bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficacy of Infection Eradication in Antibiotic Cement-Coated Intramedullary Nails for Fracture-Related Infections, Nonunions, and Fusions
Authors: Conway Janet D. , Elhessy Ahmed H. , Galiboglu Selin , Patel Nirav , Gesheff Martin G.
Published: May 2022

Antibiotic cement-coated intramedullary nails (ACCINs) are increasing in popularity as a viable solution for the treatment of fracture-related infections (FRIs), infected long bone nonunions, and arthrodeses without an external fixator. ACCINs effectively manage to fulfill three of the basic principles for eradicating osteomyelitis: dead space management, antibiotic delivery, and bone stability. We performed a retrospective review of 111 patients who were treated with ACCINs between January 2014 and December 2020. In our series, 87.4% (n = 97) of patients achieved healed and uninfected bone or stable arthrodesis at a mean follow-up of 29.2 months (range, 6–93 months). Additionally, 69.1% (n = 67) of healed patients were resolved after only one procedure, and the remainder (30.9%, n = 30) healed after one or more additional procedures. The mean number of additional procedures was 2.1 (range, 1–6 additional procedures). The overall limb salvage rate was 93.7% (n = 104). The majority of the total cohort were successfully treated in only one surgery. This study suggests that ACCINs are effective in the treatment of FRIs, infected long bone nonunions, and infected ankle and knee arthrodeses.
A Whiff of Sulfur: One Wind a Day Keeps the Doctor Away
Authors: Tiganescu Eduard , Lämmermann Markus Alexander , Ney Yannick , Abdin Ahmad Yaman , Nasim Muhammad Jawad , Jacob Claus
Published: May 2022

Reactive Sulfur Species (RSS), such as allicin from garlic or sulforaphane from broccoli, are fre-quently associated with biological activities and possible health benefits in animals and humans. Among these Organic Sulfur Compounds (OSCs) found in many plants and fungi, the Volatile Sulfur Compounds (VSCs) feature prominently, not only because of their often-pungent smell, but also because they are able to access places which solids and solutions cannot reach that easily. Indeed, inorganic RSS such as hydrogen sulfide (H2S) and sulfur dioxide (SO2) can be used to lit-erally fumigate entire rooms and areas. Similarly, metabolites of garlic, such as allyl methyl sulfide (AMS), are formed metabolically in humans in lower concentrations and reach the airways from inside the body as part of one’s breath. Curiously, H2S is also formed in the gastrointestinal tract by gut bacteria, and the question of if and for which purpose this gas then crosses the barriers and enters the body is indeed a delicate matter for equally delicate studies. In any case, nature is surprisingly rich in such VSCs, as fruits (for instance, the infamous durian) demonstrate, and therefore these VSCs represent a promising group of compounds for further studies.
Anti-Oxidative Therapy in Islet Cell Transplantation
Authors: Eguchi Natsuki , Damyar Kimia , Alexander Michael , Dafoe Donald , Lakey Jonathan R. T. , Ichii Hirohito
Published: May 2022

Islet cell transplantation has become a favorable therapeutic approach in the treatment of Type 1 Diabetes due to the lower surgical risks and potential complications compared to conventional pancreas transplantation. Despite significant improvements in islet cell transplantation outcomes, several limitations hamper long-term graft survival due to tremendous damage and loss of islet cells during the islet cell transplantation process. Oxidative stress has been identified as an omnipresent stressor that negatively affects both the viability and function of isolated islets. Furthermore, it has been established that at baseline, pancreatic β cells exhibit reduced antioxidative capacity, rendering them even more susceptible to oxidative stress during metabolic stress. Thus, identifying antioxidants capable of conferring protection against oxidative stressors present throughout the islet transplantation process is a valuable approach to improving the overall outcomes of islet cell transplantation. In this review we discuss the potential application of antioxidative therapy during each step of islet cell transplantation.
Phytochemical Characterization of Taxus baccata L. Aril with Emphasis on Evaluation of the Antiproliferative and Pro-Apoptotic Activity of Rhodoxanthin
Authors: Dumitraş Daria-Antonia , Bunea Andrea , Vodnar Dan Cristian , Hanganu Daniela , Pall Emoke , Cenariu Mihai , Gal Adrian Florin , Andrei Sanda
Published: May 2022

Taxus baccata L., an evergreen tree, was known until recently due to its high concentration of toxic compounds. The purpose of the present study was to focus on the only non-poisonous part, the red arils, which have recently been described as an important source of various bioactive constituents. To establish total phenolic, flavonoid, and carotenoid content, antioxidant capacity, and cytotoxic properties, two types of extracts were obtained. The chemical profile of the ethanolic extract was evaluated using chromatographic (HPLC-DAD-ESI+) and spectral (UV-Vis) methods, and the antioxidant activity of the ethanolic extract was assessed using DPPH and FRAP assays, yielding moderate results. In the second type of extract (methanol: ethyl acetate: petroleum ether (1:1:1, v/v/v)) we identified three carotenoids using open column chromatography and RP–PAD–HPLC, with rhodoxanthin being the most abundant. Considering the above and mainly because of the lack of information in the literature about this pigment and its biological effects, we decided to further investigate the cytotoxic activity of rhodoxanthin, the main carotenoid presented in aril, and its protective effect against H2O2-induced oxidative stress using two cell lines: human HaCaT keratinocytes and B16F10 murine malignant melanoma. The MTT and Annexin-V Apoptosis assays showed a substantial cytotoxic potential expressed in a dose-dependent manner towards the melanoma cell line, however, no obvious cytotoxic effects on human keratinocytes were noticed.
Abiotic Stresses Elicitation Potentiates the Productiveness of Cardoon Calli as Bio-Factories for Specialized Metabolites Production
Authors: D’Alessandro Rosa , Docimo Teresa , Graziani Giulia , D’Amelia Vincenzo , De Palma Monica , Cappetta Elisa , Tucci Marina
Published: May 2022

Cultivated cardoon (Cynara cardunculus L. var altilis) is a Mediterranean traditional food crop. It is adapted to xerothermic conditions and also grows in marginal lands, producing a large biomass rich in phenolic bioactive metabolites and has therefore received attention for pharmaceutical, cosmetic and innovative materials applications. Cardoon cell cultures can be used for the biotechnological production of valuable molecules in accordance with the principles of cellular agriculture. In the current study, we developed an elicitation strategy on leaf-derived cardoon calli for boosting the production of bioactive extracts for cosmetics. We tested elicitation conditions that trigger hyper-accumulation of bioactive phenolic metabolites without compromising calli growth through the application of chilling and salt stresses. We monitored changes in growth, polyphenol accumulation, and antioxidant capability, along with transcriptional variations of key chlorogenic acid and flavonoids biosynthetic genes. At moderate stress intensity and duration (14 days at 50–100 mM NaCl) salt exerted the best eliciting effect by stimulating total phenols and antioxidant power without impairing growth. Hydroalcoholic extracts from elicited cardoon calli with optimal growth and bioactive metabolite accumulation were demonstrated to lack cytotoxicity by MTT assay and were able to stimulate pro-collagen and aquaporin production in dermal cells. In conclusion, we propose a “natural” elicitation system that can be easily and safely employed to boost bioactive metabolite accumulation in cardoon cell cultures and also in pilot-scale cell culture production.
Unstructured Data Analysis for Risk Management of Electric Power Transmission Lines
Authors: Pereira Lucas H. , Pereira Rafael B. , Prado Pedro H. S. , Cunha Felipe D. , Góes Fabrício , Fiusa Roger S. , da Silva Lorrany Fernanda Lopes
Published: May 2022

Risk management of electric power transmission lines requires knowledge from different areas such as the environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we propose a novel method to calculate risk probabilities on the implementation of transmission lines based on unstructured text from a single source. It uses the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contain decisions about the electrical grid comprising most of the aforementioned areas. Since the data are unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. This method differs from previous works, which were based on structured data (numerical or categorical) from single or multiple sources. Our results show that we were able to extract relevant keywords from the ANEEL reports that enabled our proposed method to estimate the probability of 97 risks out of 233 listed by an expert.
Impact of Lowering Speed Limit on Urban Transportation Network
Authors: Jang Sunhee , Wu Seungkook , Kim Daejin , Song Ki-Han , Lee Seongkwan Mark , Suh Wonho
Published: May 2022

The purpose of this study is to analyze the impact of lowering speed limit on an urban transportation network. A microscopic traffic simulation model, Vissim was utilized to measure the impact. Also, various traffic inputs were tested with different signal coordination scenarios to investigate the impact in different traffic conditions. It was found that during early morning hours with very light traffic, the impact of lowering speed limit was significant. During congested time periods, including level of service E and F, the travel speed reduction from lowering speed limit was not significant. As suggested in other studies, the results demonstrated that lowering the speed limit does not have a significant impact on average travel speed in congested traffic networks. Also, different signal coordination was tested. As expected, signal coordination based on the lowered speed limit performed better than the case with signal coordination based on the previous higher speed limit. The results of this study are expected to provide insights when considering lowering speed limit for existing traffic networks.
Estimation of Cosmic-Ray-Induced Atmospheric Ionization and Radiation at Commercial Aviation Flight Altitudes
Authors: Makrantoni Panagiota , Tezari Anastasia , Stassinakis Argyris N. , Paschalis Pavlos , Gerontidou Maria , Karaiskos Pantelis , Georgakilas Alexandros G. , Mavromichalaki Helen , Usoskin Ilya G. , Crosby Norma , Dierckxsens Mark
Published: May 2022

The main source of the ionization of the Earth’s atmosphere is the cosmic radiation that depends on solar activity as well as geomagnetic activity. Galactic cosmic rays constitute a permanent radiation background and contribute significantly to the radiation exposure inside the atmosphere. In this work, the cosmic-ray-induced ionization of the Earth’s atmosphere, due to both solar and galactic cosmic radiation during the recent solar cycles 23 (1996–2008) and 24 (2008–2019), was studied globally. Estimations of the ionization were based on the CRAC:CRII model by the University of Oulu. The use of this model allowed for extensive calculations from the Earth’s surface (atmospheric depth 1033 g/cm2) to the upper limit of the atmosphere (atmospheric depth 0 g/cm2). Monte Carlo simulations were performed for the estimation quantities of radiobiological interest with the validated software DYASTIMA/DYASTIMA-R. This study was focused on specific altitudes of interest, such as the common flight levels used by commercial aviation.
Digital Function Modeling in Graph-Based Design Languages
Authors: Elwert Michael , Ramsaier Manuel , Eisenbart Boris , Stetter Ralf , Till Markus , Rudolph Stephan
Published: May 2022

The main focus of this paper is the integration of an integrated function modeling (IFM) framework in an engineering framework based on graph-based design languages (GBDLs). Over the last decade, GBDLs have received increasing attention as they offer a promising approach for addressing several important challenges in engineering, such as the frequent and time-consuming transfer of data between different computer aided engineering (CAE) tools. This absorbs significant amounts of manual labor in engineering design projects. GBDLs create digital system models at a meta level, encompassing all relevant information concerning a certain product design and feeding this into the relevant simulation tools needed for evaluating the impact of possible design variations on the performance of the resulting products/parts. It is possible to automate this process using digital compilers. Because of this, it is also possible to realize systematic design variations for a very large number of parameters and topological variants. Therefore, these kinds of graph-based languages are a powerful means for creating a large number of viable design alternatives and for permitting fast evaluation processes against the given specifications. While, thus far, such analyses tend to be based on a more or less fully defined system, this paper proposes an expansion of the applicability of GBDLs into the domain of product functions to cohesively link conceptual with embodiment design stages. This will also help with early systematic, automated generation and the validation of design alternatives through relevant simulation tools during embodiment design. Further, it will permit the automated exploration of function paths and enable extended analysis possibilities, such as the detection of functional bottlenecks, while enhancing the traceability of the design over the development process. For these extended analysis possibilities, a function analysis tool was developed that adopts core ideas of the failure mode and effects analysis (FMEA). In this, the functional distinction between function carriers and function-related processes allows the goal-directed assessment of component reliabilities and the detectability and importance of processes in a technical system. In the paper, the graph-based modeling of functions and the function analysis tools are demonstrated on the example of a multicopter.
Ab Initio Study of the Mechanism of Proton Migration in Perovskite LaScO3
Authors: Galashev Alexander Y. , Pavlov Dmitriy S. , Zaikov Yuri P. , Rakhmanova Oksana R.
Published: May 2022

The mechanism of proton motion in a LaScO3 perovskite crystal was studied by ab initio molecular dynamics. The calculations were performed at different temperatures, locations, and initial velocity of the proton. Different magnitudes and directions of the external electric field were also considered. It is shown that initial location and interaction between proton and its nearest environment are of great importance to the character of the proton movement, while the magnitude and direction of the initial velocity and electric field strength are secondary factors characterizing its movement through the LaScO3 crystal. Four types of proton-jumping between oxygen atoms are determined and the probability of each of them is established. Energy barriers and characteristic times of these jumps are determined. The probable distances from a proton to other types of atoms present in perovskite are calculated. It is shown that the temperature determines, to a greater extent, the nature of the motion of a proton in a perovskite crystal than the magnitude of the external electric field. The distortion of the crystal lattice and its polarization provoke the formation of a potential well, which determines the path for the proton to move and its mobility in the perovskite crystal.
The Characterization of Shale Differences Based on Petrophysical Properties and Pore Structure: A Case Study of the Longmaxi Formation in Northern Guizhou Province and the Yanchang Formation in the Ordos Basin
Authors: Wang Duo , Li Xiao , Li Guanfang , Mao Tianqiao , Zheng Bo
Published: May 2022

The pore structure and the petrophysical properties are essential parameters during the marine and continental shale reservoir evaluation. In order to the reveal the mechanism of the pore parameters differences and discuss whether the controlling factors of pore structure have extensive influence in different lithologies. Based on the analysis of N2/CO2 isotherm adsorption, FE-SEM, and geomechanical test for the marine shale samples of Longmaxi Formation and continental shale samples of Yanchang Formation are undertaken. The results indicate that (1) Continental shale is a typical transversely isotropic, while marine shale can be considered a homogeneous rock on a centimeter scale. The lithological stratification is the main reason for the difference in petrophysical properties. (2) The thermal evolution degree has significant influence on the pore structure in shale samples except for TOC content, particularly for the micropores structure. (3) Since the matrix strength of shale is much greater than the strength of bedding planes, the continental shale tends to crack and fail along the weak surface of the bedding during the axial compression, showing a lower peak strength, while the marine shale presents main crack along the principal stress direction without activating the bedding crack.
A Hierarchical Approach toward Prediction of Human Biological Age from Masked Facial Image Leveraging Deep Learning Techniques
Authors: Islam Md. Mahbubul , Baek Joong-Hwan
Published: May 2022

The lifestyle of humans has changed noticeably since the contagious COVID-19 disease struck globally. People should wear a face mask as a protective measure to curb the spread of the contagious disease. Consequently, real-world applications (i.e., electronic customer relationship management) dealing with human ages extracted from face images must migrate to a robust system proficient to estimate the age of a person wearing a face mask. In this paper, we proposed a hierarchical age estimation model from masked facial images in a group-to-specific manner rather than a single regression model because age progression across different age groups is quite dissimilar. Our intention was to squeeze the feature space among limited age classes so that the model could fairly discern age. We generated a synthetic masked face image dataset over the IMDB-WIKI face image dataset to train and validate our proposed model due to the absence of a benchmark masked face image dataset with real age annotations. We somewhat mitigated the data sparsity problem of the large public IMDB-WIKI dataset using off-the-shelf down-sampling and up-sampling techniques as required. The age estimation task was fully modeled like a deep classification problem, and expected ages were formulated from SoftMax probabilities. We performed a classification task by deploying multiple low-memory and higher-accuracy-based convolutional neural networks (CNNs). Our proposed hierarchical framework demonstrated marginal improvement in terms of mean absolute error (MAE) compared to the one-off model approach for masked face real age estimation. Moreover, this research is perhaps the maiden attempt to estimate the real age of a person from his/her masked face image.
Three-Dimensional Acoustic Analysis of a Rectangular Duct with Gradient Cross-Sections in High-Speed Trains: A Theoretical Derivation
Authors: Sun Yanhong , Qiu Yi , Liu Lianyun , Zheng Xu
Published: May 2022

Rectangular ducts used in the air-conditioning system of a high-speed train should be carefully designed to achieve optimal acoustic and flow performance. However, the theoretical analysis of the rectangular ducts with gradient cross-sections (RDGC) at frequencies higher than the one-dimensional cut-off frequency is rarely published. This paper has developed the three-dimensional analytical solutions to the wave equations of the expanding and shrinking RDGCs. Firstly, a homogeneous second-order variable coefficient differential equation is derived from the wave equations. Two coefficients of the solution to the differential equation are set to zero to ensure convergence. Secondly, the transfer matrices of the duct systems composed of multiple RDGCs are derived from the three-dimensional solutions. The transmission losses of the duct systems are then calculated from the transfer matrices and validated with the measurement. Finally, the acoustic performance and flow efficiency of the RDGCs with different geometries are discussed. The results show that the REC with double baffles distributed transversely has good performance in both acoustic attenuation and flow efficiency. This study shall provide a helpful guide for designing rectangular ducts used in high-speed trains.
OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
Authors: Mankodiya Harsh , Jadav Dhairya , Gupta Rajesh , Tanwar Sudeep , Hong Wei-Chiang , Sharma Ravi
Published: May 2022

In recent years, artificial intelligence (AI) has become one of the most prominent fields in autonomous vehicles (AVs). With the help of AI, the stress levels of drivers have been reduced, as most of the work is executed by the AV itself. With the increasing complexity of models, explainable artificial intelligence (XAI) techniques work as handy tools that allow naive people and developers to understand the intricate workings of deep learning models. These techniques can be paralleled to AI to increase their interpretability. One essential task of AVs is to be able to follow the road. This paper attempts to justify how AVs can detect and segment the road on which they are moving using deep learning (DL) models. We trained and compared three semantic segmentation architectures for the task of pixel-wise road detection. Max IoU scores of 0.9459 and 0.9621 were obtained on the train and test set. Such DL algorithms are called “black box models” as they are hard to interpret due to their highly complex structures. Integrating XAI enables us to interpret and comprehend the predictions of these abstract models. We applied various XAI methods and generated explanations for the proposed segmentation model for road detection in AVs.
Urban Expansion Monitoring Based on the Digital Surface Model—A Case Study of the Beijing–Tianjin–Hebei Plain
Authors: Wang Yanping , Dong Pinliang , Liao Shunbao , Zhu Yueqin , Zhang Da , Yin Na
Published: May 2022

Although urban expansion statistics have been widely carried out, large-scale and rapid monitoring is still worth doing in order to improve the efficiency of statistics, as well as make up for the omissions and deficiencies of construction expansion statistics with multi-year intervals. This paper presents a study of urban expansion in the Beijing–Tianjin–Hebei plain based on ALOS Global Digital Surface Model “ALOS World 3D-30 m” (AW3D30 DSM), Shuttle Radar Topography Mission (SRTM) DSM, and Landsat 7 ETM+ images. Through the evaluation of errors and the elimination of non-building changes, a relatively objective result is derived. The neighborhood block statistics of the construction height expansion reveal that from 2000 to 2009, the largest centralized construction expansion mainly occurred between the Second Ring Road and the Fifth Ring Road of Beijing, followed by Yizhuang, Shunyi, Tianjin Central City, and Langfang. Zonal statistics also show a significant imbalance in the expansion of construction in the counties of the Beijing–Tianjin–Hebei plain. For example, Chaoyang, Dongcheng, Xicheng, Xuanwu, Chongwen, Nankai, Heping, and Hexi have a larger construction expansion; however, other counties present a relatively slow rate of building expansion. Furthermore, the correlation coefficient between the statistical average building height expansion per unit area (ABHE, by our method) and the actual average completed building floor space per unit area (ACBFS) derived from the Beijing Statistical Yearbook (BSY) is 0.9436, which proves that this method is feasible. With the continuous improvement of DSM data quality in the future, the method proposed in this paper can provide rapid and large-scale statistics to study more urban construction expansion in the world.
Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
Authors: Sun Kecheng , Zeng Qinghua , Liu Jianye , Wang Shouyi
Published: May 2022

Because of the resilient frame structure, the factor graph is often used in navigation systems to solve the sensor asynchrony problem and realize plug-and-play effectively in the navigation information fusion method. To improve the fault detection performance of resilient integrated navigation systems under complex interference environments, a fault detection method in factor graph navigation framework based on INS measurements and GNSS pseudo-range measurements is proposed in this paper. The proposed method can effectively locate the fault satellite pseudo-range information based on the Chi-square fault detection method. Due to the plug-and-play characteristic of the factor graph framework, this method can quickly isolate faults to improve navigation accuracy. Finally, the effect of the method is verified by simulation and experiment. Compared with the Chi-square fault detection method, positioning accuracy is improved by more than 40%.
Digital Droplet-PCR for Quantification of Viable Campylobacter jejuni and Campylobacter coli in Chicken Meat Rinses
Authors: Govindaswamy Janani , Zeller-Péronnet Véronique , Pavlovic Melanie , Wirtz Daniela , Murr Larissa , Thärigen Diana , Colson Bertrand , Uhlig Steffen , Busch Ulrich , Huber Ingrid
Published: May 2022

The EU commission established Regulation (2017/1495) in 2017 to reduce Campylobacter on chicken skin and to decrease the number of human cases of campylobacteriosis attributable to the consumption of poultry meat. A Process Hygiene Criterion based on colony-forming unit data was set to a maximum of 1000 CFU Campylobacter spp. per gram chicken neck skin at slaughterhouses. Confronted with stressors, including cold, oxidative stress or antibiotic treatment, live cells may enter into a viable but non-cultivable state (VBNC) and lose the ability to grow, in reference to the plate count ISO 10272-2:2017 method, but still possess the potential to recover and cause infections under favorable conditions. In this study, a droplet digital PCR combined with the intercalating dye propidium monoazide (PMA) was established for quantification of C. coli and C. jejuni in chicken meat rinses. The PMA was used to inactivate DNA from dead cells in this technique. This method was successfully validated against the reference method according to ISO 16140-2:2016 for accuracy and relative trueness. Additionally, it presented a 100% selectivity for Campylobacter jejuni and C. coli. Moreover, the technical measurement uncertainty was determined according to ISO 19036:2019, and the applicability of ddPCR for quantifying C. coli and C. jejuni in chicken meat rinses was investigated on naturally contaminated samples from slaughterhouses and supermarkets. Results obtained from this study demonstrated a strong correlation to qPCR as well as the classical microbiological reference method.
Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)
Authors: Habermehl Christian , Höpfner Gregor , Berroth Jörg , Neumann Stephan , Jacobs Georg
Published: May 2022

Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.
The Sliding-Aperture Transform and Its Applicability to Deep-Level Transient Spectroscopy
Authors: Buchwald Walter R. , Peale Robert E. , Grant Perry C. , Logan Julie V. , Webster Preston T. , Morath Christian P.
Published: May 2022

A mathematical method is presented for the extraction of defect parameters from the multiexponential decays generated during deep-level transient spectroscopy experiments. Such transient phenomenon results from the ionization of charge trapped in defects located in the depletion width of a semiconductor diode. From digitized transients acquired at fixed temperatures, this method produces a rate–domain spectral signature associated with all defects in the semiconductor. For signal-to-noise ratio of 1000, defect levels with carrier emission rates differing by as little as 1.5 times may be distinguished.
Adaptive Fault-Tolerant Control for Flexible Variable Structure Spacecraft with Actuator Saturation and Multiple Faults
Authors: Hu Kai-Yu , Sun Wenjing , Yang Chunxia
Published: May 2022

This study investigated the adaptive fault-tolerant control (FTC) for a flexible variable structure spacecraft in the presence of external disturbance, multiple actuator faults, and saturation. The attitude system model of a variable structure spacecraft and actuator fault model are first given. A sliding mode-based fault detection observer and a radial basis function-based fault estimation observer were designed to detect the time of actuator fault occurrence and estimate the amplitude of an unknown fault, respectively. Then, the adaptive FTC with variable structure harmonic functions was proposed to automatically repair multiple actuator faults, which first guaranteed that the state trajectory of attitude systems without actuator saturation converges to a neighborhood of the origin. Then, another improved adaptive FTC scheme was further proposed in the actuator saturation constraint case, ensuring that all the closed-loop signals are finite-time convergence. Finally, simulation results are given to illustrate the effectiveness of the proposed method.
Neural Tracking Control of a Four-Wheeled Mobile Robot with Mecanum Wheels
Authors: Szeremeta Mateusz , Szuster Marcin
Published: May 2022

This study designed an algorithm for the intelligent control of the motion of a mobile robot with mecanum wheels. After reviewing the model kinematics and dynamics of the robot, we conducted a synthesis of the neural control algorithm to determine network weight adaptation, according to Lyapunov stability theory. Using a MATLAB/Simulink computing environment, we developed a numerical simulation for the implementation of the robot’s motion path with parametric disturbances acting on the control object. To determine the quality of the implementation of the desired motion path, a numerical test of the robot’s motion, controlled with the use of a PD controller, was conducted. The proposed control algorithm was verified on a laboratory stand equipped with a dSpace DS1103 controller board and a Husarion Panther four-wheeled mobile robot with mecanum wheels. The conducted research confirmed the improved implementation of the desired motion path by a robot controlled with the use of an intelligent control system.
A Few-Shot Learning-Based Reward Estimation for Mapless Navigation of Mobile Robots Using a Siamese Convolutional Neural Network
Authors: Kok Vernon , Olusanya Micheal , Ezugwu Absalom
Published: May 2022

Deep reinforcement learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori or that the distance to the goal can be obtained at each timestep. In artificial or simulated environments, obtaining the distance to the goal is considered a trivial task. Still, when applied to a real-world scenario, the distance must be obtained through complex localization techniques, and the use of localization techniques increases the complexity of the agent design. However, for agents navigating in unknown environments, using information about the goal to either form part of the state representation or act as the reward mechanism is usually expensive for both the robot design and for computing costs. This paper proposes using a pre-trained Siamese convolutional neural network (SCNN) to estimate the distance between an agent and its goal, thus enabling agents equipped with onboard cameras to navigate an unknown environment without needing localization sensors. This technique can be applied to environments where a goal location may be unknown, and the only information regarding the goal maybe a description of the goal state. Our experiments show that the Siamese network can learn the distance between the agent and its goal using relatively few training samples. Therefore, it is useful for mapless navigation using only visual state information and reduces the need for complex localization techniques.
Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning
Authors: Miltenović Aleksandar , Rakonjac Ivan , Oarcea Alexandru , Perić Marko , Rangelov Damjan
Published: May 2022

Gears are essential machine elements that are exposed to heavy loads. In some cases, gearboxes are critical elements since they serve as machine drivers that must operate almost every day for a more extended period, such as years or even tens of years. Any interruption due to gear failures can cause significant losses, and therefore it is necessary to have a monitoring system that will ensure proper operation. Tooth surface damage is a common occurrence in operating gears. One of the most common types of damage to teeth surfaces is pitting. It is necessary for normal gear operations to regularly determine the occurrence and span of a damaged tooth surface caused by pitting. In this paper, we propose a machine vision system as part of the inspection process for detecting pitting and monitoring its progression. The implemented inspection system uses a faster R-CNN network to identify and position pitting on a specific tooth, which enables monitoring. Prediction confidence values of pitting damage detection are between 99.5–99.9%, while prediction confidence values for teeth recognized as crucial for monitoring are between 97–99%.
Weathering Processes on Sandstone Painting and Carving Surfaces at Prehistoric Rock Sites in Southern Spain
Authors: Cañaveras Juan Carlos , Sanz-Rubio Enrique , Sánchez-Moral Sergio
Published: May 2022

The sandstones which constitute the host rock for the prehistoric artwork in the Rock Groups of Tajo de las Figuras and Peñas de Cabrera (southern Spain) show a serious degree of alteration, due both to natural processes and those related to anthropogenic and animal activity. A detailed study was carried out on the petrological and compositional characteristics of the sandstones (fresh and altered rock) in both rock groups, and on the geological and climatological characteristics of the area in which they are located. The sandstones have very similar petrological and compositional characteristics in both areas. This likeness causes the nature of the natural weathering processes to be similar in the rock areas studied. These processes can be divided in terms of the predominant mechanisms of alteration into three inter-related categories: mechanical weathering, chemical weathering, and bio-induced alteration processes. However, the different climatic conditions of the areas in which the two rock areas are located directly influences the intensity of these processes. The precipitation and the range of temperature variation with heavy winter frosts in the area of El Tajo de las Figuras are significantly higher than in the area of Peñas de Cabrera; this translates into a higher rate of weathering at El Tajo de las Figuras. Regarding the anthropogenic action, two types of influence on the deterioration can be distinguished: a direct one, which consists of scouring and wetting of the walls in order to increase the chromatic contrast; and an indirect one, which is the extraction of blocks of sandstone in the upper part of rock shelters, which in turn encourages the development of the chemical weathering processes.
Relationship between Craniocervical Posture and Sagittal Position of the Mandible: A Systematic Review
Authors: Zokaitė Gintarė , Lopatienė Kristina , Vasiliauskas Arūnas , Smailienė Dalia , Trakinienė Giedrė
Published: May 2022

Changes in craniocervical posture are a critical issue in modern society. Alterations of the mandible position in the anterior-posterior direction in association with head and neck posture are reported. The objective of the present review was to evaluate the relationship between craniocervical posture and sagittal position of the mandible and to evaluate the risk of bias inthe included studies. Electronic databases used to perform the search were PubMed, Wiley Online Library, and Cochrane. Only clinical trials that assessed sagittal craniocervical posture and mandible position in lateral cephalograms were included. Selected inclusion criteria were used to assess the finally selected studies. The upper and lower cervical spine was evaluated by seven and six studies, respectively. The risk of bias in the included studies varied from low to moderate. Literature research identified 438 records from 3 databases. Eventually, seven eligible clinical trials were included in this review. Evaluating the relationship between craniocervical posture and mandible position in the sagittal plane, it can be concluded that increased cervical inclination and head upright position are associated with the posterior position of the mandible. Attention to patients’ craniocervical posture should be paid as a part of clinical evaluation since it might be the reason for the changed mandible position.
Structural Analysis and Form-Finding of Triaxial Elastic Timber Gridshells Considering Interlayer Slips: Numerical Modelling and Full-Scale Test
Authors: Lara-Bocanegra Antonio José , Majano-Majano Almudena , Ortiz Juan , Guaita Manuel
Published: May 2022

Elastic timber gridshells are lightweight structures whose stiffness is highly dependent on multiple factors, such as boundary conditions and the semi-rigidity and eccentricity of the joints. Their structural analysis requires calibrated numerical models that incorporate all aspects influencing stiffness. Unfortunately, very little research on experimentally verified numerical models can be found. This paper focuses on the structural behaviour of a novel concept of triaxial elastic long-gridshells supported only on their short sides, called by the authors TEL-gridshells. First, the most relevant details of the construction process and the load test of a full-scale laboratory prototype are presented. Then, two finite element models for structural analysis and form-finding are proposed. Both are based on the modelling of the joints using a series of aligned couplings that allow the integration of the actual joint eccentricity and the interlayer slip by means of springs in all shear planes. The first model replicates the geometry of the prototype built from experimental measurements, focusing on stiffness calibration. The results of the load test are used to verify the proposed model and to analyse the most influential aspects on the stiffness of the structure. The second is a form-finding model that reproduces the construction process of the laboratory prototype, focusing on the residual stresses generated during the deformation process of the structural elements. From the numerical results, the structural behaviour of the prototype is discussed and some of the main aspects to be considered in the design and structural analysis of TEL-gridshells are established.
A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data
Authors: Zhou Fangrong , Wen Gang , Ma Yi , Geng Hao , Huang Ran , Pei Ling , Yu Wenxian , Chu Lei , Qiu Robert
Published: May 2022

In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.
New Challenges in Surgical Approaches for Colorectal Cancer during the COVID-19 Pandemic
Authors: Serban Dragos , Vancea Geta , Smarandache Catalin Gabriel , Balasescu Simona Andreea , Gangura Gabriel Andrei , Costea Daniel Ovidiu , Tudosie Mihail Silviu , Tudor Corneliu , Dumitrescu Dan , Dascalu Ana Maria , Tanasescu Ciprian , Tribus Laura Carina
Published: May 2022

(1) Background: The COVID-19 pandemic put a great burden on national healthcare systems, causing delays and disruptions in the medical care of non-COVID-19 patients. This paper aims to analyze the COVID-19 pandemic impact upon the quality of care in colorectal surgery. (2) Materials and Methods: We performed a retrospective study on the colorectal cancer cases operated in the Fourth Department of General Surgery, Emergency Hospital Bucharest Romania, over the period March 2020–February 2021 (pandemic group) vs. March 2019–February 2020 (non-pandemic group). (3) Results: The number of patients in the pandemic group decreased by 70% (36 vs. 118 patients), with lower accessibility from rural areas (11.1% vs. 37.2%, p = 0.035). Most cases in the pandemic group were emergencies (69% vs. 37.3%, p = 0.009), admitted for bowel obstruction (63.8% vs. 27.9%, p = 0.008). There was no in-hospital COVID-19 infection in patients operated for colorectal cancer. The 30-day mortality was significantly higher in the pandemic group (25% vs. 6.7%, p = 0.017), mostly due to septic shock (36.1% vs. 5%, p = 0.0001). (4) Conclusions: Colorectal cancer surgery may be performed safely during the COVID-19 pandemic, with strict adherence to the SARS-CoV-2 prevention protocols. However, the significant increase in colorectal cancers in the emergency was associated with worse outcomes and higher mortality during the COVID-19 pandemic.
Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
Authors: Zhou Junjie , Shuai Siyue , Wang Lingyun , Yu Kaifeng , Kong Xiangjie , Xu Zuhua , Shao Zhijiang
Published: May 2022

With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.
A Blockchain-Based Digital Asset Platform with Multi-Party Certification
Authors: Liu Feng , Feng Zhefu , Qi Jiayin
Published: May 2022

Assets such as warehouse receipts are important for enterprises, which can be used to pledge in supply chain finance (SCF). However, traditional pledges are performed manually, which inevitably encounters inefficiency and security problems such as multiple pledges. To improve asset security, we propose a blockchain-based digital asset platform (BDAP) with multi-party certification. BDAP not only has a security protocol based on the threshold ECDSA algorithm to make related participants confirm the authenticity of assets but also embeds a Verifiable Byzantine Fault Tolerant (VBFT) mechanism, randomly selecting the consensus nodes and improving the safety of the nodes. Moreover, data stored on the blockchain makes traceability possible. Through a set of experiments, we have verified the functionality and performance of BDAP. When the pressure test reaches 100 concurrent user volume, BDAP’s average response time is 1.441 s, showing a high ability to process transactions. However, now just a few open-minded banks are willing to access BDAP, it might take a long time to change the traditional perception of the participants in supply chain finance.