bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Vibroactivity of Toothed Gears with Highly Flexible Metal Clutch under Variable Load Conditions
Authors: Kuczaj Mariusz , Wieczorek Andrzej N. , Konieczny Łukasz , Burdzik Rafał , Wojnar Grzegorz , Filipowicz Krzysztof , Głuszek Grzegorz
Published: December 2022

The article provides a discussion on a methodology intended for testing of power transmission systems featuring an innovative highly torsionally flexible metal clutch patented by the co-authors of this paper. What this methodology takes into account is the amplitude and frequency analyses discussed in the article, as well as a sensing system based on diverse piezoelectric and magnetic phenomena, the Doppler effect, etc. Both contact and non-contact (laser measurement) methods were used during the tests. The purpose of the tests conducted at the stand, originally designed by the authors in accordance with the methodology proposed, was to evidence that using the innovative and patented, highly torsionally flexible metal clutch makes it possible to reduce the vibrations of multi-stage toothed gears, consequently reducing the forces affecting the gear bearings and those acting at the tooth space, which is to enable the service life of individual components of the power transmission systems intended for mining scraper conveyors to be significantly extended. Based on the studies and analyses performed by the authors, one can observe and conclude that the methodology proposed in the paper makes it possible to use an example of a relatively complex power transmission system in order to examine the relationships between the processes at work, i.e., the decline of the linear vibrations of the gear housing (which is undoubtedly positive in power transmission systems) at the expense of increasing torsional vibrations of the innovative clutch, the latter not to be considered unfavourable to users in the case analysed.
Improved Behavioral Box and Sensing Techniques for Analysis of Tactile Discrimination Tasks in Rodents
Authors: Oliveira Silva José Wanderson , Lima Renan Araújo , Morya Edgard , Brasil Fabricio Lima , Gonçalves Luiz Marcos Garcia
Published: December 2022

Neuroscience behavioral studies stand out among the research works in this area. In these studies, normally, rodents are put inside closed platforms known as behavioral boxes in order to perform tasks and have their behaviors observed by way of sensors and annotations by hand. In this work, we aim to improve this research process by developing new techniques based upon the full automation of the behavioral box processes for more easily acquiring data. We come up with a new structural design using cutting-edge technology, with enhanced spaces and better materials. We use components that can be easily purchased (or built) and developed new techniques for control and data acquisition. Our new platform allows for more precise control of the opening of the discrimination bars, which was not satisfactorily done with previous platforms. This makes possible the design of more complex decision-making experiments using camera and sensor systems, allowing a better assessment of rodent performance in the discrimination task. All the necessary materials and development documents are made available in a collaborative multi-user platform allowing work replication. With this, the present study provides a low-cost tool with ease of development and construction that can be used by laboratories that work with this type of research.
Biocompatible Piezoelectric PVDF/HA/AgNO3 Thin Film Prepared by the Solvent Casting Method
Authors: Markuniene Ieva , Rabiei Marzieh , Nasiri Sohrab , Urbaite Sigita , Palevicius Arvydas , Janusas Giedrius
Published: December 2022

In this study, new composites based on polyvinylidene fluoride (PVDF) were ornamented and prepared with hydroxyapatite (HA) and silver nitride (AgNO3). Taking into account the polarity of the solvent dimethyl sulfoxide, this solvent was used to disperse the particles. The aim of using DMSO was to create amorphous phases and the strong dipoles of the C–F bond to reduce the energy barrier and improve the electrical properties. The PVDF played the role of matrix in HA, and AgNO3 was used as reinforcing elements. X-ray diffraction of the samples directly showed the amorphous phase and mixed amorphous and crystalline phases when all three materials were used simultaneously for preparing the composite. The scanning electron microscopy (SEM) images of the samples confirmed the role of PVDF, HA, and AgNO3. Furthermore, the energy dispersive X-ray (EDX) analysis was performed and proved that the HA structure did not change when the ratio of CaP was equal to the ratio of natural HA. The electrical properties were investigated, and the amount of energy ranged from 56.50 to 125.20 mV. The final results showed that a designed device consisting of an active layer made of 0.1 g HA:0.5 g PVDF showed the highest energy barrier, the highest polarity, and surface energy, thus proving its relevance as potential material for energy harvesting applications.
PCB-Based Planar Inductive Loops for Partial Discharges Detection in Power Cables
Authors: Kaziz Sinda , Romano Pietro , Imburgia Antonino , Ala Guido , Sghaier Halim , Flandre Denis , Tounsi Fares
Published: December 2022

Partial discharge (PD) diagnosis tests, including detecting, locating, and identifying, are used to trace defects or faults and assess the degree of aging in order to monitor the insulation condition of medium- and high-voltage power cables. In this context, an experimental evaluation of three different printed circuit board (PCB)-based inductive sensor topologies, with spiral, non-spiral, and meander shapes, is performed. The aim is to assess their capabilities for PD detection along a transmission power cable. First, simulation and experimental characterization are carried out to determine the equivalent electrical circuit and the quality factor of the three sensors. PD activity was studied in the lab on a 10-m-long defective MVAC cable. The three PCB-based sensors were tested in three different positions: directly on the defective cable (P1), at a separation distance of 10 cm to 3 m (P2), and on the ground line (P3). For the three positions, all sensors’ outputs present a damped sine wave signal with similar frequencies and durations. Experimental results showed that the best sensitivity was given by the non-spiral inductor, with a peak voltage of around 500 mV in P1, 428 mV in P2, and 45 mV in P3, while the meander sensor had the lowest values, which were approximately 80 mV in P1. The frequency spectrum bandwidth of all sensors was between 10 MHz and 45 MHz. The high sensitivity of the non-spiral inductor could be associated with its interesting properties in terms of quality factor and SFR, which are due to its very low resistivity. To benchmark the performance of the designed three-loop sensors, a comparison with a commercial high-frequency current transformer (HFCT) is also made.
The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements
Authors: Szafarczyk Anna , Toś Cezary
Published: December 2022

Bathymetric LiDAR technology is a technology used for simultaneous data acquisition regarding the morphology of the bottom of water reservoirs and the surrounding coastal zone, realized from the air, e.g., by plane or drone. Contrary to the air topographic LiDAR, which uses an infrared wavelength of 1064 nm, bathymetric LiDAR systems additionally use a green wavelength of 532 nm. The green laser can penetrate the water, which makes it possible to measure the depth of shallow water reservoirs, rivers, and coastal sea waters within three Secchi depths. This article presents the theoretical basis for the construction of a green laser. Against the background of other methods of measuring the bottom of water reservoirs, the technology using waves from the visible light range is presented in detail in the assessment of the bottom morphology of shallow water reservoirs. The possibilities of using green laser in lidar bathymetry implemented in particular in non-navigable regions are shown. The results of the researchers’ work on river processes (erosion, sedimentation), design of stream restoration, determination of morphometric parameters of the riverbed, as well as assessment of the topography of the marine coastal bottom zones are summarized. The development direction of lidar bathymetry is discussed.
Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images
Authors: Wang Junjie , Bi Li , Sun Pengxiang , Jiao Xiaogang , Ma Xunde , Lei Xinyi , Luo Yongbin
Published: December 2022

Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks effective technical means. In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category weight assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a feature fusion method based on ResNet152–Xception. A coordinate attention (CA) mechanism is incorporated into the feature map to enhance the feature extraction capability of the existing model. The proposed model was conducted on two global publicly available PV-defective electroluminescence (EL) image datasets, and using CNN, Vgg16, MobileNetV2, InceptionV3, DenseNet121, ResNet152, Xception and InceptionResNetV2 as comparative benchmarks, it was evaluated that several metrics were significantly improved. In addition, the accuracy reached 96.17% in the binary classification task of identifying the presence or absence of defects and 92.13% in the multiclassification task of identifying different defect types. The numerical experimental results show that the proposed deep-learning-based defect detection method for PV cells can automatically perform efficient and accurate defect detection using EL images.
Transient Electromagnetic Processes Analysis in High Voltage Transmission Lines during Two-Phase Short Circuits
Authors: Perzyński Tomasz , Levoniuk Vitaliy , Figura Radosław
Published: December 2022

The mathematical model of a fragment of a high-voltage electric network is developed in this paper. The network consists of a long power line with distributed parameters and an equivalent three-phase active-inductive load. Neumann and Robin—Poincare boundary conditions were used to identify the boundary conditions for the long line equation. The parameter output voltage (voltage at the end of the line) is introduced into the paper for further universal use of the developed line model. On the basis of the developed mathematical model, the program code is written in the algorithmic language Visual Fortran. By means of it, oscillograms of transient electromagnetic processes of voltages and currents in the form of spatial, temporal and temporal-spatial distributions during remote two-phase short circuits in the transmission line of high voltage are obtained. Two transient electromagnetic processes are analyzed in the present work. The first one was analyzed during the switching on of the line to the normal mode of operation with the subsequent transition to the emergency mode. The second one was analyzed during the switching on the line in the mode of a remote two-phase short circuit to the ground. The results of transient electromagnetic process simulation in the form of analyzed figures are shown. All the results presented in this paper were obtained exclusively using numerical methods.
Monitoring Dewatering Fish Spawning Sites in the Reservoir of a Large Hydropower Plant in a Lowland Country Using Unmanned Aerial Vehicles
Authors: Jurevičius Linas , Punys Petras , Šadzevičius Raimondas , Kasiulis Egidijus
Published: December 2022

This paper presents research concerning dewatered areas in the littoral zones of the Kaunas hydropower plant (HPP) reservoir in Lithuania. It is a multipurpose reservoir that is primarily used by two large hydropower plants for power generation. As a result of the peaking operation regime of the Kaunas HPP, the large quantity of water that is subtracted and released into the reservoir by the Kruonis pumped storage hydropower plant (PSP), and the reservoir morphology, i.e., the shallow, gently sloping littoral zone, significant dewatered areas can appear during drawdown operations. This is especially dangerous during the fish spawning period. Therefore, reservoir operation rules are in force that limit the operation of HPPs and secure other reservoir stakeholder needs. There is a lack of knowledge concerning fish spawning locations, how they change, and what areas are dewatered at different stages of HPP operation. This knowledge is crucial for decision-making and efficient reservoir storage management in order to simultaneously increase power generation and protect the environment. Current assessments of the spawning sites are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar in such conditions. A detailed survey of a small (approximately 5 ha) area containing several potential spawning sites was carried out using Unmanned Aerial Vehicles (UAV) equipped with multispectral and conventional RGB cameras. The captured images were processed using photogrammetry and analyzed using various techniques, including machine learning. In order to highlight water and track changes, various indices were calculated and assessed, such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), and Normalized Green-Red Difference Index (NGRDI). High-resolution multispectral images were used to analyze the spectral footprint of aquatic macrophytes, and the possibility of using the results of this study to identify and map potential spawning sites over the entire reservoir (approximately 63.5 km2) was evaluated. The aim of the study was to investigate and implement modern surveying techniques to improve usage of reservoir storage during hydropower plant drawdown operations. The experimental results show that thresholding of the NGRDI and supervised classification of the NDWI were the best-performing methods for the shoreline detection in the fish spawning sites.
Methodological Considerations in the Kinematic and Kinetic Analysis of Human Movement among Healthy Adolescents: A Scoping Review of Nonlinear Measures in Data Processing
Authors: Silva Sandra , Ribeiro Fernando , Figueira Vânia , Pinho Francisco
Published: December 2022

Nonlinear measures have increasingly revealed the quality of human movement and its behaviour over time. Further analyses of human movement in real contexts are crucial for understanding its complex dynamics. The main objective was to identify and summarize the nonlinear measures used in data processing during out-of-laboratory assessments of human movement among healthy adolescents. Summarizing the methodological considerations was the secondary objective. The inclusion criteria were as follows: According to the Population, Concept, and Context (PCC) framework, healthy teenagers between 10 and 19 years old that reported kinetic and/or kinematic nonlinear data-processing measurements related to human movement in non-laboratory settings were included. PRISMA-ScR was used to conduct this review. PubMed, Science Direct, the Web of Science, and Google Scholar were searched. Studies published between the inception of the database and March 2022 were included. In total, 10 of the 2572 articles met the criteria. The nonlinear measures identified included entropy (n = 8), fractal analysis (n = 3), recurrence quantification (n = 2), and the Lyapunov exponent (n = 2). In addition to walking (n = 4) and swimming (n = 2), each of the remaining studies focused on different motor tasks. Entropy measures are preferred when studying the complexity of human movement, especially multiscale entropy, with authors also carefully combining different measures, namely entropy and fractal analysis.
An Infinity Tube with an Expansion Chamber for Noise Control in the Ductwork System
Authors: Xue Rong , Mak Cheuk Ming , Cai Chenzhi , Ma Kuen Wai
Published: December 2022

This paper proposes a muffler with simple geometry to effectively reduce low-frequency noise in ductwork systems. A muffler named infinity tube with an expansion chamber (ITEC) is developed from the infinity tube (IT). Theoretical and numerical analyses of wave propagation in the ITEC have been conducted in this paper. The transfer matrix method is adopted to predict transmission loss theoretically. The theoretical results are validated by the finite element method simulation. The comparison of the transmission loss between the IT and ITEC illustrates that the ITEC has an advantage in low-frequency noise reduction. The transmission loss results of the ITEC are compared with the Helmholtz resonator system to assess the potential for industrial application. Finally, the geometric parameters of the proposed ITEC on its noise attenuation performance have been analyzed. The proposed ITEC can effectively reduce low-frequency noise, and it is suitable for ductwork systems in constrained spaces.
Measurement System for the Environmental Load Assessment of the Scale Ship Model
Authors: Miller Anna , Rak Andrzej
Published: December 2022

The forces and moments acting on a marine vessel caused by the wind are most often modeled based on its speed measured at a standard 10 m above the sea level. There exist numerous well-known methods for modeling wind speed in such conditions. These models, by nature, are inadequate for simulating wind disturbances for free-running scale ship models sailing on lakes. Such scale models are being used increasingly for design and testing modern ship motion control systems. The paper describes the hardware and methodology used in measuring wind speed at low altitudes above the lake level. The system consists of two ultrasonic anemometers supplemented with wave sensor acting as a capacitor immersed partially in the water. Obtained measurement results show clear similarity to the values gathered during full-scale experiments. Analysis of the power spectral density functions of turbulence measured for different mean wind speeds over the lake, indicates that, at the present stage of research, the best model of wind turbulence at low altitude above the lake level can be obtained by assembling four of the known, standard turbulence models.
Cramér–Rao Bounds for DoA Estimation of Sparse Bayesian Learning with the Laplace Prior
Authors: Bai Hua , Duarte Marco F. , Janaswamy Ramakrishna
Published: December 2022

In this paper, we derive the Cramér–Rao lower bounds (CRLB) for direction of arrival (DoA) estimation by using sparse Bayesian learning (SBL) and the Laplace prior. CRLB is a lower bound on the variance of the estimator, the change of CRLB can indicate the effect of the specific factor to the DoA estimator, and in this paper a Laplace prior and the three-stage framework are used for the DoA estimation. We derive the CRLBs under different scenarios: (i) if the unknown parameters consist of deterministic and random variables, a hybrid CRLB is derived; (ii) if all the unknown parameters are random, a Bayesian CRLB is derived, and the marginalized Bayesian CRLB is obtained by marginalizing out the nuisance parameter. We also derive the CRLBs of the hyperparameters involved in the three-stage model and explore the effect of multiple snapshots to the CRLBs. We compare the derived CRLBs of SBL, finding that the marginalized Bayesian CRLB is tighter than other CRLBs when SNR is low and the differences between CRLBs become smaller when SNR is high. We also study the relationship between the mean squared error of the source magnitudes and the CRLBs, including numerical simulation results with a variety of antenna configurations such as different numbers of receivers and different noise conditions.
Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation
Authors: Thesma Vaishnavi , Mohammadpour Velni Javad
Published: December 2022

This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.
Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking
Authors: Velusamy Gandhimathi , Lent Ricardo
Published: December 2022

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock’s power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA’s High Rate Delay Tolerant Networking (HDTN) project.
Unusual Driver Behavior Detection in Videos Using Deep Learning Models
Authors: Abosaq Hamad Ali , Ramzan Muhammad , Althobiani Faisal , Abid Adnan , Aamir Khalid Mahmood , Abdushkour Hesham , Irfan Muhammad , Gommosani Mohammad E. , Ghonaim Saleh Mohammed , Shamji V. R. , Rahman Saifur
Published: December 2022

Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior.
Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model
Authors: Rodriguez Valido Manuel , Gomez-Cardenes Oscar , Magdaleno Eduardo
Published: December 2022

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.
Smart Portable Device Based on the Utilization of a 2D Disposable Paper Stochastic Sensor for Fast Ultrasensitive Screening of Food Samples for Bisphenols
Authors: Stefan-van Staden Raluca-Ioana , Chera-Anghel Irina-Alina , Gheorghe Damaris-Cristina , van  Staden Jacobus (Koos) Frederick , Badulescu Marius
Published: December 2022

Since the determination of the high toxicity of bisphenol A, alternative structures for bisphenols have been synthesized, resulting in bisphenols C, E, F, S, and Z. These bisphenols have replaced bisphenol A in plastic bottles, toys, and cans used for preserving food. Later, the toxicity and negative effects of all of these bisphenols on people’s health were proven. Therefore, there is a need for a fast ultrasensitive screening method that is able to detect the presence of these bisphenols in any condition directly from food samples. This paper presented a disposable device based on the utilization of a 2D disposable paper stochastic sensor for the fast ultrasensitive screening of food samples for bisphenols A, C, E, F, S, and Z. The 2D disposable sensor was obtained by the deposition of graphene and silver nanolayers on paper using cold plasma. Furthermore, the active side of the sensor was modified using 2,3,7,8,12,13,17,18-octaethyl-21H,23H Mn porphyrin. The limits of quantification of these bisphenols were 1 fmol L−1 for bisphenols C and E, 10 fmol L−1 for bisphenols A and F, 10 pmol L−1 for bisphenol S, and 1 pmol L−1 for bisphenol Z. The recoveries of these bisphenols in milk, canned fruits, vegetables, and fish were higher than 99.00% with RSD (%) values lower than 1.50%.
Detecting Lombard Speech Using Deep Learning Approach
Authors: Kąkol Krzysztof , Korvel Gražina , Tamulevičius Gintautas , Kostek Bożena
Published: December 2022

Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks (CNNs) and various two-dimensional (2D) speech signal representations. To reduce the computational cost and not resign from the 2D representation-based approach, a strategy for threshold-based averaging of the Lombard effect detection results is introduced. The pseudocode of the averaging process is also included. A series of experiments are performed to determine the most effective network structure and the 2D speech signal representation. Investigations are carried out on German and Polish recordings containing Lombard speech. All 2D signal speech representations are tested with and without augmentation. Augmentation means using the alpha channel to store additional data: gender of the speaker, F0 frequency, and first two MFCCs. The experimental results show that Lombard and neutral speech recordings can clearly be discerned, which is done with high detection accuracy. It is also demonstrated that the proposed speech detection process is capable of working in near real-time. These are the key contributions of this work.
Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals
Authors: Biot-Monterde Vicente , Navarro-Navarro Angela , Zamudio-Ramirez Israel , Antonino-Daviu Jose A. , Osornio-Rios Roque A.
Published: December 2022

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.
Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging
Authors: Al Ktash Mohammad , Stefanakis Mona , Wackenhut Frank , Jehle Volker , Ostertag Edwin , Rebner Karsten , Brecht Marc
Published: December 2022

UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.
Development of Analytical Methods for the Determination of N-Bromosuccinimide in Different Active Pharmaceutical Ingredients by High-Performance Ion Chromatography with Suppressed Conductivity Detection
Authors: Páll Boglárka , Kapui Imre , Kormány Róbert , Horváth Krisztián
Published: December 2022

Product safety is important for medicines. For drugs on the market, it must be demonstrated that the levels of toxic contaminants are below the permitted limits. These impurities are used as reagents or are generated during synthesis. N-bromosuccinimide is used as a brominating agent in the synthesis of some active pharmaceutical ingredients. The determination of N-bromosuccinimide is difficult due to its high reactivity. In this work, a high-performance ion chromatographic method was developed for the determination of N-bromosuccinimide. The ion chromatographic measurement can be performed in two ways, one involves the assay of the resulting bromide ion and the other is via the assay of the 3-carbamoyl propanoic acid ion produced from the succinimide. Both acid ions were analyzed on an anion exchange column by gradient elution with potassium hydroxide eluent and detection was performed by a suppressed conductivity detector. During the method development, the results showed that the measurement of bromide ion was more selective than the measurement of 3-carbamoyl propanoic acid ion. Two different types of active pharmaceutical ingredients (API), i.e., prasugrel and favipiravir, were chosen to test the developed method and sample preparation. For both APIs, sample preparation was performed in a vial and consists of liquid–liquid extraction with an alkaline reagent. Finally, the anion exchange ion chromatography method was validated at the limit value level, and harmonized with the guidelines. For prasugrel, the quantification limits and the accuracy at the limit level are 7.2 ppm and 96.4%, while for favipiravir these are 7.5 ppm and 114.7%, respectively.
Constructing Home through Unhome: Narratives of Resistance by an Iranian Asylum Seeker in Germany
Authors: Fathi Mastoureh
Published: December 2022

The recent COVID-19 pandemic uncovered some already existing but somewhat hidden inequalities in different countries. Many features of inequalities that pre-existed the COVID-19 pandemic for a long time were uncovered due to this radical shift in living arrangements globally. This paper focuses on one particular feature of these inequalities: housing situation of one Iranian asylum seeker in a heim (refugee accommodation) in Germany. Contributing to an understanding of how political resistance can be exercised through personal biographies, the paper differentiates between the notion of ‘unhome’ from home by discussing three factors: choice, anchor and the significant of others. The paper contributes to the growing scholarships around home in migration and its intersection with personal narratives.
Influence of Organizational Democracy on Organizational Citizenship Behaviors in Digital Transformation: Mediating Effects of Job Satisfaction and Organizational Commitment for Smart Services
Authors: Haskasap Enver , Saner Tulen , Eyupoglu Serife , Gunsel Haskasap Cemre S.
Published: December 2022

This study investigated the influence of organizational democracy on organizational citizenship behaviors in digital transformation, by considering the mediating effects of job satisfaction and organizational commitment for smart services. Exploratory factor analysis (EFA) was conducted to investigate the factors, which was followed by confirmatory factor analysis (CFA) and path analysis to test the hypotheses. The sample consisted of 144 full-time employees of the largest bank in North Cyprus. The findings suggest that organizational democracy had a significant positive direct effect on the job satisfaction and organizational commitment, whereas the direct effect on the organizational citizenship behaviors was not significant. The mediating effect of organizational commitment was found to be significantly positive. Job satisfaction was found not to be a significant mediator. The indirect effect of organizational democracy on the organizational citizenship behaviors was significant.
Life-Cycle Assessment of Refrigerants for Air Conditioners Considering Reclamation and Destruction
Authors: Yasaka Yoshihito , Karkour Selim , Shobatake Koichi , Itsubo Norihiro , Yakushiji Fumiaki
Published: December 2022

Fluorinated gases (F-gases) used as refrigerants in air conditioners have a significant global warming effect, so their release into the atmosphere must be minimized. The purpose of this study was to evaluate and compare the environmental impact of two treatment methods: reclamation and destruction after refrigerant recovery. Plant data for R410A, R32, R134a, and R22 were collected from Japan and Europe and evaluated in terms of greenhouse gas (GHG) emissions, energy consumption, and the Life-cycle Impact assessment Method based on Endpoint modeling 3 (LIME3). As for GHG emissions, the results per kg of used refrigerant showed that the reclamation process emitted approximately 5.7 to 15.9 kg CO2-eq less than the destruction process. In addition, the energy consumption was found to be 82.5 to 250.6 MJ lower, and, for LIME3, the results were found to be USD 0.40 to 0.97 lower for the reclamation compared with the destruction. This trend was the same regardless of the refrigerant type and location, and it was quantitatively clarified that the environmental impact was smaller for the reclamation process than for the destruction process.
Digital Transformation Characteristics of the Semiconductor Industry Ecosystem
Authors: Kim Donggi , Cho Keuntae
Published: December 2022

This study investigates the characteristics of competitive strategies, digital competencies, and management performance as digital drivers in the semiconductor industry. We constructed a conceptual research model and proposed five research hypotheses based on previous studies and analyzed the fit of the research model using IBM SPSS AMOS software. In total, 131 companies specializing in semiconductor materials, components, equipment, and fabless design comprised the sample for analysis. To improve the fit of the research model, several unnecessary paths and measurement items were removed, the final research model was selected, and the five hypotheses were tested. The results indicate that digital drivers positively affect digital competencies, which, in turn, positively affect efficiency-centered and novelty-centered business models. Regarding the relationship between these business models and management performance, only the novelty-centered business model positively affects non-financial performance. These findings could help formulate digital transformation strategies related to products/services, processes, and business models at the firm level, as well as establish policies such as regulation and support programs at the government level.
Antibiotic Consumption in Vanuatu before and during the COVID-19 Pandemic, 2018 to 2021: An Interrupted Time Series Analysis
Authors: Foxlee Nicola D. , Lui Amsaline , Mathias Agnes , Townell Nicola , Lau Colleen L.
Published: December 2022

The study objectives were to examine antibiotic consumption at Vila Central Hospital (VCH), Vanuatu between January 2018 and December 2021 and the influence of the COVID-19 pandemic on antibiotic consumption during this period. Data on antibiotic usage were obtained from the Pharmacy database. We used the WHO’s Anatomical Therapeutic Classification/Defined Daily Dose (ATC/DDD) index, VCH’s inpatient bed numbers and the hospital’s catchment population to calculate monthly antibiotic consumption. The results were expressed as DDDs per 100 bed days for inpatients (DBDs) and DDDs per 1000 inhabitants per day for outpatients (DIDs). Interrupted time series (ITS) was used to assess the influence of COVID-19 by comparing data before (January 2018 to January 2020) and during (February 2020 to December 2021) the pandemic. Ten antibiotics were examined. In total, 226 DBDs and 266 DBDs were consumed before and during COVID-19 by inpatients, respectively with mean monthly consumption being significantly greater during COVID-19 than before the pandemic (2.66 (p = 0.009, 95% CI 0.71; 4.61)). Whilst outpatients consumed 102 DIDs and 92 DIDs before and during the pandemic, respectively, the difference was not statistically significant. Findings also indicated that outpatients consumed a significantly lower quantity of Watch antibiotics during COVID-19 than before the pandemic (0.066 (p = 0.002, 95% CI 0.03; 0.11)). The immediate impact of COVID-19 caused a reduction in both inpatient and outpatient mean monthly consumption by approximately 5% and 16%, respectively, and this was followed by an approximate 1% monthly increase until the end of the study. By mid-2021, consumption had returned to pre-pandemic levels.
False Contraindications for Vaccinations Result in Sub-Optimal Vaccination Coverage in Quito, Ecuador: A Cross-Sectional Study
Authors: Andrade-Guerrero Felipe , Tapia Adriana , Andrade Vinicio , Vásconez-González Jorge , Andrade-Guerrero José , Noroña-Calvachi Carlos , Izquierdo-Condoy Juan S. , Yeager Justin , Ortiz-Prado Esteban
Published: December 2022

Vaccination coverage in Ecuador has decreased since 2013, falling short of the World Health Organization’s vaccination goal. There are several causes for this deficiency in coverage, one of these are lost vaccination opportunities, which are caused when a patient without contraindications postpones, or for other reasons fails to receive a recommended immunization. The objective of this study was to determine the state of knowledge regarding vaccination contraindications among the Metropolitan District of Quito health personnel to assess missed vaccination opportunities. Through this cross-sectional descriptive study, health personnel were surveyed online and asked 18 clinical scenarios which were created to evaluate their knowledge of the true contraindications of vaccination, and measure missed opportunities. A total of 273 surveys were collected; 74% belonged to the public health system, and the rest represented by private practitioners. Of those surveyed, 98.2% of health personnel had improperly denied vaccination at least once. We specifically found vaccinations were incorrectly denied more frequently in cases where the hypothetical patient presented mild or moderate fever cases. The use of corticosteroids, autoimmune diseases, and egg allergy were also incorrectly denied (89%, 71.4%, 72.9%, and 58.6%, respectively). Among the health personnel surveyed, there is an apparent lack of knowledge of the true contraindications of vaccination and differences in knowledge about contraindications according to personnel in charge of administering immunization to children. Our preliminary results suggest that lack of education related to side effects could be biasing medical professionals’ decisions, causing them to unnecessarily delay or deny vaccinations, which likely contributes to explaining low overall vaccination coverage in Quito, the capital city of Ecuador.
Spatial Optimization to Improve COVID-19 Vaccine Allocation
Authors: Scroggins Stephen , Goodson Justin , Afroze Tasnova , Shacham Enbal
Published: December 2022

Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers.
Vaccine-Associated Rubella Caused by the RA 27/3 Strain
Authors: Paz Manuel , Padilla Magaly , Perez Eduardo , Sauceda Jessica , Camacho Adrian
Published: December 2022

Vaccine-associated rubella is a very rare adverse effect after rubella vaccination; we report the characteristics of a young women who, after a vaccination campaign where she received three different vaccines against influenza, tetanus/diphtheria, and measles/rubella, developed a fever and rash consistent with rubella disease that was confirmed by sequencing of the virus. The evolution was favorable. The woman had two close contacts who did not develop the disease. Follow-up of the patient and her contacts was important to detect complications and for epidemiology surveillance.