bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Development of a Mathematical Model for an Industrial Process, in a System Dynamics Environment
Authors: Barrios Sánchez Jorge Manuel , Baeza Serrato Roberto , Bianchetti Marco
Published: October 2022

This research proposes a methodology based on control engineering, transforming the simulation model of system dynamics into a mathematical model expressed as a system transfer function. The differential equations of a time domain present in the Forrester diagram are transformed into a frequency domain based on the Laplace transform. The conventional control engineering technique is used to present and reduce the dynamic system in a block diagram as a mechanism for determining the structure of the system. The direct path equation and the feedback equation are determined to obtain mathematical models that explain the trajectory of the behavior of each state variable through a transfer function in response to the different inputs of the system. The research proposal is based on presenting an alternative of analytical validation for more robust decision-making to systems dynamics models, based on the explanation of the system structure through a transfer function and its analysis of stability and external controllability for the system dynamics model under study. The results are visually analyzed in a root diagram.
Verification of Fuel Consumption and Carbon Dioxide Emissions under Sulfur Restriction Policy during Oceanographic Navigation
Authors: Shen Hsueh-Chen , Tzu Fu-Ming , Lin Chitsan , Yeh Chin-Ko , Huang Wen-Yen , Pu Han-Pin , Chang Shun-Hsyung
Published: October 2022

The paper presents a comparison of the fuel oil (FO) consumption and carbon dioxide (CO2) emissions of a container ship’s 8000 twenty-foot equivalent unit (TEU) during oceanographic navigation. The evaluation has two types of FOs: a 3.4% heavy fuel oil with desulfurization (HFOWD) and a 0.5% very-low-sulfur fuel oil (VLSFO), based on the sulfur cap policy of the International Maritime Organization (IMO). The results show the average FO consumption at 130 tons/day of HFOWD and 141 tons/day of VLSFO, which means shifting to VLSFO increases fuel consumption 8.4% more than the HFOWD. The average CO2 emissions are 429 tons/day of the HFOWD and 471 tons/day of the VLSFO, indicating an 9.5% increase in CO2 emissions when the IMO adopts the low-sulfur fuel policy. Moreover, the VLSFO blending of various chemicals further deteriorates and wears out the main engine of the ship. IMO’s low-sulfur fuel policy significantly reduced the emission of sulfur oxides (SOX) and particulate matter emissions. Still, we should not ignore the fact that adopting VLSFO may cause more CO2 emissions. Therefore, while switching to low-sulfur fuels, the maritime industry should improve the related energy efficiency to reduce fuel consumption and CO2 emissions.
Multi-Objective Optimization of Rear Guide Vane of Diagonal Flow Fan Based on Robustness Design Theory
Authors: Zhou Shuiqing , Xu Biao , Lu Laifa , Jin Weiya , Mao Zijian
Published: October 2022

As an essential fluid machine in national production, the optimal design of the performance of fans has long been a focus of research. In the format of small diagonal flow fans with rear guide vanes, there are too many design variables, and the design parameters interact with each other, resulting in a degradation of the fan’s performance under actual operating conditions. Under realistic conditions, many uncertainties exist in the object of study and fluctuations in variables and parameters lead to a specific deviation of the optimization target from the design target. A method is needed for the robust and optimal design of angular flow fans under stochastic operating conditions of the design variables. In this paper, a multi-objective aerodynamic performance powerful design method is proposed using Pearson correlation analysis, combined with CFD calculation method, Latin hypercube experimental design and the Kriging agent model, to optimize the rear guide vane structural parameters of the diagonal flow fan under uncertain aerodynamic performance conditions, analyze the flow field characteristics before and after the modification, and select the optimal design model for the release. The test results show that the total pressure of the optimized fan is increased by 86 Pa, and the noise is reduced by 2.4 dB. The proposed optimization method is effective and can also be used to optimize the performance of other types of fans.
Augmented Reality in Cultural Heritage: An Overview of the Last Decade of Applications
Authors: Boboc Răzvan Gabriel , Băutu Elena , Gîrbacia Florin , Popovici Norina , Popovici Dorin-Mircea
Published: October 2022

Augmented reality is a mature technology that uses the real world as a substrate and extends it by overlaying computer-generated information. It has been applied to several domains. In particular, the technology was proven to be useful for the management and preservation of Cultural Heritage. This study provides an overview of the last decade of the use of augmented reality in cultural heritage through a detailed review of the scientific papers in the field. We analyzed the applications published on Scopus and Clarivate Web of Science databases over a period of 9 years (2012–2021). Bibliometric data consisted of 1201 documents, and their analysis was performed using various tools, including ScientoPy, VOS Viewer, and Microsoft Excel. The results revealed eight trending topics of applying augmented reality technology to cultural heritage: 3D reconstruction of cultural artifacts, digital heritage, virtual museums, user experience, education, tourism, intangible cultural heritage, and gamification. Each topic is discussed in detail in the article sections, providing insight into existing applications and research trends for each application field.
Reliability Sensitivity Analysis by the Axis Orthogonal Importance Sampling Method Based on the Box-Muller Transformation
Authors: Zhao Wei , Wu Yeting , Chen Yangyang , Ou Yanjun
Published: October 2022

The axis orthogonal importance sampling method proves to be one version of efficient importance sampling methods since the quasi-Monte Carlo simulation is its basic ingredient, in which it is now a common practice to transform low-discrepancy sequences from the uniform distribution to the normal distribution by the well-known inverse transformation. As a valid transformation method for low-discrepancy sequences, the Box-Muller transformation is introduced into the axis orthogonal importance sampling method and compared with the inverse transformation in this paper for structural reliability sensitivity analysis. Three representative quasi-random sequences with low discrepancy are presented to generate samples following the target distribution and explore the interaction with the transformation method, which is used as a sample plan along the tangent plane at the most probable failure point in the axial orthogonal importance sampling for structural reliability analysis and reliability sensitivity analysis. The numerical experiments show that the reliability sensitivity analysis method by means of the Box-Muller transformation is a good alternative to the inverse transformation to generate samples from low-discrepancy sequences to the normal distribution. In particular, the scheme of the Box-Muller transformation combined with the Sobol sequence needs fewer samples with more accuracy and is more applicable for solving reliability sensitivity analysis in various nonlinear problems.
Image-Caption Model Based on Fusion Feature
Authors: Geng Yaogang , Mei Hongyan , Xue Xiaorong , Zhang Xing
Published: October 2022

The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the image’s region-level features. Grid-level features are poor in semantic information, such as the relationship and location of objects, while regional features lack fine-grained information about images. To address this problem, this paper proposes a fusion-features-based image-captioning model, which includes the fusion feature encoder and LSTM decoder. The fusion-feature encoder is divided into grid-level feature encoder and region-level feature encoder. The grid-level feature encoder is a convoluted neural network embedded in squeeze and excitation operations so that the model can focus on features that are highly correlated to the title. The region-level encoder employs node-embedding matrices to enable models to understand different node types and gain richer semantics. Then the features are weighted together by an attention mechanism to guide the decoder LSTM to generate an image caption. Our model was trained and tested in the MS COCO2014 dataset with the experimental evaluation standard Bleu-4 score and CIDEr score of 0.399 and 1.311, respectively. The experimental results indicate that the model can describe the image in detail.
Conceiving a Digital Twin for a Flexible Manufacturing System
Authors: Magalhães Laurence C. , Magalhães Luciano C. , Ramos Jhonatan B. , Moura Luciano R. , de Moraes Renato E. N. , Gonçalves João B. , Hisatugu Wilian H. , Souza Marcelo T. , de Lacalle Luis N. L. , Ferreira João C. E.
Published: October 2022

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.
Detection of Concrete Cover of Reinforcements in Reinforced Concrete Wall by Microwave Thermography with Transmission Approach
Authors: Keo Sam Ang , Brachelet Franck , Defer Didier , Breaban Florin
Published: October 2022

Concrete cover has an important role in reinforced concrete (RC) structures because it protects reinforcement bars from the bad effects of weather, fire, and bad environmental conditions that cause the corrosion of the reinforcements. Although it is an essential parameter to be considered for structural health monitoring (SHM), its detection by infrared thermography, especially in the heating phase, has not been accessed yet. The detailed analysis and discussions of physical phenomena, known as diffraction and interference, affecting the thermograms during the detection of the steel bars by microwave thermography have given an essential key for resolving this issue. The present paper proposes an innovative methodology with microwave thermography for determining the concrete cover thickness of one-layer reinforcements (12 mm in diameter and regularly placed at 10 cm) in an RC wall (1 m × 1 m × 6.5 cm). By using the transmission approach with five angles of microwave antenna direction (0°, 15°, 30°, 45°, and 60°) and the Snell–Descartes law and linear law, the proposed methodology leads us to deduce the approximate value of the concrete cover thickness (37.74 mm), which is close to the real value (38 mm), as well as the spacing of the steel bars and dielectric constant of the concrete. The detection of the concrete cover thickness is another new remarkable achievement of infrared thermography methods.
Efficient Detection of Pre-Proinsulin by Double Antibody Sandwich ELISA
Authors: Zhu Zhu , Wang Han , Wang Li , Wei Zhou , Zheng Zhiming , Wang Peng
Published: October 2022

To detect residual pre-proinsulin (PPI) in recombinant human insulin production, an analytical method based on double-antibody sandwich ELISA was developed in this study. The BALB/c mice were immunized with PPI, and the hybridomas secreting anti-PPI monoclonal antibodies were obtained using the conventional cell fusion technique and ELISA screening. We purified the antibody using a Protein G gel column and identified its purity by SDS-PAGE. The sandwich ELISA was used to explore the pairing effect, and the specificity of the paired antibody was determined. We selected a paired antibody with relatively good specificity to establish sandwich ELISA, constructed a quantitative curve, and evaluated the accuracy and sensitivity of the method. Six anti-PPI monoclonal antibodies were obtained, named P1, P2, P3, P4, P5 and P6, of which P5 had the highest titer value. The sandwich ELISA method was established with P5 for plating and P2 as detection antibodies. The linear range of the quantitative curve of PPI by sandwich ELISA was 0.645 to 82.5 pg/mL, the recovery was 95% and the detection limit was 3.06 pg/mL. In this study, we prepared six anti-PPI monoclonal antibodies and established the sandwich ELISA method to detect PPI in process control and product release control for recombinant human insulin production.
A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental Study
Authors: Feng Xingxing , Chen Hong , Wu Gang , Zhang Anfu , Zhao Zhigao
Published: October 2022

High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibration. This paper proposes a new vibration controller by using reinforcement learning (RL) and a finite-impulse-response (FIR) filter. First, a simulator with enough physical fidelity was built for the vibration system. Then, the deep deterministic policy gradient (DDPG) algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Finally, the control policy, represented as a neural network, was run directly on a controller in real-world experiments with high-dimensional and high-frequency dynamics. The simulation results show that the maximum peak values of the power-spectrum-density (PSD) curves at specific frequencies can be reduced by over 63%. The experimental results show that the peak values of the PSD curves at specific frequencies were reduced by more than 47% (maximum over 52%). The numerical and experimental results indicate that the proposed controller can significantly attenuate various vibrations within the range from 50 Hz to 60 Hz.
Antioxidant and Antibacterial Activity of Extracts from Selected Plant Material
Authors: Kozłowska Mariola , Ścibisz Iwona , Przybył Jarosław L. , Laudy Agnieszka E. , Majewska Ewa , Tarnowska Katarzyna , Małajowicz Jolanta , Ziarno Małgorzata
Published: October 2022

Plants are a valuable source of biologically active molecules, mainly phenolic compounds. In the present study, the total phenolic content (TPC), DPPH· and ABTS+ scavenging activity as well as ferric reducing ability (FRAP) of aqueous ethanolic (70%) extracts of Cistus incanus L. and Asarum europaeum L. herb, Geum urbanum L. rhizome, Angelica archangelica L. root, white mulberry (Morus alba L.), lemon balm (Melisa officinalis L.), red raspberry (Rubus idaeus L.) and Betula pendula Roth. leaves were determined. In addition, the phenolic profiles of the studied plant extracts and antibacterial activity have been investigated. The extracts from C. incanus and G. urbanum demonstrated the highest TPC and antioxidant capacity, while the extracts from A. archangelica and white mulberry were characterized by the lowest values. A remarkable correlation was also found between the TPC and antioxidant activity of the examined extracts. HPLC analysis showed that the studied extracts were sources of both phenolic acids and flavonoids. More flavonoids than phenolic acids were identified in the extracts of C. incanus, M. alba, R. idaeus and B. pendula compared to the other extracts tested. Not all extracts showed a significant impact on the growth of the tested bacterial strains. Escherichia coli was the most sensitive strain to lemon balm extract (MIC, 0.125 mg/mL), whereas the strains of Acinetobacter baumannii and Bordetella bronchiseptica were sensitive to the G. urbanum extract (MIC, 0.125 mg/mL). Among Gram-positive bacteria, Enterococcus faecalis was the most sensitive to G. urbanum extract. In turn, Staphylococcus aureus and Staphylococcus epidermidis were sensitive to the extracts from C. incanus herb (MIC, 0.125 mg/mL), red raspberry (MIC, 0.125 mg/mL) and lemon balm leaves (MIC. 0.25 mg/mL). Based on the obtained results, the applicability of the studied plant extracts as additives to food and cosmetic products may be considered in the future.
Development of a Resource Optimization Platform for Cross-Regional Operation and Maintenance Service for Combine Harvesters
Authors: Zhang Weipeng , Zhao Bo , Zhou Liming , Qiu Conghui , Wang Jizhong , Niu Kang , Jiang Hanlu , Li Yashuo
Published: October 2022

In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources.
An Energy Aware Grid-Based Clustering Power Efficient Data Aggregation Protocol for Wireless Sensor Networks
Authors: Wang Neng-Chung , Chen Young-Long , Huang Yung-Fa , Chen Ching-Mu , Lin Wei-Cheng , Lee Chao-Yang
Published: October 2022

A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an important topic of research. A well-designed data transmission scheme can effectively extend the lifetime of a network. In this paper, we propose an energy aware grid-based clustering power efficient data aggregation protocol (GB-PEDAP) for WSNs. The proposed scheme has a two-layer architecture: the inner layer and the outer layer. The inner layer uses direct transmission to collect the data of the cluster (cell), and the outer layer uses a tree structure transmission to collect the data of the cluster head (cell head). In our simulations, the number of rounds executed by GB-PEDAP was approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy, 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP were 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can evenly disperse the energy consumption of sensors to avoid the rapid death of sensors, extending the lifetime of a WSN.
Combined Optimization of Friction-Based Isolators in Liquid Storage Tanks
Authors: Tsipianitis Alexandros , Spachis Andreas , Tsompanakis Yiannis
Published: October 2022

Large-scale tanks are widely used for storing chemicals and fuels. Their failure due to natural (e.g., earthquakes) and/or man-made hazards can lead to disastrous consequences. Nonetheless, they are often constructed in seismic-prone regions. For this reason, base isolation is often used for the seismic protection of large tanks, aiming to “decouple” the superstructure from the imposed ground motions. In this study, a combined optimization formulation is presented in order to further improve the seismic response of a base-isolated tank. The main aim is to optimize both the critical design parameters and the placement of the minimum number of isolators at the base of the tank. In particular, a Cuckoo Search (CS) optimizer is used to optimize the dynamic performance of liquid storage tanks, isolated either via single friction pendulum bearings (SFPB) or triple friction pendulum bearings (TFPB). The main objective is to minimize the eccentricity between the center of mass and the center of rigidity of the isolation system, while appropriate constraints are also imposed. Several cases are examined, while the results are compared with respect to isolator displacement fragility curves, as well as the reduced accelerations at the base of the tank. According to the findings of this study, the tank industry can significantly benefit from the proposed approach, as a more cost-efficient design of the base-isolation system of large-scale tanks can be achieved, i.e., using fewer isolators with optimal key parameters.
Enhanced Osteoblast Adhesion and Proliferation on Vacuum Plasma-Treated Implant Surface
Authors: Jeon Hyun Jeong , Jung Ara , Kim Hee Jin , Seo Jeong San , Kim Jun Young , Yum Moon Seop , Gweon Bomi , Lim Youbong
Published: October 2022

In this study, we propose a vacuum plasma device for surface treatment of dental implants. This plasma device was designed to allow direct installation of sealed implant packaging containing the dental implant. In this manner, the dental implant could be treated with plasma under a moderate vacuum environment while remaining in a sterile condition. To assess the osseointegration efficiency, in vitro experiments using sandblasted, large grit, acid etching (SLA), calcium coated-SLA (CaSLA), and calcium coated-SLA with plasma treatment (PCaSLA) were performed. The implant surface was observed with scanning electron microscope (SEM) before and after plasma treatment. Thereafter, protein adsorption, cell adhesion, proliferation, and differentiation efficiency were investigated on the surface of each implant type using saos-2, an osteoblast. Plasma treatment significantly improved protein adsorption, cell adhesion, and cell proliferation efficiency compared to both CaSLA and SLA without damaging the calcium coating. According to the findings, the proposed vacuum plasma device has shown the potential to improve osseointegration efficiency. We believe that this plasma technology can be an innovative chairside solution that can be easily handled in the clinical field with superb usability.
Cooperative Control of Interconnected Air Suspension Based on Model Predictive Control
Authors: Zhou Ying , Li Zhongxing , Yu Wenhao , Yu Yi
Published: October 2022

The suspension system is a significant part of a vehicle because it transmits the torque and force between the wheels and the frame, meeting the requirements of ride comfort. In this paper, a novel interconnected air suspension was introduced and a cooperative control between interconnected mode activation (IMA) and outsourced mode activation (OMA) was designed. To improve ride comfort in a bus at a minimal energy consumption, this cooperative control based on model predictive control (MPC) for computing the best force and the rule was defined to distribute the best suspension force generated by IMA and OMA. The simulation and experimental results showed that the proposed control strategy significantly improved ride comfort in different conditions. Compared with traditional control in the test vehicle, the RMS of the front-left sprung mass and unsprung vertical acceleration decreased by 24.2% and 14.5%, respectively, under a straight condition; the RMS of the sprung (unsprung) mass vertical acceleration reduced by 22.38% and 15.43%, respectively, under a bump condition.
Similarity Calculation via Passage-Level Event Connection Graph
Authors: Liu Ming , Chen Lei , Zheng Zihao
Published: October 2022

Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.
Multivariate Statistical Analyses and Potentially Toxic Elements Pollution Assessment of Pyroclastic Products from Mt. Etna, Sicily, Southern Italy
Authors: Caridi Francesco , Paladini Giuseppe , Mottese Antonio Francesco , Messina Maurizio , Venuti Valentina , Majolino Domenico
Published: October 2022

Potentially toxic elements contamination represents a universal problem of major concern, due to several adverse health effects on human beings when permissible concentration levels are overcome. In this sense, the assessment of potentially risky elements content in different environmental matrices plays a key role in the safeguarding of the quality of the environment, and thus of the strictly correlated public health. In this article, measurements of the average potentially toxic elements concentrations in pyroclastic products from Mt. Etna, Eastern Sicily and Southern Italy were performed together with a comparison with the allowable levels set by Italian legislation, with the aim to evaluate the level of toxicity imposed on the ecosystem. For this purpose, Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) measurements were performed to investigate any possible chemical pollution by potentially risky elements, by applying different indices such as Enrichment Factor (EF), Geo-accumulation Index (Igeo), Contamination Factor (CF) and Pollution Load Index (PLI). Finally, the multivariate statistical analysis was performed by processing potentially toxic elements content and pollution indices. It is worth noting that the used approach could be applied, in principle, for the evaluation of the chemical risk due to the presence of potentially toxic elements in a large variety of samples of particular environmental interest, and can constitute a guideline for investigations focused on the monitoring of the environmental quality.
State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning
Authors: Jayamini Widana Kankanamge Darsha , Mirza Farhaan , Naeem M. Asif , Chan Amy Hai Yan
Published: October 2022

Length of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variables with LOS and to use those factors to predict LOS. A dataset of asthma-related admissions in the Auckland region was analysed using different statistical techniques. Using those predictors, machine learning models were built to predict LOS. Demographic, diagnostic, and temporal factors were associated with LOS. Māori females had the highest average LOS among all the admissions at 2.8 days. The random forest algorithm performed well, with an RMSE of 2.48, MAE of 1.67, and MSE of 6.15. The mean predicted LOS by random forest was 2.6 days with a standard deviation of 1.0. The other three algorithms were also acceptable in predicting LOS. Implementing more robust machine learning classifiers, such as artificial neural networks, could outperform the models used in this study. Future work to further develop these models with other regions and to identify the reasons behind the shorter and longer stays for asthma patients is warranted.
Role of Alumni Program in the Prediction of Career Success in an Ecuadorian Public University
Authors: Pico-Saltos Roberto , Garzás Javier , Redchuk Andrés , Escandón-Panchana Paulo , Morante-Carballo Fernando
Published: October 2022

Alumni tracking studies at the local, regional and global levels provide quality and efficiency measurement parameters in higher education institutions and project improvements in the quality of professionals. However, there is a gap between alumni tracking and the measurement of career success, influencing the academic offer of careers relevant to labor demands. This article aims to propose a model for predicting career success through the analysis, extraction and evolutionary optimization of objective and subjective variables to determine the role of alumni tracking in a higher education institution. The methodology establishes (i) an analysis of information on the alumni program and career success, (ii) prediction models of career success using genetic algorithms, (iii) validation of prediction models and (iv) the relationship between alumni tracking and career success. The results show models for predicting career success using a genetic algorithm with high certainty percentages, where the objective variables’ weight significantly influences the predictive model. However, subjective variables show importance depending on individual characteristics and their value schemes or goals of graduates. As a recommendation, universities could include a monitoring system for their graduates, which is crucial in adapting to the curriculum, especially in strategic technical and human ethical issues.
Experimental Comparison of Editor Types for Domain-Specific Languages
Authors: Chodarev Sergej , Sulír Matúš , Porubän Jaroslav , Kopčáková Martina
Published: October 2022

The editor type can influence the user experience for a domain-specific language, but empirical evaluation of this factor is still quite limited. In this paper, we present the results of our empirical study, in which we compare the productivity of users with different kinds of editors for the same domain-specific language. We chose the domain of quiz definitions and used three editors: a text editor with syntax highlighting and code completion developed with the Xtext framework, a projectional editor created using JetBrains MPS, and an existing form-based editor—Google Forms. The study was performed on 37 graduate students of computer science. The measured time was lower for the text editor than for the form-based editor, and the form-based editor’s time was lower than the projectional one’s; however, the results were statistically insignificant. The experiment was also complemented with a survey providing insight into the perception of different editor types by users.
Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
Authors: Hao Xinyu , Xia Zhang , Jiang Mengxi , Ye Qiubo , Yang Guangsong
Published: October 2022

With the development of communication technology and the increasingly complex wireless communication channel environment, the requirements for radio modulation recognition are also increased to avoid interference and improve the efficiency of radio spectrum resources. To achieve high recognition accuracy with less computational overload, we propose a radio signal modulation recognition method based on deep learning, which uses a pruning strategy to reduce computational overload, based on the original model, CNN-LSTM-DNN (CLDNN), and the double-layer long short-term memory (LSTM). Effect factors are analyzed in terms of recognition accuracy by adjusting the parameters of each network layer. The results of the experiments show that the model not only has a greater precision improvement than some existing models, but also reduces the computational resources necessary.
Size-Dependent Mechanical Behaviors of Defective FGM Nanobeam Subjected to Random Loading
Authors: Luo Zhenxian , Shi Qinghe , Wang Lei
Published: October 2022

Considering the uncertainties of the materials and loads, the nanobeam made of functionally graded materials were investigated based on the non-local elastic theory. The spline finite point method (SFPM) was established to analyze the bending behavior of the nanobeam-based Timoshenko theory. In comparison with finite element method (FEM), SFPM has higher accuracy. Further, the multi-source uncertainties are considered, material properties are quantified as interval parameters, and loads are taken as random parameters. To deal with the problems with two types of uncertainties coexisting, a hybrid uncertain analysis model was established, and the method of polynomial chaos expansion and dimensional wise (PCE–DW) analysis was proposed to predict the response of nanobeam in the hybrid uncertain system. Numerical examples ultimately illustrate the effectiveness of the model and solution techniques, compared with MCS. The results furtherly verify the efficiency and accuracy.
DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
Authors: Tian Jiamin , Zeng Xuewen , Zhu Xiaodong
Published: October 2022

Network virtualization (NV) is considered a promising technology that may solve the problem of Internet rigidity. The resource competition of multiple virtual networks for shared substrate network resources is a challenging problem in NV called virtual network embedding (VNE). Existing approaches do not consider the differences between multi-tenant requests and adopt a single embedding method, resulting in poor performance. This paper proposes a virtual network embedding algorithm that distinguishes the network types requested by tenants. This method divides virtual network requests into ordinary requests and delay-sensitive requests according to the delay constraints, provides personalized mapping strategies for different networks, and flexibly responds to the resource requirements and quality of service (QoS) requirements of the virtual network. The simulation results show that, compared with other algorithms, the proposed algorithm improves the request acceptance ratio by about 2% to 15% and the substrate network resources are more effectively utilized.
Protection of Digital Elevation Model—One Approach
Authors: Vulić Ivan , Borisov Mirko , Prodanović Radomir , Rančić Dejan , Petrović Vladimir M. , Stankovski Stevan , Ostojić Gordana
Published: October 2022

The quality of DEMs is especially being considered based on the spatial resolution of the created digital models and the type of terrain of the interest area. DEM analysis was performed specifically for LiDAR-based DEMs, as well as a comparison with results obtained using the radar recording method (Shuttle Radar Topography Mission). The application of DEM data for security-sensitive systems requires the fulfillment of security requirements, such as authenticity, integrity, confidentiality, and non-repudiation. For this purpose, the authors have developed a model for non-repudiation and protecting DEM data. The model simulation shows it is possible to detect even the smallest changes made in the transmission or the DEM location, as well as proving the data authenticity and non-repudiation of the sender. DEM data security testing has shown that DEM data is effectively protected from the source of origin to the end entity location.
Building Function Recognition Using the Semi-Supervised Classification
Authors: Xie Xuejing , Liu Yawen , Xu Yongyang , He Zhanjun , Chen Xueye , Zheng Xiaoyun , Xie Zhong
Published: October 2022

The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results.
Privacy and Security in Federated Learning: A Survey
Authors: Gosselin Rémi , Vieu Loïc , Loukil Faiza , Benoit Alexandre
Published: October 2022

In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings from our study confirm that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN)-based attacks, while inference-based attacks are the most critical to the privacy of FL. Finally, we identify ongoing research directions on the topic. This paper could be used as a reference to promote cybersecurity-related research on designing FL-based solutions for alleviating future challenges.
A Stochastic Programming Model for Multi-Product Aggregate Production Planning Using Valid Inequalities
Authors: Gómez-Rocha José Emmanuel , Hernández-Gress Eva Selene
Published: October 2022

In this study, a mixed integer, linear, multi-stage, stochastic programming model is developed for multi-product aggregate production planning (APP). An approximation is used with a model that employs discrete distributions with three and four values and their respective probabilities of occurrence for the random variables, which are demand and production capacity, each one for every product family. The model was solved using the deterministic equivalent of the multi-stage problem using the optimization software LINGO 19.0. The main objective of this research is to determine a feasible solution to a real APP in a reasonable computational time by comparing different methods. Since the deterministic equivalent was difficult to solve, a proposal model with bounds in some decision variables was developed using some properties of the original model; both models were solved for different periods. We demonstrated that the proposed model had the same solution as the original model but required fewer iterations and CPU time, which implies an advantage in real APP. Finally, a sensitivity analysis was performed at varying service levels finding that if the service levels increase, the cost increases as well.
A Novel Acute Toxicity Bioassay and Field Trial to Evaluate Compounds for Small Hive Beetle Control
Authors: Kleckner Kaylin , De Carolis Alessandra , Jack Cameron , Stuhl Charles , Formato Giovanni , Ellis James D.
Published: October 2022

Beekeepers need new registered products to control the small hive beetle (SHB), Aethina tumida, a significant pest of western honey bee (Apis mellifera) colonies. Few approved chemical controls exist, and there is no standardized system for screening compounds against SHBs. We outline a detailed method for an acute toxicity bioassay that delivers compounds via pollen. We provide a detailed method for a field trial that delivers treated pollen in traps made from modified compact disc cases. We applied both methods in proof-of-concept experiments to assess acetamiprid as a SHB control agent. Using the laboratory bioassay, we found acetamiprid (LC50 = 20.5 µg/g) to be more toxic to SHBs than coumaphos was (LC50 = 1250 µg/g), yet less toxic to SHBs than fipronil was (LC50 = 1.78 µg/g). In our field trial, colonies treated with acetamiprid and fipronil significantly reduced (p < 0.001) live SHB populations over those of control colonies. Traps containing acetamiprid retained significantly higher (p < 0.001) numbers of dead SHBs than did traps containing fipronil. We outline the first detailed methods to assess the toxicity of compounds delivered in pollen for adult SHB control. Our proof-of-concept experiments showed acetamiprid to be a promising control agent for SHBs.
Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
Authors: Gao Pengfei , Zong Qi , Cheng Bing , Wang Haibo , Xu Ying , Zhang Binbin
Published: October 2022

Considering the low efficiency of cutting blasting in hard rock mine tunnels, a novel solution of increasing the charge diameter of the cutting holes was put forward. To investigate the influence of the charge diameter on the cutting blasting results, three different working conditions of Φ 32 mm, Φ 42 mm, and Φ 50 mm blasting holes combined with Φ 27 mm, Φ 35 mm, and Φ 45 mm cartridges, respectively, were taken as the investigation objects. At first, the theoretical destruction ranges of single cutting holes under the three different charge diameters were computed. The computed results showed that the destruction range of the cutting holes could be expanded by increasing the charge diameter, which would be beneficial to the destruction of the rock far away from the cutting holes in the cutting cavity. Subsequently, numerical simulations of cutting blasting under the three different charge diameters were performed to display the dynamic propagation process of the blasting stress wave. Importantly, the stress field intensity in the cutting cavity was enhanced significantly with the charge diameter. The stronger stress field intensities generated by the larger diameter charges were more conducive to breaking the rock in the cutting cavity into small fragments that were easy to be discarded. Ultimately, a hard rock vertical slope was used instead of the driving face to carry out the cutting blasting experiments, and the hole utilizations of the cutting blasting were 70.4%, 82.0%, and 94.0%, respectively, under the three different charge diameters, from small to large. The experimental results forcefully substantiated that a higher cutting blasting efficiency could be achieved by increasing the charge diameter of cutting holes in hard rock mine tunnels.