bluuebunny/crossref_metadata_2025 huggingface dataset:
Settlements and Subgrade Reactions of Surface Raft Foundations Subjected to Vertically Uniform Load
Authors: Chang Der-Wen , Lu Chih-Wei , Tu Yu-Jhang , Cheng Shih-Hao
Published: May 2022

The settlements and corresponding soil reactions of a surface raft foundation subjected to vertically uniform load at the sites of granular or cohesive soils have been qualitatively introduced to engineers in the past. This study intends to verify the foundation load-response mechanism using three-dimensional finite-element analyses. The Mohr–Coulomb soil model was used to simulate the nonlinear effects of the granular and cohesive soils. The coefficients of subgrade reactions of the soil were back-calculated and compared to those obtained from the rigid foundation model. For all the square surface rafts studied, non-uniform settlements with the largest value at the center can be found. For the smallest rafts located in clay, the soil reactions were found to be rather consistent. For larger rafts in clay, the soil reactions would be changed according to the soil stiffness and raft dimensions. For surface rafts in sand, the effects of soil stiffness on soil reactions appeared to be insignificant. The soil reactions were generally higher at the center and varied with raft dimensions. For the smallest raft in sand, large soil reactions can be found at the edge. The coefficients of subgrade reactions were found to be more consistent with the smallest values occurring at the center. The results of this study indicate that the load-response mechanism of the raft foundation is rather complicated. Such load-response mechanisms are strongly affected by the relative rigidity of the foundation depending on soil stiffness and foundation dimensions.
Prediction of EPB Shield Tunneling Advance Rate in Mixed Ground Condition Using Optimized BPNN Model
Authors: Fu Xuesong , Gong Quanmei , Wu Yaojie , Zhao Yu , Li Hui
Published: May 2022

Tunneling in mixed ground often results in severe torque fluctuations and a low advance rate. Therefore, choosing a reasonable set of parameters for accurate advance rate prediction is paramount to reduce cutter wear and improve tunneling efficiency. However, since the geological parameters in mixed ground conditions are diverse and uncertain, the prediction of the advance rate (AR) of EPB shield tunneling is significantly more difficult than that in homogeneous ground (i.e., full-face hard-rock ground). In addition, the operating parameters of the EPB shield tunneling can be subjective and suboptimal, and each of them has some intricate influence on AR. In this paper, an optimized back-propagation neural network by genetic algorithm (BPNN-GA) was proposed for reasonable operating parameter selection and accurate AR prediction, and four typical machine learning methods were used for comparison. Five processing strategies with different input parameters were also proposed and compared to determine the optimum selection of geological parameters in mixed ground conditions. The proposed models with strategies were adopted in the case study of the Nanjing Metro Line S6 project, and a total of 1188 rings of datasets were used for this study. The results showed that the proposed modified BPNN with the genetic algorithm could be effectively implemented for the AR prediction. It concluded that Strategy B—i.e., using the composite ratio and the geological parameters of each layer as input—was the best strategy in mixed ground conditions for advance rate prediction. Hence, a high correlation between measured and predicted AR was observed in this study with a correlation coefficient (R2) of 0.920.
A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study
Authors: Pierleoni Paola , Raggiunto Sara , Belli Alberto , Paniccia Michele , Bazgir Omid , Palma Lorenzo
Published: May 2022

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.
Nanostructured Na2CaP2O7: A New and Efficient Catalyst for One-Pot Synthesis of 2-Amino-3-Cyanopyridine Derivatives and Evaluation of Their Antibacterial Activity
Authors: Achagar Redouane , Elmakssoudi Abdelhakim , Thoume Abderrahmane , Dakir Mohamed , Elamrani Abdelaziz , Zouheir Yassine , Zahouily Mohamed , Ait-Touchente Zouhair , Jamaleddine Jamal , Chehimi Mohamed M.
Published: May 2022

A facile and novel synthesis of thirteen 2-amino-3-cyanopyridine derivatives 5(a–m) by a one-pot multicomponent reactions (MCRs) is described for the first time, starting from aromatic aldehydes, malononitrile, methyl ketones, or cyclohexanone and ammonium acetate in the presence of the nanostructured diphosphate Na2CaP2O7 (DIPH) at 80 °C under solvent-free conditions. These compounds were brought into existence in a short period with good to outstanding yields (84–94%). The diphosphate Na2CaP2O7 was synthesized and characterized by different techniques (FT-IR, XRD, SEM, and TEM) and used as an efficient, environmentally friendly, easy-to-handle, harmless, secure, and reusable catalyst. Our study was strengthened by combining five new pyrido[2,3-d]pyrimidine derivatives 6(b, c, g, h, j) by intermolecular cyclization of 2-amino-3-cyanopyridines 5(b, c, g, h, j) with formamide. The synthesized products were characterized by FT-IR, 1H NMR, and 13C NMR and by comparing measured melting points with known values reported in the literature. Gas chromatography/mass spectrometry was used to characterize the newly synthesized products and evaluate their purity. The operating conditions were optimized using a model reaction in which the catalyst amount, temperature, time, and solvent effect were evaluated. Antibacterial activity was tested against approved Gram-positive and Gram-negative strains for previously mentioned compounds.
Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
Authors: Kompella Gayatri , Singarayan Jeevakala , Antico Maria , Sasazawa Fumio , Yu Takeda , Ram Keerthi , Pandey Ajay K. , Fontanarosa Davide , Sivaprakasam Mohanasankar
Published: May 2022

Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34∘ and an average registration error of 3.45 mm and 7.76∘ is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77∘ is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.
Numerical and Experimental Validation of a Supersonic Mixing Layer Facility
Authors: Li Yudong , Chen Li , Li Hongxun , Wu Yungang , Chen Shuang
Published: May 2022

The design of a supersonic-supersonic mixing layer facility was motivated by the need for a benchmark experimental platform to study the physical phenomena underlying supersonic mixing layers. The facility is an intermittent blowdown wind tunnel characterized by a two-stream design separated by a splitter plate in the middle of the nozzle. The splitter plate ends exactly at the start of the mixing layer test section. The Mach number of the primary stream is M1 = 3 for all nozzles and the secondary streams are M2 = 2, 2.5, and 2.9 to generate different convective Mach numbers of Mc = 0.25, 0.10, and 0.01, respectively. The facility was calibrated by pressure measurements to verify the Mach number and the pressure distribution in the streamwise direction. Large-eddy simulation (LES) was performed to illustrate a full view of the turbulent compressible mixing layer flow field and to compare this against the experimental data. Optical diagnosis measurements, i.e., femtosecond laser-induced electronic excitation tagging velocimetry (FLEET) for velocity measurement and focused laser differential interferometer (FLDI) for the density fluctuation, were also performed on the facility.
The Effect on the Germination Vigour of Cucumber Seeds after Receiving Magnetic Field Treatment Pre-Sowing
Authors: Pentoś Katarzyna , Wondołowska-Grabowska Anna , Gajda Grzegorz , Babij Michał , Chohura Piotr , Zaleski Andrzej , Szpunar-Krok Ewa , Jobczyk Weronika , Romaniuk Anastazja , Gajda Daniel
Published: May 2022

In the experiment, the impact of magnetisation on cucumber seeds is examined with the use of Bitter magnets with a constant magnetic field. The magnetisation process is performed in three magnetic fields: low—200 mT, medium—1 T, and high—9 T for 15 and 60 min. After germination, the biometric parameters are determined. The results of this research show that cucumber after pre-treatment in a magnetic field of 1 T for 60 min has a similar germination capacity and root length as the control sample. However, cucumber seeds magnetised in a 1 T field for 60 min have a significantly higher dry weight than the control sample (5.50 ± 0.32 mg vs. 3.01 ± 0.18 mg). The magnetisation in 9 T for both 15 and 60 min shows that these samples have a significantly lower germination capacity (86.8 ± 4.4% and 81.4 ± 7.3% vs. 91.8 a ± 3.2%) and root length (1.78 ± 0.02 cm and 4.42 ± 0.83 cm vs. 8.21 ± 0.34 cm) compared to the control sample. The cucumber seeds pre-treated at 9 T have a significantly greater dry weight than the control sample. Additionally, our research shows that some magnetic field intensities and magnetisation durations inhibit root growth and limit germination. These results are also important as they indicate which values of magnetic fields should be avoided.
Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images
Authors: Vaiyapuri Thavavel , Liyakathunisa  , Alaskar Haya , Parvathi Ramasubramanian , Pattabiraman Venkatasubbu , Hussain Abir
Published: May 2022

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.
The Novel Role of Solvents in Non-Surgical Endodontic Retreatment
Authors: Ferreira Inês , Pina-Vaz Irene
Published: May 2022

Non-surgical endodontic retreatment is a reliable conservative option for managing post-treatment apical periodontitis. However, effective microbial control, based on the maximization of filling removal and disinfection protocols, is not yet predictable. Traditional gutta-percha solvents, which are indistinctively used for both the core and sealer filling materials, became obsolete due to unprecedented advances in endodontic technology. Nonetheless, microtomography, scanning electronic microscopy findings, and histobacteriological analysis tend to confirm the persistence of filling materials and the lack of association between root canal enlargement and superior disinfection. There is a controversy regarding the most suitable clinical protocols surrounding the shaping procedures and the supplementary disinfection steps. Based on the literature and the previous work of the team, the authors aimed to summarize the current knowledge regarding specific solvent formulations that target filling materials. Additionally, the advantage of an additional irrigation step to optimize disinfection was highlighted. This adjunctive procedure serves a dual role in the dissolution of filling materials, and in conferring an antibiofilm effect. Further research is needed to understand the novel contribution of these strategies upon clinical practice outcomes.
Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
Authors: Kim Jin-Chul , Kim Min-Hyun , Suh Han-Enul , Naseem Muhammad Tahir , Lee Chan-Su
Published: May 2022

Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination of convolutional neural networks (CNNs) and a support vector machine (SVM) classifier using dynamic facial expression data. In order to extract facial motion characteristics, dense facial motion flows and geometry landmark flows of facial expression sequences were used as inputs to the CNN and SVM classifier, respectively. CNN architectures for facial expression recognition from dense facial motion flows were proposed. The optimal weighting combination of the hybrid classifiers provides better facial expression recognition results than individual classifiers. The system has successfully classified seven facial expressions signalling anger, contempt, disgust, fear, happiness, sadness and surprise classes for the CK+ database, and facial expressions of anger, disgust, fear, happiness, sadness and surprise for the BU4D database. The recognition performance of the proposed system is 99.69% for the CK+ database and 94.69% for the BU4D database. The proposed method shows state-of-the-art results for the CK+ database and is proven to be effective for the BU4D database when compared with the previous schemes.
A Novel Speckle-Tracking Echocardiography Derived Parameter That Predicts Clinical Worsening in Children with Pulmonary Arterial Hypertension
Authors: Muntean Iolanda , Melinte Mihaela , Făgărășan Amalia , Șuteu Carmen Corina , Togănel Rodica
Published: May 2022

Pulmonary arterial hypertension is a severe, progressive disease in children, that causes right ventricular dysfunction over time. Tissue motion annular displacement is a novel speckle-tracking derived echocardiographic parameter used in assessing ventricular function. The aim of our study was to determine the prognostic value of this echocardiographic parameter in children with pulmonary arterial hypertension. We conducted a case-control study by assessing twenty children with pulmonary arterial hypertension (idiopathic or secondary) and twenty age- and sex-matched controls, using clinical (WHO functional class, 6-min walking test), laboratory (brain natriuretic peptide level) and echocardiographic parameters (conventional and speckle-tracking derived tissue motion annular displacement) at enrolment and after one year of follow-up. According to their WHO functional class altering after one year, the pulmonary arterial hypertension patients were divided into two groups: non-worsening (eleven) and worsening (nine). The conventional echocardiographic parameters and all measured tricuspid tissue motion annular displacement indices (lateral, septal, midpoint and midpoint fractional displacement—TMADm%) were significantly lower in both pulmonary arterial hypertension groups (non-worsening and worsening) compared to controls. Comparing the worsening and non-worsening groups, only the TMADm% and brain natriuretic peptide level was significantly lower in worsening in comparison with non-worsening pulmonary arterial hypertension children (p = 0.010 and p = 0.018, respectively). In receiver-operating characteristic curve analysis, we found a cut-off value of 16.15% for TMADm% and a cut-off value of 34.35 pg/mL for the brain natriuretic peptide level that can predict worsening in pulmonary arterial hypertension children. In conclusion, tricuspid annulus midpoint fractional displacement, an angle-dependent speckle-tracking derived parameter, could be a good additional parameter in the assessment of the longitudinal right ventricular systolic function and in prediction of clinical worsening in children with pulmonary arterial hypertension.
Mathematical Description of Changes of Dried Apple Characteristics during Their Rehydration
Authors: Górnicki Krzysztof , Kaleta Agnieszka , Kosiorek Krzysztof
Published: May 2022

The mathematical description of changes of dried apples characteristics (mass gain, volume increase, dry matter loss, rehydration indices, and colour) during their rehydration was performed. The effect of conditions of both processes on model parameters were also considered. Apple slices (3 and 10 mm) and cubes (10 mm) were dried in natural convection (drying air velocity 0.01 m/s), forced convection (0.5 and 2 m/s), and fluidisation (6 m/s). Drying air temperatures (Td) were equal to 50, 60, and 70 °C. The rehydration process was carried out in distilled water at the temperatures (Tr) of 20, 45, 70, and 95 °C. Mass gain, volume increase, and dry matter loss were modelled using the following empirical models: Peleg, Pilosof–Boquet–Batholomai, Singh and Kulshrestha, Lewis (Newton), Henderson–Pabis, Page, and modified Page. Colour changes were described through applying the first-order model. Artificial neural networks (feedforward multilayer perceptron) were applied to make the rehydration indices and colour variations (ΔE) dependent on characteristic dimension, Td, drying air velocity, and Tr. The Page and the modified Page models can be considered to be the most appropriate in order to characterise the mass gain (RMSE = 0.0143–0.0619) and the volume increase (RMSE = 0.0142–0.1130), whereas the Peleg, Pilosof–Bouquet–Batholomai, and Singh and Kulshrestha models were found to be the most appropriate to characterise dry matter loss (RMSE = 0.0116–0.0454). The ANNs described rehydration indices and ΔE satisfactorily (RMSE = 0.0567–0.0802). Both considered process conditions influenced (although in different degree) the changes of the considered dried apple characteristics during their rehydration.
Specific Emitter Identification Based on Ensemble Neural Network and Signal Graph
Authors: Xing Chenjie , Zhou Yuan , Peng Yinan , Hao Jieke , Li Shuoshi
Published: May 2022

Specific emitter identification (SEI) is a technology for extracting fingerprint features from a signal and identifying the emitter. In this paper, the author proposes an SEI method based on ensemble neural networks (ENN) and signal graphs, with the following innovations: First, a signal graph is used to show signal data in a non-Euclidean space. Namely, sequence signal data is constructed into a signal graph to transform the sequence signal from a Euclidian space to a non-Euclidean space. Hence, the graph feature (the feature of the non-Euclidean space) of the signal can be extracted from the signal graph. Second, the ensemble neural network is integrated with a graph feature extractor and a sequence feature extractor, making it available to extract both graph and sequence simultaneously. This ensemble neural network also fuses graph features with sequence features, obtaining an ensemble feature that has both features in Euclidean space and non-Euclidean space. Therefore, the ensemble feature contains more effective information for the identification of the emitter. The study results demonstrate that this SEI method has higher SEI accuracy and robustness than traditional machine learning methods and common deep learning methods.
Energy Hybridization with Combined Heat and Power Technologies in Supercritical Water Gasification Processes
Authors: Benjumea José Manuel , Jiménez-Espadafor Francisco José , López-Guirao Francisco , Gutiérrez Ortiz Francisco Javier
Published: May 2022

Supercritical water gasification (SCWG) has been shown to be an effective technology to valorize a wide range of organic waste by transforming them into gases with high energy potential, such as hydrogen and methane. However, the industrial implementation of these processes is rarely extended due to the huge energy requirements during plant start-up and operation. The purpose of this study is to explore feasible ways of energy integration by hybridizing SCWG processes with combined heat and power technologies, such as exhaust gases coming from (i) internal combustion engines or (ii) gas turbines. The analysis focuses on energy consumption with the aim of optimizing the operation and design of plants. System configurations are simulated with Aspen Plus considering data from the literature for the gasification of glycerol and using typical plant capacities on an industrial scale. Results show the thermal power required in heat exchangers and the electricity generation from residual energy in hot effluents as a needed step to optimize the plant configuration and boost energy synergies with other technologies.
Study of the Lateral Bearing Capacity and Optimization Reinforcement Scheme of an Open Caisson with Consideration of Soil Disturbance
Authors: Wang An-Hui , Zhang Yan-Fang , Xia Fan , Luo Ru-Ping , Wang Ning
Published: May 2022

The bearing capacity of an open caisson under lateral loads is a key factor affecting the normal operation of an open caisson. It will inevitably have a disturbing effect on the surrounding soil layers during the sinking process of the caisson; that is, a disturbance ring will develop around the caisson. Based on the Jurong Yangtze River water supply project, the lateral bearing characteristics of the open caisson are analyzed by the numerical method with due consideration to soil disturbance, and the reinforcement scheme is optimized. The numerical results show that when the thickness of the disturbance ring is less than 0.5 m the disturbance ring has little effect on the lateral bearing capacity of the open caisson. An arc-shaped cement–soil reinforcement at the loading area can effectively improve the lateral bearing performance of the caisson. The optimized reinforcement thickness is 2 times that of the disturbance ring, and the reinforcement angle is approximately 60°.
A Systematic Review and Analysis of Intelligence-Based Pathfinding Algorithms in the Field of Video Games
Authors: Lawande Sharmad Rajnish , Jasmine Graceline , Anbarasi Jani , Izhar Lila Iznita
Published: May 2022

This paper provides a performance comparison of different pathfinding Algorithms used in video games. The Algorithms have been classified into three categories: informed, uninformed, and metaheuristic. Both a practical and a theoretical approach have been adopted in this paper. The practical approach involved the implementation of specific Algorithms such as Dijkstra’s, A-star, Breadth First Search, and Greedy Best First. The comparison of these Algorithms is based on different criteria including execution time, total number of iterations, shortest path length, and grid size. For the theoretical approach, information was collected from various papers to compare other Algorithms with the implemented ones. The Unity game engine was used in implementing the Algorithms. The environment used was a two-dimensional grid system.
IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications
Authors: Ali Redha , Hardie Russell C. , Narayanan Barath Narayanan , Kebede Temesguen M.
Published: May 2022

Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets which are capable of generating salient features for a particular problem. Then, we build up ever larger and more powerful networks by combining these SubNets in different configurations. At each stage, only one new SubNet module undergoes learning updates. This reduces the computational resource requirements for training and aids in network optimization. We compare IMNets against classic and state-of-the-art deep learning architectures such as AlexNet, ResNet-50, Inception v3, DenseNet-201, and NasNet for the various experiments conducted in this study. Our proposed IMNS design leads to high average classification accuracies of 97.0%, 97.9%, and 88.6% for malaria, diabetic retinopathy, and tuberculosis, respectively. Our modular design for deep learning achieves the state-of-the-art performance in the scenarios tested. The IMNets produced here have a relatively low computational complexity compared to traditional deep learning architectures. The largest IMNet tested here has 0.95 M of the learnable parameters and 0.08 G of the floating-point multiply–add (MAdd) operations. The simpler IMNets train faster, have lower memory requirements, and process images faster than the benchmark methods tested.
LiverNet: Diagnosis of Liver Tumors in Human CT Images
Authors: Alawneh Khaled , Alquran Hiam , Alsalatie Mohammed , Mustafa Wan Azani , Al-Issa Yazan , Alqudah Amin , Badarneh Alaa
Published: May 2022

Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant. The 3D segmented liver from the LiTS17 dataset is passed through a Convolutional Neural Network (CNN) to detect and classify the existing tumors as benign or malignant. In this work, we propose a novel light CNN with eight layers and just one conventional layer to classify the segmented liver. This proposed model is utilized in two different tracks; the first track uses deep learning classification and achieves a 95.6% accuracy. Meanwhile, the second track uses the automatically extracted features together with a Support Vector Machine (SVM) classifier and achieves 100% accuracy. The proposed network is light, fast, reliable, and accurate. It can be exploited by an oncological specialist, which will make the diagnosis a simple task. Furthermore, the proposed network achieves high accuracy without the curation of images, which will reduce time and cost.
A Scalable Deep Network for Graph Clustering via Personalized PageRank
Authors: Zhao Yulin , Li Xunkai , Zhu Yinlin , Li Jin , Wang Shuo , Jiang Bin
Published: May 2022

Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.
Technological Parameters of Rotating Electrochemical and Electrobiological Disk Contactors Depending on the Effluent Quality Requirements
Authors: Rodziewicz Joanna , Mielcarek Artur , Janczukowicz Wojciech , Bryszewski Kamil , Jabłońska-Trypuć Agata , Wydro Urszula
Published: May 2022

Soilless tomato cultivation wastewater, with typically low COD, high concentrations of phosphorus, and oxidized forms of nitrogen, may be effectively treated in a rotating electrochemical disk contactor (RECDC) and in a bioelectrochemical reactor (BER), such as a rotating electrobiological disk contactor (REBDC). The aim of this study was to determine the technological parameters of both reactors, i.e., electric current density (J) and hydraulic retention time (HRT), depending on the effluent quality requirements. The study was conducted with four one-stage RECDCs and with four one-stage REBDCs, at four hydraulic retention times, i.e., 4, 8, 12, and 24 h, and electric current densities of 0.63, 1.25, 2.50, 5.00, and 10.00 A/m2. It was demonstrated that soilless tomato cultivation wastewater could be effectively treated in electrochemical and electrobiological disk contactors, and then discharged to sewage system facilities. In a RECDC, the highest denitrification (53.4%) and dephosphatation (99.8%) performance was achieved at J = 10.0 A/m2 and HRT = 24 h. If the effluents are to be discharged to natural reservoirs, their effective treatment is only feasible in a REBDC. The bioelectrochemical disk contactor ensured over 90% dephosphatation effectiveness. At HRT = 24 h and all electric current densities studied, the concentrations of pollutants in the effluent met requirements set for industrial wastewater discharged into natural waters and the ground. By applying J = 2.5 A/m2 and HRT = 24 h in the REBDC, it was possible to achieve a phosphorus concentration below 3.0 mg P/L and concentrations of ammonia nitrogen and nitrites lower than the permissible levels for treated industrial wastewater introduced to waters and to the ground. Given the nitrate concentration (exceeding 30 mg N/L), an external carbon source is recommended to aid a treatment process that uses a technological system with a REBDC. Technological schemes were proposed for wastewater treatment plants (WWTPs) with a RECDC and a REBDC, for discharging treated wastewater to natural waters, the ground, and sewage systems.
Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones
Authors: Thanh Mai Thi Giang , Van Toan Ngo , Ngoc Vo Truong Nhu , Tra Nguyen Thu , Giap Cu Nguyen , Nguyen Duc Minh
Published: May 2022

A mobile-phone-based diagnostic tool, which most of the population can easily access, could be a game changer in increasing the number of examinations of people with dental caries. This study aimed to apply a deep learning algorithm in diagnosing the stages of smooth surface caries via smartphone images. Materials and methods: A training dataset consisting of 1902 photos of the smooth surface of teeth taken with an iPhone 7 from 695 people was used. Four deep learning models, consisting of Faster Region-Based Convolutional Neural Networks (Faster R-CNNs), You Only Look Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD), were tested to detect initial caries lesions and cavities. The reference standard was the diagnosis of a dentist based on image examination according to the International Caries Classification and Management System (ICCMS) classification. Results: For cavitated caries, YOLOv3 and Faster R-CNN showed the highest sensitivity among the four tested models, at 87.4% and 71.4%, respectively. The sensitivity levels of these two models were only 36.9 % and 26% for visually non-cavitated (VNC). The specificity of the four models reached above 86% for cavitated caries and above 71% for VNC. Conclusion: The clinical application of YOLOv3 and Faster R-CNN models for diagnosing dental caries via smartphone images was promising. The current study provides a preliminary insight into the potential translation of AI from the laboratory to clinical practice.
Improving Particle Swarm Optimization Analysis Using Differential Models
Authors: Hsiao Sung-Jung , Sung Wen-Tsai
Published: May 2022

This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm’s performance can be greatly improved by increasing the step size parameter and using different transformation methods. This analytical method improves the performance of the PSO algorithm, and it is a feasible idea. This paper uses simple analysis to find that many algorithms are improved by using the difference model. Through simple analysis, this paper finds that many AI-related algorithms have been improved by using differential models. The PSO algorithm can be regarded as the social behavior of biological groups such as birds foraging and fish swimming. Therefore, these behaviors described above are an ongoing process and are more suitable for using differential models to improve the analysis of PSO. The simulation results of the experiment show that the differential evolution PSO algorithm based on the Runge–Kutta method can effectively avoid premature results and improve the computational efficiency of the algorithm. This research analyzes the influence of the differential model on the performance of PSO under different differenced conditions. Finally, the analytical results of the differential equation model of this paper also provide a new analytical solution.
Cycle Mutation: Evolving Permutations via Cycle Induction
Authors: Cicirello Vincent A.
Published: May 2022

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation operator whose inspiration is the well-known cycle crossover operator, and the concept of a permutation cycle. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best. As a prerequisite, we develop new permutation distance measures: cycle distance, k-cycle distance, and cycle edit distance. The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. We experimentally validate these findings showing cycle mutation’s strengths on problems such as QAP and LCS, and its limitations on problems such as the TSP, while also showing that it is less prone to local optima than commonly used alternatives. We integrate cycle mutation into the open source Chips-n-Salsa library, and the new distance metrics into the open source JavaPermutationTools library.
Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
Authors: Söylemez Mehmet , Tekinerdogan Bedir , Kolukısa Tarhan Ayça
Published: May 2022

Microservice architecture (MSA) is an architectural style for distributed software systems, which promotes the use of fine-grained services with their own lifecycles. Several benefits of MSA have been reported in the literature, including increased modularity, flexible configuration, easier development, easier maintenance, and increased productivity. On the other hand, the adoption of MSA for a specific software system is not trivial and a number of challenges have been reported in the literature. These challenges should be evaluated carefully concerning project requirements before successful MSA adoption. Unfortunately, there has been no attempt to systematically review and categorize these challenges and the potential solution directions. This article aims at identifying the state of the art of MSA and describing the challenges in applying MSA together with the identified solution directions. A systematic literature review (SLR) is performed using the published literature since the introduction of MSA in 2014. Overall, 3842 papers were discovered using a well-planned review protocol, and 85 of them were selected as primary studies and analyzed regarding research questions. Nine basic categories of challenges were identified and detailed into 40 sub-categories, for which potential solution directions were explored. MSA seems feasible, but the identified challenges could impede the expected benefits when not taken into account. This study identifies and synthesizes the reported challenges and solution directions, but further research on these directions is needed to leverage the successful MSA adoption.
Simulation of Coupled Heat-Mass Transfer in Sea Cucumbers with Heat Pump Drying
Authors: Zhao Haibo , Dai Jiaao , Wu Kun
Published: May 2022

To study the heat and moisture transfer behavior in sea cucumbers with heat pump convective drying, a multi-physical field coupled model including the velocity field, temperature field, and dilute matter transfer in the porous media of sea cucumbers is built. The heat pump drying experiments verified the results of numerical simulation. The results show that the moisture content on the dry basis of a sea cucumber decreases from the surface to the inside. The sea cucumber’s surface temperature increases gradually with drying until it reaches an equilibrium temperature, while the time required decreases with the increase in wind speed. The dry basis moisture content at the center of three different samples decreases from the initial values of 17.29, 13.48, and 7.73 to 3.25, 1.80, and 0.66, respectively. Further, the surface temperatures reached 40 °C, 33 °C, and 30 °C, which are the same as the inlet temperature. The simulated results were within 11% of the experimental ones, showing good consistency for both the dry basis moisture contents and temperatures. This research proves that a coupled heat and mass transfer model based on the porous medium theory can well describe the heat and mass transfer characteristics of sea cucumber with heat pump drying. It is also useful for studies on drying process optimization and quality improvement of aquatic products with heat pump drying.
Mine-Microseismic-Signal Recognition Based on LMD–PNN Method
Authors: Li Qiang , Li Yingchun , He Qingyuan
Published: May 2022

The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.
Dynamic Navigation System for Immediate Implant Placement in the Maxillary Aesthetic Region
Authors: Battista Eric , Gasparro Roberta , Cacciola Maria , Sammartino Gilberto , Marenzi Gaetano
Published: May 2022

(1) Background: The achievement of an optimal implant position is still a critical consideration in implantology, especially in the aesthetic area. Dynamic navigation is a computer-aided procedure that allows the surgeon to follow on a screen the three-dimensional position of instruments in real time during implant site preparation. The aim of this proof-of-concept study was to assess the clinical and radiographical performance and accuracy of the surgical workflow during maxillary immediate implant placement assisted by DTX studio Implant software planning and X-Guide Navigation. (2) Methods: Twelve consecutive patients requiring at least one implant in the maxillary aesthetic region were treated. Clinical outcome measures were biological complications and implant or prosthetic success rate. The accuracy was measured by calculating the deviation between the real implant position obtained from the postoperative cone beam computed tomography (CBCT) scan and the planned implant position. (3) Results: The average deviation at the implant shoulder was 0.77 ± 0.25 mm and at the apical point was 1.2 ± 0.61 mm. The depth error was 0.5 ± 0.21 mm. The axis deviation was 2.5 ± 0.41 degrees. No biological complications or implant and prosthetic failures occurred after mean 6-month follow-up. (4) Conclusions: Within the limitations of this study, it seems that the dynamic navigation system for implant placement in the maxillary aesthetic region is accurate for prosthetically driven implant placement.
Mechanisms and Evolution of Cracks in Prestressed Concrete Sleepers Exposed to Time-Dependent Actions
Authors: Li Dan , You Ruilin , Kaewunruen Sakdirat
Published: May 2022

Railway sleepers are an important component of track systems, which can be manufactured by timber, concrete, steel and any other engineered materials. They serve as rail supports and transfer loads from trains to substructures of track systems. In railway networks worldwide, prestressed concrete sleepers are more common than other material types because of their cost-efficacy, environmental friendliness, higher stability and performance, and durability. However, certain types of damage can still appear due to diverse load spectra and aggressive environmental conditions. The causes of cracking observed in prestressed concrete sleepers are usually induced by impact loads. The most affected sections are at the midspan and the rail-seat area of sleepers. Over a long term, time-dependent actions also affect the structural performance of prestressed concrete sleepers. This paper intends to determine the time-dependent crack phenomena in prestressed concrete sleepers under static conditions. Nonlinear finite element method (FEM) has been developed and validated by full-scale experimental tests of prestressed concrete sleepers in accordance with EN13230. In this study, equivalent losses of prestress as a result from time-dependent actions and resultant behaviours are considered. Their influences on crack initiation and propagation in prestressed concrete sleepers have been demonstrated. The results exhibit that the crack simulation can accurately predict the cracking behaviours and the time-dependent behaviour of prestressed concrete sleepers. This insight is critically essential to experimental load rating prediction, that can appropriately estimate the remaining life of aged railway concrete sleepers exposed to time-dependent actions.