bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrasound and Magnetic Resonance Imaging of the Tongue in Obstructive Sleep Apnoea
Authors: Molnár Viktória , Lakner Zoltán , Molnár András , Tárnoki Dávid László , Tárnoki Ádám Domonkos , Kunos László , Jokkel Zsófia , Tamás László
Published: September 2022

To examine the geometrical parameters of the tongue in obstructive sleep apnoea (OSA) based on sex, age and BMI parameters and ultrasound (US) and MRI. The presence of OSA and tongue-based obstruction can be predicted using these parameters. Of 100 patients, 64% were diagnosed with OSA according to overnight polysomnography. MRI and US devices were used to measure tongue parameters. The location of the obstruction was identified using drug-induced sleep endoscopy. Statistical analysis was performed using the quadratic discriminant analysis. Men presented higher tongue volumes and axial diameter during Müller’s maneuver (MM) of US and coronal diameter of the MRI. In women, all examined MRI parameters were significantly correlated with apnoea-hypopnea index (AHI). A stronger correlation between BMI and AHI parameters was observed in women than in men. Using our algorithm, which includes tongue parameters and anthropometric values, the presence of OSA could be predicted in 91% with US and 82% with MRI. The detection of tongue-based obstruction was successful in 89% using US and 87% using MRI, whereas tongue-based obstruction was successful in 70% using US. Using MRI and US of the tongue combined with basic anthropometric parameters, the presence of OSA and tongue-based obstruction can be identified with high precision.
Finite Element Analysis of Punching Shear of Reinforced Concrete Slab–Column Connections with Shear Reinforcement
Authors: Jia Yueqiao , Chiang Jeffrey C. L.
Published: September 2022

Shear reinforcement is one of the common measures used to strengthen slab–column connections. It can make the joint’s load capacity increase. However, when the connection is subjected to an unbalanced bending moment, the reduction effect of the shear reinforcement on the bending moment is worth investigating. Nonlinear finite element analysis was performed on reinforced concrete slabs with shear reinforcement for various moment-to-shear (M/V) ratios. The effects of the number and diameter of shear reinforcement and different loading methods on the slab were investigated, as along with a correction to the ACI-318 and China Concrete Structure Design code GB50010 formula for the moment transfer coefficient. The 3D finite element modeling was performed using appropriate element types and a constitutive model for concrete. The concrete damage plasticity model parameters were calibrated using the experimental results of a specimen. The amount of shear reinforcement had a certain contribution to the strength of the slab, but the diameter of the shear reinforcement had little effect. The plate’s bearing capacity decreased as the M/V increased. The corrected moment transfer coefficient formula demonstrates that M/V is positively correlated with the coefficient and that the calculated values specified in ACI-318 and GB50010 are overly conservative.
Learning to Optimise a Swarm of UAVs
Authors: Duflo Gabriel , Danoy Grégoire , Talbi El-Ghazali , Bouvry Pascal
Published: September 2022

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density.
Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
Authors: Barayan Mohammed A. , Qawas Arwa A. , Alghamdi Asma S. , Alkhallagi Turki S. , Al-Dabbagh Raghad A. , Aldabbagh Ghadah A. , Linjawi Amal I.
Published: September 2022

Background: Identifying the diagnostic value of bitewing radiographs (BW) is highly dependent on the operator’s knowledge and experience. The aim of this study is to assess the effectiveness of machine learning (ML) to classify the BW according to their diagnostic quality. Methods: 864 BW radiographs from records of 100 patients presented at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia were assessed. The radiographic errors in representing proximal contact areas (n = 1951) were categorized into diagnostic and non-diagnostic. Labeling and training of the BW were done using Roboflow. Data were divided into validation, training, and testing sets to train the pre-trained model Efficientdet-d0 using TensorFlow. The model’s performance was assessed by calculating recall, precision, F1 score, and log loss value. Results: The model excelled at detecting “overlap within enamel” and “overlap within restoration (clear margins) with F1 score of 0.89 and 0.76, respectively. The overall system errors made by the built model showed a log loss value of 0.15 indicating high accuracy of the model. Conclusions: The model is a “proof of concept” for the effectiveness of ML in diagnosing the quality of the BW radiographs based on the contact areas. More dataset specification and optimization are needed to overcome the class imbalance.
Tennis Attack: An Exergame Utilizing a Natural User Interface to Measure and Improve the Simple Reaction Time
Authors: Politopoulos Nikolaos , Tsiatsos Thrasyvoulos
Published: September 2022

The present article demonstrates the process of designing, developing, implementing, and evaluating an active video game or exergame. The main goal of our proposed exergame is to develop the simple reaction time of players. The main target group are simple users; however, it can work on tennis specialists. Herein, we used the exergame to investigate the hypothesis that players can improve their reaction time through practice. To achieve this, players’ simple reaction time was measured at the start of the game. Then, the players took part in a 4-week training session. At the end of the training session, the simple reaction time of players was measured again and a questionnaire was completed. Another goal of this paper was to investigate the difference between perceived usefulness (general usability, usefulness, and user interface satisfaction) of experts and non-experts (sports science students and computer science students) and pro-gamers and casual gamers. The findings were encouraging. The majority of the players reported that the gaming experience was very satisfying and the game was easy to use and learn. Moreover, after the analysis, it was discovered that this game can significantly improve the simple reaction time of all players. This improvement was independent from the players’ background.
Body Weight Distribution and Body Sway in Healthy Female Adults Aged between 51 and 60 Years in Germany—Standard Values
Authors: Ohlendorf Daniela , Keller Julia , Sosnov Polyna , Ackermann Hanns , Keil Fee , Maurer-Grubinger Christian , Holzgreve Fabian , Oremek Gerhard , Groneberg David A.
Published: September 2022

Background: In order to determine possible pathological deviations in body weight distribution and body sway, it is helpful to have reference values for comparison: gender and age are two main influencing factors. For this reason, it was the aim of the present study to present reference values for women between 51 and 60 years of age. Methods: For this study, 101 subjectively healthy female Germans aged between 51 and 60 years (55.16 ± 2.89 years) volunteered and were required to stand in a habitual posture on a pressure measuring platform. Results: The average BMI of this age group was 25.02 ± 4.55 kg/m². The left and right foot showed an almost evenly balanced load distribution with a median load of 52.33% on the left foot [tolerance interval (TR) 38.00%/68.03%; confidence interval (CI) 51.00%/53.33%] and 47.67% on the right foot [TR 31.97%/62.00%; CI 46.67%/49.00%]. The measured median load of the forefoot was 33.33% [TR 21.37%/54.60%; CI 30.67%/36.00%] and that of the rear foot was 66.67% [TR 45.50%/78.63%; CI 64.00%/69.33%]. The median body sway in the frontal plane was 11 mm [TR 5.70 mm/26.30 mm; CI 10.00 mm/11.67 mm] and that of the sagittal plane was 16 mm [TR 7.37 mm/34.32 mm; CI 14.67 mm/18.67 mm]. The median ellipse area was 1.17 cm² [TR 0.29 cm²/4.96 cm²; CI 0.98 cm²/1.35 cm²], the median ellipse width was 0.91 cm [TR 0.42 cm/1.9 cm; CI 0.84 cm/1.02 cm] and its height was 0.40 cm [TR 0.22 cm/0.89 cm; CI 0.38 cm/0.43 cm]. Conclusions: The left-to-right ratio is almost balanced. The load distribution of the forefoot to the rear foot is approximately 1:2. The median body sway values for the frontal and sagittal planes (11 and 16 mm, respectively) agree with other values. The values for the height, body weight and the BMI are comparable to the values of average German women at this age; therefore, the measured values show a presentable cross section of women in the 51–60 age group in Germany. The present data can be used as a basis for women aged 51–60 years and can support the detection of possible dysfunctions as well as injury prevention in the parameters of postural control.
The Tolerance, Absorption, and Transport Characteristics of Macleaya cordata in Relation to Lead, Zinc, Cadmium, and Copper under Hydroponic Conditions
Authors: Zhang Hongxiao , Zhou Wenli , Chen Yahua , Xu Huawei , Hou Dianyun , Lv Shufang , Sun Xijing , Wang Fayuan , Yang Liming
Published: September 2022

Heavy metal pollution has potential hazards to plant, animal, and human health, and phytoremediation is recognized as a safe and efficient technique for the revegetation of heavy-metal-polluted soil. Macleaya cordata was found in heavily tailing areas with fast growth rates, large biomass, and huge taproots. In our study, the seedlings of M. cordata were exposed to cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn) in a Hoagland solution. After 20 days, the tolerance index as well as the content and distribution of Cd, Pb, Cu, and Zn in roots, stems, and leaves were determined. The results showed M. cordata had higher tolerance to Pb and Zn than to Cd and Cu under hydroponic culture conditions. Pb and Cu mainly accumulated in the roots, and the translocation efficiency to the shoots was very low, while about three-quarters of Zn concentrations in the plants were accumulated in the shoots; even the Cd content per shoot of M. cordata exceeded some Cd hyperaccumulators. In the present study, the metal ions in the roots or leaves of M. cordata were firstly determined in situ using dithizone staining, and the degree of root-tip staining was consistent with the amountof the total metal content in the roots. The addition of Zn or Cu in the Pb treatment solution increased the Pb content in the stems and leaves of M. cordata, while the addition of Zn or Cu in the Cd treatment solution had the opposite effect. Pb or Cd in the compound treatment decreased the Zn content in all parts of M. cordata. Our results suggest that Pb can be transported above ground via some special pathways in M. cordata. The different absorption and transport mechanisms of M. cordata in relation to Cd, Zn, Cu, and Pb can be important for the plant to be applied for the remediation of compound-polluted soil or water.
Deep Neural Network Concept for a Blind Enhancement of Document-Images in the Presence of Multiple Distortions
Authors: Mohsenzadegan Kabeh , Tavakkoli Vahid , Kyamakya Kyandoghere
Published: September 2022

In this paper, we propose a new convolutional neural network (CNN) architecture for improving document-image quality through decreasing the impact of distortions (i.e., blur, shadows, contrast issues, and noise) contained therein. Indeed, for many document-image processing systems such as OCR (optical character recognition) and document-image classification, the real-world image distortions can significantly degrade the performance of such systems in a way such that they become merely unusable. Therefore, a robust document-image enhancement model is required to preprocess the involved document images. The preprocessor system developed in this paper places “deblurring” and “noise removal and contrast enhancement” in two separate and sequential submodules. In the architecture of those two submodules, three new parts are introduced: (a) the patch-based approach, (b) preprocessing layer involving Gabor and Blur filters, and (c) the approach using residual blocks. Using these last-listed innovations results in a very promising performance when compared to the related works. Indeed, it is demonstrated that even extremely strongly degraded document images that were not previously recognizable by an OCR system can now become well-recognized with a 91.51% character recognition accuracy after the image enhancement preprocessing through our new CNN model.
A Method for Enterprise Architecture Model Slicing
Authors: Guo Hong , Li Jingyue , Gao Shang , Smite Darja
Published: September 2022

Enterprise Architecture (EA) has been applied widely in industry as it brings substantial benefits to ease communication and improve business-IT alignment. However, due to its high complexity and cost, EA still plays a limited role in many organizations. Existing research recommends realizing more of the EA potential. EA can be developed for specific purposes, accumulated in a digital repository, and reused when needed later. Due to the diversity and inconsistency of the repository, it is challenging to find relevant EA data and reuse it. In the present research, we propose using slicing techniques to extract EA models for reuse. We validate the method with an official EA repository hosted by The Open Group. The result shows that the method could facilitate extracting existing EA model components for developing new EA artifacts to save cost, alleviate maintenance effort, and help keep the repository consistent for future (re)use.
Convolutional Neural Network Algorithm Trained with Anteroposterior Radiographs to Diagnose Pre-Collapse Osteonecrosis of the Femoral Head
Authors: Kim Jeoung Kun , Choi Gyu-Sik , Kwak Seong Yeob , Chang Min Cheol
Published: September 2022

A convolutional neural network (CNN) is a representative deep-learning algorithm that has a significant advantage in image recognition and classification. Using anteroposterior pelvic radiographs as input data, we developed a CNN algorithm to determine the presence of pre-collapse osteonecrosis of the femoral head (ONFH). We developed a CNN algorithm to differentiate between ONFH and normal radiographs. We retrospectively included 305 anteroposterior pelvic radiographs (right hip: pre-collapsed ONFH = 79, normal = 226; left hip: pre-collapsed ONFH = 62, normal = 243) as data samples. Pre-collapsed ONFH was diagnosed using pelvic magnetic resonance imaging data for each patient. Among the 305 cases, 69.8% of the included data samples were randomly selected as the training set, 21.0% were selected as the validation set, and the remaining 9.2% were selected as the test set to evaluate the performance of the developed CNN algorithm. The area under the curve of our developed CNN algorithm on the test data was 0.912 (95% confidence interval, 0.773–1.000) for the right hip and 0.902 (95% confidence interval, 0.747–1.000) for the left hip. We showed that a CNN algorithm trained using pelvic radiographs would help diagnose pre-collapse ONFH.
Stability Conditions in Lignite Open Pits from Romania, Case Study: Oltețu Open Pit
Authors: Lazar Maria , Faur Florin , Apostu Izabela-Maria
Published: September 2022

The problem of the slope stability of open pit mines is one of constant interest and great importance, both during the period of operation, but also post-closure. The research focused on the Oltețu open pit (located in Berbesti Mining Basin, Romania) and was directed in such a way as to allow consideration in the stability analyses of natural (predisposing the investigated area to landslides) and anthropogenic (specific to open pit mining) factors and causes as well as their combined effect. The field investigations (observations on the technical condition of the slopes, discussions with the technical personnel from Oltețu open pit, and sampling) were completed with analyses and laboratory tests (physical–mechanical properties of rocks in the composition of the slopes). The stability analyses took into account different hypotheses related to the actual geometry of the working slopes, and a predictive analysis was also carried out for the forecasted evolution of the working fronts and lateral slopes. Following stability analyses, it was found that for most slopes, the stability reserve is insufficient to allow continuing lignite exploitation under safe conditions. The last part of the paper presents the solutions identified by the authors (adoption of new geometries of the working front and lateral slopes) in order to increase the stability reserve to a minimum acceptable level, which would allow the safe continuation of lignite extraction, and, in the end, some practical recommendations are briefly presented.
Experimental Study on a Reinforced Concrete Element to Extract the Durability Index with the Automated Visualization
Authors: Lajevardi Seyed Mohammad Sadegh , Moghadas Nejad Fereidoon , Ravanshadnia Mehdi
Published: September 2022

Reinforced concrete (RC) durability is a crucial feature to estimate long-term quality and structural performance. The degradation model is vital for the resource planning of maintenance projects. This model will extract data by updating the status of structures and trending the components’ state over time in terms of durability. Surface erosion, spalling, cracks, and other defects exposed on RC components lead to increase factors adversely affecting concrete durability in structures. This research presents an approach based on automated visualization for extracting quantitative indexes as well as visual inspection without the subjective interspersion of humans or probable human errors during the inspection. The durability index (Di) will extract according to damage probability and defects growth in order to extract the severity of failure and risk. Measurement operation by automated software has been double-checked by manual measurement tools, and data will verify randomly in this method. The results show that, in this component, the damaged area increases by 24% after a definite time. According to degradation models, this component may pass the relative thresholds for the limit for the state of operations to fail. This significant difference between expected time and designing time determines the Di, equal to 5 out of 10.
A ML-Based Wind Speed Prediction Model with Truncated Real-Time Decomposition and Multi-Resolution Data
Authors: Feng Hui , Jin Yao , Laima Shujin , Han Feiyang , Xu Wengchen , Liu Zhiqiang
Published: September 2022

Accurate wind speed prediction is of importance for long-span cross-sea bridges. To this end, data decomposition techniques are usually employed to promote accuracy of the prediction model. Since wind speed data come sequentially, real-time decomposition should be adopted. However, real-time decomposition may degrade the accuracy due to the end effect. In this paper, a novel scheme of real-time decomposition that is a combination of truncated real-time decomposition and multi-resolution data is developed. Specifically, truncated real-time decomposition firstly denoises the data and eliminates the end effect; high-resolution data are then introduced to compensate for the information loss. Further, a novel wind speed prediction model that consists of the proposed scheme and neural networks is proposed. Specifically, two gated recurrent unit neural networks are employed to extract features from the obtained original-resolution and high-resolution data, respectively, and a multi-layer perceptron is adopted to utilize extracted features and make predictions. The proposed model is validated on the monitoring wind speed data of two long-span cross-sea bridges. Specifically, the mean absolute error and the root of mean square error of the proposed model on the two datasets are 0.334, 0.445 and 0.233 and 0.316 m/s, which are smaller than benchmark models and demonstrate superiority of the proposed model.
The Effects of Extracts from Buckwheat Hulls on the Quality Characteristics of Chicken Meatballs during Refrigerated Storage
Authors: Pietrzak Dorota , Zwolan Adam , Chmiel Marta , Adamczak Lech , Cegiełka Aneta , Hać-Szymańczuk Elżbieta , Ostrowska-Ligęza Ewa , Florowski Tomasz , Oszmiański Jan
Published: September 2022

Buckwheat hulls, due to their high content of phenolic compounds, could be used as a promising food ingredient, the use of which would reduce the waste generated during the production of buckwheat groats. The aim of this study was to investigate the influence of buckwheat hull extracts on the quality of chicken meatballs. Meatballs were produced according to three different treatments: without extracts (Control) and with water (WE) and ethanolic (EE) extracts of buckwheat hulls. The phenolic compositions of the extracts were analysed and their effects on the colour, lipid oxidation, microbiological and sensory qualities of the chicken meatballs were studied. The ethanolic extract of buckwheat hulls was characterised by a total polyphenolic content more than double that of the water extract. Rutin was the major phenolic compound identified in the extracts, with the ethanolic extract containing more than four times as much rutin as the water extract. Oxidative changes in lipids in the meatballs prepared with extracts of buckwheat hulls occurred more slowly than those in Control. This was indicated by lower TBARS values and the longest fat induction time. The results suggested that, although the ethanolic extract of buckwheat hulls was characterised by a higher content of polyphenolic compounds compared to the water extract, both additives showed similar antioxidant activities in chicken meatballs during 14 days of refrigerated storage.
Analytical, Stochastic and Experimental Solution of the Osteosynthesis of the Fifth Metatarsal by Headless Screw
Authors: Vlčková Kateřina , Frydrýšek Karel , Bajtek Vojtěch , Demel Jiří , Pleva Leopold , Havlíček Miroslav , Pometlová Jana , Madeja Roman , Kratochvíl Jiří , Krpec Pavel , Osemlak Paweł , Čabanová Kristina , Olšovská Eva , Vaculová Jana
Published: September 2022

This paper evaluates the various approaches to strength and stiffness analysis of fracture osteosynthesis using a headless Herbert screw. The problem has been extensively addressed using several scientific approaches, namely the analytical approach, stochastic approach, experimental approach, and (marginally) using the finite elements method. The problem is illustrated on the use of a prototype headless screw Ti: 4.0/1.4 × 30/7 (manufacturer: Medin, Czech Republic) and the surgical treatment of the fifth metatarsal fracture. Mathematical equations for the analytical calculation of the maximum stresses in the screw were established for tensile/compression loading. This problem is also interesting because of its static indetermination in tension and compression; for this reason, it was necessary to use the deformation condition, i.e., the relationship between screw extension and bone contraction. The stochastic (probabilistic) approach, i.e., application of the Monte Carlo method, takes advantage of the mathematical equations derived during the analytical solution by respecting of the natural variabilities and uncertainties. The analytical and stochastic approaches were validated by measurements on porcine bones and by the finite element method. The data measured experimentally were also processed and used for deriving an equation, appropriately approximating the data. The main part of the measurement was to determine the axial force generated during osteosynthesis with a headless screw. The obtained compressional force was used to determine the maximal stress in the screw and bone. Finally, the methods were compared. In this paper, comprehensive and original approaches based on the authors’ experience with multiple methods are presented. Obtained results are necessary for headless screw designers during optimalization of the implants and are also useful for surgeons developing new surgical techniques. This biomechanical problem was solved in cooperation with the engineering industry and physicians to improve the quality of care for patients with trauma in orthopedics and surgery.
Semiautomated Statistical Discontinuity Analyses from Scanline Data of Fractured Rock Masses
Authors: Zangerl Christian , Koppensteiner Matthias , Strauhal Thomas
Published: September 2022

Quantitative statistical discontinuity analysis of fractured rock masses is commonly applied in the fields of engineering geology, rock mechanics, slope stability, and hydrogeology. This study presents a workflow for the semiautomatic determination of basic discontinuity parameters, such as spacing, frequency, trace length, and termination, from scanline surveys written for the open-source software Octave. The aim is to provide theoretical background information and scripts for a quick introduction to all interested parties from academia and consulting, in order to promote the use of widely known and accepted statistical evaluation methods. Data from a study site in granodioritic rock are analyzed in the context of the proposed workflow. These test data and all scripts (m-files) used in the study are provided in order to minimize initial training time. The scripts provided herein are kept short and simple, but can be used as a basis for advanced automation of the workflow and presentation of the results.
Numerical Investigation of the Influence of the Wake of Wind Turbines with Different Scales Based on OpenFOAM
Authors: Tian Wenxin , Tie Hao , Ke Shitang , Wan Jiawei , Zhao Xiuyong , Zhao Yuze , Zhang Lidong , Wang Sheng
Published: September 2022

The wake of a wind turbine has an important influence on the output power of wind farms. Staggered height layout is an emerging method for the layout optimization of wind farms. In order to study the effect of a staggered height layout on the overall power output of wind farms in depth, we established a combination of two large wind turbines and three small wind turbines arranged laterally between the two large wind turbines, and set four working conditions with different distances between the small wind turbines and the downstream large wind turbines as the research objects. The wind turbine array is analyzed by numerical simulation The layouts add three small wind turbines between the two large wind turbines, and each row of small wind turbines has a different distance from the downstream large wind turbines. The results show that as the distance from the upstream large wind turbine increases, the power of the three small wind turbines on the downstream wind turbine tends to be positive. The numerical simulation suggests that under the inflow wind speed, the closer to the downstream large wind turbine, the higher the wind speed is at the hub height.
Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
Authors: He Yicheng , Yang Kai , Wang Xiaoqing , Huang Haisong , Chen Jiadui
Published: September 2022

In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs.
The Role of Soil Structure Interaction on the Seismic Resilience of Isolated Structures
Authors: Forcellini Davide
Published: September 2022

Resilience has become an interesting parameter to assess the seismic risk connected with functionality of structures. In this regard, losses due to earthquakes may be significantly reduced by applying isolation at the base of the structures. However, design of isolation needs to consider the effects of soil deformability and all the connected effects of Soil Structure Interaction (SSI). In particular, soil deformability may reduce significantly the benefit of base isolation and thus the computation of resilience needs to consider such conditions. This paper aims to consider the issue by considering several isolated configurations on different soil conditions and for each of them, the seismic resilience has been computed. Numerical simulations have been performed in order to calculate the resilience of the various configurations and then this parameter was chosen a reference for comparing the isolation models on different soil conditions.
A New Imbalanced Encrypted Traffic Classification Model Based on CBAM and Re-Weighted Loss Function
Authors: Qin Jiayu , Liu Guangjie , Duan Kun
Published: September 2022

The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. In this paper, we propose a new imbalanced encrypted traffic classification model. The proposed model is based on the improved convolutional block attention module (CBAM) and re-weighted cross-entropy focal loss (CEFL) function. The model exploits the redefined imbalance degree to construct a weight function, which is used to reassign the weights of the categories. The improved CBAM based on the redefined imbalance degree can make the model pay more attention to the characteristics of the minority samples, and increase the representation ability of these samples. The re-weighted CEFL loss function can be used to expand the effective loss gap between minority and majority samples. The method is validated on the public ISCX Tor 2016 dataset. The experimental results show that the performance of the new classification model is better than the baseline methods, and the proposed method can remarkably push the precision of the minority categories to 93.28% (14.63%↑), recall to 91.71% (16.98%↑), and F1 score to 92.49% (16.23%↑).
New Perspectives in Chemical and Functional Properties of Natural Products
Authors: Leszczyńska Teresa , Kapusta-Duch Joanna , Piątkowska Ewa
Published: September 2022

Natural, i.e., unprocessed products are becoming more and more popular amongconsumers and food producers [...]
DHT-Based Blockchain Dual-Sharding Storage Extension Mechanism
Authors: Zhao Jindong , Zhang Donghong , Liu Wenxuan , Qiu Xiuqin , Brusic Vladimir
Published: September 2022

The expansion of blockchain storage has become a major problem limiting the application of blockchain. From the perspective of improving the scalability of blockchain storage, a DHT (distributed hash table)-based blockchain dual-sharding storage extension mechanism (DBDSM) is proposed. The nodes in the network are divided into m DHT clusters. Each cluster includes n nodes, and stores 1/m of the transaction data, and the nodes within each cluster store part of data allocated to that cluster. In this way, node storage pressure is alleviated. Furthermore, a hybrid query mechanism has been designed to achieve efficient querying of transaction data, without changing the original state data query. Simulation results showed without changing the original state data query, that the storage space consumed by the nodes is only s/(m × n) of that used in the traditional method; when the number of faulty nodes in the cluster does not exceed s − 1, the integrity of blockchain data can still be ensured. For transaction data queries, the average number of hops was 1.99, greatly improving query efficiency in the sharded state.
Menhat Helmy and the Emergence of Egyptian Women Art Teachers and Artists in the 1950s
Authors: Kane Patrick Matthew
Published: September 2022

The rise of Egyptian women artists and art teachers at the end of the 1940s appeared in tandem with an active women’s movement that asserted the agency of women in modern Egyptian public life. In this article, we discuss the art career of Menhat Helmy (1925–2004), a 1949 arts graduate of the ma`had al-ali li-ma`lumat al-funun al-jamila (Higher Institute for Women Teachers of the Fine Arts), located in the working-class district of Bulaq in Cairo, and who was among the first Egyptian graduates of the Slade School of Art in London. In a series of etchings executed from around 1956 and through the 1960s, Helmy produced a visual commentary on the dignity of Bulaq’s residents, with emphasis on the active presence of women in its neighborhood and public spaces. Helmy may be viewed in context with the feminism of her fellow women artists, including Gazbia Sirry (1925–2021) and Inji Efflatoun (1924–1986), and in relation to Efflatoun’s two books on feminist causes. As new professional artists and teachers, they advocated the promotion of education and vocational choice for women. Helmy’s choice of this neighborhood as a subject for art allows a comparison to theories about Bulaq’s development and its locus for the arts for which a multidisciplinary approach is required.
Electron Impact Ionization of Adenine: Partial Cross Sections
Authors: Rehman Mohammad Atiqur , Krishnakumar E.
Published: September 2022

Electron ionization of a genetically important nucleobase, adenine, was investigated from threshold to 500 eV using crossed electron beam–effusive molecular beam geometry and time-of-flight mass spectrometry. We measured the complete set of absolute partial cross sections for adenine using the relative flow technique (RFT) up to an electron energy of 500 eV. Normalization to absolute values was performed using electron ionization cross sections for argon and the vapor pressure data of adenine. The total cross sections obtained by summing the partial cross sections were compared with the existing theoretical and experimental data. The appearance energies of various fragment ions were also measured and compared with the reported data. The prominence of ions with mass (HCN)n+ (n = 1 to 5) indicated a possible pathway to form adenine in the interstellar medium through aggregation of HCN units. Analysis of the partial cross sections for various groups of fragment ions as a function of electron energy was found to give insights into their composition.
Decision Optimization for Water and Electricity Shared Resources Based on Fusion Swarm Intelligence
Authors: Yang Xiaohua , Yang Hao , Bao Jing , Shen Xin , Yan Rong , Pan Nan
Published: September 2022

As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation.
Coefficient Inequalities for Multivalent Janowski Type q-Starlike Functions Involving Certain Conic Domains
Authors: Ur Rehman Muhammad Sabil , Ahmad Qazi Zahoor , Al-shbeil Isra , Ahmad Sarfraz , Khan Ajmal , Khan Bilal , Gong Jianhua
Published: September 2022

In the current work, by using the familiar q-calculus, first, we study certain generalized conic-type regions. We then introduce and study a subclass of the multivalent q-starlike functions that map the open unit disk into the generalized conic domain. Next, we study potentially effective outcomes such as sufficient restrictions and the Fekete–Szegö type inequalities. We attain lower bounds for the ratio of a good few functions related to this lately established class and sequences of the partial sums. Furthermore, we acquire a number of attributes of the corresponding class of q-starlike functions having negative Taylor–Maclaurin coefficients, including distortion theorems. Moreover, various important corollaries are carried out. The new explorations appear to be in line with a good few prior commissions and the current area of our recent investigation.
A Molecular-Based Q-Tensor Hydrodynamic Theory of Smectic Liquid Crystals
Authors: Feng Xinxin , Li Sirui
Published: September 2022

The Doi–Onsager molecular theory is capable of providing a rather accurate description of the local behavior of molecules; however, its computation is extremely time-consuming, since some higher-dimensional variables are typically involved. Therefore, establishing a computable reduced model that can capture essential physical properties is an important issue. In this work, we derived a reduced Q-tensor hydrodynamic theory that described smectic phases with density variations from the Doi–Onsager molecular theory using the Bingham closure approximation. The coefficients in the tensor model were derived from those in the molecular model. The energy dissipation law was inherited from the tensor model. Some special cases for the model were also discussed.
On Robust Global Error Bounds for a Class of Uncertain Piecewise Linear Inequality Systems
Authors: Tan Wen , Guo Xiaole , Sun Xiangkai
Published: September 2022

This paper is concerned with the radius of robust global error bounds for an uncertain piecewise linear inequality system where the uncertain data are assumed to be in polytope uncertain sets. We first present a dual characterization for robust global error bounds of this uncertain piecewise linear inequality system. Then, we establish upper and lower bounds for the radius of robust global error bounds of the system of uncertain piecewise linear inequalities in terms of the Minkowski function generalized by the polytope uncertain sets. Moreover, we also investigate robust global error bounds for this uncertain piecewise linear inequality system when the uncertain polytope sets are symmetric sets.
Factor Prioritization for Effectively Implementing DevOps in Software Development Organizations: A SWOT-AHP Approach
Authors: Noorani Noor , Zamani Abu , Alenezi Mamdouh , Shameem Mohammad , Singh Priyanka
Published: September 2022

DevOps (development and operations) is a collective and multidisciplinary organizational effort used by many software development organizations to build high-quality software on schedule and within budget. Implementing DevOps is challenging to implement in software organizations. The DevOps literature is far away from providing a guideline for effectively implementing DevOps in software organizations. This study is conducted with the aim to develop a readiness model by investigating the DevOps-related factors that could positively or negatively impact DevOps activities in the software industry. The identified factors are further categorized based on the internal and external aspects of the organization, using the SWOT (strengths, weaknesses, opportunities, threats) framework. This research work is conducted in three different phases: (1) investigating the factors, (2) categorizing the factors using the SWOT framework, and finally, (3) developing an analytic hierarchy process (AHP)-based readiness model of DevOps factors for use in software organizations. The findings would provide a readiness model based on the SWOT framework. The proposed framework could provide a roadmap for organizations in the software development industry to evaluate and improve their implementation approaches to implement a DevOps process.
Identifying Stock Prices Using an Advanced Hybrid ARIMA-Based Model: A Case of Games Catalogs
Authors: Chen You-Shyang , Chou Chih-Lung (Jerome) , Lee Yau-Jung (Mike) , Chen Su-Fen , Hsiao Wen-Ju
Published: September 2022

At the beginning of 2020, the COVID-19 pandemic struck the world, affecting the pace of life and the economic behavioral patterns of people around the world, with an impact exceeding that of the 2008 financial crisis, causing a global stock market crash and even the first recorded negative oil prices. Under the impact of this pandemic, due to the global large-scale quarantine and lockdown measures, game stocks belonging to the stay-at-home economy have become the focus of investors from all over the world. Therefore, under such incentives, this study aims to construct a set of effective prediction models for the price of game stocks, which could help relevant stakeholders—especially investors—to make efficient predictions so as to achieve a profitable investment niche. Moreover, because stock prices have the characteristics of a time series, and based on the relevant discussion in the literature, we know that ARIMA (the autoregressive integrated moving average) prediction models have excellent prediction performance. In conclusion, this study aims to establish an advanced hybrid model based on ARIMA as an excellent prediction technology for the price of game stocks, and to construct four groups of different investment strategies to determine which technical models of investment strategies are suitable for different game stocks. There are six important directions, experimental results, and research findings in the construction of advanced models: (1) In terms of the experiment, the data are collected from the daily closing prices of game-related stocks on the Taiwan Stock Exchange, and the sample range is from 2014 to 2020. (2) In terms of the performance verification, the return on investment is used as the evaluation standard to verify the availability of the ARIMA prediction model. (3) In terms of the research results, the accuracy of the model in predicting the prices of listed stocks can reach the 95% confidence interval predicted by the model 14 days after the closing price, and the OTC stocks fall within the 95% confidence interval for 3 days. (4) In terms of the empirical study of the rate of return, the investors can obtain a better rate of return than the benchmark strategy by trading the game stocks based on the indices set by the ARIMA model in this study. (5) In terms of the research findings, this study further compares the rate of return of trading strategies with reference to the ARIMA index and the rate of return of trading strategies with reference to the monitoring indicator, finding no significant difference between the two. (6) Different game stocks apply for different technical models of investment strategies.
Regularization Cuckoo Search Algorithm for Multi-Parameter Optimization of the Multi-Laminated Controlled Release System
Authors: Zhang Xinming , Li Yi , Fan Youhua
Published: September 2022

The multi-laminated controlled release system has been widely used in the modern pharmaceutical industry because of its simple structure and safety, which can control the drug release behavior by regulating its internal parameters. To achieve three desired drug release behaviors of constant rate, linear reduction, and nonlinearity, which can be controlled by the optimization of the controllable parameters in the controlled release system, such as initial concentration and diffusion coefficient, a new regularization cuckoo search algorithm based on the cuckoo search algorithm and Tikhonov regularization method is proposed. The numerical examples confirm the stability, effectiveness, and feasibility of the proposed method. The effect of the number of model layers on the optimization results is also analyzed.
Quadrature Rules for the Fm-Transform Polynomial Components
Authors: Perfilieva Irina , Pham Tam , Ferbas Petr
Published: September 2022

The purpose of this paper is to reduce the complexity of computing the components of the integral Fm-transform, m≥0, whose analytic expressions include definite integrals. We propose to use nontrivial quadrature rules with nonuniformly distributed integration points instead of the widely used Newton–Cotes formulas. As the weight function that determines orthogonality, we choose the generating function of the fuzzy partition associated with the Fm-transform. Taking into account this fact and the fact of exact integration of orthogonal polynomials, we obtain exact analytic expressions for the denominators of the components of the Fm-transformation and their approximate analytic expressions, which include only elementary arithmetic operations. This allows us to effectively estimate the components of the Fm-transformation for 0≤m≤3. As a side result, we obtain a new method of numerical integration, which can be recommended not only for continuous functions, but also for strongly oscillating functions. The advantage of the proposed calculation method is shown by examples.