bluuebunny/crossref_metadata_2025 huggingface dataset:
A Simple Model of Ballistic Conduction in Multi-Lead Molecular Devices
Authors: Fowler Patrick W. , Pickup Barry T.
Published: December 2021

A fully analytical model is presented for ballistic conduction in a multi-lead device that is based on a π-conjugated carbon framework attached to a single source lead and several sink leads. This source-and-multiple-sink potential (SMSP) model is rooted in the Ernzerhof source-and-sink potential (SSP) approach and specifies transmission in terms of combinations of structural polynomials based on the molecular graph. The simplicity of the model allows insight into many-lead devices in terms of constituent two-lead devices, description of conduction in the multi-lead device in terms of structural polynomials, molecular orbital channels, and selection rules for active and inert leads and orbitals. In the wide-band limit, transmission can be expressed entirely in terms of characteristic polynomials of vertex-deleted graphs. As limiting cases of maximum connection, complete symmetric devices (CSD) and complete bipartite symmetric devices (CBSD) are defined and solved analytically. These devices have vanishing lead-lead interference effects. Illustrative calculations of transmission curves for model small-molecule systems are presented and selection rules are identified.
B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
Authors: Kerdegari Hamideh , Phung Nhat Tran Huy , McBride Angela , Pisani Luigi , Nguyen Hao Van , Duong Thuy Bich , Razavi Reza , Thwaites Louise , Yacoub Sophie , Gomez Alberto ,   VITAL Consortium
Published: December 2021

The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging. Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection in LUS videos. The spatial attention allows the model to focus on the most task relevant parts of the image by learning a saliency map. The temporal attention generates an attention score for each attended frame to identify the most relevant frames from an input video. Our model not only identifies videos where B-lines show, but also localizes, within those videos, B-line related features both spatially and temporally, despite being trained in a weakly-supervised manner. We evaluate our approach on a LUS video dataset collected from severe dengue patients in a resource-limited hospital, assessing the B-line detection rate and the model’s ability to localize discriminative B-line regions spatially and B-line frames temporally. Experimental results demonstrate the efficacy of our approach for classifying B-line videos with an F1 score of up to 83.2% and localizing the most salient B-line regions both spatially and temporally with a correlation coefficient of 0.67 and an IoU of 69.7%, respectively.
YOLOv4-MN3 for PCB Surface Defect Detection
Authors: Liao Xinting , Lv Shengping , Li Denghui , Luo Yong , Zhu Zichun , Jiang Cheng
Published: December 2021

Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.
An Ancient Mesiodens Investigated by Cone Beam CT
Authors: Beschiu Laura Maria , Craiovan Bogdan Alin , Popa Stefan , Micle Dorel , Ardelean Lavinia Cosmina , Rusu Laura Cristina
Published: December 2021

This article presents the complex case study of an ancient skeleton presenting a maxillary supernumerary tooth (mesiodens). The skeleton was found in an archaeological site in the western part of Romania and was dated back to the Eneolithic period, some 5500 years ago. The aim of this article is to analyze the mesiodens and the jaws in light of current knowledge regarding anomalies of dental development of past and present-time populations. The cranial remains were investigated from an orthodontic perspective through inspection and cone beam computed tomography (CBCT) scan. The occlusion was analyzed according to Angle’s classification and observations were made regarding the presence of calculus, dental wear, presence of caries, bone loss and ante-mortem tooth loss. Observations were made regarding tooth dimensions and comparisons were made with modern population. In this specific case, the mesiodens caused minimum disruption within the upper dental arch and the occlusion. Additionally, it did not have any negative impact on the adjacent teeth or cause any other complication. Judging by the analysis of the jaws, the presence of the mesiodens had little impact on the overall oral health of the person. As suggested by other findings in the scientific literature, the mesiodens is an anomaly found throughout all historical periods, from the oldest archaeological sites to present day.
Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
Authors: Xu Xinyong , Liu Xuhui , Jiang Li , Ali Khan Mohd Yawar
Published: December 2021

The Concrete Damaged Plasticity (CDP) constitutive is introduced to study the dynamic failure mechanism and the law of damage development to the aqueduct structure during the seismic duration using a large-scale aqueduct structure from the South-to-North Water Division Project (SNWDP) as a research object. Incremental dynamic analysis (IDA) and multiple stripe analysis (MSA) seismic fragility methods are introduced. The spectral acceleration is used as the scale of ground motion record intensity measure (IM), and the aqueduct pier top offset ratio quantifies the limit of structural damage measure (DM). The aqueduct structure’s seismic fragility evaluation curves are constructed with indicators of different seismic intensity measures to depict the damage characteristics of aqueduct structures under different seismic intensities through probability. The results show that penetrating damage is most likely to occur on both sides of the pier cap and around the pier shaft in the event of a rare earthquake, followed by the top of the aqueduct body, which requires the greatest care during an earthquake. The results of two fragility analysis methodologies reveal that the fragility curves are very similar. The aqueduct structure’s first limit state level (LS1) is quite steep and near the vertical line, indicating that maintaining the excellent condition without damage in the seismic analysis will be challenging. Except for individual results, the overall fragility results are in good agreement, and the curve change rule is the same. The exceedance probability in the case of any ground motion record IM may be estimated using only two factors when using the MSA approach, and the computation efficiency is higher. The study of seismic fragility analysis methods in this paper can provide a reference for the seismic safety evaluation of aqueducts and similar structures.
Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach
Authors: Miani Matteo , Dunnhofer Matteo , Rondinella Fabio , Manthos Evangelos , Valentin Jan , Micheloni Christian , Baldo Nicola
Published: December 2021

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.
Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
Authors: Dobiš Michal , Dekan Martin , Sojka Adam , Beňo Peter , Duchoň František
Published: December 2021

This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.
Controlling the Pump-Probe Optical Response in Asymmetric Tunneling-Controlled Double Quantum Dot Molecule—Metal Nanoparticle Hybrids
Authors: Kosionis Spyridon G. , Paspalakis Emmanuel
Published: December 2021

In the present work, we investigate the modified nonlinear pump-probe optical properties due to the excitonic–plasmonic interaction of a double semiconductor quantum dot (SQD) molecule coupled to a metal nanoparticle (MNP). More specifically, we study the absorption and the dispersion spectra of a weak electromagnetic field in a hybrid structure with two counterparts, a molecule of two coupled SQDs, and a spherical MNP driven by a field of high intensity. We solve the relevant density matrix equations, calculate the first-order optical susceptibility of the probe field in the strong pumping regime, and investigate the way in which the distance between the two counterparts modifies the optical response, for a variety of values of the physical constants of the system, including the pump-field detuning, the tunnelling rate, and the energy separation gap associated with the excited states of the coupled SQDs.
Strata Movement of the Thick Loose Layer under Strip-Filling Mining Method: A Case Study
Authors: Chang Qingliang , Yao Xingjie , Leng Qiang , Cheng Hao , Wu Fengfeng , Zhou Huaqiang , Sun Yuantian
Published: December 2021

Filling mining plays an important role in controlling surface subsidence. To study the movement of overburdened rock in filling mining under thick loose layers, a numerical simulation combing field measurement in CT30101 working face in the Mahuangliang coal mine was tested. The results show that different filling rates and filling body strength have different influences on roof and surface movement. The filling rate has a greater impact, which is the main control factor. The filling stress and roof tensile stress decrease gradually with roadway filling. The filling body stress and roof tensile stress in the first and second rounds are far greater than in the fourth round. After the completion of filling, the first and second round of filling bodies mainly bear the overburden, and the total deformation of the surrounding rock of the main transport roadway is very small, and therefore the displacement of the overburdened rock is controllable. The field monitoring results also show that the overburdened rock became stable after several fillings rounds. Combing the numerical modeling and field tests results, this study can be a guideline for similar geological conditions especially for coal mining under thick loose layers and thin bedrock.
Justification of the Reduction Possibility of Sulfur Oxides and Fly Ash Emissions during Co-Combustion of Coal and Waste from Woodworking Enterprises
Authors: Yankovsky Stanislav , Tolokol’nikov Anton , Gorshkov Alexander , Misyukova Albina , Kuznetsov Geniy
Published: December 2021

In this work, we experimentally investigated the effect of widespread biomass (woodworking waste—pine sawdust) in the composition of mixed fuel, formed also using the widespread steam coals metalignitous (D) and lean (T), on the concentration of sulfur, nitrogen and carbon oxides in flue gases. Investigations of composite fuels with a mass of at least 5 g were carried out in a reactor with continuous recording of the composition of the flue gases formed during the thermal decomposition of the investigated fuels. Thermal decomposition of fuels was carried out in the temperature range from 293 K to 873 K. It was found that an increase in the proportion of wood components in mixed fuels based on two different coals from 10% to 50% leads to a significant decrease in the concentration of sulfur oxides from 11% to 95.8% relative to the concentration of the formation of sulfur oxides in a homogeneous coal, respectively. It was found that an increase in the proportion of the wood component in the mixture with grade D coal up to 50% leads to a significant increase in the content of calcium sulfates (45.1%) and aluminum (43.2%) in the blended fuel. The increase in the content of these salts in the ash of mixed fuels based on T coal and wood is 35.1% and 38.6%, respectively. The obtained research results allowed us to conclude that woodworking wastes are an effective addition to the coals of various deposits, which would help to reduce anthropogenic-induced gas emissions when they are co-combusting in the furnaces of power boilers.
Dynamic Risk Assessment of Ultra-Shallow-Buried and Large-Span Double-Arch Tunnel Construction
Authors: Wang Jianxiu , Cao Ansheng , Wu Zhao , Sun Zhipeng , Lin Xiao , Sun Lei , Liu Wuji , Liu Xiaotian , Li Huboqiang , Sun Yuanwei , Long Yanxia
Published: December 2021

Ultra-shallow-buried and large-span double-arch tunnels face complex risks during construction. The risk sources are hidden, complicated, and diverse. The dynamic risk assessment problem cannot be solved satisfactorily by using the static method as an insufficient amount of research has been conducted. The land part of the Xiamen Haicang double-arch tunnel was selected as the background for the dynamic risk assessment of ultra-shallow-buried and large-span double-arch tunnel construction. The construction process was divided into five stages: pre-construction preparation; ground and surrounding rock reinforcement; pilot tunnel excavation; and the single-and the double-tunnel excavations of the main tunnel. Through consultation with tunnel experts, six first-level and thirty second-level risk evaluation indexes were proposed. The benchmark weight of the dynamic risk assessment index was determined by using the analytic hierarchy process. The weight of the risk evaluation index was revised according to the monitoring data and the construction stage. The fuzzy evaluation matrix of the construction risk membership degree was obtained by using the fuzzy comprehensive assessment method, and the calculation results were analyzed using the subsection assignment method. Control measures were suggested according to the risk assessment results. The risk assessment result of the double tunnel excavation stage of the main tunnel was level II, and the risk level was the highest among the five construction stages. The risk assessment result of the ground and surrounding rock reinforcement stage was level IV, and the risk level was the lowest. The dynamic construction safety risk assessment based on the fuzzy comprehensive assessment method is more timely, accurate, and reasonable than the traditional assessment method. The method can be adopted in similar engineering projects.
A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling
Authors: Azab Eman , Nafea Mohamed , Shihata Lamia A. , Mashaly Maggie
Published: December 2021

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.
Assessment of Active Wheelset Steering System Using Computer Simulations and Roller Rig Tests
Authors: Kalivoda Jan , Bauer Petr , Novák Zdeněk
Published: December 2021

The paper is created within a project which aims to design a system of active wheelset steering for an electric four-axle locomotive. The wheelset steering system enables reduction in forces acting in the wheel-rail contacts in a curved track and consequently a reduction in wear and maintenance costs of both vehicles and rails is achieved. The project consists of three main parts: computer simulations, scaled roller rig experiments, and field tests. The paper is focused on the fundamental aspects of the first and the second part on the project. Track curvature estimation based on the rotation of the bogies towards the car body is proposed and assessed by computer simulations across varying track radiuses, vehicle speeds, and friction conditions. The scaled roller rig has been innovated in order to simulate bogie run in a curved track with uncompensated value of lateral acceleration and instrumented with a system of measurement of lateral wheel-rail forces. The experimental bogie has been equipped with systems of active wheelset steering and measurement of axle-box forces. The experiment setup, newly developed and applied systems of forces measurement and wireless signal transmission, and results of the first experiments are described in detail. Performed computer simulations and scaled roller rig experiments show that active wheelset steering is effective and practically implementable method of reducing guiding forces acting between railway vehicle wheels and rails in a curved track.
Modulation Characteristics of Period-One Oscillations in Quantum Cascade Lasers
Authors: Zhao Binbin , Peng Yibo , Wang Xingguang , Wang Cheng
Published: December 2021

Quantum cascade lasers subject to tilted optical feedback produce periodic oscillations, quasi-periodic oscillations, and low-frequency oscillations. This work presents the modulation characteristics of period-one (P1) oscillations in a quantum cascade laser with tilted optical feedback. The electrical signal at the oscillation frequency is more than 50 dB higher than the noise level, and the electrical linewidth is less than 2.0 kHz. This electrical linewidth is about four orders of magnitude narrower than the optical linewidth (around 16 MHz) of the free-running laser, which suggests that the optical sidebands induced by the P1 oscillations are highly coherent with the main optical mode. In addition, the modulation depth of the optical signal is found to be in the range of 1% to 3.5%. In addition, it is verified in the simulations that the P1 oscillations induce not only amplitude modulation but also frequency modulation due to the phase-amplitude coupling effect.
Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System
Authors: Chae Seungheon , Choi Ahnryul , Jung Hyunwoo , Kim Tae Hyong , Kim Kyungran , Mun Joung Hwan
Published: December 2021

Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.
Sand–Tire Shred Mixture Performance in Controlling Surface Explosion Hazards That Affect Underground Structures
Authors: Dadkhah Hamed , Kalatehjari Roohollah , Hajihassani Mohsen , Kharghani Mehdi , Asteris Panagiotis G.
Published: December 2021

Blasting is an unavoidable activity in geotechnical engineering, road and tunnel construction, and mining and quarrying. However, this activity can expose the environment to various hazards that are challenging to control and, at the same time, critical for the safety of site workers, equipment, and surrounding structures. This research aims to evaluate the ability of sand–tire shred mixtures to reduce peak blast pressure, which is the leading cause of damage to underground structures under surface explosion. ABAQUS software is used to model the material behavior under explosion and is validated using the results of previous studies and an empirical equation. Different scenarios are created by using mixture layers with different thicknesses (2, 4, and 6 m) and tire shred contents (10%, 20%, and 30%) that are subjected to various surface explosion charges (100, 500, 1000, and 5000 kg). The thickness of the mixture layer is found to be directly related to the dissipation of explosion energy. However, the percentage of the rubber content in the mixture is only significant in reducing peak blast pressure when a thick enough mixture layer is used. The results confirm the adequate performance of the correctly chosen sand–tire shred mixtures in reducing peak blast pressure and protecting the underground structure from surface explosion hazards.
A Simplified Stress Analysis of Functionally Graded Beams and Influence of Material Function on Deflection
Authors: Althoey Fadi , Ali Elias
Published: December 2021

This paper aims at providing a simplified analytical solution for functionally graded beam stress analysis and optimized material gradation on the beam deflection. The power-law (P-FGM) and exponential (E-FGM) material functions were considered for an exact solution of the normal and shear stress distributions across the beam thickness. Optimization of material function on the FGM beam deflection, which is new of its kind, was also investigated considering both simply supported and cantilever beams. It was observed that the non-dimensional normal stress and shear stress are independent of the elastic moduli values of the constituent materials but rather depends on both the ratio of the elastic moduli and the location across the beam thickness in the E-FGM material function model. This observation was first validated from available kinds of literature and through numerical simulation using ABAQUS and extended to the P-FGM stress analysis. The maximum deflection on the FGM beam occurred for a homogenous steel beam while the minimum deflection was observed on the beam with a P-FGM material function. The results of this work demonstrate that if properly designed and optimized, FGMs can provide an alternative material solution in structural applications.
Intercity Online Car-Hailing Travel Demand Prediction via a Spatiotemporal Transformer Method
Authors: Li Hongbo , Wang Jincheng , Ren Yilong , Mao Feng
Published: December 2021

Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing demand has often led to a supply–demand imbalance where there is a mismatch between the immediate availability of car-hailing services and the number of passengers in certain areas. Accurate prediction of online car-hailing demand promotes efficiencies and minimizes resources and time waste. However, many prior related studies often fail to fully utilize spatiotemporal characteristics. With the development of newer deep-learning models, this paper aims to solve online car-hailing problems with an ST-transformer model. The spatiotemporal characteristics of online car-hailing data are analyzed and extracted. The study region is divided into subareas, and the demand for each subarea is summed at a specific time interval. Historical demand of the areas is used to predict future demand. The results of the ST-transformer outperformed other baseline models, namely, VAR, SVR, LSTM, LSTNet, and transformers. The validated results suggest that the ST-transformer is more capable of capturing spatiotemporal characteristics compared to the other models. Additionally, compared to others, the model is less affected by data sparsity.
Response-Only Parametric Estimation of Structural Systems Using a Modified Stochastic Subspace Identification Technique
Authors: Lin Chang-Sheng , Wu Yi-Xiu
Published: December 2021

The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to develop the appropriate algorithms for ambient modal estimation. The conventional SSI technique, including two types of covariance-driven and data-driven algorithms, is employed for parametric identification of a system subjected to stationary white excitation. By introducing the procedure of solving the system matrix in SSI-COV in conjunction with SSI-DATA, the SSI technique can be efficiently performed without using the original large-dimension data matrix, through the singular value decomposition of the improved projection matrix. In addition, the computational efficiency of the SSI technique is also improved by extracting two predictive-state matrixes with recursive relationship from the same original predictive-state matrix, and then omitting the step of reevaluating the predictive-state matrix at the next-time moment. Numerical simulations and experimental verification illustrate and confirm that the present method can accurately implement modal estimation from stationary response data only.
Energy Management Strategy for Hybrid Multimode Powertrains: Influence of Inertial Properties and Road Inclination
Authors: Tota Antonio , Galvagno Enrico , Dimauro Luca , Vigliani Alessandro , Velardocchia Mauro
Published: December 2021

Multimode hybrid powertrains have captured the attention of automotive OEMs for their flexible nature and ability to provide better and optimized efficiency levels. However, the presence of multiple actuators, with different efficiency and dynamic characteristics, increases the problem complexity for minimizing the overall power losses in each powertrain operating condition. The paper aims at providing a methodology to select the powertrain mode and set the reference torques and angular speeds for each actuator, based on the power-weighted efficiency concept. The power-weighted efficiency is formulated to normalize the efficiency contribution from each power source and to include the inertial properties of the powertrain components as well as the vehicle motion resistance forces. The approach, valid for a wide category of multimode powertrain architectures, is then applied to the specific case of a two-mode hybrid system where the engagement of one of the two clutches enables an Input Split or Compound Split operative mode. The simulation results obtained with the procedure prove to be promising in avoiding excessive accelerations, drift of powertrain components, and in managing the power flow for uphill and downhill vehicle conditions.
Onsite Testing for Nonlinear Analysis of an Earthquake Damaged Historical Church in Italy
Authors: Santini Silvia , Baggio Carlo , Sabbatini Valerio , Sebastiani Claudio
Published: December 2021

Analysis and diagnosis of historical masonry buildings are frequently affected by uncertainties due to unexpected behaviors caused by cumulative damage, material decay or transformations. This research work follows a methodology in the structural analysis of the historical masonry church of San Filippo Neri in Macerata, severely damaged after the Central Italy Earthquake occurred in October 2016. The PRiSMa laboratory (Proof testing and Research in Structures and Materials) of Roma Tre University carried out an extensive onsite testing campaign, including NDT tests as sonic tomography and endoscopy, and minor destructive technique as double flat jack test, together with dynamic monitoring under ambient vibrations, to investigate the state of conservation of the building. The onsite testing results were then implemented in an accurate finite element model, which was tuned up by means of global dynamic response provided by OMA (operational modal analysis) and updated, after the sensitivity analysis, through the Douglas-Reid method. Finally, nonlinear static and dynamic analyses were performed to investigate the state of damage of the church and reduce its uncertainties. This methodology will support the design of strengthening measures to achieve a higher level of safety concerning both needs of protection and conservation, thereby avoiding ineffectual or amiss interventions.
Continuous Mobile User Authentication Using Combined Biometric Traits
Authors: Reichinger Dominik , Sonnleitner Erik , Kurz Marc
Published: December 2021

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures.
Probabilistic Forecast of Low Level Wind Shear of Gimpo, Gimhae, Incheon and Jeju International Airports Using Ensemble Model Output Statistics
Authors: Choi Hee-Wook , Kim Yeon-Hee , Han Keunhee , Kim Chansoo
Published: December 2021

Wind shear can occur at all flight levels; however, it is particularly dangerous at low levels, from the ground up to approximately 2000 feet. If this phenomenon can occur during the take-off and landing of an aircraft, it may interfere with the normal altitude change of the aircraft, causing delay and cancellation of the aircraft, as well as economic damage. In this paper, to estimate the probabilistic forecasts of low-level wind shear at Gimpo, Gimhae, Incheon and Jeju International Airports, an Ensemble Model Output Statistics (EMOS) model based on a left-truncated normal distribution with a cutoff zero was applied. Observations were obtained from Gimpo, Gimhae, Incheon and Jeju International Airports and 13 ensemble member forecasts generated from the Limited-Area Ensemble Prediction System (LENS), for the period December 2018 to February 2020. Prior to applying to EMOS models, statistical consistency was analyzed by using a rank histogram and kernel density estimation to identify the uniformity of ensembles with corresponding observations. Performances were evaluated by mean absolute error, continuous ranked probability score and probability integral transform. The results showed that probabilistic forecasts obtained from the EMOS model exhibited better prediction skills when compared to the raw ensembles.
Pigmented Epithelioid Melanocytomas and Their Mimics; Focus on Their Novel Molecular Findings
Authors: Bayraktar Erol C. , Jour George
Published: December 2021

Pigmented epithelioid melanocytoma (PEM) is a unique tumor with significantly pigmented appearance and indolent behavior; however, it can demonstrate cytological atypia and metastasize to local lymph nodes. Clinical and histomorphological overlap between PEM and its lower or higher-grade mimics can make it difficult to distinguish in certain cases. Genomic, transcriptomic and epigenetic data indicate that PEMs are molecularly distinct entities from other melanocytic neoplasms and melanomas. In addition, methylation studies are emerging as a tool that can be useful in difficult cases. In this review, we focus on the clinical, histopathologic and recent insights in the molecular features of pigmented epithelioid melanocytic melanocytomas and their mimics. We also present a challenging case that was resolved using methylation analysis providing a proof of concept for using epigenetic studies for similar challenging cases.
Static Body Balance in Children and Expert Adults Ballroom Dancers: Insights from Spectral Analysis of Shifts
Authors: Cicchella Antonio
Published: December 2021

Objective: The purpose of this study was to assess the differences in maintaining body balance (influence of different sensorial sub-systems) in a representative sample of active Dance Sport competitors (children and adults). Methods: Overall, 13 children and 15 high-level adults sport dancers underwent a static equilibrium test on a force platform, in which 17 different parameters were examined, including a spectral analysis of shifts using an FFT algorithm that can assess the contribution of different somatic-sensory systems on maintaining body balance. Results: Younger subjects rely on their somatic-sensory reactions to maintain their balance, while adults rely more on the vestibular system, according to shifts’ spectral analysis. No differences were noted between the male and female participants. Conclusions: Children predominantly use the somatic-sensory system in body balance, while adults make more use of the vestibular system. According to these results and due to the trainability phases, exercises that challenge the somato-sensorial system are recommended to train balance in young dancers, while exercises that challenge the vestibular system are recommended in adult dancers which who have not developed exceptional somato-sensory balance abilities during their growth and training history.
The Mediterranean Diet Positively Affects Resting Metabolic Rate and Salivary Microbiota in Human Subjects: A Comparison with the Vegan Regimen
Authors: Daniele Simona , Scarfò Giorgia , Ceccarelli Lorenzo , Fusi Jonathan , Zappelli Elisa , Biagini Denise , Lomonaco Tommaso , Di Francesco Fabio , Franzoni Ferdinando , Martini Claudia
Published: December 2021

Salivary microbiota, comprising bacteria shed from oral surfaces, has been shown to be individualized, temporally stable, and influenced by macronutrient intake and lifestyle. Nevertheless, the effect of long-term dietary patterns on oral microbiota composition and the relationship between oral microbiota composition and metabolic rate remains to be examined. Herein, salivary microbiota composition and metabolic profile were analyzed in human subjects with vegan (VEG) or Mediterranean (MED) long-term dietary patterns. MED subjects presented significantly higher percentages of Subflava and Prevotella species as compared to VEG ones. Moreover, MED subjects showed a lower carbohydrate and a higher lipid consumption than VEG subjects, and, accordingly, a significantly higher basal metabolic rate (BMR) and a lower respiratory quotient (RQ). Prevotella abundance was demonstrated to be inversely related to RQ and carbohydrate consumption, whereas Subflava percentages were demonstrated to be positively correlated to BMR. Lactobacillus abundance, which was inversely related to Subflava presence in MED subjects, was associated with decreased BMR (Harris–Benedict) values. Overall, our data evidence the influence of macronutrient intake on metabolic profile and oral microbiota and confirm the positive effects of the Mediterranean diet on BMR and on the abundance of microbial species associated with a better macronutrient metabolism.
Comparative Transcriptomic Analysis of Regenerated Skins Provides Insights into Cutaneous Air-Breathing Formation in Fish
Authors: Huang Songqian , Sun Bing , Huang Longfei , Yang Lijuan , Liu Chuanshu , Zhu Jinli , Gao Jian , Cao Xiaojuan
Published: December 2021

Cutaneous air-breathing is one of the air-breathing patterns in bimodal respiration fishes, while little is known about its underlying formation mechanisms. Here, we first investigated the skin regeneration of loach (Misgurnus anguillicaudatus, a cutaneous air-breathing fish) and yellow catfish (Pelteobagrus fulvidraco, a water-breathing fish) through morphological and histological observations. Then, the original skins (OS: MOS, POS) and regenerated skins (RS: MRS, PRS) when their capillaries were the most abundant (the structural foundation of air-breathing in fish) during healing, of the two fish species were collected for high-throughput RNA-seq. A total of 56,054 unigenes and 53,731 unigenes were assembled in loach and yellow catfish, respectively. A total of 640 (460 up- and 180 down-regulated) and 4446 (2340 up- and 2106 down-regulated) differentially expressed genes (DEGs) were respectively observed in RS/OS of loach and yellow catfish. Subsequently, the two DEG datasets were clustered in GO, KOG and KEGG databases, and further analyzed by comparison and screening. Consequently, tens of genes and thirteen key pathways were targeted, indicating that these genes and pathways had strong ties to cutaneous skin air-breathing in loach. This study provides new insights into the formation mechanism of cutaneous air-breathing and also offers a substantial contribution to the gene expression profiles of skin regeneration in fish.
Four In Silico Designed and Validated qPCR Assays to Detect and Discriminate Tilletia indica and T. walkeri, Individually or as a Complex
Authors: Tremblay Émilie D. , Carey Julie , Bilodeau Guillaume J. , Hambleton Sarah
Published: December 2021

Several fungi classified in the genus Tilletia are well-known to infect grass species including wheat (Triticum). Tilletia indica is a highly unwanted wheat pathogen causing Karnal bunt, subject to quarantine regulations in many countries. Historically, suspected Karnal bunt infections were identified by morphology, a labour-intensive process to rule out other tuberculate-spored species that may be found as contaminants in grain shipments, and the closely-related pathogen T. walkeri on ryegrass (Lolium). Molecular biology advances have brought numerous detection tools to discriminate Tilletia congeners (PCR, qPCR, etc.). While those tests may help to identify T. indica more rapidly, they share weaknesses of targeting insufficiently variable markers or lacking sensitivity in a zero-tolerance context. A recent approach used comparative genomics to identify unique regions within target species, and qPCR assays were designed in silico. This study validated four qPCR tests based on single-copy genomic regions and with highly sensitive limits of detection (~200 fg), two to detect T. indica and T. walkeri separately, and two newly designed, targeting both species as a complex. The assays were challenged with reference DNA of the targets, their close relatives, other crop pathogens, the wheat host, and environmental specimens, ensuring a high level of specificity for accurate discrimination.
First Responders to Hyperosmotic Stress in Murine Astrocytes: Connexin 43 Gap Junctions Are Subject to an Immediate Ultrastructural Reorganization
Authors: Beckmann Anja , Recktenwald Johanna , Ferdinand Alice , Grißmer Alexander , Meier Carola
Published: December 2021

In a short-term model of hyperosmotic stress, primary murine astrocytes were stimulated with a hyperosmolar sucrose solution for five minutes. Astrocytic gap junctions, which are mainly composed of Connexin (Cx) 43, displayed immediate ultrastructural changes, demonstrated by freeze–fracture replica immunogold labeling: their area, perimeter, and distance of intramembrane particles increased, whereas particle numbers per area decreased. Ultrastructural changes were, however, not accompanied by changes in Cx43 mRNA expression. In contrast, transcription of the gap junction regulator zonula occludens (ZO) protein 1 significantly increased, whereas its protein expression was unaffected. Phosphorylation of Serine (S) 368 of the Cx43 C–terminus has previously been associated with gap junction disassembly and reduction in gap junction communication. Hyperosmolar sucrose treatment led to enhanced phosphorylation of Cx43S368 and was accompanied by inhibition of gap junctional intercellular communication, demonstrated by a scrape loading-dye transfer assay. Taken together, Cx43 gap junctions are fast reacting elements in response to hyperosmolar challenges and can therefore be considered as one of the first responders to hyperosmolarity. In this process, phosphorylation of Cx43S368 was associated with disassembly of gap junctions and inhibition of their function. Thus, modulation of the gap junction assembly might represent a target in the treatment of brain edema or trauma.
CD44: A Multifunctional Mediator of Cancer Progression
Authors: Hassn Mesrati Malak , Syafruddin Saiful Effendi , Mohtar M. Aiman , Syahir Amir
Published: December 2021

CD44, a non-kinase cell surface transmembrane glycoprotein, has been widely implicated as a cancer stem cell (CSC) marker in several cancers. Cells overexpressing CD44 possess several CSC traits, such as self-renewal and epithelial-mesenchymal transition (EMT) capability, as well as a resistance to chemo- and radiotherapy. The CD44 gene regularly undergoes alternative splicing, resulting in the standard (CD44s) and variant (CD44v) isoforms. The interaction of such isoforms with ligands, particularly hyaluronic acid (HA), osteopontin (OPN) and matrix metalloproteinases (MMPs), drive numerous cancer-associated signalling. However, there are contradictory results regarding whether high or low CD44 expression is associated with worsening clinicopathological features, such as a higher tumour histological grade, advanced tumour stage and poorer survival rates. Nonetheless, high CD44 expression significantly contributes to enhanced tumourigenic mechanisms, such as cell proliferation, metastasis, invasion, migration and stemness; hence, CD44 is an important clinical target. This review summarises current research regarding the different CD44 isoform structures and their roles and functions in supporting tumourigenesis and discusses CD44 expression regulation, CD44-signalling pathways and interactions involved in cancer development. The clinical significance and prognostic value of CD44 and the potential of CD44 as a therapeutic target in cancer are also addressed.
The Role of Pumilio RNA Binding Protein in Plants
Authors: Huh Sung Un
Published: December 2021

Eukaryotic organisms have a posttranscriptional/translational regulation system for the control of translational efficiency. RNA binding proteins (RBPs) have been known to control target genes. One type of protein, Pumilio (Pum)/Puf family RNA binding proteins, show a specific binding of 3′ untranslational region (3′ UTR) of target mRNA and function as a post-transcriptional/translational regulator in eukaryotic cells. Plant Pum protein is involved in development and biotic/abiotic stresses. Interestingly, Arabidopsis Pum can control target genes in a sequence-specific manner and rRNA processing in a sequence-nonspecific manner. As shown in in silico Pum gene expression analysis, Arabidopsis and rice Pum genes are responsive to biotic/abiotic stresses. Plant Pum can commonly contribute to host gene regulation at the post-transcriptional/translational step, as can mammalian Pum. However, the function of plant Pum proteins is not yet fully known. In this review, we briefly summarize the function of plant Pum in defense, development, and environmental responses via recent research and bioinformatics data.