The maximum energy capacity of a flexible rockfall barrier in resisting landslide debris is four-times that of resisting a rockfall.
Influence of the Harvesting Region on Batch Homogeneity of Ipe Wood (Tabebuia sp.) Based on Its Physical and Mechanical Properties
Authors: dos Santos Herisson Ferreira , de Moraes Matheus Henrique Morato , de Oliveira Ivanildo Amorim , de Freitas Ludmila , de Moura Aquino Vinícius Borges , Menezes Isabella Silva , Fraga Iuri Fazolin , Lahr Francisco Antonio Rocco , Mascarenhas Fernando Júnior Resende , de Almeida Filho Fernando Menezes , Christoforo André Luis
Published: August 2022

This paper aims to evaluate the hypothesis regarding the homogeneity of wood batches, as provided in the Brazilian standard for wooden structures ABNT NBR 7190 through an experimental study simulating the condition of a sawmill where the control of the harvest region, location of the sample in the trunk, edaphoclimatic condition, relief, and vegetation are variables that cannot be controlled. An experimental study was performed on 15 properties (3 physical and 12 mechanical) of Ipe (Tabebuia sp.) wood. Batches from three harvesting regions (Mucajaí [RR-A], Bonfim [RR-B], and Cláudia [MT]) were used to verify the existence of significant differences between the properties of the three areas. The properties were obtained following the test methods established in ABNT NBR 7190-3, with 540 specimens being manufactured and characterized, prior to being submitted to a statistical analysis, where ANOVA was applied with the Anderson–Darling normality test, the homogeneity of variance of multiple comparisons, and the Tukey test. Statistical analysis indicated that the wood harvesting regions showed significant differences in six (40%) and non-significant differences in nine (60%) of the fifteen properties evaluated. Among the properties evaluated, the compressive strength parallel to the grain (fc0) showed significant differences. This property is used to classify the wood strength class according to the Brazilian standard ABNT NBR 7190-1. The batches from the harvesting regions RR-A, RR-B, and MT were classified using the strength classes D60, D50, and D50, respectively. These results directly impact the consideration of the homogeneity of the batches, considering that the sawmills receive samples from different regions. For future research, a numerical method can be applied to consider the influence of the wood harvesting area.
Drought Hardening of European Beech (Fagus sylvatica L.) and Silver Fir (Abies alba Mill.) Seedlings in Mixed Cultivation
Authors: Yang Fengli , Burzlaff Tim , Rennenberg Heinz
Published: August 2022

To alleviate the enhanced frequency, duration, and intensity of drought as a consequence of global warming, admixing drought-sensitive European beech (Fagus sylvatica L.) with deep rooting silver fir (Abies alba Mill.) has been proposed. However, information on the performance of the admixtures of seedlings of these tree species at limited water availability has so far not been reported. In the present study, we investigated the significance of water deprivation in mixtures of beech and fir seedlings on the foliar relative water content (RWC), δ13C signature, total C and N contents, and C:N ratios of both species in a drought-rewetting cycle. Surprisingly, moderate drought triggered increased RWC in beech leaves and current year fir needles indicating drought hardening. The enhanced foliar RWC was preserved after rewatering in beech leaves, but not in current year fir needles. Drought did not significantly affect δ13C abundance in beech leaves, but enhanced the δ13C abundance (less negative values) in current and one-year old fir needles, indicating stomatal control in fir needles but not in beech leaves upon moderate drought. Total C contents of beech leaves were significantly increased upon drought and rewatering, but remained constant in fir needles. Foliar total N increased in both species upon drought and decreased upon rewatering. Accordingly, C:N ratios decreased in response to drought and recovered after rewatering. These results suggest that drought hardening may be achieved at least partially via osmotic adjustment by different compatible solutes in beech leaves and fir needles. No apparent effects of the number of neighbours were observed, although more fir neighbours tended to increase the RWC and total C contents of beech leaves. These results indicate that drought hardening in mixtures of beech and fir seedlings is largely independent of the number of interspecies neighbours.
Changes in Chemical Composition, Crystallizability, and Microstructure of Decayed Wood-Fiber-Mat-Reinforced Composite Treated with Copper Triazole Preservative
Authors: Bao Minzhen , Tang Rongqiang , Bao Yongjie , He Sheng , Chen Yuhe , Li Neng
Published: August 2022

Wood-fiber-mat-reinforced composites (WFMRCs) possess excellent physical and mechanical properties and provide high structural performance, making them a suitable engineering structural material. However, WFMRCs are susceptible to biological attack by fungi and insects when they are used in outdoor environments. In this study, the efficacy of copper triazole (CuAz) preservative treatment in protecting WFMRC against decay by white- and brown-rot fungi (Trametes versicolor and Gloeophyllum trabeum, respectively) was evaluated. Both fungi caused a mass loss in the untreated scrimber of more than 15%, while the CuAz preservative treatment reduced the mass loss to 5%. The measurement results show that CuAz treatment could effectively reduce the degradation rate of three major components of wood; inhibit fungal colonization and degradation; and improve the decay resistance of WFMRC. The atmospheric impregnation of CuAz preservative is more suitable for the material features of WFMRC than vacuum impregnation and can be considered for practical industrial applications. This study provides technical support for the protection and outdoor application of WFMRCs.
Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
Authors: Zhu Hongtao , Xu Huahu , Ma Xiaojin , Bian Minjie
Published: August 2022

Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise, due to expression-independent intra-class differences. In this paper, we propose a Dual Path Stacked Attention Network (DPSAN) to better cope with the above challenges. Firstly, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences. Secondly, by providing the global image and segmented local image regions as training data for the integrated dual path model, the overfitting problem of the deep network due to a lack of data can be effectively mitigated. Finally, this paper also designs a stacked attention module to weight the fused feature maps according to the importance of each part for expression recognition. For the cropping scheme, this paper chooses to adopt a cropping method based on the fixed four regions of the face image, to segment out the key image regions and to ignore the irrelevant regions, so as to improve the efficiency of the algorithm computation. The experimental results on the public datasets, CK+ and FERPLUS, demonstrate the effectiveness of DPSAN, and its accuracy reaches the level of current state-of-the-art methods on both CK+ and FERPLUS, with 93.2% and 87.63% accuracy on the CK+ dataset and FERPLUS dataset, respectively.
Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis
Authors: Afrifa Stephen , Zhang Tao , Appiahene Peter , Varadarajan Vijayakumar
Published: August 2022

With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.
The Fuel Moisture Index Based on Understorey Hygrochron iButton Humidity and Temperature Measurements Reliably Predicts Fine Fuel Moisture Content in Tasmanian Eucalyptus Forests
Authors: Bowman David M. J. S. , Furlaud James M. , Porter Meagan , Williamson Grant J.
Published: August 2022

Fine fuel moisture content (FFMC) is a key determinant of wildfire occurrence, behaviour, and pyrogeographic patterns. Accurate determination of FFMC is laborious, hence managers and ecologists have devised a range of empirical and mechanistic measures for FFMC. These FFMC measures, however, have received limited field validation against field-based gravimetric fuel moisture measurements. Using statistical modelling, we evaluate the use of the relationship between gravimetric FFMC and the Fuel Moisture Index (FMI), based on Hygrochron iButton humidity and temperature dataloggers. We do this in Tasmanian wet and dry Eucalyptus forests subjected to strongly contrasting disturbance histories and, hence, percentage of canopy cover. We show that 24 h average FMI based on data from Hygrochron iButtons 0.75 m above the forest floor provides reliable estimates of gravimetric litter fuel moisture (c. 1 h fuels) that are strongly correlated with near surface gravimetric fuel moisture sticks (c. 10 h fuels). We conclude FMI based on Hygrochron iButton data provides ecologists with an economic and effective method to retrospectively measure landscape patterns in fuel moisture in Tasmanian forests.
Factors Influencing Risk during Wildfires: Contrasting Divergent Regions in the US
Authors: Noonan-Wright Erin , Seielstad Carl
Published: August 2022

(1) Background: Federal land managers in the US are charged with risk-based decision-making which requires them to know the risk and to direct resources accordingly. Without understanding the specific factors that produce risk, it is difficult to identify strategies to reduce it. (2) Methods: Risk characterized by U.S. land managers during wildfires was evaluated from 2010–2017 to identify factors driving risk perceptions. Annotation from 282 wildfires in two regions with distinctive risk profiles, the Northwest and Southwest Geographic Areas, were qualitatively coded using the risk assessment framework of hazards, values, and probability from the Relative Risk Assessment in the Wildland Fire Decision Support System (WFDSS). (3) Results: The effects of climate on seasonal severity, fuel condition, and fire behavior emerged as the most influential factors driving risk perceptions and characterizations of risk in both regions. Monsoonal precipitation extended the longevity of landscape barriers, especially in the Southwest. The results suggest that a scarcity of values at risk and a mild fire environment produce low risk fires regardless of location, while high risk fires reflect specific local values and geography, under the umbrella of dry climate. (4) Conclusions: the climatic contrasts between the two regions highlight how influential climate change will be on future characterizations of wildfire risk.
Unsteady Fluid Flows in the Slab Mold Using Anticlogging Nozzles
Authors: González-Solórzano María Guadalupe , Dávila Rodolfo Morales , Guarneros Javier , Calderón-Ramos Ismael , Muñiz-Valdés Carlos Rodrigo , Nájera-Bastida Alfonso
Published: August 2022

The characterization of the fluid flow of liquid steel in a slab mold, using two nozzle designs under unclogged and clogged conditions, is performed using physical and mathematical simulations. Nozzle A, with an expanding and contracting geometry, yields larger sub-meniscus experimental velocities than nozzle B, with internal flow deflectors. The numerical predictions indicate quick time-changing velocity profiles in the submeniscus region between the mold’s narrow face and the nozzles. The flow deflectors in nozzle B have two effects; the high dissipation rate of kinetic energy in the upper-half length induces lower velocities in the ports than nozzle A. The neutralization of the biased flow caused by the sliding gate allows a balanced fluid through the ports. According to the results, nozzle A yields velocity profiles in the sub-meniscus region with larger standard deviations than nozzle B, leading to an unstable bath surface. The clogged nozzles produced biased-asymmetrical flow patterns in the mold, finding approximated matchings between numerical predictions and experimental measurements. The internal protrusions of the deposits lead to covariance losses of the bath surface wave heights. The use of internal deflectors helped to decrease the amount of clog material in nozzle B.
Multi-Scale Structures and Functional Properties of Quinoa Starch Extracted by Alkali, Wet-Milling, and Enzymatic Methods
Authors: Junejo Shahid Ahmed , Wang Jun , Liu Ying , Jia Rui , Zhou Yibin , Li Songnan
Published: August 2022

The purpose of this study is to investigate the effects of starch extraction methods (alkali, wet-milling, and enzymatic) on the multi-scale structures and functional properties of quinoa starch. When the enzymatic method was compared with alkali and wet-milling, it showed higher protein content (2.4%), larger mean size of aggregated granules (44.1 μm), higher relative crystallinity (29.0%), scattering intensity (17.8 α.u.), absorbance ratio of 1047/1022 (0.9), single and double helical content (8.2% and 23.1%), FWHM ratio (1.5), and average molecular weight and radius of gyration (1.58 × 107 g/mol and 106.8 nm), respectively. Similarly, quinoa starch by enzymatic extraction had a higher onset (82.1 °C), peak (83.8 °C), and conclusion (86.3 °C) temperatures, as well as an enthalpy change (6.8 J/g). It further showed maximum hardness (238.8 N), gumminess (105.6 N), chewiness (80.2 N), SDS content (7.5% of raw and 4.8% of cooked), and RS content (15.4% of raw and 13.9% of cooked), whereas it contained minimum RDS content (77.1% of raw and 81.9% of cooked). The results suggest that extraction of starch by the enzymatic method could be a viable approach to retain the native structure of starch and may eventually improve the glycemic response.
Consumers’ Attitude towards Sustainability in Italy: Process of Validation of a Duly Designed Questionnaire
Authors: Aureli Vittoria , Nardi Alessandra , Peluso Daniele , Scognamiglio Umberto , Rossi Laura
Published: August 2022

This study aimed to describe the process of validation of a questionnaire assessing Italian consumers’ perception of food sustainability. The study has a multiphase design. Phase 1 consisted in translating and structuring the questionnaire. Phase 2 aimed at assessing the validity of the content by experts. Phase 3 consisted of a pilot study (n = 150) carried out to revise the questionnaire based on the reactions of consumers representing the target group of the assessment. The questionnaire showed adequate content validity for 11 out of 14 questions (>0.79) and S-CVI/Ave > 0.80. Cronbach’s alpha values ranged from 0.08 to 0.90. The construct with insufficient results (0.08) was changed because it failed to correlate with the rest of the questionnaire. The factor analysis permitted the identification of questions that needed improvement in terms of comprehensibility, elimination of redundancies, and repetitions. The validated questionnaire included 12 questions (71 response options); 3 sections were identified: food sustainability knowledge (4 questions-30 items); sources of proteins alternative to meat (3 questions-20 items); eating behaviors (5 questions-21 items). This study showed the importance of validation before the administration on a large scale of a questionnaire on a topic such as sustainability still lacking large support from consensus documents.
Simulation of Fluid Flow during Egg Pickling under Different Inlet and Outlet Conditions in a Pulsed Pressure Tank with Liquid Circulation
Authors: Zhang Jing-Shou , Zielinska Magdalena , Wang Hui , Liu Yu-Qi , Xu Yu-Fan , Xiao Hong-Wei
Published: August 2022

Pulsed pressure pickling is an emerging highly efficient osmotic dehydration technique. However, the immobility of the pickling liquid and the material, the formation of layers, and the uneven pickling efficiency in different sections make it difficult to use industrially. This work aims at improving and optimizing the conditions of fluid flow in the pickling tank with a liquid-cycle system to reduce the unevenness in the production process. Fluid flow around the eggs was numerically investigated by solving three-dimensional Reynolds-averaged Navier–Stokes equations within the flow regime, adopting different angles and positions at the inlet and outlet. The simulation results show that the inlet with a radial deflection of 35° and the outlet with axial direction were characterized by the best flow efficiency. Under these conditions, the average flow velocity and the global uniformity index were 0.153 m/s and 0.407, respectively. Furthermore, the experiments were carried out using an equivalent scale model of the pulsed pressure equipment with liquid circulation. The experimental results showed that, under optimal conditions, the salt content in all four layers of the egg white was about 2.8% after 48 h. This study provides a solution to ensure the constant salinity of different layers of pickled eggs and to improve pickling efficiency, especially in industrial-scale production.
Investigating the Efficacy of Tasmannia lanceolata Extract in Inactivating Fungi and Prolonging the Shelf Life of Date Fruit
Authors: Al-Asmari Fahad , Akter Saleha , Mereddy Ram , Sultanbawa Yasmina
Published: August 2022

Date palm (Phoenix dactylifera L.) is one of the world’s oldest cultivated plants. Post-harvest losses of date palm due to fungal contamination reached up to 50% of the total production. This study aimed to investigate the potential of the extract of Tasmanian pepper leaf (TPL) and the non-thermal treatment of photosensitization mediated by curcumin in reducing the fungal contamination and enhancing the shelf life of date palm. In the in vivo storage study, the dates were treated with three different concentrations of TPL extract 12.5, 25, and 50 µg/mL and stored at 30 °C. The findings obtained for the treatment with TPL extract exhibited potent antifungal activity against most of the tested fungi, where minimum inhibitory concentrations (MICs) and minimum fungicidal concentrations (MFCs) were < 25 µg/mL for polygodial, the bioactive compound in TPL. The shelf life of date palm treated by 50 µg/mL polygodial was extended up to 21 days, thrice as much as the untreated controls. In contrast, a lower concentration of TPL extract (25 µg/mL polygodial) revealed up to a 15-day shelf life extension compared to untreated dates (7 days). The results obtained from the study suggested that using TPL extracts against pathogenic and spoilage fungi occurring in fresh date fruits is a promising treatment for the shelf life extension of fresh date fruits at room temperature.
Control of Residues of Antimicrobial Substances by Screening Methods in Raw Milk Based on Participation in Proficiency Tests in Poland, 2017–2021
Authors: Łuszczyńska Magdalena , Gołaś-Prądzyńska Marlena , Rola Jolanta Grażyna
Published: August 2022

Proficiency testing (PT) is an important tool for evaluation of the competences of laboratories which test milk for residues of antimicrobial substances. It also warrants the reliability of the obtained test results, which is important to the clients of a laboratory. In 2017–2021, the Polish National Reference Laboratory organized 10 rounds of proficiency testing on raw milk samples according to the ISO/IEC 17043 standard. The milk samples were fortified with selected antimicrobial substances. All participating laboratories used commercial receptor and/or microbiological diagnostic kits in the proficiency tests. The results obtained by the laboratories were compared to assigned results and were defined as compliant or non-compliant. In total, 7533 results were obtained, and 104 (1.4%) were assessed as non-compliant. The percentage of laboratories which obtained a positive result in proficiency tests ranged by testing round from 81.8% to 100%. Based on proficiency testing results, it can be concluded that laboratories carry out tests correctly. The proven proficiency by a laboratory increases the confidence of its clients in its service of antimicrobial residue monitoring in milk.
Hydrogel Beads of Amidoximated Starch and Chitosan as Efficient Sorbents for Inorganic and Organic Compounds
Authors: Loghin Diana Felicia , Bazarghideanu Melinda Maria , Vasiliu Silvia , Racovita Stefania , Zaharia Marius-Mihai , Vasiliu Tudor , Mihai Marcela
Published: August 2022

The synthesis of hydrogel beads involving natural polymers is, nowadays, a leading research area. Among natural polymers, starch and chitosan represent two biomolecules with proof of efficiency and low economic impact in various utilization fields. Therefore, herein, the features of hydrogel beads obtained from chitosan and three sorts of starch (potato, wheat and rise starches), grafted with acrylonitrile and then amidoximated, were deeply investigated for their use as sorbents for heavy metal ions and dyes. The hydrogel beads were prepared by ionotropic gelation/covalent cross-linking of chitosan and functionalized starches. The chemical structure of the hydrogel beads was analyzed by FT-IR spectroscopy; their morphology was revealed by optical and scanning electron microscopies, while the influence of the starch functionalization strategies on the crystallinity changes was evaluated by X-ray diffraction. Molecular dynamics simulations were used to reveal the influence of the grafting reactions and grafted structure on the starch conformation in solution and their interactions with chitosan. The sorption capacity of the hydrogel beads was tested in batch experiments, as a function of the beads’ features (synthesis protocol, starch sort) and simulated polluted water, which included heavy metal ions (Cu2+, Co2+, Ni2+ and Zn2+) and small organic molecules (Direct Blue 15 and Congo red).