bluuebunny/crossref_metadata_2025 huggingface dataset:
Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
Authors: Cheung Calvin , Rawashdeh Samir , Mohammadi Alireza
Published: October 2022

Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans under different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error.
Data Matrix Based Low Cost Autonomous Detection of Medicine Packages
Authors: Lima José , Rocha Cláudia , Rocha Luísa , Costa Paulo
Published: October 2022

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.
Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems
Authors: Shieh Chin-Shiuh , Nguyen Thanh-Tuan , Lin Wan-Wei , Nguyen Dinh-Cuong , Horng Mong-Fong
Published: October 2022

The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms.
Modelling the Defect Processes of Materials for Energy Applications
Authors: Sgourou Efstratia N. , Daskalopulu Aspassia , Goulatis Ioannis , Panayiotatos Yerassimos , Solovjov Andrei L. , Vovk Ruslan V. , Chroneos Alexander
Published: October 2022

The technological requirement for ever more efficient materials for the energy and electronics sectors has led to the consideration of numerous compositionally and structurally complicated systems. These systems include solid solutions that are difficult to model using electronic structure calculations because of the numerous possibilities in the arrangement of atoms in supercells. The plethora of such possible arrangements leads to extensive and large numbers of potential supercells, and this renders the investigation of defect properties practically intractable. We consider recent advances in oxide interfaces where studies have demonstrated that it is feasible to tune their defect processes effectively. In this review, we aim to contribute to the ongoing discussion in the community on simple, efficient and tractable ways to realise research in solid solutions and oxide interfaces. The review considers the foundations of relevant thermodynamic models to extract point defect parameters and the special quasirandom structures method to model the supercell of solid solutions. Examples of previous work are given to highlight these methodologies. The review concludes with future directions, systems to be considered and a brief assessment of the relevant methodologies.
Theoretical Analysis of Rock Blasting Damage in Construction of Tunnels Closely Under-Passing Sewage Box Culverts
Authors: Xu Jiancong , Xue Huihao , Rui Guorong
Published: October 2022

With the large-scale construction of urban traffic tunnels in China, it has become common to underpass existing buildings and structures such as sewage box culverts and pipelines using the drilling-blasting method. How to analyze accurately the blasting damage of surrounding rock and reasonably determine the safe distance between tunnel and box culvert or pipelines is an urgent issue to be solved. In this paper, the Cowper-Symonds plastic kinetic hardening model was improved using both rock initial damage degree and damage modification coefficient considering rock residual strength. The proposed model was implemented into LS-DYNA. The proposed damage model was used to evaluate the blasting construction of rock tunnels closely under-passing sewage box culverts. The results of numerical simulation using the proposed damage model shows that the blasting damage range of rock with a damage degree of more than 0.5 very significantly reduces from 1.0 m to 0.3 m as the spacing between the box culvert and the tunnel increases from 1.0 m to 4.0 m, and the evolution process of rock blasting damage can be well-presented. Moreover, the safe distance between tunnel and box culvert in blasting construction can be reasonably determined to be no less than 4.0 m. The findings in this paper could be significant for guiding the blasting construction of rock tunnels closely under-passing sewage box culverts.
Parametric Analysis on the Effect of Dynamic Interaction between Nonlinear Soil and Reinforced Concrete Frame
Authors: Wang Jishuai , Yang Jun
Published: October 2022

The effect of dynamic soil–structure interaction on the seismic demand of a reinforced concrete frame is of great significance to seismic design, retrofit, and damage evaluation. To investigate the degree of influence of the consideration of the soil–structure interaction on the structural seismic response, an efficient numerical model considering the nonlinearities of both a reinforced concrete frame and soil was developed and validated against a shaking table test. Subsequently, detailed parametric analyses on the dynamic soil–structure interaction effect were conducted, where the influences of the length and diameter of the pile, span number and frequency of the structure, soil property, and natural uncertainty of the seismic record were investigated. The research results indicate that the base shear of the pile-supported reinforced concrete frame generally increases with a larger pile length and pile diameter. The influence of the span number and pile diameter on the soil–structure interaction effect is up to 40% in some cases while that of the pile length is within 10% in general. Consideration of the soil–structure interaction can also considerably increase the structural base shear in certain cases and the growth can be greater than 30%. The dynamic soil–structure interaction effect predominantly depends on the structure frequency, spectral characteristic and peak acceleration of the seismic record, and soil shear wave velocity while the influence of the pile diameter and number of spans cannot be neglected in some cases.
BIM-Based Management System for Off-Site Construction Projects
Authors: Jang YeEun , Son JeongWook , Yi June-Seong
Published: October 2022

Offsite construction (OSC) is one of the alternative methods for the various challenges that the construction sector faces today. This study developed a management system based on building information modeling (BIM) to execute OSC projects successfully. Because OSC differs from the conventional onsite building method, the authors studied and analyzed several project cases and interviewed the participants and stakeholders. The OSC method has unique characteristics in the aspects of the projects’ location and time, production process, flow, method, facility, and environment. Moreover, before the system development, we analyzed the OSC project management, usability, and system requirements. These requirements were turned into a BIM-based system following a waterfall model, with six management menus: drawing, schedule, production, logistics, installation and progress monitoring, and progress payment. This study implemented each menu’s vital functions within the system more effectively due to the BIM-based technological features, such as object-oriented data processing, visualization, high interoperability, linkage, and integration. The developed system was applied to four projects. The test resulted in a streamlined work process, improved activity, and less input time and workload than in a non-BIM-based management environment. These findings indicated that the proposed BIM-based system enabled OSC project management to perform better.
A New Fault Diagnosis Method for Unbalanced Data Based on 1DCNN and L2-SVM
Authors: Hu Baoquan , Liu Jun , Zhao Rongzhen , Xu Yue , Huo Tianlong
Published: October 2022

In general, the measured health condition data from rolling bearings usually exhibit imbalanced distribution. However, traditional intelligent fault diagnosis methods usually assume that the data categories are balanced. To improve the diagnosis accuracy of unbalanced datasets, a new fault diagnosis method for unbalanced data based on 1DCNN and L2-SVM is proposed in this paper. Firstly, to prevent the minority class samples from being heavily suppressed by the rectified linear unit (ReLU) activation function in the traditional convolutional neural network (CNN), ReLU is improved by linear and scaled exponential linear units (SELUs). Secondly, to solve the problem where the cross-entropy loss treats all input samples equally, it is replaced by the L2-support vector machine (L2-SVM) loss. Furthermore, a dynamic adjustment parameter is introduced to assign less misclassification cost to the majority of class samples. Finally, we add a new modulation factor that reduces the weight of more distinguishable samples to generate more focus on training indiscernible samples. The proposed method is carried out on two kinds of bearing datasets. The experimental results illustrate a significant improvement in recognition accuracy and the higher diagnosis performance of the model when dealing with unbalanced data compared with other intelligent methods.
Development of an Estimation Method for Depth of Spalling Damage in Concrete Pavement by Ultrasonic Velocity Measurement
Authors: Yeon Gyumin , Yoo Hojun , Hong Sungjin , Cho Jeongyeon , Kim Intai
Published: October 2022

As the amount of aged pavement increases, functional damage, such as spalling, occurs frequently on Portland Cement Concrete pavement (PCC) in South Korea. However, the existing management method does not properly reflect the scope of deterioration of the pavement causing early damage. To overcome the problem of the existing repair method, this study evaluated the deterioration of functional damage on the surface of the slab as soundness through ultrasonic velocity measurement method among non-destructive testing (NDT) techniques and suggested a method to estimate the depth of deterioration. To develop a method for estimating the depth of the deterioration a slab, a preliminary investigation was conducted to check the range of ultrasonic velocity of concrete pavement in South Korea and to evaluate the variability of NDT equipment. Based on the ultrasonic velocity, the sound rating of concrete pavement was graded from 5 for “very good” to 0 for “very poor”, and the tendency of the ultrasonic velocity to increase according to the depth of the deteriorated areas was confirmed.
A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations
Authors: Galo Fernandes Rafael Augusto , Silva Rocha Rizol Paloma Maria , Nascimento Andreas , Matelli José Alexandre
Published: October 2022

Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and enable shut-in wells to work again. One of the major challenges while using these tools is the motor stalling, which can lead to serious damage to the PDM and lost time events in drilling and workover rigs. These events result in total losses of hundreds of thousands of dollars, and their avoidance mostly depends on trained and fully aware equipment operators. If a PDM starts to stall, the pumping needs to be halted immediately or the tool may fail. This paper describes the use of a Fuzzy Inference System (FIS) to detect the stalling events as they start to happen using the acquisition data from the coiled tubing unit, the output of the FIS could then trigger an alarm for the operator to take the proper action or remotely stop the pump. The FIS was implemented in Python and tested with real CT milling acquisition data. When tested using real data, the system analyzed 68,458 acquisition points and detected 94% of the stalling events across this data during its first seconds, whereas, during the real job, a CT operator could take longer to notice this event and take the proper action, or even take no action. If the FIS was applied on a real coiled tubing acquisition system, it could reduce PDMs over-pressurization due to stalling, leading to an increase on its useful life and decrease on premature failure. As of now there is no similar system in the market or published and this kind of operation is fully performed using human supervision only.
Effect of Lead Ion Contamination on the Microstructure of Guilin Red Clay
Authors: Song Yu , Dong Shuaishuai , Ding Song , Geng Yukun , Huang Yaoyi , Li Hui
Published: October 2022

The heavy metal contamination of red clay in Guilin is a serious problem. Lead ions pollute red clay and have a series of effects, which affect the macroscopic properties of red clay. However, fundamentally, the effects occur because the internal microstructure of red clay is eroded by Pb2+, which results in the change in the macroscopic properties of red clay. Therefore, we adopted a mercury injection experiment and used electronic microscope Pb2+ to explore the microscopic mechanism through which red clay is internally influenced. From the mercury injection experiment, we found that an increase in the concentration of Pb2+ increased soil pore diameter and volume, and that a higher heavy metal content of Pb2+ had a greater effect on red clay cementation. Using scanning electron microscopy, we found that when the micro-image magnification was 500 and 20,000 times, the inside of the red clay pore increased with the increase in the concentration of Pb2+, showing that the heavy metal within the microstructure damaged the red clay. The above two experiments showed that heavy metal ions increase the intergranular fractures of red clay, and the thickness of the double layer reduces, which results in the weakening of the interaction force between particles.
A Probe-Based qPCR Method, Targeting 16S rRNA Gene, for the Quantification of Paenibacillus larvae Spores in Powdered Sugar Samples
Authors: Carra Elena , Galletti Giorgio , Carpana Emanuele , Bergamini Federica , Loglio Giulio , Bosi Filippo , Palminteri Stefano , Bassi Stefano
Published: October 2022

Paenibacillus larvae (P. larvae) is responsible for American foulbrood (AFB), the most severe bacterial disease of honeybees. The enumeration of P. larvae spores in substrates taken from hives allows for the identification of the contamination levels of the colonies, mostly in those with atypical symptoms or with asymptomatic infections; in these cases, it is essential for the effective control of American foulbrood (AFB). In this work we described a new quantitative TaqMan® probe-based real-time PCR (qPCR) assay, targeting the 16S rRNA gene of P. larvae, used for the quantification of P. larvae spores in powdered sugar samples collected from hives, in comparison to the culture. A total of 105 colonies were selected, belonging to 10 apiaries with different levels of infection, located in northern Italy. The proportions of positive colonies was 54% (57/105) with the culture and 66% (69/105) with qPCR. A significant difference between the two methods was found with McNemar’s test (p = 0.02). Out of the 51 positive samples by both methods, 45 showed higher infection by qPCR compared to the culture. A close concordance with the clinical–epidemiological status of the hives was observed by both methods, with higher infection levels found by qPCR.
SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection
Authors: Zhang Jiawei , Ma Pingli , Jiang Tao , Zhao Xin , Tan Wenjun , Zhang Jinghua , Zou Shuojia , Huang Xinyu , Grzegorzek Marcin , Li Chen
Published: October 2022

This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection.
Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
Authors: Lu Jiaxin , Qi Hengnian , Wu Xiaoping , Zhang Chu , Tang Qizhe
Published: October 2022

In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still a challenge to be tackled by research. Offline static handwriting has a unique structure and strong interpretability, while online handwriting contains dynamic information, such as timing and pressure. Therefore, this paper proposes an authentic signature identification method, integrating dynamic and static features. The dynamic data and structural style of the signature are extracted by dot matrix pen technology, the global and local features, time and space features are fused and clearer and understandable features are applied to signature identification. At the same time, the classification of a forged signature is more detailed according to the characteristics of signature and a variety of machine learning models and a deep learning network structure are used for classification and recognition. When the number of classifications is 5, it is better to identify simple forgery signatures. When the classification number is 15, the accuracy rate is mostly about 96.7% and the highest accuracy reaches 100% on CNN. This paper focuses on feature extraction, incorporates the advantages of dynamic and static features and improves the classification accuracy of signature identification.
Rapid Identification of Foodborne Pathogens in Limited Resources Settings Using a Handheld Raman Spectroscopy Device
Authors: Gonzalez-Gonzalez Cid Ramon , Hansen Mark , Stratakos Alexandros Ch.
Published: October 2022

Rapid and precise methods to detect pathogens are paramount in ensuring food safety and selecting appropriate disinfection treatments. Raman spectrometry is a promising technology being investigated for detecting pathogens and achieving rapid, culture-free, and label-free methods. Nonetheless, previous Raman techniques require additional steps, including the preparation of slides that could introduce significant variability. In this study, we investigated the capability of a Raman handheld device for rapid identification of monocultures of Listeria monocytogenes, Salmonella Typhimurium, Escherichia coli O157:H7, and Staphylococcus aureus, and the combination of co-cultures in BHI broth suspension by utilising principal component analysis (PCA) and support vector machine (SVM) classification of Raman spectra. The detection method accurately identified monocultures (0.93 ± 0.20), achieving good discrimination after 24 h of bacterial growth. However, the PCA–SVM system was less accurate for classifying co-cultures (0.67 ± 0.35). These results show that this method requires an isolation step followed by biomass enrichment (>8 log10 CFU/mL) for accurate identification. The advantage of this technology is its simplicity and low-cost preparation, achieving high accuracy in monocultures in a shorter time than conventional culture-dependent methods.
Nutritive Profile of Canned Goat Meat Food with Added Carrot
Authors: Chomanov Urishbay , Kenenbay Gulmira , Tursynov Alibek , Zhumalieva Torgyn , Tultabayev Nurzhan , Suychinov Anuarbek
Published: October 2022

This study describes the effect of adding carrot on the nutritional and biological value of canned goat meat. Four batches of canned goat meat were produced: control (without carrot), and three experimental treatments T10, T20, and T30 that was canned goat meat with the addition of 10, 20, and 30% carrot, respectively. Canned goat meat with 30% added carrot had the lowest fat (5.76%) and protein (17.08%) content. The mass fraction of fiber was significantly increased, up to 1.96% in T10, 2.33% in T20, and 2.71% in T30. The same trend was observed for β-carotene content (from 0.78 mg/100 g in the control sample to 1.91 mg/100 g in T30). Among the amino acids, histidine was significantly increased in T30 (p < 0.05), and lysine in T20 and T30 (p < 0.05). There was also evidence of increased vitamin A, B6 (pyridoxine), B9 (folic acid), and B5 (pantothenic acid) content in experimental samples than in the control sample. Among the minerals, the most significant increase was in potassium, magnesium, and phosphorus levels in samples with carrot. Organoleptic evaluation showed differences in color, taste, consistency, and odor. Thus, the addition of carrots increases the nutritive value and improves the palatability of canned goat meat.
Virtual Humans in Museums and Cultural Heritage Sites
Authors: Sylaiou Stella , Fidas Christos
Published: October 2022

This article presents the results of a survey on the use of digital avatars and agents in museums and places of cultural interest. The optimization of virtual agents in the cultural heritage domain is an interdisciplinary undertaking and this paper investigates pertinent research and solutions and suggests ways forward. The research questions examined relate to (a) the technological characteristics of cultural heritage-related uses of users’ avatars and virtual agents, and patterns that emerge, and (b) suggestions for future research based on this article’s findings. We reviewed relevant publications and analysed the approaches presented to identify trends and issues that could lead to conclusions on the existing state of the field and, moreover, infer and suggest future directions. The main findings relate to a trend toward onsite, sophisticated installations or applications with increasing investment in mixed reality. Moreover, emphasis shifts toward optimising agents such as virtual guides or companions, mediators of cultural content and engaging facilitators. Behavioural Realism (BR), featured mostly in virtual reality installations, greatly fosters engagement according to the reviewed research, and we conclude that mixed reality onsite applications, which are gathering pace, should reach their degree of sophistication and combine the strengths of both MR and BR.
Special Issue on Microgrids/Nanogrids Implementation, Planning, and Operation
Authors: Benbouzid Mohamed , Muyeen S. M. , Zia Muhammad Fahad
Published: October 2022

Today’s power system faces the challenges of environmental protection, increasing global demand for electricity, high-reliability requirements, clean energy, and planning restrictions [...]
Elastic Analysis of Three-Layer Concrete Slab Based on Numerical Homogenization with an Analytical Shear Correction Factor
Authors: Staszak Natalia , Szymczak-Graczyk Anna , Garbowski Tomasz
Published: October 2022

Sandwich structures are widely used in construction, as well as in the aviation, spaceship, and electronics industries. The interesting result, among others, is the fact that individual layers can be freely selected to meet the planned requirements. In the case of sandwich structures in construction, they must meet the requirements of load-bearing capacity, thermal, and acoustic insulation, and additionally, they must be resistant to biological and chemical corrosion. The paper presents calculation algorithms for Hoff’s three-layer panels. In the first case, the well-known and proven method of finite differences in variation terms was used, assuming actual geometrical and material parameters. In the second case, the numerical homogenization method of the layered panel was used, replacing the stiffnesses of individual layers with a homogeneous equivalent plate with substitute stiffness corrected in shearing by an analytically derived shear correction factor. A comparative analysis of the results of the calculations with the use of both approaches was carried out. A good agreement between the displacement values and the calculated cross-sectional forces was obtained. On this basis, it can be assumed that the static analysis of a slab by simplified methods using numerical homogenization with an analytical shear correction factor is appropriate and can be applied to layer structures.
The Application of Game-Based Teaching for Water Resources Science Learning
Authors: Yang Han-Chung , Lee Meng-Tsung , Lin Li-Chun
Published: October 2022

It is essential to encourage scientific understanding of water resources. However, how to achieve this for different objects needs to be researched further. Therefore, the purpose of this study is to analyze the impact of game-based teaching on elementary school students’ learning satisfaction in water resources science applications. Students who take the course are asked to complete a learning satisfaction questionnaire at the end. A total of 420 questionnaires were distributed, with 388 being effective, a 92% effective ratio. The questionnaire data was used to conduct the study and analysis. The Cronbach’s α for all factors exceeded 0.7, indicating that the questionnaire items are stable and internally consistent. The results of descriptive statistics, factor analysis, independent sample t-test, regression analysis, and correlation analysis revealed that students were extremely satisfied with game-based water resources science teaching. According to factor analysis, three key factors influenced learning satisfaction: “learning style,” “learning interest,” and “learning process.” Furthermore, “learning interest” was positively related to “learning style” and “learning process,” with both having a significant effect on “learning interest.” The findings of this study also show that water resources science applications can be taught and promoted in a variety of ways.
Selected Problems in a Two-Vehicle Impact Collision Modeling
Authors: Aleksandrowicz Iwo , Zalewski Jarosław , Aleksandrowicz Piotr
Published: October 2022

In this paper, a potential use of the basic parameters of a road collision in a forensic activity was analyzed. A selected case of a frontal, eccentric, and oblique collision between two motor vehicles was analyzed from the point of view of both a computer simulation and a model. The case has been presented as an attempt to identify the collision parameters necessary to conduct the analytical calculations useful in analyzing specific road accidents. The simulation results were obtained in V-SIM software, which is widely used in collision reconstructions by forensic experts and appraisers. It was further analyzed from the point of view of a mathematical model, with the use of the force–impulse method and avoiding the use of the coefficients of restitution in the normal and tangential directions versus the adopted coordinate frame. In the analytical calculations, apart from the masses and the velocities of vehicles, the collision angles and the vicarial coefficient of adhesion between the colliding vehicles (µ) are also important. The approach presented in the article enables an expert to verify the obtained results of a computer program simulation.
Electrochemical Synthesis and Structural Characteristics of New Carbon-Based Materials Generated in Molten Salts
Authors: Ji Deqiang , Zhang Fajin , Qiao Zhiqiang , Zhang Jing , Wu Hongjun , Wang Guanzhong
Published: October 2022

This article is devoted to providing a new feasible route to realize carbon dioxide reduction and resource utilization. With the wide electrochemical window, high thermal stability and fast mass transfer rate of molten salt electrolyte, new carbon-based materials can be synthesized on the surface of the inexpensive Fe cathode. EDS (Electron-Dispersive-Spectroscope), SEM (Scanning-Electron-Microscope) and BET (Brunauer-Emmett-Teller) analyzers are selected to detect the critical element, microstructures and specific surface area of the new carbon-based materials generated via electrolysis. It is demonstrated that eutectic carbonates’ electrochemical reduction, ranging from 450 °C to 750 °C prefers to produce carbons with no high-value structure. While carbon products are observed with honeycomb-like and platelet structures at 450 °C with an increase in current density. Additionally, the feedstock CO2 could be converted into carbon-based materials with high value such as high surface area carbon, spherical carbon and cellular porous carbon production by optimizing the electrolysis parameters of temperature, current density and molten salt conformation. This paper shows a viable way for one-pot CO2 utilization and facile production of micro-scale structure carbon materials, in line with the concept of sustainable development.
Stability Analysis of Surrounding Rock in the Diversion Tunnel at the Xulong Hydropower Station based on RFPA3D and Microseismic Monitoring
Authors: Qian Hongjian , Tan Zhou , Li Biao
Published: October 2022

To study the surrounding rock stability of the excavated geologically weak section of the #2 diversion tunnel in the Xulong Hydropower Station, a quasi-3D numerical model was built using the Realistic Failure Process Analysis (RFPA3D) system to simulate the damage and failure process consisting of crack initiation, growth, and penetration in the rock mass after tunnel excavation, and reveal the instability failure mechanism inside the rock mass. Moreover, the microseismic monitoring technology was employed to delineate potential danger areas in the surrounding rock of the tunnel and explore possible instability failure modes. Results indicate that the surrounding rock of the tunnel profile failed as different degrees during the excavation process, most obviously near the vault and corners of the side wall, where tensile failure predominated. As the excavation proceeded, microseismic events increased gradually at the vault and corners of the side wall, and the energy from acoustic emissions accumulated steadily, thus raising the possibility of collapse and rock bursts in this area. The research results can provide technical support for the construction of the diversion tunnel project in the Xulong Hydropower Station and serve as a guide for the construction of similar geologically weak underground projects.
Reverse Time Migration Imaging Using SH Shear Wave Data
Authors: Zhou Chengyao , Yin Wenjie , Yang Jun , Nie Hongmei , Li Xiangyang
Published: October 2022

In this paper, we discussed the reverse time migration imaging of compressional wave (P-wave) and horizontally polarized shear wave (SH shear wave) seismic data, together with P- and SH shear wave constrained velocity model building. In the Sanhu area in Qaidam Basin, there are large areas of gas clouds, which leads to poor P-wave seismic imaging. The P and SH shear wave seismic data of a co-located survey line with the same acquisition geometry were used to access their imaging capability using reverse time migration. We first estimated the change in P-wave and SH shear wave velocity ratio using pre-stack time migration (PSTM) for constraining the overall depth domain velocity model. Additionally, we then used an eighth-order finite difference scheme for P-wave reverse time migration on a variable grid and used the sixth-order combined compact difference (CCD) wave field simulation method for SH shear wave reverse time migration on a regular grid. The results show that the constrained velocity model produces a good match in the overall geological structure shown in the P-wave and SH shear wave reverse time migration results. However, in the gas cloud areas, SH shear wave reverse time migration has obvious imaging advantages, which can clearly image the structure inside the gas clouds.
A TSV-Structured Room Temperature p-Type TiO2 Nitric Oxide Gas Sensor
Authors: Yeh Yu-Ming , Chang Shoou-Jinn , Wang Pin-Hsiang , Hsueh Ting-Jen
Published: October 2022

Planar MOS/MEMS gas sensors have been widely studied and applied, but the detection of exhaled gas has been little developed. The flow rate of exhaled gas affects the suspension structure of the MEMS gas sensor and the operating temperature of the gas sensor. Therefore, this study uses the Bosch process and the atomic layer deposition (ALD) process to prepare a room-temperature (RT) TSV-structured TiO2 gas sensor. The results indicated that the TiO2 sensing film is uniformed and covers the through-silicon via (TSV) structure and the TiO2 sensing film is confirmed to be a p-type MOS. In terms of gas sensing at room temperature, the response of the sensor increases with the increasing NO concentration. The sensor response is 16.5% on average, with an inaccuracy of <± 0.5% for five cycles at 4 ppm NO concentration. For gas at 10 ppm, the response of the sensor to NO is 24.4%, but the sensor produces almost no response to other gases (CO, CO2, SO2, and H2S). The RT TiO2 gas sensor with a TSV structure exhibits good stability, reversibility, and selectivity to NO gas.
Does Acute Caffeine Intake before Evening Training Sessions Impact Sleep Quality and Recovery-Stress State? Preliminary Results from a Study on Highly Trained Judo Athletes
Authors: Filip-Stachnik Aleksandra
Published: October 2022

No previous study has analyzed the impact of a low caffeine dose ingested before an evening training session on sleep and recovery-stress state. Nine highly trained judo athletes underwent a randomized, double-blind, placebo-controlled crossover experiment in which each athlete acted as their own control. Each athlete performed two identical trials after the ingestion of (i) a placebo and (ii) 3 mg of caffeine per kg of body mass, administered 60 min before an evening randori training session. Sleep was assessed using actigraphy and a Karolinska Sleep Diary (KSD), while the recovery-stress state was assessed using a short recovery and stress scale the morning following the trial. No significant differences were observed in any actigraphy sleep measures between conditions, or in the recovery-stress state (p > 0.05 for all). However, sleep quality assessed using the KSD was worse following caffeine ingestion compared with the placebo (3.0 ± 1.0 vs. 3.9 ± 0.6, respectively; p = 0.03, ES: 1.09). The ingestion of 3 mg/kg of caffeine before an evening training session has no impact on actigraphy-derived sleep measures or recovery-stress state. However, it leads to a substantial decrease in self-reported sleep quality.
Comparison of Machine Learning Algorithms Used for Skin Cancer Diagnosis
Authors: Bistroń Marta , Piotrowski Zbigniew
Published: October 2022

The paper presents a comparison of automatic skin cancer diagnosis algorithms based on analyses of skin lesions photos. Two approaches are presented: the first one is based on the extraction of features from images using simple feature descriptors, and then the use of selected machine learning algorithms for the purpose of classification, and the second approach uses selected algorithms belonging to the subgroup of machine learning—deep learning, i.e., convolutional neural networks (CNN), which perform both the feature extraction and classification in one algorithm. The following algorithms were analyzed and compared: Logistic Regression, k-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine, and four CNN–VGG-16, ResNet60, InceptionV3, and Inception-ResNetV2 In the first variant, before the classification process, the image features were extracted using 4 different feature descriptors and combined in various combinations in order to obtain the most accurate image features vector, and thus the highest classification accuracy. The presented approaches have been validated using the image dataset from the ISIC database, which includes data from two categories—benign and malignant skin lesions. Common machine learning metrics and saved values of training time were used to evaluate the effectiveness and the performance (computational complexity) of the algorithms.
On the Omori Law in the Physics of Earthquakes
Authors: Zavyalov Alexey , Zotov Oleg , Guglielmi Anatol , Klain Boris
Published: October 2022

This paper proposes phenomenological equations that describe various aspects of aftershock evolution: elementary master equation, logistic equation, stochastic equation, and nonlinear diffusion equation. The elementary master equation is a first-order differential equation with a quadratic term. It is completely equivalent to Omori’s law. The equation allows us to introduce the idea of proper time of earthquake source “cooling down” after the main shock. Using the elementary master equation, one can pose and solve an inverse problem, the purpose of which is to measure the deactivation coefficient of an earthquake source. It has been found for the first time that the deactivation coefficient decreases with increasing magnitude of the main shock. The logistic equation is used to construct a phase portrait of a dynamical system simulating the evolution of aftershocks. The stochastic equation can be used to model fluctuation phenomena, and the nonlinear diffusion equation provides a framework for understanding the spatiotemporal distribution of aftershocks. Earthquake triads, which are a natural trinity of foreshocks, main shock, and aftershocks, are considered. Examples of the classical triad, the mirror triad, the symmetrical triad, as well as the Grande Terremoto Solitario, which can be considered as an anomalous symmetrical triad, are given. Prospects for further development of the phenomenology of earthquakes are outlined.
Celector®: An Innovative Technology for Quality Control of Living Cells
Authors: Zia Silvia , Roda Barbara , Maggio Alessia , Marrazzo Pasquale , Pizzuti Valeria , Alviano Francesco , Bonsi Laura , Marassi Valentina , Zattoni Andrea , Reschiglian Pierluigi
Published: October 2022

Among the in vitro and ex vivo models used to study human cancer biology, cancer cell lines are widely utilized. The standardization of a correct tumor model including the stage of in vitro testing would allow for the development of new high-efficiency drug systems. The poor correlation between preclinical in vitro and in vivo data and clinical trials is still an open issue, hence the need for new systems for the quality control (QC) of these cell products. In this work, we present a new technology, Celector®, capable of the label-free analysis and separation of cells based on their physical characteristics with full preservation of their native properties. Two types of cancer cell lines were used: HL60 as cells growing in suspension and SW620 as adherent cells. Cell lines in general show a growth variability depending on the passage and method of culture. Celector® highlights physical differences that can be correlated to cell viability. This work demonstrates the use of Celector® as an analytical platform for the QC of cells used for drug screening, with fundamental improvement of preclinical tests. Cells with a stable doubling time under analysis can be collected and used as standardized systems for high-quality drug monitoring.
Novel Fault Diagnosis of Bearings and Gearboxes Based on Simultaneous Processing of Spectral Kurtoses
Authors: Gelman Len , Persin Gabrijel
Published: October 2022

Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage.
Geometric Nonlinear Analysis of the Catenary Cable Element Based on UPFs of ANSYS
Authors: Xu Binlin , Tian Zhongchu , Deng Jihua , Zhang Zujun
Published: October 2022

The catenary cable element has more advantages than other nonlinear truss elements but is less used in commercial programs. In this paper, the initial geometric configuration of the element is solved iteratively by the dichotomous method. Then the Updated Lagrangian (UL) Formulation for the two-node catenary cable element is combined with the element secondary development tool provided by ANSYS platform-User Programmable Features (UPFs) to develop a three- dimensional cable element—user101. The algorithm and procedure of this paper are verified through examples and a real bridge. The study shows that the developed cable element user101 is more accurate and faster than the ANSYS self-contained element. The method can effectively use the computational theory of nonlinear cable elements with catenary geometry and combine it with ANSYS commercial program, which saves computation time without reducing accuracy and has good practicality.
Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
Authors: Li Ming , Tang Xiafei , Zhang Qichun , Zou Yiqun
Published: October 2022

For the bearing-only target motion analysis (TMA), the pseudolinear Kalman filter (PLKF) solves the complex nonlinear estimation of the motion model parameters but suffers serious bias problems. The pseudolinear Kalman filter under the minimum mean square error framework (PL-MMSE) has a more accurate tracking ability and higher stability compared to the PLKF. Since the bearing signals are corrupted by non-Gaussian noise in practice, we reconstruct the PL-MMSE under Gaussian mixture noise. If some prior information, such as state constraints, is available, the performance of the PL-MMSE can be further improved by incorporating state constraints in the filtering process. In this paper, the mean square and estimation projection methods are used to incorporate PL-MMSE with linear constraints, respectively. Then, the linear approximation and second-order approximation methods are applied to merge PL-MMSE with nonlinear constraints, respectively. Simulation results demonstrate that the constrained PL-MMSE algorithms result in lower mean square errors and bias norms, which demonstrates the superiority of the constrained algorithms.
Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
Authors: Xiong Zhangming , Zhu Daofei , Liu Dafang , He Shujing , Zhao Luo
Published: October 2022

With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data. Aiming at the problem that the training data are contaminated with anomalies in autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposes a data anomaly detection method that combines an isolated forest (iForest) and autoencoder algorithm. In this method (iForest-AE), the iForest algorithm was used to calculate the anomaly score of energy data, and the data with a lower anomaly score were selected for model training. After the test data passed through the autoencoder trained by normal data, the data whose reconstruction error was larger than the threshold were determined as an anomaly. Experiment results on the electricity consumption dataset showed that the iForest-AE method achieved an F1 score of 0.981, which outperformed other detection methods, and a significant advantage in anomaly detection.
Special Issue: Control, Optimization and Planning of Power Distribution Systems
Authors: Soma Gian Giuseppe
Published: October 2022

The use of renewable energy sources is moving the generation from the top to the bottom of power systems, where traditionally only loads existed [...]