bluuebunny/crossref_metadata_2025 huggingface dataset:
Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis
Authors: Alsheibi Abdullah Zaini , Valavanis Kimon P. , Iqbal Asif , Aman Muhammad Naveed
Published: May 2022

With the advancement in voice-communication-based human–machine interface technology in smart home devices, the ability to decompose the received speech signal into a signal of interest and an interference component has emerged as a key requirement for their successful operation. These devices perform their tasks in real time based on the received commands, and their effectiveness is limited when there is a lot of ambient noise in the area in which they operate. Most real-time speech enhancement algorithms do not perform adequately well in the presence of high amounts of noise (i.e., low input-signal-to-noise ratio). In this manuscript, we propose a speech enhancement framework to help these algorithms in situations when the noise level in the received signal is high. The proposed framework performs noise suppression in the frequency domain by generating an approximation of the noisy signals’ short-time Fourier transform, which is then used by the speech enhancement algorithms to recover the underlying clean signal. This approximation is performed by using the proposed block principal component analysis (Block-PCA) algorithm. To illustrate efficacy of the proposed framework, we present a detailed performance evaluation under different noise levels and noise types, highlighting the effectiveness of the proposed framework. Moreover, the proposed method can be used in conjunction with any speech enhancement algorithm to improve its performance under moderate to high noise scenarios.
The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach
Authors: Sarvia Filippo , De Petris Samuele , Ghilardi Federica , Xausa Elena , Cantamessa Gianluca , Borgogno-Mondino Enrico
Published: May 2022

Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification.
Crop Sequencing to Improve Productivity and Profitability in Irrigated Double Cropping Using Agricultural System Simulation Modelling
Authors: Zeleke Ketema , McCormick Jeff
Published: May 2022

Land and water are two major inputs for crop production. Simulation modelling was used to determine crop sequences that maximise farm return. Crop yield was determined for different irrigation scheduling scenarios based on the fraction of available soil water (FASW). Farm returns ($ ML−1 and $ ha−1) were evaluated for seven crop sequences. Three irrigation water price scenarios (dry, median, wet) were considered. The yield of summer crops increased with irrigation. For winter crops, despite increase in irrigation, the yield would not increase. The optimum irrigation (ML ha−1) was: soybean 8.2, maize 10.4, wheat 2.5, barley 3.1, fababean 2.5, and canola 2.7. The water productivity curve of summer crops has a parabolic shape, increasing with FASW, reaching a maximum value at FASW 0.4–0.6, and then decreasing. The water productivity of winter crops decreases as FASW increases following a power function. Gross margins are positive when water is cheap ($60 ML−1) and when water has a median price ($124 ML−1). When water is expensive ($440 ML−1), positive gross margin would be obtained only for the continuous wheat scenario. Deficit irrigation of summer crops leads to significant yield loss. Supplemental irrigation of winter crops results in the highest gross margin per unit of water.
Ameliorative Effects of Anti-Clostridial Egg Yolk Antibodies (IgYs) in Experimentally-Induced Avian Necrotic Enteritis
Authors: Abadeen Zain Ul , Javed Muhammad Tariq , Jamil Tariq , Nasir Azam Ali
Published: May 2022

The present study was planned to evaluate the ameliorative effects of egg yolk antibodies (EYAs) in broiler chicken. For this purpose, 80-day-old broiler chickens were divided into four groups (A–D), where group A was kept as negative control. Experimental infection with C. perfringens (1 × 108 cfu/mL) was induced via oral route on days 17, 18 and 19 of the experiment in groups B, C and D. Groups C and D were passively immunized by anti-clostridial IgYs @ 1 mL per bird via oral and oral and intramuscular (I/M) routes respectively, on days 21 to 24, and on days 22 and 24 of the experiment, respectively. Two necropsies were performed (the first on day 26th and the second on day 35th). Birds in group B showed behavioral signs e.g., laziness, depression and diarrhea, gross post-mortem lesions e.g., increase in the relative weights (RW), due to acute swelling and congestion of liver and kidneys and ballooning and hemorrhages of jejunum and microscopic lesions e.g., congestion and necrosis in liver and kidneys’ parenchyma and disrupted epithelium with fewer goblet cells in jejunum, compared to the group A. Birds in groups C and D, showed significant improvements in clinical and behavioral signs, RW of liver, kidneys and jejunum, swelling, congestion and mononuclear cells’ infiltration in liver and kidneys and damages in the jejunal-wall, compared to group B. The most significant changes were found in birds of group C. Our study revealed ameliorative effects of EYAs on certain biological parameters however, further studies would be needed to justify a safer production and a reliable application of EYAs in NE outbreaks.
Adverse Effects of Single Neutrophil Extracellular Trap-Derived Components on Bovine Sperm Function
Authors: Moya Claudia , Rivera-Concha Rodrigo , Pezo Felipe , Uribe Pamela , Schulz Mabel , Sánchez Raúl , Hermosilla Carlos , Taubert Anja , Gärtner Ulrich , Zambrano Fabiola
Published: May 2022

Neutrophil extracellular traps (NETs) play a key role in fertilisation by eliminating microorganisms and entrapping spermatozoa in the female reproductive tract (FRT). The deleterious effects of NETs on spermatozoa have been previously described; however, individual exposure to NET-derived components in bull spermatozoa has not been explored. The aim of this study was to evaluate the effects of the main NET-derived proteins, histone 2A (H2A), neutrophil elastase (ELA), myeloperoxidase (MPO), pentraxin 3 (PTX), cathepsin G (Cat-G), and cathelicidin LL37 (LL-37), at concentrations of 1, 10, and 30 μg/mL, on sperm parameters. Sperm were selected and incubated with different NET-derived proteins for 4 h. Membrane and acrosome integrity, lipoperoxidation, and membrane phospholipid disorders were also evaluated. Bovine polymorphonuclear neutrophil (PMN)/sperm co-cultures were evaluated by scanning electron microscopy and immunofluorescence. All NET-derived proteins/enzymes resulted in a reduction in membrane integrity, acrosome integrity, and lipoperoxidation at a concentration of 30 μg/mL. Bovine PMN/sperm co-cultures showed marked NET formation in the second hour. In conclusion, all NET-derived proteins/enzymes exerted cytotoxic effects on bull sperm, and this effect should be considered in future investigations on the uterine microenvironment and the advancement of spermatozoa in the FRT.
Effects of Dietary Supplementation with Glycerol Monolaurate (GML) or the Combination of GML and Tributyrin on Growth Performance and Rumen Microbiome of Weaned Lambs
Authors: Li Yi , Wang Heze , Zhang Yulei , Li Xilong , Jiang Xianren , Ding Hongbiao
Published: May 2022

Our objective was to evaluate the effects of dietary supplementation with glycerol monolaurate (GML) or the combination (Solider, SOL) of GML and tributyrin (TB) on the growth performance and rumen microbiome of weaned lambs. Thirty-six male Hu lambs (11.46 ± 0.88 kg BW and 40 ± 5 days of age) were divided into three treatment groups: (1) CON: basal diet, (2) GML: basal diet supplemented with GML at 1.84 g/kg DM, and (3) SOL: basal diet supplemented with SOL at 3 g/kg DM. GML increased the final BW (p = 0.04) and ADG (p = 0.02) compared with CON. There were no significant differences in the DMI (p > 0.10) among the three treatment groups. GML and SOL tended to decrease the dry matter intake/average daily gain (p = 0.07) compared with CON. GML tended to increase the apparent digestibility of CP (p = 0.08) compared with CON. SOL increased the apparent digestibility of NDF (p = 0.04) compared with CON. The Chao1 and Shannon indexes of SOL were both significantly higher than those of the other groups (p = 0.01). LefSE analysis showed that Bifidobacteriaceae of the Bifidobacteriales was enriched in the GML group. In addition, compared with GML, SOL reduced the relative abundance of Actinobacteria (p < 0.01) and increased the relative abundance of Verrucomicrobia (p = 0.05), and GML reduced the relative abundance of Ruminococcus (p = 0.03). Our results indicated that dietary supplementation with GML or SOL improved growth performance and feed conversion, and changed the rumen microbiome of weaned lambs.
The Cryopreserved Sperm Traits of Various Ram Breeds: Towards Biodiversity Conservation
Authors: Vozaf Jakub , Svoradová Andrea , Baláži Andrej , Vašíček Jaromír , Olexiková Lucia , Dujíčková Linda , Makarevich Alexander V. , Jurčík Rastislav , Ďúranová Hana , Chrenek Peter
Published: May 2022

The aim of our research was to compare three Slovak sheep breeds in the quality parameters of cryopreserved sperm. The ejaculates of Slovak Dairy (SD), Native Wallachian (NW), and Improved Wallachian (IW) sheep rams (n = 12) were collected by electro-ejaculation. Heterospermic samples were created from suitable ejaculates, separately for each breed (at least 90% of total and 80% of progressive motility). Samples were equilibrated in a Triladyl® diluent and frozen by automated freezing. Sperm samples were subjected to the motility, morphology, (CASA), viability and apoptosis (DRAQ7/Yo-Pro-1), fertilizing capability (penetration/fertilization test (P/F) in vitro) and acrosomal status (transmission electron microscopy) assays before freezing and after thawing. It was found that there were no significant differences (p < 0.05) between the evaluated breeds in motility, viability, apoptosis, morphological properties, and fertilizing ability of cryopreserved sperm. Significant differences occurred in acrosomal status. Our results demonstrate that the use of the selected cryopreservation protocol is suitable for at least three different sheep breeds, which can greatly benefit the biodiversity protection and simplifies the creation of an animal genetic resources gene bank.
Neonatal Piglet Temperature Changes: Effect of Intraperitoneal Warm Saline Injection
Authors: Tucker Bryony S. , Petrovski Kiro R. , Kirkwood Roy N.
Published: May 2022

Piglets are poor at thermoregulation immediately following birth and take up to 24 h to recover from their initial temperature drop. The present study aimed to determine if providing piglets with a 15 mL intraperitoneal injection of warm (45 °C) saline at birth would improve their internal temperature recovery to 24 h of age, and how the treatment interacted with birth weight (BWC = 1; ≤0.80 kg, BWC = 2; 0.81 kg to 1.10 kg, and BWC = 3; >1.10 kg), rectal temperature at 1.5 h (RC = 1; ≤32.0 °C, RC = 2; 32.10 °C to 35.0 °C, and RC = 3; ≥35.10 °C), and colostrum intake (CI = 1; <200 g and CI = 2, ≥200 g) to affect preweaning survival. Treated BWC1 piglets had improved rectal temperatures from 2 to 24 h. BWC3 piglets who consumed insufficient colostrum also had improved rectal temperature between 1 and 24 h post-birth. Colostrum intake was improved with saline injection in BWC2 piglets of RC1 and RC3 (p < 0.001) and BWC3-RC3 piglets (p < 0.001). Treated BWC1 improved survival to 20 d (p < 0.001). Irrespective of BWC, piglets from all RC had greater survival when injected with saline. The greatest difference was for piglets in RC1, likely due to all BWC1 piglets falling within this category. The results suggest that an intraperitoneal injection of warmed saline is an effective way to improve piglet temperature recovery to 24 h, colostrum intake, and survival in low-birth-weight piglets. These findings will be helpful for producers who have large numbers of low-birth-weight piglets born and are able to provide individual care.
Impact of Dog’s Age and Breed on Dog Owner’s Physical Activity: A German Longitudinal Study
Authors: Hielscher-Zdzieblik Benedikt , Froboese Ingo , Serpell James , Gansloßer Udo
Published: May 2022

Dog ownership contributes positively to physical activity (PA). The impact of different dog breeds and age on PA is less investigated in longitudinal studies. This study aimed to evaluate PA changes in dog owners as their dogs’ ages increased and to explore whether there are differences in PA between owners of different breeds over a three-year period. Owners of different dog breeds were categorized into nine groups according to the perceived energy level and size of the breed. PA was monitored using an online questionnaire for three consecutive years. Linear mixed models (LMM) showed a small, but significant decrease in total PA, leisure time walking, dog-related PA and dog walking over three years. No decreases were found if only participants who attended at all time points were included. In all LMM analyses, a significant relationship between the dog breed and the outcomes of PA were shown. At baseline, dog owners performed different types of activities depending on their dog breed. In conclusion, owners of different dog breeds differ in their types of PA. The study emphasizes that age, size and energy level of the dog does not per se have an impact on dog owners PA.
Whole-Genome Characterisation of ESBL-Producing E. coli Isolated from Drinking Water and Dog Faeces from Rural Andean Households in Peru
Authors: Medina-Pizzali Maria Luisa , Venkatesh Apoorva , Riveros Maribel , Cuicapuza Diego , Salmon-Mulanovich Gabriela , Mäusezahl Daniel , Hartinger Stella M.
Published: May 2022

E. coli that produce extended-spectrum β-lactamases (ESBLs) are major multidrug-resistant bacteria. In Peru, only a few reports have characterised the whole genome of ESBL enterobacteria. We aimed to confirm the identity and antimicrobial resistance (AMR) profile of two ESBL isolates from dog faeces and drinking water of rural Andean households and determine serotype, phylogroup, sequence type (ST)/clonal complex (CC), pathogenicity, virulence genes, ESBL genes, and their plasmids. To confirm the identity and AMR profiles, we used the VITEK®2 system. Whole-genome sequencing (WGS) and bioinformatics analysis were performed subsequently. Both isolates were identified as E. coli, with serotypes -:H46 and O9:H10, phylogroups E and A, and ST/CC 5259/- and 227/10, respectively. The isolates were ESBL-producing, carbapenem-resistant, and not harbouring carbapenemase-encoding genes. Isolate 1143 ST5259 harboured the astA gene, encoding the EAST1 heat-stable toxin. Both genomes carried ESBL genes (blaEC-15, blaCTX-M-8, and blaCTX-M-55). Nine plasmids were detected, namely IncR, IncFIC(FII), IncI, IncFIB(AP001918), Col(pHAD28), IncFII, IncFII(pHN7A8), IncI1, and IncFIB(AP001918). Finding these potentially pathogenic bacteria is worrisome given their sources and highlights the importance of One-Health research efforts in remote Andean communities.
An Observational Study of MDR Hospital-Acquired Infections and Antibiotic Use during COVID-19 Pandemic: A Call for Antimicrobial Stewardship Programs
Authors: Shbaklo Nour , Corcione Silvia , Vicentini Costanza , Giordano Susanna , Fiorentino Denise , Bianco Gabriele , Cattel Francesco , Cavallo Rossana , Zotti Carla Maria , De Rosa Francesco Giuseppe
Published: May 2022

The pandemic caused by the COVID-19 virus has required major adjustments to healthcare systems, especially to infection control and antimicrobial stewardship. The objective of this study was to describe the incidence of multidrug-resistant (MDR) hospital-acquired infections (HAIs) and antibiotic consumption during the three waves of COVID-19 and to compare it to the period before the outbreak at Molinette Hospital, located in the City of Health and Sciences, a 1200-bed teaching hospital with surgical, medical, and intensive care units. We demonstrated an increase in MDR infections: particularly in K. pneumoniae carbapenemase-producing K. pneumoniae (KPC-Kp), A. baumannii, and MRSA. Fluoroquinolone use showed a significant increasing trend in the pre-COVID period but saw a significant reduction in the COVID period. The use of fourth- and fifth-generation cephalosporins and piperacillin–tazobactam increased at the beginning of the COVID period. Our findings support the need for restoring stewardship and infection control practices, specifically source control, hygiene, and management of invasive devices. In addition, our data reveal the need for improved microbiological diagnosis to guide appropriate treatment and prompt infection control during pandemics. Despite the infection control practices in place during the COVID-19 pandemic, invasive procedures in critically ill patients and poor source control still increase the risk of HAIs caused by MDR organisms.
Correction: Stelmaszczyk et al. Ultrafast Time-of-Flight Method of Gasoline Contamination Detection Down to ppm Levels by Means of Terahertz Time-Domain Spectroscopy. Appl. Sci. 2022, 12, 1629
Authors: Stelmaszczyk Kamil , Karpierz-Marczewska Ewelina , Mikhnev Valeri , Cywinski Grzegorz , Skotnicki Thomas , Knap Wojciech
Published: May 2022

The authors wish to make the following corrections to their paper [...]
A Modelling Study on the Comparison of Predicted Auditory Nerve Firing Rates for the Personalized Indication of Cochlear Implantation
Authors: Aichinger Philipp
Published: May 2022

The decision of whether to perform cochlear implantation is crucial because implantation cannot be reversed without harm. The aim of the study was to compare model-predicted time–place representations of auditory nerve (AN) firing rates for normal hearing and impaired hearing with a view towards personalized indication of cochlear implantation. AN firing rates of 1024 virtual subjects with a wide variety of different types and degrees of hearing impairment were predicted. A normal hearing reference was compared to four hearing prosthesis options, which were unaided hearing, sole acoustic amplification, sole electrical stimulation, and a combination of the latter two. The comparisons and the fitting of the prostheses were based on a ‘loss of action potentials’ (LAP) score. Single-parameter threshold analysis suggested that cochlear implantation is indicated when more than approximately two-thirds of the inner hair cells (IHCs) are damaged. Second, cochlear implantation is also indicated when more than an average of approximately 12 synapses per IHC are damaged due to cochlear synaptopathy (CS). Cochlear gain loss (CGL) appeared to shift these thresholds only slightly. Finally, a support vector machine predicted the indication of a cochlear implantation from hearing loss parameters with a 10-fold cross-validated accuracy of 99.2%.
Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System
Authors: Lee Sangil , Ryu Kwangyeol
Published: May 2022

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes.
Food Risk Entropy Model Based on Federated Learning
Authors: Yu Jiaojiao , Chen Yizhou , Wang Zhenyu , Liu Jin , Huang Bo
Published: May 2022

The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for evaluating the safety of agricultural products, such as pass rates and residue rates, can only qualitatively describe the level of pesticide residues. Isolated data leads to low data utilization, data is distributed between different terminals or departments and cannot be shared, while the security of private data needs to be ensured. Therefore, we propose a risk entropy model based on federated learning. The model is able to quantitatively describe the risk level of agricultural products and achieve data fusion without exposing private data in the federated learning model. In this paper, a total of 90,510 agricultural product data samples from 2015 to 2019 are collected, with each sample containing 58 indicators. The experimental results show that the developed food safety risk entropy model can quantitatively reflect the level of risk in the target region and time interval. In addition, we have developed a multidimensional data analysis tool based on federated learning, which can achieve data integration across multiple regions and departments.
Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach
Authors: Le Minhhuy , Luong Van Su , Nguyen Dang Khoa , Le Dang-Khanh , Lee Jinyi
Published: May 2022

An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm3 with only 20% of training data and could be increased to 99.0% with 60–80% of the training data.
Seismic Fragility and Risk Assessment of a Nuclear Power Plant Containment Building for Seismic Input Based on the Conditional Spectrum
Authors: Park Ji-Hun , Shin Dong-Hyun , Jeon Seong-Ha
Published: May 2022

A procedure for the seismic fragility assessment of nuclear power plants by applying ground motions compatible with the conditional probability distribution of a conditional spectrum (CS) is presented with a case study of a containment building. Three CSs were constructed using different control frequencies to investigate the influence of the control frequency. Horizontal component-to-component directional variability was introduced by randomly rotating the horizontal axes of the recorded ground motions. Nonlinear lumped mass stick models were constructed using variables distributed by Latin hypercube sampling to model the uncertainty. An incremental dynamic analysis was performed, and seismic fragility curves were calculated. In addition, a seismic input based on a uniform hazard response spectrum (UHRS) was applied to the seismic fragility assessment for comparison. By selecting a control frequency dominating the seismic response, the CS-based seismic input produces an enhanced ‘high confidence of low probability of failure’ capacity and lower seismic risk than the UHRS-based seismic input.
An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
Authors: Lv Qian , Cai Liuxi , Yu Xiaoling , Ma Haihui , Li Yun , Shu Yue
Published: May 2022

The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this paper, a novel fault detection method for the reciprocating compressor based on digital image processing and artificial neural network (ANN) was proposed. This method is implemented to the sectionalized pressure–volume (p–V) curves, which are obtained by dividing a working cycle in the cylinder into four thermal processes, including expansion, suction, compression and discharge. Hit-or-miss transform is adopted to extract the comprehensive gradients of expansion and compression curves, and vertical projection transform is applied to extract the vertical projection features. Finally, all of the features are fed to an ANN to do classification. To validate the proposed method, a seeded fault testing was conducted to collect real running data. The results showed that the new approach shows a good performance, with a high classification accuracy of 97.9%.
Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
Authors: Gao Hui , Deng Miaolei , Zhao Wenjun , Zhang Dexian
Published: May 2022

Crowd counting is an important part of crowd analysis and has been widely applied in the field of public safety and commercial management. Although researchers have proposed many crowd counting methods, there is little research on non-uniform population distribution. In this research, a new scene adaptive segmentation network (SASNet) is proposed that can focus on crowd area to estimate accurately crowd density in population heterogeneous distribution. First, an image segmentation module is designed that can adaptive horizontal segment an image according to different density levels, and then obtains a close-up view image and a distant view image. Second, a dual branches network based on convolution neural network (CNN) is exploited that contains a distant view network (DVNet) and a close-up view network (CVNet), so as to extract different scales of image features and then generate density maps by each branch, respectively, so that the crowd counting module has robustness on different scales of target. Finally, a comparative experiment on three well-known crowd counting datasets shows that SASNet achieved stabilized performance and robustness in population heterogeneous distribution.
An Experimental Ultrasound Database for Tomographic Imaging
Authors: Franceschini Stefano , Ambrosanio Michele , Gifuni Angelo , Grassini Giuseppe , Baselice Fabio
Published: May 2022

In the framework of non-destructive testing and imaging, ultrasound tomography can have an important role in several applications, especially in the biomedical field. The motivation beyond the use of this imaging technique lies in the possibility of obtaining quantitative imaging which is also operator-independent, conversely to conventional approaches. Thus, the need for public data sets for testing inverse scattering approaches is always persisting. To this aim, this paper introduces an experimental multiple-input-multiple-output ultrasound tomographic database whose acquisitions were performed by an air-matched in-house system designed and built by the Authors. The proposed database provides several cases with single and multiple objects of different shapes, sizes, and materials, to be imaged in laboratory-controlled conditions. Therefore, these scenarios can represent interesting options for the preliminary testing of tomographic ultrasound imaging approaches.
GNSS-RTK Adaptively Integrated with LiDAR/IMU Odometry for Continuously Global Positioning in Urban Canyons
Authors: Zhang Jiachen , Wen Weisong , Huang Feng , Wang Yongliang , Chen Xiaodong , Hsu Li-Ta
Published: May 2022

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.
Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation
Authors: Ahmed Shakeel , Kamal Khurram , Ratlamwala Tahir Abdul Hussain , Mathavan Senthan , Hussain Ghulam , Alkahtani Mohammed , Alsultan Marwan Bin Muhammad
Published: May 2022

The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil are usually forecasted using the numerical solution of the Reynolds-averaged Navier–Stokes equation. However, very heavy computational expenses and lengthy progression intervals are associated with this method. Advancements in computational power and efficiency throughout the present era have considerably reduced these costs; however, for many practical applications, performing numerical simulations is still a very computationally expensive and time-consuming task. The application of machine learning techniques has seen a sharp rise in various fields over recent years, including fluid dynamics, and they have proved their worth. In the present study, a famous machine learning model that is known as the back-propagation neural network was implemented for the prediction of the aerodynamic coefficients of airfoils. The most important aerodynamic properties of the coefficient of lift and the coefficient of drag were predicted by providing the model with the name, flow Reynolds number, Mach number and the angle of attack of the airfoils with respect to the incoming flows as input parameters. The dataset for the current study was obtained by performing CFD simulations using the RANS-based Spalart–Allmaras turbulence model on four different NACA series airfoils under varying aerodynamic conditions. The data that were obtained from the CFD simulations were divided into two subsets: 70% were used as training data and the remaining 30% were used as validation and testing data. The BPNN showed promising results for the prediction of the aerodynamic coefficients of airfoils under different conditions. An RMSE value of 3.57 × 10−7 was achieved for the best performance validation case with 28 epochs when there were 10 neurons in the hidden layer. The regression plot also depicted a close to perfect fit between the predicted and actual values for the regression curves.
Rough IPFCM Clustering Algorithm and Its Application on Smart Phones with Euclidean Distance
Authors: Chen Chih-Ming , Chang Sheng-Chieh , Chuang Chen-Chia , Jeng Jin-Tsong
Published: May 2022

New interval clustering technology for symbolic data analysis (SDA) on smart phones is shown to be beneficial for mobile computing devices for smart data analysis in this paper. A new interval clustering method that combined the rough set with interval possibilistic fuzzy C-means (IPFCM) algorithm under Euclidean distance is proposed and implemented on smart phones. Symbolic clustering algorithms (SCAs) have been widely used for pattern recognition, data mining, artificial intelligence, etc. In general, the SCA is unsupervised classification that is divided into groups according to symbolic data sets. However, the traditional interval fuzzy C-means (IFCM) clustering method still has noisy and data overlapping problems associated with these symbolic interval data. Hence, a new rough set with the interval possibilistic fuzzy C-means (RIPFCM) clustering algorithm with Euclidean distance was proposed to address the symbolic interval data (SID). That is, the proposed method can perform better than the traditional IFCM clustering algorithm for SID clustering in noisy environments and with data overlapping problems. The new RIPFCM algorithm under the Euclidean distance method was proposed to deal with SID on new applications in smart phones. Consequently, this method shows the expansion of the smart phone’s computing power and its future application in new SDA.
Model Test Study of Offshore Wind Turbine Foundation under the Combined Action of Wind Wave and Current
Authors: Zhang Xiaoling , Liu Chengrui , Ye Jianhong
Published: May 2022

The wind turbine system in the offshore area will be subjected to the combined action of complex marine environment dynamic loads. At present, there is a certain advantage to using traditional mechanical devices to simulate marine environmental loads. In this study, the self-developed complex dynamic load test loading system is adopted to carry out a series of model tests for pile foundations. The theoretical calculation results of wind, wave and current load are applied to the wind turbine system, and the responses of the pile and soil are analyzed according to the actual model test. The main conclusions are as follows: the development of pile displacement is basically linear with the logarithm of cycle times, and pile–soil interaction is directly affected by the form of load, the results of dynamic response obtained in this test are closer to that under the actual marine environmental load.
Understanding the Influence of Diverse Non-Volatile Media on Rheological Properties of Thermophilic Biological Sludge and Evaluation of Its Thixotropic Behaviour
Authors: Collivignarelli Maria Cristina , Todeschini Sara , Bellazzi Stefano , Carnevale Miino Marco , Caccamo Francesca Maria , Calatroni Silvia , Baldi Marco , Manenti Sauro
Published: May 2022

In this study, the rheological properties of thermophilic biological sludge (TBS) have been investigated evaluating the influence of non-volatile solids (NVS). Calcium carbonate, sand, and sodium bentonite were separately added to the sludge to evaluate the effect of concentration and type of NVS. Results show that TBS consistency coefficient significantly enhanced increasing sodium bentonite concentration. On the contrary, calcium carbonate and sand showed relatively small influence on the rheological properties of TBS. Thixotropic behaviour of TBS has also been investigated and is more pronounced at higher shear rate (1000 s−1). Double exponential fitting model was the best choice to represent thixotropic behaviour in case of low (100 s−1) and high shear rate (1000 s−1), while a single-exponential model represents the best option in case of medium shear rate (400 s−1).
The Associations between Plantar Force Distribution and Successfulness in Short-Fire Shooting among Special Police Officers
Authors: Kasović Mario , Štefan Lovro , Bilobrk Mate , Sladin Damir , Štefan Andro , Štrbac Ivana , Jencikova Katerina
Published: May 2022

The main purpose of the study was to determine whether a pistol shooting efficiency score could be predicted by plantar force distribution patterns. In this cross-sectional study, participants were special police male officers (N = 30), members of the Anti-Terrorist Unit ‘Lučko’ (agemean±SD = 40 ± 6 years, heightmean±SD = 180 ± 5 cm, weightmean±SD = 89 ± 8 kg). Shooting efficiency at a target 10 m away was tested on a scale from 0 to 5, while standing on a Zebris pedobarographic platform. Higher absolute (N; β = −0.19, p = 0.002) and relative (%; β = −0.12, p = 0.043) forces beneath the hindfoot were associated with poorer shooting efficiency. A significant positive association between the relative force beneath the forefoot and shooting efficiency was found, i.e., higher relative forces beneath the forefoot region exhibited better shooting values (β = 0.12, p = 0.043). When the force was normalized by weight (N/kg), similar associations remained. This study shows that higher force values under the hindfoot region may lead to a lower shooting performance, while higher force values under the forefoot region can increase shooting performance.
The Mortars of Florence Riverbanks: Raw Materials and Technologies of Lungarni Historical Masonry
Authors: Calandra Sara , Salvatici Teresa , Centauro Irene , Cantisani Emma , Garzonio Carlo Alberto
Published: May 2022

The study of structural masonry joined to geohydrological hazards in cultural heritage represents a multidisciplinary theme, which requires consideration of several aspects, among them the characterization of the materials used. In this paper, a first complete chemical, minero-petrographic, and physico-mechanical characterization of core samples taken from the masonry of two Florence riverbanks (Lungarno degli Acciaiuoli and Lungarno delle Grazie) is performed in order to identify the raw materials, technologies, and state of conservation and to support the planning of maintenance and restoration interventions. The physico-mechanical characterization of the riverbanks allows their stability to be determined. Such investigations allow identification of the level of compactness and cohesion of masonry; this information is useful for planning emergency interventions and for supporting planned restoration activities. The results provide valid support for the design of riverbank safety projects, to mitigate the risk of their collapse and to decrease the flood risk in the historic center of Florence.
A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems
Authors: Arthur John Kingsley , Zhou Conghua , Mantey Eric Appiah , Osei-Kwakye Jeremiah , Chen Yaru
Published: May 2022

Recommender systems (RS) have been widely deployed in many real-world applications, but usually suffer from the long-standing user/item cold-start problem. As a promising approach, cross-domain recommendation (CDR), which has attracted a surge of interest, aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Traditional machine learning and deep learning methods are not designed to learn from complex data representations such as graphs, manifolds and 3D objects. However, current trends in data generation include these complex data representations. In addition, existing research works do not consider the complex dimensions and the locality structure of items, which however, contain more discriminative information essential for improving the performance accuracy of the recommender system. Furthermore, similar outcomes between test samples and their neighboring training data restrained in the kernel space are not fully realized from the recommended objects belonging to the same object category to capture the embedded discriminative information effectively. These challenges leave the problem of sparsity and the cold-start of items/users unsolved and hence impede the performance of the cross-domain recommender system, causing it to suggest less relevant and undistinguished items to the user. To handle these challenges, we propose a novel deep learning (DL) method, Discriminative Geometric Deep Learning (D-GDL) for cross-domain recommender systems. In the proposed D-GDL, a discriminative function based on sparse local sensitivity is introduced into the structure of the DL network. In the D-GDL, a local representation learning (i.e., a local sensitivity-based deep convolutional belief network) is introduced into the structure of the DL network to effectively capture the local geometric and visual information from the structure of the recommended 3D objects. A kernel-based method (i.e., a local sensitivity deep belief network) is also incorporated into the structure of the DL framework to map the complex structure of recommended objects into high dimensional feature space and achieve an effective recognition result. An improved kernel density estimator is created to serve as a weighing function in building a high dimensional feature space, which makes it more resistant to geometric noise and computation performance. The experiment results show that the proposed D-GDL significantly outperforms the state-of-the-art methods in both sparse and dense settings for cross-domain recommendation tasks.
The University of Lisbon’s Short Professional Course in BIM: Practice, Construction, Structures and Historic Buildings
Authors: Sampaio Alcinia Zita
Published: May 2022

The implementation of building information modeling (BIM) methodology in the construction industry has wide applicability with recognized benefits when designing, constructing, and operating buildings. To stay competitive in business, companies are urged to recruit professionals that offer brand-new knowledge and skillsets. To meet this demand, BIM training regarding the concept, range of applications, and tools available is required within the construction profession. A recent short course organized by the University of Lisbon, Portugal, actualized with the most relevant achievements in Master’s degree research, was offered to professionals in the industry, namely, architects and civil engineers coming from diverse engineering areas such as the environment, construction, maintenance, contracting and surveyors, and from patrimonial enterprises and public organizations, as well as city councils. The proposed action covers the areas of construction (conflict analysis, planning, and material quantity), structures (interoperability, analyses, and the transfer of information between software types), and the most recent heritage building information modeling (HBIM) perspectives. The methodology used was based on the presentation of case studies related to situations of conflict between disciplines, interoperability problems, and the structural rehabilitation of old buildings. The difficulties found in the course are mainly due to the heterogeneity of the participating population, who have different interests and specific perspectives. The participants followed the course with great interest and satisfaction, formulating several questions directed at the particular field of expertise of each professional. In general, the participants indicated a positive score, and changes in response to negative feedback will be adopted in future courses. The course aims to contribute to the dissemination of the potential of BIM in the design, construction, and refurbishment of historical buildings.