bluuebunny/crossref_metadata_2025 huggingface dataset:
An Optimization of Oregano, Thyme, and Lemongrass Essential Oil Blend to Simultaneous Inactivation of Relevant Foodborne Pathogens by Simplex–Centroid Mixture Design
Authors: Torres Neto Luiz , Monteiro Maria , Machado Maxsueli , Galvan Diego , Conte Junior Carlos
Published: November 2022

(1) Background: This study aimed to use the simplex–centroid mixture design methodology coupled with a microdilution assay to predict optimal essential oil (EO) formulations against three potential foodborne pathogens simultaneously through the desirability (D) function. (2) Methods: Oregano (ORE; Origanum vulgare), thyme (THY; Thymus vulgaris), and lemongrass (LG; Cymbopogon citratus) and their blends were evaluated concerning minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for Salmonella enterica serotype Enteritidis, Escherichia coli and Staphylococcus aureus. (3) Results: THY combined with ORE or LG were the most promising EO formulations in inhibiting and killing each bacterium separately. Regarding the simultaneous effect, the optimal proportion for maximum inhibition was composed of 75% ORE, 15% THY, and 10% LG, while for maximum inactivation was 50% ORE, 40% THY, and 10% LG. (4) Conclusion: The multiresponse optimization allowed identifying an EO blend to simultaneously control three potential foodborne pathogens. This first report could be a helpful natural and green alternative for the industry to produce safer food products and mitigate public health risks.
Study on the Influence of Internal Water Pressure on the Internal Force of Circular Hydraulic Tunnel Lining
Authors: Zhu Yueyue , Liu Cheng
Published: November 2022

The internal water pressure condition influences the internal force of the circular hydraulic tunnel lining. However, calculating the lining’s internal force of this type of tunnel still lacks practical theory. Based on the modified routine method and the theory of structural mechanics, the internal stress model of the tunnel section is established in this paper. The general calculation formula of lining internal force is deduced by considering arbitrary water level height and different water conveyance pressures. The formula is used to calculate the internal force of the lining under the action of internal water pressure and the influence laws of water level height and water conveyance pressure are explored, respectively. In addition, case analysis was carried out for several typical projects. The results show that the maximum internal force of the lining increases with the increase of water pressure and inner radius and the maximum internal force is in a fixed special position when the water is conveyed under pressure. When the water is conveyed without pressure, the internal force of the lining will increase with the increased water level. However, the maximum bending moment and axial force will reduce at the special water level. This calculation theory considering different working conditions of internal water pressure solves the calculation problem of the internal force of a circular hydraulic tunnel. It improves the design theory of tunnel structure and provides a theoretical basis for this type of tunnel’s structural design and safe operation.
Mechanical Properties of the Functionally Graded Lining for a Deep Buried Subway Tunnel
Authors: Li De-Chun , Zhang Tong-Tong , Cui Zhen-Dong , Chen Jin-Ming , Xu Xiang-Qing , Xu Chong , Zhang Zhao-Wei , Song Gang
Published: November 2022

With the rapid development of the subway rail transit, tunnels are buried at an increasing depth, raising the requirements of bearing capacity and waterproofness for linings. Functionally graded materials are introduced into the design of linings to save costs, and concrete with different elastic moduli is equipped at different positions to reduce the waste of materials, compared to the homogeneous lining. The significance of this study includes that the functionally graded lining for the buried subway tunnel is under the non-uniform confining pressure and the calculation model of internal force and deformation for the functionally graded lining is established. The elastic modulus of the lining is set to vary with the angle in the form of a power function, and the function parameters are analyzed on the basis of this model. The results show that the radial displacement of the lining axis decreases with the increase in a and b, but the deformation mode remains the same, and the reduction in deformation is smaller and smaller. With the increase in a and b, the distribution trend of the moment remains the same. The lateral pressure coefficient λ has a great impact on the safety of the structure, which exceeds the influence of the function parameters on the safety of the structure. The displacement of the lining axis and the section moment change linearly with the increase in λ. With the increase in λ, the shape of the lining changes significantly, which shows that the side with larger pressure deforms to the inside and the side with smaller pressure expands to the outside. When the maximum deformation occurs at 0°, the parameter a should be larger than b. When the maximum deformation occurs at 90°, the parameter b should be larger than a, so as to minimize the cost of materials and reduce the structural deformation. Finally, the numerical simulation is conducted to verify the theoretical results, showing that the calculation model of internal force and deformation is suitable for the cylinder with t/R≤0.2, and there is a certain gap between the theoretical calculation and numerical simulation, but the largest gap of the displacement is within 8%. Compared with Function I, Function II has some advantages in reducing the maximum deformation of the structure, but the advantages are relatively low. The analysis results have significant reference value for designers and relevant scholars.
The Effectiveness of Dental Bleaching during Orthodontic Treatment with Clear Aligners: A Systematic Review
Authors: Khashashneh Majd , Ratnayake Jithendra , Choi Joanne Jung Eun , Mei Li , Lyons Karl , Brunton Paul A.
Published: November 2022

The objective of this article was to systematically review the literature to determine how effective tooth whitening procedures are when carried out in tandem with orthodontic clear aligners. This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Online searches of PubMed, Medline via Ovid, Science Direct, the Cochrane database of systematic reviews via Ovid, Scopus, and Google Scholar as well as manual searches of the references of the selected articles were conducted. Clinical studies which assessed the efficacy of dental bleaching using orthodontic clear aligners were included in the present study. Data from the included studies were extracted and assessed for risk of bias using the Cochrane risk of bias tool. Three studies met the inclusion and exclusion criteria and were included in this study. One study compared the efficiency of tooth bleaching between clear aligners and conventional bleaching trays. One study evaluated the effectiveness of dental bleaching using clear aligners by comparing the tooth shade of those patients to a control group of patients who did not receive dental bleaching during orthodontic clear aligner treatment. One study evaluated the effectiveness of different concentrations of different bleaching agents using clear aligners. All the included studies showed an improved tooth shade when using clear aligners as bleaching trays.
A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
Authors: Tan Chengjun , Zhao Hua , Uddin Nasim , Yan Banfu
Published: November 2022

Recently, the ‘drive-by’ or vehicle scanning technique has attracted increasing attention over the last decade for the purpose of bridge health monitoring. The feasibility of this technique has been demonstrated by many field tests. In comparison to conventional bridge SHM, the concept of the drive-by bridge technique shows many advantages in terms of efficiency, economy, convenience, and mobility. It has been verified that wavelet transforms can successfully identify bridge damage and its location using the responses of a moving vehicle. However, the validity of this method is challenged by road roughness. This paper proposes a wavelet-based approach to detect bridge defects using wavelet energy. In addition, a damage index based on component wavelet energy is developed to localize the damage. A numerical simulation is modeled to verify the feasibility of the proposed approach, and the result shows that the proposed approach performs well even when considering road roughness in the vehicle and bridge interaction. Moreover, the effects of road surface profile, vehicle velocity, vehicle mass, noise signal, and different damage severity on the proposed approach are investigated. The proposed approach shows a great potential application in bridge health monitoring using indirect measurements from a moving vehicle.
Three-Phase Fault Arc Phase Selection Based on Global Attention Temporal Convolutional Neural Network
Authors: Yu Qiongfang , Zhao Liang , Yang Yi
Published: November 2022

For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method based on a global temporal convolutional network. First, this method builds a low-voltage three-phase arc fault data acquisition platform and establishes a dataset. Second, the experimental data were decomposed by variational mode decomposition and analyzed in the time-frequency domain. The decomposed data are reconstructed and used as input to the model. Finally, in order to reduce the fault features lost during the causal convolution operation, the global attention mechanism is used to extract deep fault characterization to identify faults and their differences. The experimental results show that the accuracy of the three-phase arc fault arc phase selection of the model can reach 98.62%, and the accuracy of single-phase fault detection can reach 99.39%. This model can effectively extract three-phase arc fault and phase characteristics. This paper provides a new idea for series fault arc detection and three-phase fault arc phase selection research.
Analysis of Jet Structure and Physical Properties in the Coalfields of Northern China
Authors: Gao Yuping , Gao Jinyu , Hu Kaitong , Ma Yufei , Zhang Yuanzhi
Published: November 2022

Archeological discoveries have identified China as one of the first countries in the world to use jet. However, many differences are evident between the jet currently found in existing mines and the archaeological discoveries of cultural relics in terms of texture and quality according to the definition of organic gem jet in gemology. This paper reports the results of microscopic analysis and coal quality analysis of the coal and jet samples from coal seams in Fushun Open-pit Mine and Datong Coalfield. The findings reveal that the physical and chemical composition of coal in different mining areas differs markedly. However, the differences between jet and coal in both mining areas are similar; that is, jet has lighter density and greater hardness (2–4) compared to coal, as well as elasticity (engravability), and both jet and coal occur in the (rock slurry) hydrothermal environment. Lastly, the analysis shows that the formation of jet depends on rubber-like hydrocarbon coal with a high degree of corruption in a sedimentary environment under the vulcanization of a hydrothermal, high-sulfur environment.
Geometric–Statistical Model for Middle-Ear Anatomy and Ventilation
Authors: Rădulescu Marian , Mocanu Adela-Ioana , Dascălu Ionela Teodora , Schipor Mihai-Adrian , Mocanu Horia
Published: November 2022

The ventilation of the middle-ear (ME) is achieved by the mucosa covering the bony cavities of this segment, which we have previously defined as consisting of two distinct epithelial areas, each representing an independent organ with characteristic function: the D-Organ and the F-Organ. The D-Organ corresponds to the epithelium covering the Antrum walls (belonging to the central cavities of the middle-ear) and the walls of mastoid and petrous cavities (peripheral cavities of the ME); it ensures the D-Function, the biophysical process comparable to that of energy-consuming ionic membrane pumps, works against electrical trans-membrane gradients to transfer gas molecules against trans-membrane and trans-cellular pressure gradients. The F-Organ corresponds to the epithelium covering the Protympanum, Tympanic Cavity and Aditus ad Antrum (central cavities of ME). The F-Function is represented by the permeability of cell membranes for respiratory gases. This is a general function of all cells and the size of the cellular membrane surface (luminal and basal) and the height of the cell (distance between the two membranes) determines the diffusion flow for each molecular type of gas. The present work aims to give an original point of view on middle-ear geometry and precedence over ME mucosa affliction or structural-anatomic type of the mastoid (pneumatic, pneumato-diploic, diploic, sclerotic). This type of approach to the problem has never been attempted since the two organs have never been previously defined. We aim to establish a clear topographic structure for these two organs within the reference system represented by the anatomy of ME cavities and to establish the reasons why the mastoid and petrous cavitary system grow or stop growing at a certain point in the life of an individual.
Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study
Authors: Gupta Praveen Kumar , Ghosh Goutam , Kumar Virendra , Paramasivam Prabhu , Dhanasekaran Seshathiri
Published: November 2022

Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved continuous bridge under various seismic loadings. The effectiveness of LRBs on the bridge response control was determined by considering various aspects, such as the changes in ground motion characteristics, multidirectional effects, the degree of seismic motion, and the variation of incident angles. Three recorded ground motions were considered in this study, representing historical earthquakes with near-field, far-field, and forward directivity effects. The effectiveness of the bi-directional behavior considering the interaction effect of the bearing and pier was also studied. The finite element method was adopted. A sensitivity study of the bridge response related to the bearing design parameters was carried out for the considered ground motions. The importance of non-linearity and critical design parameters of LRBs were assessed. It was found that LRBs resulted in a significant increase in deck displacement for Turkey ground motion, which might be due to the forward directivity effect. The bi-directional effect is crucial for the curved bridge as it enhances the displacement significantly compared to uni-directional motion.
Section Margin Allocation Method for Renewable Energy Power Generation Clusters Considering the Randomness of Source and Load Power
Authors: Yuan Jingbo , Huang Chun , Shen Yatao , Liu Yingtong
Published: November 2022

It is difficult to adapt the traditional section margin distribution method to the power system with a high proportion of renewable energy generation (REG). This paper proposes a section margin allocation method of REG clusters considering the randomness of source and load power. Firstly, the probability density function of the predicted REG power error is estimated, the probability that REG stations meet the output command after the section margin distribution is calculated, and the output realization probability of each REG station is discussed. The typical operation mode set of the REG cluster is then obtained by clustering the REG stations according to the operation history, and the load rise space of the REG cluster under each typical operation mode is calculated. Considering the randomness of REG power and its load power, the section margin is allocated to each REG station in the REG cluster so as to ensure that each REG station reaches the highest output probability. On the premise of ensuring the safe and stable operation of the grid, this method facilitates the management of REG clusters and the accommodation capacity of the power system for REG. Simulations of the power system of IEEE 39 nodes verify the rationality and validity of the section margin allocation method.
Development of a Finite Element Model of the Cervical Spine and Validation of a Functional Spinal Unit
Authors: Silva Afonso J. C. , Alves de Sousa Ricardo J. , Fernandes Fábio A. O. , Ptak Mariusz , Parente Marco P. L.
Published: November 2022

The cervical spine is a common site of injury in the vertebral column, with severe injuries often associated with damage to the spinal cord. Several studies have been performed to better understand the mechanisms of such situations and develop ways to treat or even prevent them. Among the most advantageous and most widely used methods are computational models, as they offer unique features such as providing information on strains and stresses that would otherwise be difficult to obtain. Therefore, the main objective of this work is to help better understand the mechanics of the neck by creating a new finite element model of the human cervical spine that accurately represents most of its components. The initial geometry of the cervical spine was obtained using the computer tomography scans of a 46-year-old female. The complete model was then sectioned, and a functional spinal unit consisting of the C6–C7 segment was simulated to initiate the validation process. The reduced model was validated against experimental data obtained from in vitro tests that evaluated the range of motion of various cervical segments in terms of flexion–extension, axial rotation, and lateral bending.
Functionality Analysis of Derailment Containment Provisions through Full-Scale Testing—I: Collision Load and Change in the Center of Gravity
Authors: Bae Hyun-Ung , Kim Kyoung-Ju , Park Sang-Yun , Han Jeong-Jin , Park Jong-Chan , Lim Nam-Hyoung
Published: November 2022

In order to reduce the large damage caused by train derailment, protective facilities of various shapes and conditions can be installed on railroad tracks. These protective facilities are referred to as derailment containment provisions (DCPs) and three different types are used worldwide. However, there are no clear standards for DCP design such as installation location, size, and design load, and the performance verification of DCPs installed in the actual railway field is not sufficiently performed. In this paper, the functionality of DCP type I was analyzed experimentally. A method for estimating the collision (impact) load acting on the DCP was proposed. In addition, the containment effect of DCP type I according to the change in the vehicle’s center of gravity was identified through a comparative analysis of the dynamic motion such as roll, pitch, and yaw.
CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection
Authors: Shen Yanping , Zheng Kangfeng , Yang Yanqing , Liu Shuai , Huang Meng
Published: November 2022

Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer.
Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
Authors: Tan Jiyuan , Zhang Limin , Zhong Zhaogen
Published: November 2022

Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification.
Study of the Evolution of the Performance Ratio of Photovoltaic Plants Operating in a Utility-Scale Installation Located at a Subtropical Climate Zone Using Mixed-Effects Linear Modeling
Authors: Montes Carlos , Dorta-Guerra Roberto , González-Díaz Benjamín , González-Pérez Sara , Ocaña Luis , Llarena Elena
Published: November 2022

This paper assessed the evolution of the performance ratio (PR) of a utility-scale photovoltaic (PV) installation that operates at subtropical climate conditions. The period of study encompassed 8 years, and the PR was calculated according to the ICE 61724 standard with a monthly resolution. A linear mixed effects model (LME) is a suitable tool for analyzing longitudinal data. Three LME models were assessed to provide the degradation rate. The “null model” evaluates the general relationship between PR and time with a monthly declination rate (ΔPR%) of 0.0391%/month. The “typology model” considered the relationship between PR and, as covariates, time, Manufacturer, Technology, and NominalP. Only the ΔPR% related to NominalP was found to be significant, so that, when the nominal power of a type of PV module used for a PV production unit is increased by one unit, the ΔPR% of the corresponding unit increases by 0.000897%/month. Finally, the “location model” took into account the relationship between PR and, as covariates, time, Edge, and LengthSt. These last two factors were significant, resulting in an increase of 0.0132%/month for a PV unit located at the edge of the facility and 0.00117%/month and per PV production unit when considering the length of a street, respectively.
Virtual and Augmented Reality as a Novel Opportunity to Unleash the Power of Radiotherapy in the Digital Era: A Scoping Review
Authors: Marvaso Giulia , Pepa Matteo , Volpe Stefania , Mastroleo Federico , Zaffaroni Mattia , Vincini Maria Giulia , Corrao Giulia , Bergamaschi Luca , Mazzocco Ketti , Pravettoni Gabriella , Orecchia Roberto , Jereczek-Fossa Barbara Alicja
Published: November 2022

Although radiation therapy (RT) provides several therapeutic advantages in terms of cancer control and quality of life, it continues to be a poorly understood field by most students and health workers. Theoretical lessons are not sufficient, while practical exercitations are time-consuming, both in terms of man- and machine-hours. Furthermore, RT candidates often have several prejudices that may affect their treatment choices, favoring the more well-known surgical or chemotherapy approaches or resulting in a high level of anxiety during treatment. Moreover, the misperception of low treatment control and its related side effects could worsen the patients’ psychological distress, already brought by a cancer diagnosis. Augmented reality (AR) and virtual reality (VR) could be a valid instrument for promoting the awareness of radiation oncology as a discipline with its own identity and respect in the scientific community. The aim of the present work is to provide a glance at the recent developments in AR/VR to support students’ education, personnel training and patients’ empowerment in this clinical setting. The main findings of our work show that such technologies have already become a reality in many institutions worldwide and it has been shown to be an effective strategy for raising educational standards, improving health workers’ skills and promoting patients’ well-being and compliance. These results seem to promote the further implementation of AR/VR technologies and their development as a driving force of a much-hoped-for revolution in the way patients are treated and radiation oncology is taught.
Local Site Effects Investigation in Durres City (Albania) Using Ambient Noise, after the 26 November 2019 (M6.4) Destructive Earthquake
Authors: Theodoulidis Nikos , Dushi Edmond , Duni Llambro , Grendas Ioannis , Panou Areti , Hajrullai Ardit , Kuka Neki , Koci Rexhep
Published: November 2022

Site characterization of metropolitan areas, especially after an earthquake, is of paramount importance for interpretation of spatial damage distribution and taking measures that assure realistic design actions to strengthen existing constructions and create new ones. Such a case is the city of Durres, Albania, that was hit by the disastrous earthquake of 26 November 2019 (M6.4). Significant differences in structural damage were observed throughout the city, despite its uniform epicentral distance (approximately 15 km); this could be either due to varying vulnerability of the affected constructions and/or to spatial variation of strong ground motion in the city, resulting from local site effects; the latter factor was investigated in this study. This was achieved by taking single station ambient noise measurements throughout the city, at approximately 80 sites. Ambient noise measurements are favorable, as acquiring ambient noise data is an easy and effective noninvasive approach within urban environments. Measurements were processed using the widely applied Horizontal-to-Vertical Spectral Ratio (HVSR) method, following the SESAME project (2004) guidelines. Their fundamental and dominant frequencies, fo and fd, respectively, were calculated and related to the iso-depth contours of the investigated area, as well as their corresponding amplitudes, Ao, and Ad. These experimental parameters and the HVSR curves were used to group all examined sites into classes with similar properties. This clustering provided a zonation map with four categories consisting of similar shapes and amplitudes, applicable to the city of Durres. This map can be utilized as a first level zonation of local site effects for the city. In addition, dynamic properties of soil profiles in selected sites were investigated and tested using 1D synthetic ambient noise data, based on the Hisada (1994, 1995) simulation method, and compared to experimental HVSRs in proximity to the selected sites. A comparison of the proposed four categories zonation map to the observed damage of the 26 November 2019, mainshock is attempted and evaluated. The four categories zonation map with similar expected local site effects proposed in this study can be used as a first level seismic microzonation of Durres. Undoubtedly, corrections for 2D/3D effects on ground shaking must be applied to sites lying in the edges of the Durres basin.
Developments in 3D Visualisation of the Rail Tunnel Subsurface for Inspection and Monitoring
Authors: McDonald Thomas , Robinson Mark , Tian Gui Yun
Published: November 2022

Railway Tunnel SubSurface Inspection (RTSSI) is essential for targeted structural maintenance. ‘Effective’ detection, localisation and characterisation of fully concealed features (i.e., assets, defects) is the primary challenge faced by RTSSI engineers, particularly in historic masonry tunnels. Clear conveyance and communication of gathered information to end-users poses the less frequently considered secondary challenge. The purpose of this review is to establish the current state of the art in RTSSI data acquisition and information conveyance schemes, in turn formalising exactly what constitutes an ‘effective’ RTSSI visualisation framework. From this knowledge gaps, trends in leading RTSSI research and opportunities for future development are explored. Literary analysis of over 300 resources (identified using the 360-degree search method) informs data acquisition system operation principles, common strengths and limitations, alongside leading studies and commercial tools. Similar rigor is adopted to appraise leading information conveyance schemes. This provides a comprehensive whilst critical review of present research and future development opportunities within the field. This review highlights common shortcomings shared by multiple methods for RTSSI, which are used to formulate robust criteria for a contextually ‘effective’ visualisation framework. Although no current process is deemed fully effective; a feasible hybridised framework capable of meeting all stipulated criteria is proposed based on identified future research avenues. Scope for novel analysis of helical point cloud subsurface datasets obtained by a new rotating ground penetrating radar antenna is of notable interest.
TBRm: A Time Representation Method for Industrial Knowledge Graph
Authors: Cao Keyan , Zheng Chuang
Published: November 2022

With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security.
Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks
Authors: Peralta Billy , Sepúlveda Tomás , Nicolis Orietta , Caro Luis
Published: November 2022

Currently, air pollution is a highly important issue in society due to its harmful effects on human health and the environment. The prediction of pollutant concentrations in Santiago de Chile is typically based on statistical methods or classical neural networks. Existing methods often assume that historical values are known at a fixed geographic point, such that air pollution can be predicted at a future hour using time series analysis. However, these methods are inapplicable when it is necessary to know the pollutant concentrations at every point of the space. This work proposes a method that addresses the space-time prediction of PM2.5 concentration in Santiago de Chile at any spatial points through the use of the LSTM recurrent network model. In particular, by considering historical values of air pollutants (PM2.5, PM10 and nitrogen dioxide) and meteorological variables (temperature, wind speed and direction and relative humidity), measured at fixed monitoring stations, the proposed model can predict PM2.5 concentrations for the next 24 h in a new location where measurements are not available. This work describes the experiments carried out, with particular emphasis on the pre-processing step, which constitutes an important factor for obtaining relatively good results. The proposed multilayer LSTM model obtained R2 values equal to 0.74 and 0.38 in seven stations when considering forecasts of 1 and 24 h, respectively. As future work, we plan to include more input variables in the proposed model and to use attention-based networks.
Track Line Recognition Based on Morphological Thinning Algorithm
Authors: Niu Weilong , Chen Zan , Zhu Yihui , Sun Xiaoguang , Li Xuan
Published: November 2022

In the field of intelligent driving of freight trains, determining the track line ahead of the train is an important function in the autopilot technology of such trains. Combining the characteristics of freight railway tracks, we conduct an in-depth analysis of the shortcomings of object detection technology in extracting track lines and propose an improved Zhang–Suen (ZS) thinning theory for a railway track line recognition algorithm. Through image preprocessing and single pixel thinning steps, a continuous track line is obtained and then processed by a denoising algorithm to obtain a complete track line. Experimental results show that the track extracted by our method has good continuity and less noise. It can simultaneously perform track detection on straight roads, curves and turnouts, and is suitable for changing weather conditions such as sunny daytime, mild rainy daytime, cloudy daytime, night with lamp lighting and night without lamp lighting conditions.
Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
Authors: Macias-Aguayo Jaime , Garcia-Castro Lizzi , Barcia Kleber F. , McFarlane Duncan , Abad-Moran Jorge
Published: November 2022

In recent years, Industry 4.0 (I4.0) has been a recurrent theme in the literature on Lean Six Sigma (LSS), given the synergies that can arise from their combination. However, their joint implementation presents several challenges. In this article, a systematic literature review (SLR) of research on I4.0 and LSS integration was performed. This review involved five database platforms and included seventy-four articles providing state-of-the-art knowledge on the topic, focusing on the barriers to and enablers of integration. As a result, 20 integration barriers were identified, highlighting the high implementation cost, long learning curve, and technology incompatibility as the main barriers. Seventeen enablers were found to facilitate and guarantee implementation success, highlighting investment in IT infrastructure and employee training, stakeholder involvement, and top management support. In addition, the article discusses actions to facilitate I4.0 and LSS integration in practice, determined by connecting the identified enablers to their corresponding barriers. Finally, the SLR identifies several avenues for future research.
Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
Authors: Bist Ramesh Bahadur , Chai Lilong
Published: November 2022

Poultry farming plays a key role in agricultural air emissions. Particulate matter (PM) level tends to be high in broiler and cage-free layer houses, that may impair health and welfare of animals and their caretakers. To protect public health and welfare, the occupational exposure limit for PM10 and PM2.5 (i.e., PM diameters that are generally ≤10 and 2.5 μm, respectively) are suggested not to exceed 150 µg m−3 and 35 µg m−3, respectively, based on 24-h concentrations thresholds as suggested by US. EPA. However, the levels of PM10 and PM2.5 in poultry houses could be 100 times higher than that limit. For instance, PM10 and PM2.5 levels in cage-free henhouses are higher than 15,000 µg/m3 and 3500 µg/m3 in wintertime. Therefore, it is critical to identify the primary factors affecting PM generation in poultry houses and apply corresponding mitigation strategies. This review paper summarizes PM emission factors, mitigating strategies, and impacts on birds’ and caretakers’ health, and welfare. Generally, PM emissions are affected by various factors, including housing types, seasonal and diurnal variation, manure management, bedding materials, ventilation rates, and birds’ activities. High PM concentrations in poultry houses impair birds’ and caretakers’ liver, kidneys, and respiratory systems. Thus, different mitigating strategies are discussed in this study for addressing those issues. Effective mitigation strategies include frequent house cleaning, optimum light intensity, liquid spraying, bedding management, and air filtration systems. However, mitigation strategies can be cost-prohibitive and have side effects. Therefore, poultry farms should select mitigation strategies based on farm location, climate conditions, environmental policies, and available resources (government assistance programs).
Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
Authors: Hahn Bongsu
Published: November 2022

In this paper, we propose a conceptual framework for a sensor fusion system that can detect objects in a dense smoke environment with a visibility of less than 1 m. Based on the review of several articles, we determined that by using a single thermal IR camera, a single Frequency-Modulated Continuous-Wave (FMCW) radar, and multiple ultrasonic sensors simultaneously, the system can overcome the challenges of detecting objects in dense smoke. The four detailed methods proposed are as follows: First, a 3D ultrasonic sensor system that detects the 3D position of an object at a short distance and is not affected by temperature change/gradient; Second, detecting and classifying objects such as walls, stairs, and other obstacles using a thermal IR camera; Third, a 2D radial distance measurement method for a distant object using an FMCW radar; Fourth, sensor fusion for 3D position visualization of multiple objects using a thermal IR camera, 3D ultrasonic sensor system, and FMCW radar. Finally, a conceptual design is presented based on the proposed methodologies, and their theoretical usefulness is discussed. The framework is intended to motivate future research on the development of a sensor fusion system for object detection in dense smoke environments.
Common-Mode Driven Synchronous Filtering of the Powerline Interference in ECG
Authors: Neycheva Tatyana , Dobrev Dobromir , Krasteva Vessela
Published: November 2022

Powerline interference (PLI) is a major disturbing factor in ground-free biopotential acquisition systems. PLI produces both common-mode and differential input voltages. The first is suppressed by a high common-mode rejection ratio of bioamplifiers. However, the differential PLI component evoked by the imbalance of electrode impedances is amplified together with the diagnostic differential biosignal. Therefore, PLI filtering is always demanded and commonly managed by analog or digital band-rejection filters. In electrocardiography (ECG), PLI filters are not ideal, inducing QRS and ST distortions as a transient reaction to steep slopes, or PLI remains when its amplitude varies and PLI frequency deviates from the notch. This study aims to minimize the filter errors in wide deviation ranges of PLI amplitudes and frequencies, introducing a novel biopotential readout circuit with a software PLI demodulator–remodulator concept for synchronous processing of both differential-mode and common-mode signals. A closed-loop digital synchronous filtering (SF) algorithm is designed to subtract a PLI estimation from the differential-mode input in real time. The PLI estimation branch connected to the SF output includes four stages: (i) prefilter and QRS limiter; (ii) quadrature demodulator of the output PLI using a common-mode driven reference; (iii) two servo loops for low-pass filtering and the integration of in-phase and quadrature errors; (iv) quadrature remodulator for synthesis of the estimated PLI using the common-mode signal as a carrier frequency. A simulation study of artificially generated PLI sinusoids with frequency deviations (48–52 Hz, slew rate 0.01–0.1 Hz/s) and amplitude deviations (root mean square (r.m.s.) 50–1000 μV, slew rate 10–200 μV/s) is conducted for the optimization of SF servo loop settings with artificial signals from the CTS-ECG calibration database (10 s, 1 lead) as well as for the SF algorithm test with 40 low-noise recordings from the Physionet PTB Diagnostic ECG database (10 s, 12 leads) and CTS-ECG analytical database (10 s, 8 leads). The statistical study for the PLI frequencies (48–52 Hz, slew rate ≤ 0.1 Hz/s) and amplitudes (≤1000 μV r.m.s., slew rate ≤ 40 μV/s) show that maximal SF errors do not exceed 15 μV for any record and any lead, which satisfies the standard requirements for a peak ringing noise of < 25 μV. The signal-to-noise ratio improvement reaches 57–60 dB. SF is shown to be robust against phase shifts between differential- and common-mode PLI. Although validated for ECG signals, the presented SF algorithm is generalizable to different biopotential acquisition settings via surface electrodes (electroencephalogram, electromyogram, electrooculogram, etc.) and can benefit many diagnostic and therapeutic medical devices.
Long-Term Water Quality Prediction Using Integrated Water Quality Indices and Advanced Deep Learning Models: A Case Study of Chaohu Lake, China, 2019–2022
Authors: Yao Siyi , Zhang Yongheng , Wang Peng , Xu Zhipeng , Wang Yongmei , Zhang Youhua
Published: November 2022

The rapid development of urban industrialization has had many negative effects on the quality of water sources around cities. Long-term prediction of water quality can be of great help to the conservation of water environment. This case tries to use several popular deep learning models, such as RNN, LSTM, MLP, and Transformer-based models to predict the long-term integrated water quality index in the Chaohu Lake area. The dataset is derived from daily monitoring data from four monitoring sites within Chaohu Lake from 2019 to 2022, and the long-term prediction performance of the model is evaluated using MAE and MSE as evaluation metrics. The experimental results showed that all models selected in this case achieved good results within the study area, but Informer performed more prominently (MSE = 0.2455, MAE = 0.2449) as the length of the prediction series increased. Our results demonstrate the effectiveness of popular deep learning models in the field of WQI prediction, especially the significant advantage of transformer-based models represented by Informer in long-term water quality prediction, which will further provide an effective modern tool for water quality monitoring and management.
Forensic Analysis of Novel SARS2r-CoV Identified in Game Animal Datasets in China Shows Evolutionary Relationship to Pangolin GX CoV Clade and Apparent Genetic Experimentation
Authors: Jones Adrian , Massey Steven E. , Zhang Daoyu , Deigin Yuri , Quay Steven C.
Published: November 2022

Pangolins are the only animals other than bats proposed to have been infected with SARS-CoV-2 related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic. Here, we examine the novel SARS2r-CoV we previously identified in game animal metatranscriptomic datasets sequenced by the Nanjing Agricultural University in 2022, and find that sections of the partial genome phylogenetically group with Guangxi pangolin CoVs (GX PCoVs), while the full RdRp sequence groups with bat-SL-CoVZC45. While the novel SARS2r-CoV is found in 6 pangolin datasets, it is also found in 10 additional NGS datasets from 5 separate mammalian species and is likely related to contamination by a laboratory researched virus. Absence of bat mitochondrial sequences from the datasets, the fragmentary nature of the virus sequence and the presence of a partial sequence of a cloning vector attached to a SARS2r-CoV read suggests that it has been cloned. We find that NGS datasets containing the novel SARS2r-CoV are contaminated with significant Homo sapiens genetic material, and numerous viruses not associated with the host animals sampled. We further identify the dominant human haplogroup of the contaminating H. sapiens genetic material to be F1c1a1, which is of East Asian provenance. The association of this novel SARS2r-CoV with both bat CoV and the GX PCoV clades is an important step towards identifying the origin of the GX PCoVs.
Electrical Stunning Has the Potential to Delay Fillet Degradation Post-Harvest in Red Seabream (Pagrus major)
Authors: Angelakopoulos Rafael , Dimitroglou Arkadios , Papaharisis Leonidas , Moutou Katerina A.
Published: November 2022

Ensuring the humane harvest of farmed fish without compromising the quality of the fresh product is paramount to the welfare of fish and in meeting consumer demands. Electrical stunning is a quick and effective way to render fish unconscious and it has emerged as the suggested harvest method by EFSA and OIE. The present study evaluated the effects of electrical stunning on the biochemical processes that lead to fillet degradation postmortem, in the red seabream (Pagrus major). Two distinct electrical stunning conditions (low and high) were compared along with the conventional harvest method (an ice slurry). The activity patterns of calpain, collagenase, and cathepsin B and L were assessed and compared to stereological changes in white muscles at different time points up to 13 days post-harvest. Histological examinations, independent of the harvest technique, revealed a progressively declining trend in fiber volume density and increasing interfibrillar spaces over time, indicative of degradation activity within and between the muscle fibers. Strong correlations between the stereological measures and the individual protease activities were recorded. The higher current condition (electric field 1.8 V/cm and velocity 1.6 m/s) consistently exhibited the lowest protease activity levels and the slowest pace of stereological changes, making it the suggested method of all harvest methods explored.