bluuebunny/crossref_metadata_2025 huggingface dataset:
A New SQP Methodology for Coordinated Transformer Tap Control Optimization in Electric Networks Integrating Wind Farms
Authors: Grigoraș Gheorghe , Neagu Bogdan-Constantin , Ivanov Ovidiu , Livadariu Bogdan , Scarlatache Florina
Published: January 2022

The real-time control of optimal power flow (OPF) in electric networks represents, in the last period, a challenge for the Distribution Network Operators (DNOs) and Transmission System Operators (TSOs) under the conditions of large-scale integration of renewable energy sources. The paper focused on the voltage management in the electric networks with wind farms connected, proposing a fast Successive Quadratic Programming (SQP)-based centralized control (CC) methodology of the on-load tap changers (OLTC) corresponding to the transformers from the electric substations, which can be included in the Optimal Power Flow (OPF) module of the Supervisory Control and Data Acquisition (SCADA) Master System. The SQP-CC aims to ensure an optimal voltage level inside a variation range, as narrow as possible, which leads to minimize power losses in the electric networks. Treating the OPF problem as an SQP problem and accelerating convergence using the conjugate reduced gradient method led to very few iterations and a fast computational time, recommending the implementation of the methodology for real-time work. The effectiveness of the SQP-CC was demonstrated in a test network having two voltage levels (220 and 110 kV) with two wind farms integrated, considering two scenarios, without and with wind farms connected. The optimal tap positions of the transformers led to very close voltage variations between the buses of the network, quantified through an average voltage drop between the initial and final buses by 0.004 pu compared with 0.031 pu recorded in the scenario with both wind farms injecting power into the network and without coordinated control of the OLTCs. The energy-saving was over 30% in both scenarios, 33.5% (without wind farms) and 41.27% (both wind farms injecting power in the network). These results highlighted the positive effects of the proposed SQP-CC methodology on the real-time optimal operation of the electric networks integrating wind farms.
Influence of Ball Bearing Size on the Flight and Damage Characteristics of Blast-Driven Ball Bearings
Authors: Langdon Genevieve S. , Qi Ruixuan , Cloete Trevor J. , Chung Kim Yuen Steeve
Published: January 2022

This paper presents insights into the influence of ball size on the flight characteristics and damage of a ball bearing embedded in a rear detonated cylindrical charge. It includes results from a post-test damage analysis of ball bearings from previously reported experiments. Computational simulations using Ansys Autodyn were used to provide extra information about the velocity variation during flight and the damage sustained by the ball bearings during the blast event. The influence of bearing size (diameter and mass) was investigated using the validated simulation models to extend the dataset beyond the initial experimental work. The peak bearing velocity is influenced by the charge mass to ball bearing mass ratio and the aspect ratio of the charge. Larger ball bearings require extra momentum to accelerate them to higher velocities, but their higher surface area means a greater portion of the explosive charge is involved in transferring kinetic energy to the projectile. Tensile spalling was to be the major damage mechanism within the ball bearings. The charge aspect ratio also influenced the hydrostatic pressure propagation within the ball bearing itself, affecting the location and degree of internal cracking within the bearings. These findings will prove valuable to blast protection engineers considering the effects of embedded projectiles in improvised explosive devices.
Interplay between Binary and Three-Body Interactions and Enhancement of Stability in Trapless Dipolar Bose–Einstein Condensates
Authors: Subramaniyan Sabari , Ramavarmaraja Kishor Kumar , Ramaswamy Radha , Malomed Boris A.
Published: January 2022

We investigate the nonlocal Gross–Pitaevskii (GP) equation with long-range dipole-dipole and contact interactions (including binary and three-body collisions). We address the impact of the three-body interaction on stabilizing trapless dipolar Bose–Einstein condensates (BECs). It is found that the dipolar BECs exhibit stability not only for the usual combination of attractive binary and repulsive three-body interactions, but also for the case when these terms have opposite signs. The trapless stability of the dipolar BECs may be further enhanced by time-periodic modulation of the three-body interaction imposed by means of Feshbach resonance. The results are produced analytically using the variational approach and confirmed by numerical simulations.
Optimization of Protocol for Construction of Fungal ITS Amplicon Library for High-Throughput Illumina Sequencing to Study the Mycobiome of Aspen Leaves
Authors: Siddique Abu Bakar , Albrectsen Benedicte Riber , Ilbi Hulya , Siddique Abu Bakar
Published: January 2022

High-Throughput Illumina Sequencing (HTS) can be used to study metagenomes, for example, those of importance for plant health. However, protocols must be optimized according to the plant system in question, the focal microorganisms in the samples, the marker genes selected, and the number of environmental samples. We optimized the protocol for metagenomic studies of aspen leaves, originating from varied genotypes sampled across the growing season, and consequently varying in phenolic composition and in the abundance of endo- and epiphytic fungal species. We optimized the DNA extraction protocol by comparing commercial kits and evaluating five fungal ribosomal specific primers (Ps) alone, and with extended primers that allow binding to sample-specific index primers, and we then optimized the amplification with these composite Ps for 380 samples. The fungal DNA concentration in the samples varied from 561 ng/µL to 1526 ng/µL depending on the DNA extraction kit used. However, binding to phenolic compounds affected DNA quality as assessed by Nanodrop measurements (0.63–2.04 and 0.26–2.00 absorbance ratios for 260/280 and 260/230, respectively), and this was judged to be more important in making our choice of DNA extraction kit. We initially modified the PCR conditions after determining the concentration of DNA extract in a few subsamples and then evaluated and optimized the annealing temperature, duration, and number of cycles to obtain the required amplification and PCR product bands. For three specific Ps, the extended Ps produced dimers and unexpected amplicon fragments due to nonspecific binding. However, we found that the specific Ps that targeted the ITS2 region of fungal rDNA successfully amplified this region for every sample (with and without the extension PP) resulting in the desired PCR bands, and also allowing the addition of sample-specific index primers, findings which were successfully verified in a second PCR. The optimized protocol allowed us to successfully prepare an amplicon library in order to subject the intended 380 environmental samples to HTS.
Towards a Vectorial Approach to Predict Beef Farm Performance
Authors: Abbona Francesca , Vanneschi Leonardo , Giacobini Mario
Published: January 2022

Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.
Analysis of Some Power Quality Parameters at the Points of Common Coupling of Photovoltaic Plants Based on Data Measured by Inverters
Authors: Santiago Isabel , García-Quintero Javier , Mengibar-Ariza Gonzalo , Trillo-Montero David , Real-Calvo Rafael J. , Gonzalez-Redondo Miguel
Published: January 2022

With the increasing implementation of renewable energies, the impact that this type of installation can have on the electricity supply grid is of great importance. In this context, the aim of this work is to analyse how the production and injection of electricity generated by a series of small photovoltaic (PV) installations influence some parameters of the electricity grid in the low-voltage (LV) distribution networks to which they are connected, by analysing the basic data provided by the inverters of these installations. The presence of a slight rise in the grid voltage values in the Point of Common Coupling (PCC) as the production of the PV plants increases has been verified, with maximum slopes between 0.95 and 0.00027 V/kW, but which do not result in voltage values close to the limits set by the regulations. In addition to assessing this impact, the results obtained made it possible to determine the hosting capacity that the networks into which these installations inject their energy would have for this type of installation so as not to have a detrimental effect on the voltage values of the grid. The possible influence of the production of PV installations on the voltage imbalance between phases or on the frequency of the grid has also been analysed in this work. Although the values recorded by the inverters have limitations in their measurements that do not make them valid for assessing events such as the presence of harmonics or flickers, they do allow a first analysis to be made of the influence of PV plant production on some grid parameters, without the need to incorporate additional measurement systems in these renewable installations. However, it is important to be aware of the limitations of the measurements of this equipment and, as far as possible, to choose inverter models that carry out this type of measurement as completely as possible.
Molecular Dynamics of Solids at Constant Pressure and Stress Using Anisotropic Stochastic Cell Rescaling
Authors: Del Tatto Vittorio , Raiteri Paolo , Bernetti Mattia , Bussi Giovanni
Published: January 2022

Molecular dynamics simulations of solids are often performed using anisotropic barostats that allow the shape and volume of the periodic cell to change during the simulation. Most existing schemes are based on a second-order differential equation that might lead to undesired oscillatory behaviors and should not be used in the equilibration phase. We recently introduced stochastic cell rescaling, a first-order stochastic barostat that can be used for both the equilibration and production phases. Only the isotropic and semi-isotropic variants have been formulated and implemented so far. In this paper, we develop and implement the equations of motion of the fully anisotropic variant and test them on Lennard-Jones solids, ice, gypsum, and gold. The algorithm has a single parameter that controls the relaxation time of the volume, results in the exponential decay of correlation functions, and can be effectively applied to a wide range of systems.
Wearable Inertial Measurement Unit to Measure External Load: A Full-Season Study in Professional Soccer Players
Authors: Nobari Hadi , Gonçalves Luiz G. , Aquino Rodrigo , Clemente Filipe Manuel , Rezaei Manuchehr , Carlos-Vivas Jorge , Pérez-Gómez Jorge , Pueo Basilio , Ardigò Luca Paolo
Published: January 2022

The aim of this study was to describe weekly acute workload (wAW), chronic workload (wCW), acute: chronic workload ratio (wACWR), training monotony (wTM), and training strain (wTS) variations over a full season across playing positions. Twenty-one professional soccer players were daily monitored during 48 consecutive weeks. Total distance, sprint total distance (STD), high-speed running distance (HSRd), maximum speed, number of the repeated sprints, and body load (BL) were obtained during training and matches using a Wearable Inertial Measurement Unit. The wAW was determined for each external load measure. The wCW, wACWR, and wTM were calculated based on BL metric. Higher values of weekly STD were observed in lateral defenders/wingers (LDW) compared to central defenders/forwards (CDF) (p = 0.009; ES = Large) and midfielders (MDF) (p = 0.034; ES = Large). Additionally, weekly HSRd was higher in LDW vs. CDF (p = 0.016; ES = Large) and MDF (p = 0.011; ES = Large). The CDF presented a lower weekly number of repeated sprints than LDW (p = 0.021; ES = Large). In conclusion, weekly external load metrics were position-dependent over the season. Moreover, LDW a presented greater weekly STD, HSRd, and number of repeated sprints compared to other positions.
Multimodal Diarization Systems by Training Enrollment Models as Identity Representations
Authors: Mingote Victoria , Viñals Ignacio , Gimeno Pablo , Miguel Antonio , Ortega Alfonso , Lleida Eduardo
Published: January 2022

This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system.
Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset
Authors: Ma Tao , Wu Lizhou , Zhu Shuairun , Zhu Hongzhou
Published: January 2022

Predicting clay sensitivity is important to geotechnical engineering design related to clay. Classification charts and field tests have been used to predict clay sensitivity. However, the imbalanced distribution of clay sensitivity is often neglected, and the predictive performance could be more accurate. The purpose of this study was to investigate the performance that extreme gradient boosting (XGboost) method had in predicting multiclass of clay sensitivity, and the ability that synthetic minority over-sampling technique (SMOTE) had in addressing imbalanced categories of clay sensitivity. Six clay parameters were used as the input parameters of XGBoost, and SMOTE was used to deal with imbalanced classes. Then, the dataset was divided using the cross-validation (CV) method. Finally, XGBoost, artificial neural network (ANN), and Naive Bayes (NB) were used to classify clay sensitivity. The F1 score, receiver operating characteristic (ROC), and area under the ROC curve (AUC) were considered as the performance indicators. The results revealed that XGBoost showed the best performance in the multiclassification prediction of clay sensitivity. The F1 score and mean AUC of XGBoost were 0.72 and 0.89, respectively. SMOTE was useful in addressing imbalanced issues, and XGBoost was an effective and reliable method of classifying clay sensitivity.
Wide-Aperture Bimorph Deformable Mirror for Beam Focusing in 4.2 PW Ti:Sa Laser
Authors: Samarkin Vadim , Alexandrov Alexander , Galaktionov Ilya , Kudryashov Alexis , Nikitin Alexander , Rukosuev Alexey , Toporovsky Vladimir , Sheldakova Julia
Published: January 2022

The bimorph deformable mirror with a diameter of 320 mm, including 127 control electrodes, has been developed and tested. The flatness of the initial mirror surface of about 1 μm (P-V) was achieved by mechanically adjusting the mirror substrate fixed in the metal mount. To correct for the aberrations and improve the beam focusing in the petawatt Ti:Sa laser, the wide-aperture adaptive optical system with the deformable mirror and Shack–Hartmann wavefront sensor was developed. Correction of the wavefront aberrations in the 4.2 PW Ti:Sa laser using the adaptive system provided increases the intensity in the focusing plane to a value of 1.1 × 1023 W/cm2
In Vitro Antibacterial Activity of Marine Microalgae Extract against Vibrio harveyi
Authors: Jusidin Mohd Ridzwan , Othman Rafidah , Shaleh Sitti Raehanah Muhamad , Ching Fui Fui , Senoo Shigeharu , Oslan Siti Nur Hazwani
Published: January 2022

Marine microalgae may produce antibacterial substances. At the exponential phase of growth, four species of marine microalgae were examined for their potential to create secondary metabolites that limit the growth of Vibrio harveyi: Nannochloropsis oceanica, Chaetoceros gracilis, Isochrysis sp., and Thalassiosira weissflogii. V. harveyi is a pathogenic bacteria that can cause severe mortality and loss in aquaculture. Disc diffusion assay and co-culture assay were used to determine antibacterial activity. On TSA % NaCl media, the disc impregnated with microalgae and extracted with ethanol, methanol, saline water, and dimethyl sulfoxide (DMSO) was tested against V. harveyi at concentrations of 1.0 × 105, 106 and 107 CFU mL−1. The disc diffusion assay revealed that N. oceanica extracted with ethanol had the largest inhibitory zone against V. harveyi. Meanwhile, only N. oceanica, Isochrysis sp., and T. weissflogii reduced the growth of V. harveyi (105 CFU mL−1) in the co-culture assay (p < 0.05). The current findings reveal that the hydrophilic chemicals in microalgae extract have antibiotic activity against the highly virulent V. harveyi, which causes vibriosis, a serious disease in farmed fish and aquaculture cultivation around the world.
In Search of the 1654 Seismic Source (Central Italy): An Obscure, Strong, Damaging Earthquake Occurred Less than 100 km from Rome and Naples
Authors: Cucci Luigi , Cinti Francesca R.
Published: January 2022

The M6.3 earthquake that occurred in southern Lazio (Central Italy) in 1654 is the strongest seismic event to have occurred in the area. However, our knowledge about this earthquake is scarce and no study has been devoted to the individuation of its causative source. The main purpose of this study is putting together all of the information available for this shock to provide reliable landmarks to identify its seismic source. To this end, we present and discuss historical, hydrological, geological, and seismological data, both reviewed and newly acquired. An important, novel part of this study relies on an analysis of the coseismic hydrological changes associated with the 1654 earthquake and on the comparison of their distribution with models of the coseismic strain field induced by a number of potential seismogenic sources. We find more satisfactory results when imposing a lateral component of slip to the faults investigated. In particular, oblique left-lateral sources display a better fit between strain and hydrological signatures. Finally, the cross-analysis between the results from modeling and the other pieces of evidence collected point to the Sora fault, with its trend variability, as the probable causative source of the 1654 earthquake.
Dynamic Programming Ring for Point Target Detection
Authors: Fu Jingneng , Zhang Hui , Luo Wen , Gao Xiaodong
Published: January 2022

To improve the detection efficiency of a long-distance dim point target based on dynamic programming (DP), this paper proposes a multi-frame target detection algorithm based on a merit function filtering DP ring (MFF-DPR). First, to reduce the influence of noise on the pixel state estimation results, a second-order DP named the MFF-DP is proposed. The current states of pixels on an image plane are estimated by maximizing the addition of the merit functions of the previous two frames and the observation data of the current frame. In addition, to suppress the diffusion of the merit function, the sequential and reverse observation data are connected in a head-to-tail manner to form a ring structure. The MFF-DP is applied to the ring structure, and the merit function of the MFF-DPR is obtained by averaging the merit functions of the sequential and reverse MFF-DPs. Finally, the target trajectory is obtained by correlating the extreme points of the merit functions of the MFF-DPR. The simulation and analysis results show that by merely adding a ring structure, the detection probability of the traditional DP can be improved by up to 40% when detecting point targets under the SNR of 1.8. The point target detection algorithm based on the MFF-DPR can achieve significantly better performance in point target detection compared with the traditional DPs with or without a ring structure. The proposed algorithm is suitable for radars and infrared point target detection systems.
Geophysical Characterization in the Shallow Water Estuarine Lakes of the Southern Everglades, Florida
Authors: Kiflai Michael Eyob , Whitman Dean , Price René M. , Frankovich Thomas A. , Madden Christopher J.
Published: January 2022

Anthropogenic activities have greatly modified freshwater flows through Everglades National Park (ENP) such that saltwater has intruded extensively inland from the coastline, causing coastal lakes and their ecosystems to be exposed to varying salinity conditions. The Comprehensive Everglades Restoration Plan (CERP) makes an effort to restore the quantity, quality, timing, and distribution of freshwater flow in ENP with a goal of reducing salinity conditions within the coastal communities and adjacent estuaries. An understanding of the temporal and spatial variations of surface water and shallow groundwater salinity in the coastal lakes of ENP is needed to evaluate restoration efforts. Geophysical surveys were conducted between 2016 to 2019 using electrical resistivity and electromagnetic (EM) methods in the coastal lakes of ENP. A mean local formation factor of 10.7 ± 1.8 was calculated for the region by comparing the lakes’ bottom formation inverted electrical resistivity soundings with coincident pore water resistivity measured in groundwater wells. The conductivity of surface and groundwater increased during the dry season, reflecting decreased precipitation, increased evapotranspiration, and the increasing influence of saline water from Florida Bay. Spatially, salinity in the lakes increased from west to east in the surface water with an opposite trend observed in the shallow groundwater. Along the south to north inland direction, the salinity of both surface water and groundwater decreased. This study demonstrates that floating electrical resistivity and EM methods can characterize the subsurface formation resistivity and describe temporal and spatial patterns of surface and shallow groundwater conductivity.
Electrical Conductivity and Compressive Strength of Cement Paste with Multiwalled Carbon Nanotubes and Graphene Nanoplatelets
Authors: Lim Kwang Mo , Lee Joo Ha
Published: January 2022

Many studies have been conducted using carbon-based nanomaterials (CBNs) for improving the electrical conductivity and mechanical properties of cementitious composites, but their practical use is yet to be achieved. Several methods have been attempted to secure the dispersibility in the cementitious composite matrix of CBNs, such as multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs). In this study, MWCNTs and GNPs were noncovalently functionalized using melamine, a low-cost chemical, and ball milling, a simple process commonly used in industrial fields. Additionally, MWCNTs and GNPs having one- and two-dimensional shapes were mixed with the cement paste to examine their effect on electrical conductivity and compressive strength. Following the experiment, it was shown that the electrical conductivity was improved via the noncovalent functionalization of MWCNT and GNP. The compressive strength increased up to approximately 0.30–0.60% of the CBNs content; however, for CBN content higher than 0.60%, the compressive strength decreased. The hybrid MWCNT and GNP mixture had a negligible effect on the electrical conductivity and compressive strength.
Skeletal and Dentoalveolar Effects Induced by the Paolone-Kaitsas Appliance in the Treatment of Class II Malocclusion: A Controlled Retrospective Study on Lateral Cephalograms
Authors: Gavazzi Camilla , Franceschi Debora , Pierleoni Felicita , Barone Valeria , Kaitsas Francesco , Paolone Maria Giacinta , Franchi Lorenzo , Giuntini Veronica
Published: January 2022

The aim of this study was to assess the skeletal and dentoalveolar effects induced by the Paolone-Kaitsas functional appliance (PK appliance) in the treatment of growing patients affected by Class II malocclusion. A group of 25 Class II patients, treated with the PK appliance followed by fixed appliances, was evaluated with lateral cephalograms at the start (9.6 ± 1.6 years) and at the end of treatment (13.0 ± 1.5 years), and was compared with a matched untreated Class II control group of 23 subjects selected from the web archive of the American Association of Orthodontists Foundation Craniofacial Growth Legacy. Statistical comparisons were performed with the Student’s t-tests. The treated group showed a significant decrease in SNA (−2.2°), ANB (−2.2°), and Wits appraisal (−3.4 mm), a significant increase in the SN-palatal plane angle (1.1°), and a significant improvement in overjet (−2.9 mm), overbite (−2.5 mm), and molar relationship (3.6 mm). The PK appliance produced favorable dentoalveolar and skeletal effects: it inhibited maxillary growth without effects on the mandible and it also induced a downward inclination of the palatal plane.
Improving the Electromagnetic Vibration Energy Harvester Performance by Using a Double Coil Structure
Authors: Costanzo Luigi , Lo Schiavo Alessandro , Vitelli Massimo
Published: January 2022

The power generation capability of an electromagnetic vibration energy harvester augmented with an additional coil was investigated and compared with that of a standard single coil electromagnetic energy harvester. A single degree of freedom model and the corresponding equivalent electric circuit were employed for the analysis of the standard and of the augmented harvesters. The harvester model was validated by means of an accurate experimental characterization of a commercial electromagnetic harvester, i.e., the model-D by ReVibe. The electric circuits for the standard and for the augmented harvesters were implemented by electronic components and experimentally tested to determine the maximum power they are able to generate in four test conditions. Results from simulations and from experiments showed significant improvement of the power extraction performance exhibited by the double coil energy harvester, particularly at frequencies lower than the harvester mechanical resonance frequency.
Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
Authors: Gonzalez-Lopez Jose A. , Gomez-Alanis Alejandro , Pérez-Córdoba José L. , Green Phil D.
Published: January 2022

In this paper, we propose a novel algorithm called multiview temporal alignment by dependence maximisation in the latent space (TRANSIENCE) for the alignment of time series consisting of sequences of feature vectors with different length and dimensionality of the feature vectors. The proposed algorithm, which is based on the theory of multiview learning, can be seen as an extension of the well-known dynamic time warping (DTW) algorithm but, as mentioned, it allows the sequences to have different dimensionalities. Our algorithm attempts to find an optimal temporal alignment between pairs of nonaligned sequences by first projecting their feature vectors into a common latent space where both views are maximally similar. To do this, powerful, nonlinear deep neural network (DNN) models are employed. Then, the resulting sequences of embedding vectors are aligned using DTW. Finally, the alignment paths obtained in the previous step are applied to the original sequences to align them. In the paper, we explore several variants of the algorithm that mainly differ in the way the DNNs are trained. We evaluated the proposed algorithm on a articulatory-to-acoustic (A2A) synthesis task involving the generation of audible speech from motion data captured from the lips and tongue of healthy speakers using a technique known as permanent magnet articulography (PMA). In this task, our algorithm is applied during the training stage to align pairs of nonaligned speech and PMA recordings that are later used to train DNNs able to synthesis speech from PMA data. Our results show the quality of speech generated in the nonaligned scenario is comparable to that obtained in the parallel scenario.
Brake-Disc Holes and Slit Shape Design to Improve Heat Dissipation Performance and Structural Stability
Authors: Park Soojin , Lee Kibum , Kim Sunwoo , Kim Jinho
Published: January 2022

A brake disc decelerates the vehicle through friction with the brake pads. When the brake system is overheated, brake fade can occur, in which the friction coefficient drops significantly. Additionally, an over-heated brake system may cause vapor lock, in which the brake hydraulic fluid is vaporized. These phenomena can lead to the loss of braking power and cause a fatal accident. Therefore, brake systems must have stable braking and heat dissipation performance. Having through-holes and slits on the friction surface of the rotor has been adopted to improve the heat dissipation performance, but the holes become stress points and potentially cause cracks. Therefore, brake systems should be designed to have structural stability as well as good heat dissipation. In this study, finite element (FE) modeling was developed to analyze the structural stability and heat dissipation performance of a brake system, and structural and thermal simulations were performed in ANSYS, a CAE software package. In addition, to minimize concentrated stress and temperature, optimal design of the shape and pattern of holes and slits was carried out using PIAnO, an integrated optimal design software package. The first step of design optimization was performed while considering the shape and pattern of the disc holes and slits as design factors. Among the design factors, those with the largest effects on the objective functions were found and set as new design factors to perform the second step. The designs were compared to existing discs. Through the optimization presented in this paper, it is expected that the performance of the braking system will improve and the life of the brake parts will be increased.
The Design of a Lightweight Cable Aerial Manipulator with a CoG Compensation Mechanism for Construction Inspection Purposes
Authors: AlAkhras Ayham , Sattar Ilham Hassan , Alvi Muhammad , Qanbar Mohammed W. , Jaradat Mohammad A. , Alkaddour Muhannad
Published: January 2022

Conventionally, aerial manipulators, when used for inspection, use drone rotors to stabilize the center of gravity (CoG) shifts, which highly affects its performance. This paper discusses the development of a self-balancing lightweight cable aerial manipulator that can be used for construction inspection purposes. The design is based on a 3D-printed, three degrees of freedom (DoF), planar cable manipulator that is mounted on an extended platform below it as a counter-balance mechanism. The actuators control the manipulator links through a cable system, allowing them to be mounted at the system base to reduce the moving mass of the manipulator during operation. The counter-balance mechanism compensates for any shifts in the CoG of the system by actively sliding a counter-balance weight, mainly a battery, which powers the setup. This mechanism can be attached beneath an off-the-shelf quadrotor to solve the problem of CoG shifts. CoG shifts are due to the manipulator operation when a payload or inspection tool is attached to the end effector to perform a given task. For construction integrity inspection, the aerial manipulator must remain stable during the push or slide processes on both flat and curved surfaces while the non-destructive tests are carried out. To validate the effectiveness of the proposed design, an experimental setup was used, and comparisons were made between the compensated and uncompensated tilt angles of the aerial manipulator. Significant tilt angle reductions were observed with an average of 94.69% improvement, undergoing different manipulator motions during different operation scenarios, as a result of an active compensation of the CoG shift and lightweight design of the system, without sacrificing the functionality of the manipulator for the task.
Research of Strength, Frost Resistance, Abrasion Resistance and Shrinkage of Steel Fiber Concrete for Rigid Highways and Airfields Pavement Repair
Authors: Kos Željko , Kroviakov Sergii , Kryzhanovskyi Vitalii , Grynyova Iryna
Published: January 2022

High-early strength fiber-reinforced concretes are effective materials for the full depth repair of rigid highway and airfield pavements. A comprehensive study was carried out on the influence of the amount of steel anchor fiber and hardening accelerator on properties that are important for repairing concrete. A two-factor experiment was carried out, in which the influence of the hardening accelerator and fiber dosages on the strength, frost resistance, wear resistance and shrinkage of repaired steel-fiber-reinforced concrete for rigid pavements was studied. The investigated concretes contained 400 kg/m3 of cement and polycarboxylate plasticizer in the amount of 1.2% of the cement content. It has been established that the optimal concrete compositions are with the amount of Sika Rapid 3 hardening accelerator from 1 to 2% of the cement content and the steel fiber amount from 60 to 90 kg/m3. Optimal fiber-reinforced concrete compositions have a reduced shrinkage during hardening, and at the age of 2 days they have a compressive strength of at least 55 MPa and a flexural strength of at least 8.5 MPa. At the design age, the fiber-reinforced concrete compressive strength is 85–90 MPa, its flexural strength ranges from 15.5 to 17.5 MPa, it has a frost resistance of F200 and abrasion not higher than 0.24 g/cm2. These properties ensure the high durability of the repair material.
A Robust Controller for Upper Limb Rehabilitation Exoskeleton
Authors: Blanco-Ortega Andrés , Vázquez-Sánchez Luis , Adam-Medina Manuel , Colín-Ocampo Jorge , Abúndez-Pliego Arturo , Cortés-García Claudia , García-Beltrán Carlos Daniel
Published: January 2022

In this paper, a portable exoskeleton for the rehabilitation of upper extremities of three degrees of freedom (DOF) is proposed. With these degrees of freedom, the exoskeleton provides the movements of flexion–extension and abduction–adduction of the arm and flexion–extension of the forearm. A robust generalized proportional integral (GPI) controller for trajectory tracking to provide smooth movements for rehabilitation with the exoskeleton is proposed. This controller only requires output measurements and is robust against different types of disturbances. Simulation results are presented in the MSC Adams® software environment in co-simulation with Matlab-Simulink® to show the controller’s performance against different types of disturbances. The results of a PID type controller are also contrasted with the results of the GPI controller.
The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI
Authors: Zhang Jiaxing , Han Ge , Mao Huiqin , Pei Zhipeng , Ma Xin , Jia Weijie , Gong Wei
Published: January 2022

Methane is the second most important greenhouse gas after carbon dioxide. The intensity and distribution of methane source/sink in China are unknown. We collected the column-averaged dry air mixing ratio of CH4 (abbreviated as XCH4 hereafter) from TROPOMI for the period from 2018 to 2021, to study spatial distribution and temporal change of atmospheric CH4 concentration, providing clues and foundations for understanding the source/sink in China. It was found that the distribution of XCH4 is roughly high in the East, low in the West, high in the South and low in the North. Additionally, an evidently positive linear relationship between XCH4 and population density was witnessed, suggesting anthropogenic emissions may account for a large portion of total methane emissions. XCH4 exhibits evident seasonal characteristics, with the peak in summer and trough in winter, regardless of the different regions. Moreover, we used XCH4 anomalies to identify the emission sources and found its great potential in the detection of methane emission from mining plants, landfill, rice fields and even geological fracture zones.
Investigations on Wind Characteristics for Typhoon and Monsoon Wind Speeds Based on Both Stationary and Non-Stationary Models
Authors: Qin Zhiqing , Xia Dandan , Dai Liming , Zheng Qingsong , Lin Li
Published: January 2022

Stationary models are usually applied for wind characteristics analysis. However, nonstationarity has been found in the field measurements of typhoons in recent studies; therefore, using traditional models with stationary assumptions to conduct wind characteristics is inadequate. In this research, data acquisition of typhoon wind speeds and monsoon are conducted based on the wind field measurements. Wind speeds of typhoon “Maria” passing through Pintan, Fujian Province, China and the monsoon from 2017.10–2018.10 were obtained to investigate wind characteristics. The run test method is utilized to show that non-stationarity exists in both typhoon and monsoon wind speed, and the percent of non-stationary increases with the increase in time interval. Additionally, results show that stronger non-stationarity exists in typhoon wind speed compared with monsoons. Based on a self-adaptive procedure to extract time varying mean wind speed, a non-stationary model is established to compare with the non-stationary model, which has been applied in the traditional wind characteristic analysis. The fluctuating wind characteristics such as turbulence intensity, gust factor, turbulence integral scale, and wind speed spectrum are analyzed to compare the two models. Results show that the difference of such characteristics between the two models increases with the time interval, indicating the necessity of consideration of non-stationary models, especially for design specifications with larger time intervals. Influences of time intervals are investigated, and relevant recommendations are provided for wind resistance specifications. Our conclusions may provide reference for wind resistance design in engineering applications.
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
Authors: Bochenek Bogdan , Ustrnul Zbigniew
Published: January 2022

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.
Fractional Calculus—Theory and Applications
Authors: Macías-Díaz Jorge E.
Published: January 2022

In recent years, fractional calculus has witnessed tremendous progress in various areas of sciences and mathematics [...]
Accurate Time-Domain Modeling of Arbitrarily Shaped Graphene Layers Utilizing Unstructured Triangular Grids
Authors: Amanatiadis Stamatios , Zygiridis Theodoros , Kantartzis Nikolaos
Published: January 2022

The accurate modeling of curved graphene layers for time-domain electromagnetic simulations is discussed in the present work. Initially, the advanced properties of graphene are presented, focusing on the propagation of strongly confined surface plasmon polariton waves at the far-infrared regime. Then, the implementation of an unstructured triangular grid was examined, based on the Delaunay triangulation method. The electric-field components were placed at the edges of the triangles, while two different techniques were proposed for the sampling of the magnetic ones. Specifically, the first one suggests that the magnetic component is placed at the triangle’s circumcenter providing more accurate results, although instability may occur for nonacute triangles. On the other hand, the magnetic field was sampled at the triangle’s centroid, considering the second technique, ensuring the algorithm’s stability, but further approximations were required, leading to a slight accuracy reduction. Moreover, the updating equations in the time-domain were extracted via an appropriate approximation of Maxwell equations in their integral form. Finally, graphene was introduced in the computational domain as an equivalent surface current density, whose location matches the corresponding electric components. The validity of our methodology was successfully performed via the comparison of graphene surface wave propagation properties to their theoretical values, whereas the global error determination indicates the minimal triangle dimensions. Additionally, an instructive setup comprising a circular graphene scatterer was analyzed thoroughly, to reveal our technique’s advantages compared to the conventional staircase discretization.
Modern Problems of Mathematical Physics and Their Applications
Authors: Juraev Davron Aslonqulovich , Noeiaghdam Samad
Published: January 2022

There are many applications of mathematical physics in several fields of basic science and engineering [...]
Post-Quantum Midpoint-Type Inequalities Associated with Twice-Differentiable Functions
Authors: Sitthiwirattham Thanin , Murtaza Ghulam , Ali Muhammad Aamir , Promsakon Chanon , Sial Ifra Bashir , Agarwal Praveen
Published: January 2022

In this study, first we establish a p,q-integral identity involving the second p,q-derivative, and then, we use this result to prove some new midpoint-type inequalities for twice-p,q-differentiable convex functions. It is also shown that the newly established results are the refinements of the comparable results in the literature.
Nutraceuticals in HIV and COVID-19-Related Neurological Complications: Opportunity to Use Extracellular Vesicles as Drug Delivery Modality
Authors: Kodidela Sunitha , Godse Sandip , Kumar Asit , Nguyen Xuyen H. , Cernasev Alina , Zhou Lina , Singh Ajay Kumar , Bhat Hari K. , Kumar Santosh
Published: January 2022

People living with HIV/AIDS (PLWHA) are at an increased risk of severe and critical COVID-19 infection. There is a steady increase in neurological complications associated with COVID-19 infection, exacerbating HIV-associated neurocognitive disorders (HAND) in PLWHA. Nutraceuticals, such as phytochemicals from medicinal plants and dietary supplements, have been used as adjunct therapies for many disease conditions, including viral infections. Appropriate use of these adjunct therapies with antiviral proprieties may be beneficial in treating and/or prophylaxis of neurological complications associated with these co-infections. However, most of these nutraceuticals have poor bioavailability and cannot cross the blood–brain barrier (BBB). To overcome this challenge, extracellular vesicles (EVs), biological nanovesicles, can be used. Due to their intrinsic features of biocompatibility, stability, and their ability to cross BBB, as well as inherent homing capabilities, EVs hold immense promise for therapeutic drug delivery to the brain. Therefore, in this review, we summarize the potential role of different nutraceuticals in reducing HIV- and COVID-19-associated neurological complications and the use of EVs as nutraceutical/drug delivery vehicles to treat HIV, COVID-19, and other brain disorders.
Cognitive Impairment in Heart Failure—A Review
Authors: Goh Fang Qin , Kong William K. F. , Wong Raymond C. C. , Chong Yao Feng , Chew Nicholas W. S. , Yeo Tiong-Cheng , Sharma Vijay Kumar , Poh Kian Keong , Sia Ching-Hui
Published: January 2022

Cognitive impairment (CI) is common in heart failure (HF). Patients with HF demonstrate reduced global cognition as well as deficits in multiple cognitive domains compared to controls. Degree of CI may be related to HF severity. HF has also been associated with an increased risk of dementia. Anatomical brain changes have been observed in patients with HF, including grey matter atrophy and increased white matter lesions. Patients with HF and CI have poorer functional independence and self-care, more frequent rehospitalisations as well as increased mortality. Pathophysiological pathways linking HF and CI have been proposed, including cerebral hypoperfusion and impaired cerebrovascular autoregulation, systemic inflammation, proteotoxicity and thromboembolic disease. However, these mechanisms are poorly understood. We conducted a search on MEDLINE, Embase and Scopus for original research exploring the connection between HF and CI. We then reviewed the relevant literature and discuss the associations between HF and CI, the patterns of brain injury in HF and their potential mechanisms, as well as the recognition and management of CI in patients with HF.