bluuebunny/crossref_metadata_2025 huggingface dataset:
Distribution and Prevalence of Theileria orientalis Genotypes in Adult Lactating Dairy Cows in South West Region of Western Australia
Authors: Leong Chi-Cheng , Oskam Charlotte L. , Barbosa Amanda D. , Aleri Joshua W.
Published: January 2023

Bovine anaemia caused by Theileria orientalis group (BATOG) causes significant production and economic losses in Australia’s cattle industry. The pathogenic T. orientalis genotypes reported in Australian cattle are type 1 (Chitose) and type 2 (Ikeda). The present study aimed to determine the prevalence and distribution of T. orientalis genotypes in adult lactating cows in Western Australia (WA) dairy herds. A total of 100 whole blood samples from lactating cows from 10 farms were obtained and screened for T. orientalis using polymerase chain reaction (PCR). Sanger sequencing was subsequently used to characterise T. orientalis genotypes isolated from positive samples. A total of thirteen cows (13%; 95% CI: 7.1–21.2%) were positive for T. orientalis, and six out of ten farms (60%; 95% CI: 26.2–87.8%) housed at least one T. orientalis-positive cow. The distribution of T. orientalis was found to be wide and dense in the South west region of WA and the southern coast of WA. The predominant T. orientalis genotype identified was Ikeda (n = 11, 11%; 95% CI: 5.6–18.8%), while the Buffeli genotype was identified in WA for the first time, albeit at a low prevalence (n = 1, 1%; 95% CI: 0.0–5.4%). This study has provided useful epidemiological evidence on the prevalence and distribution of T. orientalis in adult lactating dairy cows in WA dairy farms, and on the importance of conducting widespread surveillance programs for the understanding of BATOG in WA.
Guidance on the Conduct of Clinical Research within OECD Countries during the Early Stages of the COVID-19 Pandemic: A Systematic Review
Authors: Bhutkar Renu , Collins Jack C. , O’Reilly Claire L. , El-Den Sarira
Published: January 2023

Background: In response to the COVID-19 pandemic, member countries of the Organisation for Economic Co-operation and Development (OECD) rapidly published guidance regarding the conduct of clinical research. A systematic review was conducted to explore the recommendations issued in relation to the commencement, continuation and termination of clinical research during the early phases of the pandemic. Methods: Searches consisting of the terms “COVID-19”, “clinical research”, and “guidance”, were conducted in PubMed, Embase, MEDLINE, Trip, Guidelines International Network, and Google in April–May 2021 (up to 4 May 2021). Data were extracted from guidance published from OECD member countries and mapped to inductively-developed categories. Results: 9419 references were systematically screened, resulting in the inclusion of 46 publications from 27 OECD countries. Thirty-three sources made recommendations regarding monitoring, risk-benefit assessments and information technology. There was limited specific recommendations made in relation to personal protective equipment (PPE) in the included guidance. Findings demonstrate that guidance differed by publication date demonstrating the rapidly evolving environment within which research was conducted. Importantly, many organisations opted to endorse existing guidance published by the United States’ Food and Drug Administration and the European Medicines Agency rather than develop their own recommendations. Conclusions: Given the rapidly evolving nature of the pandemic, particularly in the early stages, findings demonstrate the global response in relation to clinical research conduct, thereby providing important insights for future public health emergencies.
Optical Properties of LiGdF4 Single Crystal in the Terahertz and Infrared Ranges
Authors: Komandin Gennady A. , Lebedev Sergey P. , Korableva Stella L. , Morozov Oleg A. , Kyashkin Vladimir M. , Semashko Vadim V. , Fedorov Pavel P.
Published: January 2023

The basic mechanisms of absorption of THz range radiation in optically perfect LiGdF4 single crystals were studied using the broadband experimental data and the dielectric response function analysis within the harmonic oscillator model. The polarized IR reflection spectra have allowed one to determine the phonon contribution in the absorption coefficient in the THz range, while transmission spectra in the THz range were used to obtain the birefringence value and the effects of various mulitparticle processes. Additionally, we established the optical and electrodynamic parameters of the LiGdF4 single crystal, which are necessary for the design of nonlinear optical devices.
Elemental Profile, General Phytochemical Composition and Bioaccumulation Abilities of Selected Allium Species Biofortified with Selenium under Open Field Conditions
Authors: Vuković Sandra , Moravčević Djordje , Gvozdanović-Varga Jelica , Dojčinović Biljana , Vujošević Ana , Pećinar Ilinka , Kilibarda Sofija , Kostić Aleksandar Ž.
Published: January 2023

Allium species are known as a rich source of many compounds with potential healing effects. Biofortification is recognized as an effective agrotechnical measure for raising the level of biogenic elements—especially microelements in the edible parts of these species, so Allium can be considered as a ‘natural dietary supplement’. The aim of this research was to test the effects of foliar application of Se fertilizer (Na2SeO4) in different doses (control—0, 10, 20 and 30 g per ha) on the content of macro, microelements and secondary metabolites (SMs)—free phenolics, flavonoids and hydroxycinnamic acid derivatives in the edible parts, i.e., leaves of two selected Allium species in Serbia (A. odorum and A. schoenoprasum), which grew in open field conditions over the course of two growing seasons. The bioaccumulation factor (BAF), as an indicator of the ability of plants to accumulate biogenic elements, was also determined. Although with no full regularity, the dose of 10 g of Se per ha yielded the highest content for the most biogenic elements for both alliums in the first growing season, i.e., a dose of 20 g of Se per ha for A. schoenoprasum, and a dose of 30 g of Se per ha for A. odorum in the second growing season. The obtained results justified the Se-biofortification of different alliums. The BAF values indicated the ability of both Allium species to accumulate S, K and P in their leaves during both growing seasons. The accumulation of potentially toxic elements was not recorded for either species, emphasizing the safety of the produced plant material for human consumption. Additionally, Se-treated plants had higher SM contents compared to control plants. The growing season also showed an impact on SM content; i.e., in the second season, characterized as drought-stressed, the synthesis of SMs was significantly higher compared to that in the first season. Further research should be directed towards finding the appropriate dose of Se, expanded in the sense of conducting research in controlled conditions, as well as different ways of applying Se fertilizer. The idea of this study was also to popularize the examined Allium species, which are rarely grown in the territory of Serbia.
Current Trends for Lavender (Lavandula angustifolia Mill.) Crops and Products with Emphasis on Essential Oil Quality
Authors: Crișan Ioana , Ona Andreea , Vârban Dan , Muntean Leon , Vârban Rodica , Stoie Andrei , Mihăiescu Tania , Morea Adriana
Published: January 2023

Lavender is in the research spotlight due to its increasing economic importance, while market demand is expected to continue to grow. Among the hundreds of essential-oil-bearing plants, Lavandula angustifolia Mill. remains one of the most valuable. This paper explores the lavender chain timeline from crop to products, examining the expanding knowledge on the characteristics, phytochemical profile and functional potential of lavender that could lead to new products and uses. Lavender crops can be expanded without competing for productive land, instead using marginal, contaminated or unproductive land. A novel cultivation trend proposes leveraging agri-background biodiversity, arbuscular mycorrhiza and the natural enemies of pests for healthy crops. Together with breeding efforts targeting highly performant genotypes with complex volatile profiles coupled with resistance to specific biotic (particularly Phytoplasma) and abiotic (salt, heavy metals) stressors, industry could have a steady supply of high-quality raw material. Besides the expansion of the uses of essential oil in cosmetics, pharmaceuticals, food and environmental and agri-applications, novel channels have appeared for the use of the solid by-product, which is rich in polyphenols and polysaccharides; these channels have the potential to create additional streams of value. The stabilization and optimization of techno-functional delivery systems through the encapsulation of essential oil can extend shelf-life and enhance biological activity efficiency.
Study of the Chemical Profile and Anti-Fungal Activity against Candida auris of Cinnamomum cassia Essential Oil and of Its Nano-Formulations Based on Polycaprolactone
Authors: Rosato Roberto , Napoli Edoardo , Granata Giuseppe , Di Vito Maura , Garzoli Stefania , Geraci Corrada , Rizzo Silvia , Torelli Riccardo , Sanguinetti Maurizio , Bugli Francesca
Published: January 2023

Background: Candida auris represents an emerging pathogen that results in nosocomial infections and is considered a serious global health problem. The aim of this work was to evaluate the in vitro antifungal efficacy of Cinnamomum cassia essential oil (CC-EO) pure or formulated in polycaprolactone (PCL) nanoparticles against ten clinical strains of C. auris. Methods: nanoparticles of PCL were produced using CC-EO (nano-CC-EO) and cinnamaldehyde (CIN) through the nanoprecipitation method. The chemical profile of both CC-EO and nano-CC-EO was evaluated using SPME sampling followed by GC-MS analysis. Micro-broth dilution tests were performed to evaluate both fungistatic and fungicidal effectiveness of CC-EO and CIN, pure and nano-formulated. Furthermore, checkerboard tests to evaluate the synergistic action of CC-EO or nano-CC-EO with micafungin or fluconazole were conducted. Finally, the biofilm disrupting activity of both formulations was evaluated. Results: GC-MS analysis shows a different composition between CC-EO and nano-CC-EO. Moreover, the microbiological analyses do not show any variation in antifungal effectiveness either towards the planktonic form (MICCC-EO = 0.01 ± 0.01 and MICnano-CC-EO = 0.02 ± 0.01) or the biofilm form. No synergistic activity with the antifungal drugs tested was found. Conclusions: both CC-EO and nano-CC-EO show the same antimicrobial effectiveness and are potential assets in the fight against C. auris.
Effect of Production Technology Intensity on the Grain Yield, Protein Content and Amino Acid Profile in Common and Durum Wheat Grain
Authors: Sułek Alicja , Cacak-Pietrzak Grażyna , Różewicz Marcin , Nieróbca Anna , Grabiński Jerzy , Studnicki Marcin , Sujka Katarzyna , Dziki Dariusz
Published: January 2023

Products derived from wheat grains are an important source of protein in the daily diet of people in many parts of the world. The biological value of protein is determined by its amino acid composition and the proportions of the individual amino acids. Synthesis of these compounds in wheat grains is influenced by genetic factors, as well as habitat conditions and the agrotechnology applied in cultivation. The aim of this study was to assess the effect of production technology (integrated, intensive) on the grain yield and the content amino acid profile of protein in common and durum wheat grain. Field research was conducted at the Experimental Station IUNG-PIB in Osiny (Poland) in two growing seasons. It was found that grain yield significantly depended on the weather conditions in the years of harvesting and genotype, but did not depend on the production technology. On the other hand, the protein content and their amino acid composition depended significantly on the production technology and genotype. A significantly higher content of protein substances was found in durum wheat grain. Increasing the intensity of production technology had a positive effect on the total protein content and the content of individual amino acids, both exogenous and endogenous. The amino acid limiting the biological value of protein contained in grains of both wheat species was lysine, and the deficiency of this amino acid was significantly lower in grain protein from intensive than integrated cultivation technology.
Prediction of Surface Location Error Considering the Varying Dynamics of Thin-Walled Parts during Five-Axis Flank Milling
Authors: Tang Yuyang , Zhang Jun , Hu Weixin , Liu Hongguang , Zhao Wanhua
Published: January 2023

Surface location error (SLE) caused by forced vibration is a key factor to determine the quality of the finished part. When machining thin-walled structures with sculptured surfaces, the complicated milling process is significantly influenced by the vibration due to the flexibility of the part. The dynamics of the part are dominant and vary with the material removal during machining. This paper presents a prediction method of SLE considering the varying dynamics of thin-walled parts in five-axis flank milling. The in-process part is decomposed into unmachined and machined portions, which are both modelled based on the thin-plate theory. The dynamics models of the two portions are coupled using the substructure method. Coordinate transformation based on the screw theory and the general cutting dynamics model for five-axis flank milling is employed to transform the cutting force vectors and frequency response function (FRF) to the same coordinate system for the prediction of SLE. The proposed method is validated with five-axis flank milling tests and SLE measurements on a thin-walled twisted part. It is shown that the average error of the proposed method for SLE prediction is less than 5 μm, and the calculation is almost 8 times faster than the typical finite element method.
Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data
Authors: Chang Wanjun , Sun Dong , Du Qidong
Published: January 2023

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.
Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
Authors: Ali Sharafat , Alam Fakhrul , Arif Khalid Mahmood , Potgieter Johan
Published: January 2023

The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN
Authors: Yang Dan , Zhang Xinyi , Zhou Ti , Wang Tao , Li Jiahui
Published: January 2023

In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.
A Comparative Investigation of Automatic Speech Recognition Platforms for Aphasia Assessment Batteries
Authors: Mahmoud Seedahmed S. , Pallaud Raphael F. , Kumar Akshay , Faisal Serri , Wang Yin , Fang Qiang
Published: January 2023

The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated that CNN-based speech recognition algorithms outperform LDA and off-the-shelf platforms. The ResNet-50 architecture of CNN yielded an accuracy of 99.64 ± 0.26% on the healthy dataset. Even though Microsoft Azure was not trained on the same healthy dataset, it still generated comparable results to the LDA and superior results to Google’s speech recognition platform.
Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems
Authors: Xiao Shengyuan , Wang Shuo , Ge Liang , Weng Hengxiang , Fang Xin , Peng Zhenming , Zeng Wen
Published: January 2023

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.
Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation
Authors: Iadarola Grazia , Daponte Pasquale , De Vito Luca , Rapuano Sergio
Published: January 2023

Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling are analyzed in general terms. Attention is specifically focused on the idea of exploiting the condition of the signals that, despite their large bandwidth, have a small information content in the frequency domain. However, as clarified in the paper, employing AICs in measurement instrumentation necessarily entails their characterization, through the analysis of their building blocks and the corresponding non-idealities, in order to improve the signal reconstruction.
Biological Intelligence Inspired Trajectory Design for Energy Harvesting UAV Networks
Authors: Liu Xuanlin , Wang Sihua , Yin Changchuan
Published: January 2023

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the deployment profitability is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability.
Methods for Reducing Subdivision Error within One Signal Period of Single-Field Scanning Absolute Linear Encoder
Authors: Yang Fan , Lu Xinji , Kilikevičius Artūras , Gurauskis Donatas
Published: January 2023

Optical encoders are widely used in accurate displacement measurement and motion-control technologies. Based on different measurement methods, optical encoders can be divided into absolute and incremental optical encoders. Absolute linear encoders are commonly used in advanced computer numerical control (CNC) machines. The subdivision error within one signal period (SDE) of the absolute linear encoder is vital to the positioning accuracy and low velocity control of CNC machines. In our paper, we study the working principle of the absolute linear encoder. We proposed two methods for reducing the SDE of the absolute linear encoder, a single-field scanning method based on the shutter-shaped Moiré fringe, as well as a method for suppressing harmonics through a phase shift of index grating. We established a SDE measuring device to determine the absolute linear encoder’s SDE, which we measured using a constant-speed approach. With our proposed methods, the SDE was reduced from ±0.218 μm to ±0.135 μm, which is a decrease of 38.07%. Our fast Fourier transformation (FFT) analysis of the collected Moiré fringe signals demonstrated that the third-, fifth-, and seventh-order harmonics were effectively suppressed.
A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
Authors: Galante-Sempere David , Khemchandani Sunil Lalchand , del Pino Javier
Published: January 2023

A 1.4-dB Noise Figure (NF) four-stage K-band Monolithic Microwave Integrated Circuit (MMIC) Low-Noise Amplifier (LNA) in UMS 100 nm GaAs pHEMT technology is presented. The proposed circuit is designed to cover the 5G New Release n258 frequency band (24.25–27.58 GHz). Momentum EM post-layout simulations reveal the circuit achieves a minimum NF of 1.3 dB, a maximum gain of 34 dB, |S11| better than –10 dB from 23 GHz to 29 GHz, a P1dB of –18 dBm and an OIP3 of 24.5 dBm. The LNA draws a total current of 59.1 mA from a 2 V DC supply and results in a chip size of 3300 × 1800 µm2 including pads. We present a design methodology focused on the selection of the active device size and DC bias conditions to obtain the lowest NF when source degeneration is applied. The design procedure ensures a minimum NF design by selecting a device which facilitates a simple input matching network implementation and obtains a reasonable input return loss thanks to the application of source degeneration. With this approach the input matching network is implemented with a shunt stub and a transmission line, therefore minimizing the contribution to the NF achieved by the first stage. Comparisons with similar works demonstrate the developed circuit is very competitive with most of the state-of-the-art solutions.
A Study of Speech Recognition for Kazakh Based on Unsupervised Pre-Training
Authors: Meng Weijing , Yolwas Nurmemet
Published: January 2023

Building a good speech recognition system usually requires a lot of pairing data, which poses a big challenge for low-resource languages, such as Kazakh. In recent years, unsupervised pre-training has achieved good performance in low-resource speech recognition, but it is rarely used in Kazakh and other Central and West Asian languages. In this paper, wav2vec2.0 is improved by integrating a Factorized TDNN layer to better preserve the relationship between the voice and the time step before and after the quantization, which is called wav2vec-F. The unsupervised pre-training strategy was used to learn the potential speech representation from a large number of unlabeled audio data and was applied to the cross-language ASR task, which was optimized using the noise contrast binary classification task. At the same time, speech synthesis is used to promote the performance of speech recognition. The experiment shows that wav2vec-F can effectively utilize the unlabeled data from non-target languages, and the multi-language pre-training is obviously better than the single-language pre-training. The data enhancement method using speech synthesis can bring huge benefits. Compared with the baseline model, Librispeech’s test-clean dataset has an average reduction of 1.9% in the word error rate. On the Kazakh KSC test set, the pre-training using only Kazakh reduced the word error rate by 3.8%. The pre-training of a small amount of Kazakh speech data synthesized by multi-language combined with TTS achieved a word error rate of 8.6% on the KSC test set when the labeled data were only 10 h, which was comparable to the results of the previous end-to-end model when the labeled data were 30 times less.
The Performance of Inertial Measurement Unit Sensors on Various Hardware Platforms for Binaural Head-Tracking Applications
Authors: Franček Petar , Jambrošić Kristian , Horvat Marko , Planinec Vedran
Published: January 2023

Binaural synthesis with head tracking is often used in spatial audio systems. The devices used for head tracking must provide data on the orientation of the listener’s head. These data need to be highly accurate, and they need to be provided as fast and as frequently as possible. Therefore, head-tracking devices need to be equipped with high-quality inertial measurement unit (IMU) sensors. Since IMUs readily include triaxial accelerometers, gyroscopes, and magnetometers, it is crucial that all of these sensors perform well, as the head orientation is calculated from all sensor outputs. This paper discusses the challenges encountered in the process of the performance assessment of IMUs through appropriate measurements. Three distinct hardware platforms were investigated: five IMU sensors either connected to Arduino-based embedded systems or being an integral part of one, five smartphones across a broad range of overall quality with integrated IMUs, and a commercial virtual reality unit that utilizes a headset with integrated IMUs. An innovative measurement method is presented and proposed for comparing the performance of sensors on all three platforms. The results of the measurements performed using the proposed method show that all three investigated platforms are adequate for the acquisition of the data required for calculating the orientation of a device as the input to the binaural synthesis process. Some limitations that have been observed during the measurements, regarding data acquisition and transfer, are discussed.
Multisensory Cues for Gait Rehabilitation with Smart Glasses: Methodology, Design, and Results of a Preliminary Pilot
Authors: Imbesi Silvia , Corzani Mattia
Published: January 2023

Recent advances in mobile technology have shown that augmented unisensory feedback can be leveraged to improve gait using wearable systems, but less is known about the possible benefits and usability of multisensory (i.e., multimodal) feedback. This paper introduces the preliminary results of an innovative research project aiming to develop an mHealth system including Android smart glasses, and providing multisensory cues for gait rehabilitation of people affected by Parkinson’s disease in and out of the medical context. In particular, the paper describes a preliminary pilot focusing on the design of visual, auditory, and haptic cues, and testing the design methodologies to be used in further developments of the project. Considered research questions were: Which kinds of images, sounds, and vibrations mostly influence gait speed, stride length, and cadence? Which are the ones stressing the user the least? Which ones induce the most immediate reaction? Thus, in this starting part of the research project, different typologies of sensory cues were designed, tested, and evaluated considering quantitative and qualitative parameters to properly answer the research questions.
Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review
Authors: Bonnechère Bruno , Timmermans Annick , Michiels Sarah
Published: January 2023

The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot—or can only partially—be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.
Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor
Authors: Callihan Michael , Cole Heather , Stokley Holly , Gunter Joshua , Clamp Kaitlyn , Martin Alexis , Doherty Hannah
Published: January 2023

Background: With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system. Methods: Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy. Results: High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance. Conclusions: The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.
How the Processing Mode Influences Azure Kinect Body Tracking Results
Authors: Büker Linda , Quinten Vincent , Hackbarth Michel , Hellmers Sandra , Diekmann Rebecca , Hein Andreas
Published: January 2023

The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.
A Chemiresistor Sensor Array Based on Graphene Nanostructures: From the Detection of Ammonia and Possible Interfering VOCs to Chemometric Analysis
Authors: Freddi Sonia , Vergari Michele , Pagliara Stefania , Sangaletti Luigi
Published: January 2023

Sensor arrays are currently attracting the interest of researchers due to their potential of overcoming the limitations of single sensors regarding selectivity, required by specific applications. Among the materials used to develop sensor arrays, graphene has not been so far extensively exploited, despite its remarkable sensing capability. Here we present the development of a graphene-based sensor array prepared by dropcasting nanostructure and nanocomposite graphene solution on interdigitated substrates, with the aim to investigate the capability of the array to discriminate several gases related to specific applications, including environmental monitoring, food quality tracking, and breathomics. This goal is achieved in two steps: at first the sensing properties of the array have been assessed through ammonia exposures, drawing the calibration curves, estimating the limit of detection, which has been found in the ppb range for all sensors, and investigating stability and sensitivity; then, after performing exposures to acetone, ethanol, 2-propanol, sodium hypochlorite, and water vapour, chemometric tools have been exploited to investigate the discrimination capability of the array, including principal component analysis (PCA), linear discriminant analysis (LDA), and Mahalanobis distance. PCA shows that the array was able to discriminate all the tested gases with an explained variance around 95%, while with an LDA approach the array can be trained to accurately recognize unknown gas contribution, with an accuracy higher than 94%.
Framework to Emulate Spacecraft Orbital Positioning Using GNSS Hardware in the Loop
Authors: Forero David , Esteban Segundo , Rodríguez-Polo Óscar
Published: January 2023

The paper presents a framework to emulate spacecraft orbits using GNSS hardware in the loop that enables the evaluation of new orbital positioning algorithms. The framework software generates the spacecraft orbit and the GNSS signals, including the most common perturbations. These signals are modulated and transmitted by a software-defined radio and received by a commercial GPS receiver. The system is validated using a test orbit, where the GPS receiver accurately determines the spacecraft positions. Moreover, using raw data provided by the receiver, the spacecraft positions have also been determined by software for a low earth orbit, in which civil GPS receivers do not work.
Real-Time Temperature Correction of Medical Range Fiber Bragg Gratings Dosimeters
Authors: Lebel-Cormier Marie-Anne , Boilard Tommy , Beaulieu Luc , Bernier Martin
Published: January 2023

The interest in fiber Bragg gratings dosimeters for radiotherapy dosimetry lies in their (i) submillimeter size, (ii) multi-points dose measurements, and (iii) customizable spatial resolution. However, since the radiation measurement relies on the thermal expansion of the surrounding polymer coating, such sensors are strongly temperature dependent, which needs to be accounted for; otherwise, the errors on measurements can be higher than the measurements themselves. In this paper, we test and compare four techniques for temperature compensation: two types of dual grating techniques using different coatings, a pre-irradiation and post-irradiation temperature drift technique, which is used for calorimetry, and finally, we developed a real-time interpolated temperature gradient for the multi-points dosimetry technique. We show that, over these four tested techniques, the last one outperforms the others and allows for real-time temperature correction when an array of 13 fiber Bragg gratings spatially extending over the irradiation zone is used. For a 20 Gy irradiation, this technique reduces the measurement errors from 200% to about 10%, making it suitable for a radiotherapy dose range. Temperature correction for medical low-dose range dosimetry is a first in our field and is essential for clinical FBG dosimetry applications.
Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System
Authors: Henry Azriel , Gautam Sunil , Khanna Samrat , Rabie Khaled , Shongwe Thokozani , Bhattacharya Pronaya , Sharma Bhisham , Chowdhury Subrata
Published: January 2023

Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.
Parameter Optimization and Development of Mini Infrared Lidar for Atmospheric Three-Dimensional Detection
Authors: Kuang Zhiqiang , Liu Dong , Wu Decheng , Wang Zhenzhu , Li Cheng , Deng Qian
Published: January 2023

In order to conduct more thorough research on the structural characteristics of the atmosphere and the distribution and transmission of atmospheric pollution, the use of remote sensing technology for multi-dimensional detection of the atmosphere is needed. A light-weight, low-volume, low-cost, easy-to-use and low-maintenance mini Infrared Lidar (mIRLidar) sensor is developed for the first time. The model of lidar is established, and the key optical parameters of the mIRLidar are optimized through simulation, in which wavelength of laser, energy of pulse laser, diameter of telescope, field of view (FOV), and bandwidth of filter are included. The volume and weight of the lidar system are effectively reduced through optimizing the structural design and designing a temperature control system to ensure the stable operation of the core components. The mIRLidar system involved a 1064 nm laser (the pulse laser energy 15 μJ, the repetition frequency 5 kHz), a 100 mm aperture telescope (the FOV 1.5 mrad), a 0.5 nm bandwidth of filter and an APD, where the lidar has a volume of 200 mm × 200 mm × 420 mm and weighs about 13.5 kg. It is shown that the lidar can effectively detect three-dimensional distribution and transmission of aerosol and atmospheric pollution within a 5 km detection range, from Horizontal, scanning and navigational atmospheric measurements. It has great potential in the field of meteorological research and environmental monitoring.
Autonomous Driving Control Based on the Technique of Semantic Segmentation
Authors: Tsai Jichiang , Chang Che-Cheng , Li Tzu
Published: January 2023

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously, this incurs the uncertainty of safety. Recently, in the literature, several studies have been proposed for the above-mentioned issue via Artificial Intelligence (AI). The achievement is exactly the aim that we look forward to, i.e., the autonomous vehicle. In this paper, we realize the autonomous driving control via Deep Reinforcement Learning (DRL) based on the CARLA (Car Learning to Act) simulator. Specifically, we use the ordinary Red-Green-Blue (RGB) camera and semantic segmentation camera to observe the view in front of the vehicle while driving. Then, the captured information is utilized as the input for different DRL models so as to evaluate the performance, where the DRL models include DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient). Moreover, we also design an appropriate reward mechanism for these DRL models to realize efficient autonomous driving control. According to the results, only the RDPG strategies can finish the driving mission with the scenario that does not appear/include in the training scenario, and with the help of the semantic segmentation camera, the RDPG control strategy can further improve its efficiency.
Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning
Authors: Doborjeh Maryam , Liu Xiaoxu , Doborjeh Zohreh , Shen Yuanyuan , Searchfield Grant , Sanders Philip , Wang Grace Y. , Sumich Alexander , Yan Wei Qi
Published: January 2023

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.