bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Toxic Lithium Levels on ECG—Findings from the LiSIE Retrospective Cohort Study
Authors: Truedson Petra , Ott Michael , Lindmark Krister , Ström Malin , Maripuu Martin , Lundqvist Robert , Werneke Ursula
Published: October 2022

(1) Background: Few studies have explored the impact of lithium intoxication on the heart. (2) Methods: We examined electrocardiogram (ECG) changes associated with lithium intoxication in the framework of the LiSIE (Lithium—Study into Effects and Side Effects) retrospective cohort study. We analysed ECGs before, during, and after intoxication. (3) Results: Of the 1136 patients included, 92 patients had experienced 112 episodes of lithium intoxication. For 55 episodes, there was an ECG available at the time; for 48 episodes, there was a reference ECG available before and/or after the lithium intoxication. Lithium intoxication led to a statistically significant decrease in heart rate from a mean 76 beats/min (SD 16.6) before intoxication to 73 beats/min (SD 17.1) during intoxication (p = 0.046). QTc correlated only weakly with lithium concentration (ρ = 0.329, p = 0.014). However, in 24% of lithium intoxication episodes, there were QT prolongations. In 54% of these, QTc exceeded 500 ms; patients with chronic intoxications being more affected. (4) Conclusions: Based on summary statistics, effects of lithium intoxication on HR and QTc seem mostly discrete and not clinically relevant. However, QT prolongation can carry a risk of becoming severe. Therefore, an ECG should always be taken in patients presenting with lithium intoxication.
Differential Regulation of Genes by the Glucogenic Hormone Asprosin in Ovarian Cancer
Authors: Kerslake Rachel , Sisu Cristina , Panfilov Suzana , Hall Marcia , Khan Nabeel , Jeyaneethi Jeyarooban , Randeva Harpal , Kyrou Ioannis , Karteris Emmanouil
Published: October 2022

Background: Ovarian cancer (OvCa) is one of the most lethal forms of gynaecological malignancy. Altered energy metabolism and increased aerobic glycolysis in OvCa are hallmarks that demand attention. The glucogenic hormone asprosin is often dysregulated in metabolic disorders such as insulin resistance, diabetes (type 2 and gestational), and preeclampsia. Despite association with metabolic disorders, its role in energy metabolism within the tumour microenvironment is yet to be explored. Here, we study the role of asprosin in OvCa using transcriptomics and expand on functional studies with clinical samples. Methods: RNA sequencing, functional gene enrichment analysis, Western blotting and ImageStream. Results: Following treatment with 100 nM of asprosin, the serous OvCa cell line, SKOV-3, displayed 160 and 173 gene regulatory changes, at 4 and 12 h respectively, when compared with control samples (p < 0.05 and Log2FC > 1). In addition to energy metabolism and glucose-related pathways, asprosin was shown to alter pathways associated with cell communication, TGF-β signalling, and cell proliferation. Moreover, asprosin was shown to induce phosphorylation of ERK1/2 in the same in vitro model. Using liquid biopsies, we also report for novel expression of asprosin’s predicted receptors OR4M1 and TLR4 in cancer-associated circulating cells; with significant reduction seen between pre-chemotherapy and end of first line chemotherapy, in addition to patients under maintenance with bevacizumab +/− olaparib for OR4M1. Conclusions: In relation to OvCa, asprosin appears to regulate numerous signalling pathways in-vitro. The prognostic potential of OR4M1 in liquid biopsies should also be explored further.
Accuracy of Six Intraocular Lens Power Calculations in Eyes with Axial Lengths Greater than 28.0 mm
Authors: Moshirfar Majid , Durnford Kathryn M. , Jensen Jenna L. , Beesley Daniel P. , Peterson Telyn S. , Darquea Ines M. , Ronquillo Yasmyne C. , Hoopes Phillip C.
Published: October 2022

The purpose of this study was to compare the accuracy of several intraocular (IOL) lens power calculation formulas in long eyes. This was a single-site retrospective consecutive case series that reviewed patients with axial lengths (AL) > 28.0 mm who underwent phacoemulsification. The Wang–Koch (WK) adjustment and Cooke-modified axial length (CMAL) adjustment were applied to Holladay 1 and SRK/T. The median absolute error (MedAE) and the percentage of eyes with prediction errors ±0.25 diopters (D), ±0.50 D, ±0.75 D, and ±1.00 D were used to analyze the formula’s accuracy. This study comprised a total of 35 eyes from 25 patients. The Kane formula had the lowest MedAE of all the formulas, but all were comparable except Holladay 1, which had a significantly lower prediction accuracy with either AL adjustment. The SRK/T formula with the CMAL adjustment had the highest accuracy in predicting the formula outcome within ±0.50 D. The newer formulas (BU-II, EVO, Hill-RBF version 3.0, and Kane) were all equally predictable in long eyes. The SRK/T formula with the CMAL adjustment was comparable to these newer formulas with better outcomes than the WK adjustment. The Holladay 1 with either AL adjustment had the lowest predictive accuracy.
Technical and Economic Viability of Distributed Recycling of Low-Density Polyethylene Water Sachets into Waste Composite Pavement Blocks
Authors: Tsala-Mbala Celestin , Hayibo Koami Soulemane , Meyer Theresa K. , Couao-Zotti Nadine , Cairns Paul , Pearce Joshua M.
Published: October 2022

In many developing countries, plastic waste management is left to citizens. This usually results in landfilling or hazardous open-air burning, leading to emissions that are harmful to human health and the environment. An easy, profitable, and clean method of processing and transforming the waste into value is required. In this context, this study provides an open-source methodology to transform low-density polyethylene drinking water sachets, into pavement blocks by using a streamlined do-it-yourself approach that requires only modest capital. Two different materials, sand, and ashes are evaluated as additives in plastic composites and the mechanical strength of the resulting blocks are tested for different proportion mix of plastic, sand, and ash. The best composite had an elastic modulus of 169 MPa, a compressive strength of 29 MPa, and a water absorptivity of 2.2%. The composite pavers can be sold at 100% profit while employing workers at 1.5× the minimum wage. In the West African region, this technology has the potential to produce 19 million pavement tiles from 28,000 tons of plastic water sachets annually in Ghana, Nigeria, and Liberia. This can contribute to waste management in the region while generating a gross revenue of 2.85 billion XOF (4.33 million USD).
Effect of Nano-Zirconia Addition on Mechanical Properties of Metakaolin-Based Geopolymer
Authors: Saukani Muhammad , Lisdawati Ayu Novia , Irawan Heri , Iqbal Rendy Muhamad , Nurjaya Dwi Marta , Astutiningsih Sotya
Published: October 2022

Geopolymer is an emerging material alternative to Portland cement and has potential as a refractory material. Adding filler in geopolymer material is a strategy to increase the advantages of its physical and mechanical properties. It has been previously reported that adding nanoparticles can increase the compressive strength value, but there is no reported interaction between nanoparticles and geopolymer during the geopolymerization process. This study aims to study the effect of adding nano-zirconia fillers on the physical and mechanical changes of metakaolin-based geopolymers with nano-zirconia fillers. The geopolymer samples were made with 100 g of metakaolin as the base material and nano-zirconia in amounts of 2 g, 5 g, 10 g, and 15 g. Further characterization was carried out by XRD, FTIR, and SEM-EDX. This study showed that the compressive strength of the MZr05 sample increased significantly by 58.7% compared to the control sample. The test results of the structure and functional groups did not lead to any new compounds formed in the geopolymerization reaction. Therefore, the interaction of metakaolin geopolymer with nano-zirconia only creates an interfacial bonding.
Bio-Based Epoxies: Mechanical Characterization and Their Applicability in the Development of Eco-Friendly Composites
Authors: Naik Nithesh , Shivamurthy B. , Thimmappa B. H. S. , Guo Zhanhu , Bhat Ritesh
Published: October 2022

The combination of awareness of harmful industrial processes, environmental concerns, and depleting petroleum-based resources has spurred research in developing sustainable materials from renewable sources. Natural bio-based polymers have replaced synthetic polymers because of growing concern about environmental sustainability. As a result of heating and distilling cashew nutshell liquid (CNSL), cardanol has emerged as a promising bio-retrieved component that can be used to make bio-based epoxy. The current work intends to investigate the mechanical properties of three kinds of cardanol-based bio-based epoxies in anticipation of widespread use. Vickers hardness, tensile and flexural strength are used to characterize mechanical properties. Additionally, a water absorption test is carried out to examine the weight gain properties of all the bio-based epoxy variants selected. FormuLITE 2 (FormuLITE 2501A + FormuLITE 2401B) exhibited the highest Vickers hardness, tensile and flexural strength among the three variants. Moreover, it exhibited a water absorption rate nearly equivalent to that of the conventional LY556/HY951, and thus, FormuLITE 2, the bio-based epoxy resin having 34% of bio-content blended with conventional epoxy, proves to be the best option out of the selected bio-based epoxies to be used further as the matrix material for the fabrication of biocomposites.
Zebrafish Model of Stickler Syndrome Suggests a Role for Col2a1a in the Neural Crest during Early Eye Development
Authors: Williams Antionette L. , Bohnsack Brenda L.
Published: October 2022

Most cases of Stickler syndrome are due to autosomal-dominant COL2A1 gene mutations leading to abnormal type II collagen. Ocular findings include axial eye lengthening with vitreal degeneration and early-onset glaucoma, which can result in vision loss. Although COL2A1 is a major player in cartilage and bone formation, its specific role in eye development remains elusive. We investigated the role of Col2a1a in neural crest migration and differentiation during early zebrafish eye development. In situ hybridization, immunofluorescence, live imaging, exogenous treatments [10 μM diethylaminobenzaldehyde (DEAB), 100 nM all-trans retinoic acid (RA) and 1–3% ethanol (ETOH)] and morpholino oligonucleotide (MO) injections were used to analyze wildtype Casper (roy−/−;nacre−/−), TgBAC(col2a1a::EGFP), Tg(sox10::EGFP) and Tg(foxd3::EGFP) embryos. Col2a1a colocalized with Foxd3- and Sox10-positive cells in the anterior segment and neural crest-derived jaw. Col2a1a expression was regulated by RA and inhibited by 3% ETOH. Furthermore, MO knockdown of Col2a1a delayed jaw formation and disrupted the ocular anterior segment neural crest migration of Sox10-positive cells. Interestingly, human COL2A1 protein rescued the MO effects. Altogether, these results suggest that Col2a1a is a downstream target of RA in the cranial neural crest and is required for both craniofacial and eye development.
Decellularized Pancreatic Tail as Matrix for Pancreatic Islet Transplantation into the Greater Omentum in Rats
Authors: Berkova Zuzana , Zacharovova Klara , Patikova Alzbeta , Leontovyc Ivan , Hladikova Zuzana , Cerveny David , Tihlarikova Eva , Nedela Vilem , Girman Peter , Jirak Daniel , Saudek Frantisek
Published: October 2022

Infusing pancreatic islets into the portal vein currently represents the preferred approach for islet transplantation, despite considerable loss of islet mass almost immediately after implantation. Therefore, approaches that obviate direct intravascular placement are urgently needed. A promising candidate for extrahepatic placement is the omentum. We aimed to develop an extracellular matrix skeleton from the native pancreas that could provide a microenvironment for islet survival in an omental flap. To that end, we compared different decellularization approaches, including perfusion through the pancreatic duct, gastric artery, portal vein, and a novel method through the splenic vein. Decellularized skeletons were compared for size, residual DNA content, protein composition, histology, electron microscopy, and MR imaging after repopulation with isolated islets. Compared to the other approaches, pancreatic perfusion via the splenic vein provided smaller extracellular matrix skeletons, which facilitated transplantation into the omentum, without compromising other requirements, such as the complete depletion of cellular components and the preservation of pancreatic extracellular proteins. Repeated MR imaging of iron-oxide-labeled pancreatic islets showed that islets maintained their position in vivo for 49 days. Advanced environmental scanning electron microscopy demonstrated that islets remained integrated with the pancreatic skeleton. This novel approach represents a proof-of-concept for long-term transplantation experiments.
Smart Bacteria-Responsive Drug Delivery Systems in Medical Implants
Authors: Yang Yijie , Jiang Xue , Lai Hongchang , Zhang Xiaomeng
Published: October 2022

With the rapid development of implantable biomaterials, the rising risk of bacterial infections has drawn widespread concern. Due to the high recurrence rate of bacterial infections and the issue of antibiotic resistance, the common treatments of peri-implant infections cannot meet the demand. In this context, stimuli-responsive biomaterials have attracted attention because of their great potential to spontaneously modulate the drug releasing rate. Numerous smart bacteria-responsive drug delivery systems (DDSs) have, therefore, been designed to temporally and spatially release antibacterial agents from the implants in an autonomous manner at the infected sites. In this review, we summarized recent advances in bacteria-responsive DDSs used for combating bacterial infections, mainly according to the different trigger modes, including physical stimuli-responsive, virulence-factor-responsive, host-immune-response responsive and their combinations. It is believed that the smart bacteria-responsive DDSs will become the next generation of mainstream antibacterial therapies.
Meropenem/Vaborbactam and Cefiderocol as Combination or Monotherapy to Treat Multi-Drug Resistant Gram-Negative Infections: A Regional Cross-Sectional Survey from Piedmont Infectious Disease Unit Network (PIDUN)
Authors: Lupia Tommaso , Corcione Silvia , Shbaklo Nour , Montrucchio Giorgia , De Benedetto Ilaria , Fornari Valentina , Bosio Roberta , Rizzello Barbara , Mornese Pinna Simone , Brazzi Luca , De Rosa Francesco Giuseppe
Published: October 2022

Meropenem/vaborbactam (MV) and cefiderocol were recently approved by the Food and Drug Administration and European Medicines Agency and are among the most promising antibacterial in treatment regimens against multi-drug resistant (MDR) gram-negative bacilli. A survey with close-ended questions was proposed to infectious disease (ID) and intensive care unit (ICU) physicians of Piedmont and Valle d’Aosta Region’s hospitals. The aim was to collect data about habits and prescriptions of cefiderocol and MV. Twenty-three physicians (11 ID specialists and 12 anesthesiologists) in 13 Italian hospitals took part in the survey. Both cefiderocol and MV were mostly used as target therapy after a previous treatment failure and after ID specialist consult. The most frequent MDR pathogen in hospitals was Klebsiella pneumoniae carbapenemase-producing bacteria (KPC), followed by P. aeruginosa and A. baumannii. MDRs were more frequently isolated in ICU. In conclusion, cefiderocol was used in empiric regimens when A. baumannii was suspected, while MV was more used in suspect of KPC. MV and cefiderocol can be the first option in empiric treatment for critically ill patients in settings with high risk of MDR. The treatment should then be followed by rapid de-escalation when microbiological results are available.
Physicochemical and Antibacterial Properties of Conventional and Two Premixed Root Canal Filling Materials in Primary Teeth
Authors: Hachem Claire El , Chedid Jean Claude Abou , Nehme Walid , Kaloustian Marc Krikor , Ghosn Nabil , Sahnouni Hafsa , Mancino Davide , Haikel Youssef , Kharouf Naji
Published: October 2022

In this study, some physicochemical and antibacterial properties of three root canal filling materials for primary teeth, Calplus “CP” (Prevest DenPro, Lewes, DE, USA), Bio-C Pulpecto “Bio-CP” (Angelus, Basil, Londrina, Paraná, Brazil), and Zinc Oxide and Eugenol “ZOE” (Prevest DenPro, Lewes, DE, USA) were compared. For each material, the pH, solubility, contact angle, and crystalline microstructure under SEM were evaluated. Their antibacterial activity against Enterococcus faecalis was determined through direct tests. The Kruskal–Wallis test was used to analyze the results using a one-way analysis of variance on ranks. All the materials had an alkaline pH at 3, 24, and 72 h, with CalPlus having the highest (p < 0.05). Bio-CP was more soluble during the evaluation period (24 h) than ZOE and CalPlus (p < 0.05). Bio-CP and ZOE demonstrated the creation of crystallite structures on their surfaces after immersion in PBS at 37 °C, whereas CalPlus showed none. The lowest contact angle was observed for Bio-CP (53 ± 1.5°); contact angles of (86 ± 4°) and (96 ± 1°), respectively, were observed after 10 s of the deposition of the water drop for CalPlus and ZOE. In conclusion, according to this study, there is still a need to develop new filling materials for primary teeth. ZOE, CalPlus and Bio-CP demonstrated different physicochemical and antibacterial properties, but none of the materials had optimal properties and could be considered the most suitable filling material for primary teeth pulpectomy. Bioceramics in their current state are not an alternative. The physicochemical and antibacterial properties still need improvement to fit the intricate anatomy of primary teeth.
Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures
Authors: Abuhussein Mohammed , Robinson Aaron
Published: October 2022

The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs to the network or as labels for training. The majority of the available image segmentation algorithmic development and results focus on visible image modalities. Therefore, in this treatment, the authors present the results of a study designed to identify and improve current semantic methods for infrared scene segmentation. Specifically, the goal is to propose a novel approach to provide tile-based segmentation of occlusion clouds in Long Wave Infrared images. This work complements the collection of well-known semantic segmentation algorithms applicable to thermal images but requires a vast dataset to provide accurate performance. We document performance in applications where the distinction between dust cloud tiles and clear tiles enables conditional processing. Therefore, the authors propose a Gray Level Co-Occurrence Matrix (GLCM) based method for infrared image segmentation. The main idea of our approach is that GLCM features are extracted from local tiles in the image and used to train a binary classifier to provide indication of tile occlusions. Our method introduces a new texture analysis scheme that is more suitable for image segmentation than the solitary Gabor segmentation or Markov Random Field (MRF) scheme. Our experimental results show that our algorithm performs well in terms of accuracy and a better inter-region homogeneity than the pixel-based infrared image segmentation algorithms.
Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
Authors: Alsaaidah Bayan , Al-Hadidi Moh’d Rasoul , Al-Nsour Heba , Masadeh Raja , AlZubi Nael
Published: October 2022

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors
Authors: Deng Mingxing , Zhang Quanyong , Zhang Kun , Li Hui , Zhang Yikai , Cao Wan
Published: October 2022

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be obtained are small and the scale of the different defects on the picture of the defect is different. Therefore, a simple, flexible, and efficient convolutional neural network (CNN) called accurate-detection CNN (ADCNN) to inspect MEMS pressure-sensor-chip packaging is proposed in this paper. The ADCNN is based on the faster region-based CNN, which improved the performance of the network by adding random-data augmentation and defect classifiers. Specifically, the ADCNN achieved a mean average precision of 92.39% and the defect classifier achieved a mean accuracy of 97.2%.
Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection
Authors: Hernandez-Torres Sofia I. , Boice Emily N. , Snider Eric J.
Published: October 2022

Tissue phantoms are important for medical research to reduce the use of animal or human tissue when testing or troubleshooting new devices or technology. Development of machine-learning detection tools that rely on large ultrasound imaging data sets can potentially be streamlined with high quality phantoms that closely mimic important features of biological tissue. Here, we demonstrate how an ultrasound-compliant tissue phantom comprised of multiple layers of gelatin to mimic bone, fat, and muscle tissue types can be used for machine-learning training. This tissue phantom has a heterogeneous composition to introduce tissue level complexity and subject variability in the tissue phantom. Various shrapnel types were inserted into the phantom for ultrasound imaging to supplement swine shrapnel image sets captured for applications such as deep learning algorithms. With a previously developed shrapnel detection algorithm, blind swine test image accuracy reached more than 95% accuracy when training was comprised of 75% tissue phantom images, with the rest being swine images. For comparison, a conventional MobileNetv2 deep learning model was trained with the same training image set and achieved over 90% accuracy in swine predictions. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification.
Using Paper Texture for Choosing a Suitable Algorithm for Scanned Document Image Binarization
Authors: Lins Rafael Dueire , Bernardino Rodrigo , Barboza Ricardo da Silva , De Oliveira Raimundo Correa
Published: October 2022

The intrinsic features of documents, such as paper color, texture, aging, translucency, the kind of printing, typing or handwriting, etc., are important with regard to how to process and enhance their image. Image binarization is the process of producing a monochromatic image having its color version as input. It is a key step in the document processing pipeline. The recent Quality-Time Binarization Competitions for documents have shown that no binarization algorithm is good for any kind of document image. This paper uses a sample of the texture of the scanned historical documents as the main document feature to select which of the 63 widely used algorithms, using five different versions of the input images, totaling 315 document image-binarization schemes, provides a reasonable quality-time trade-off.
A Model of Pixel and Superpixel Clustering for Object Detection
Authors: Nenashev Vadim A. , Khanykov Igor G. , Kharinov Mikhail V.
Published: October 2022

The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1, 2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward’s and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined “semantic” segmentation.
A Very Fast Image Stitching Algorithm for PET Bottle Caps
Authors: Zhu Xiao , Liu Zixiao , Zhang Xin , Sui Tingting , Li Ming
Published: October 2022

In the beverage, food and drug industry, more and more machine vision systems are being used for the defect detection of Polyethylene Terephthalate (PET) bottle caps. In this paper, in order to address the result of cylindrical distortions that influence the subsequent defect detection in the imaging process, a very fast image stitching algorithm is proposed to generate a panorama planar image of the surface of PET bottle caps. Firstly, the three-dimensional model of the bottle cap is established. Secondly, the relative poses among the four cameras and the bottle cap in the three-dimensional space are calculated to obtain the mapping relationship between three-dimensional points on the side surface of the bottle cap and image pixels taken by the camera. Finally, the side images of the bottle cap are unfolded and stitched to generate a planar image. The experimental results demonstrate that the proposed algorithm unfolds the side images of the bottle cap correctly and very fast. The average unfolding and stitching time for 1.6-megapixel color caps image can reach almost 123.6 ms.
Permeability of Additive Manufactured Cellular Structures—A Parametric Study on 17-4 PH Steels, Inconel 718, and Ti-6Al-4V Alloys
Authors: Liu Ting-Wei , Cheng Tien-Lin , Chiu Kuo-Chi , Chen Jhewn-Kuang
Published: October 2022

Cellular structures of metallic alloys are often made for various industrial applications by additive manufacturing. The permeability for fluid flow in these cellular structures is important. The current investigated the gas fluidity of cellular structures made by selective laser melting (SLM). The porosity and permeability of the SLM cellular structures were measured for 17-4 PH stainless steel, Inconel 718, and Ti-6Al-4V alloys. The relations between porosity and energy density are expressed using the power law. The characteristic molar energies were 1.07 × 105, 9.02 × 104, and 7.11 × 104 J/mole for 17-4 PH steel, Ti-6Al-4V, and Inconel 718 alloys, respectively. 17-4 PH steel gave rise to higher porosity at the same energy density when compared with Ti-6Al-4V and Inconel 718 alloy. The values of these molar energy density are related to the heat needed to melt the alloys, viscosity, and thermal conductivity. It was further shown that air permeability is not only concerned with the percentage of porosity in the cellular materials, but it also relates to the tortuosity of pore pathways formed in the cellular materials. At the same porosity, Inconel 718 demonstrates higher air permeability in comparison with that of Ti-6Al-4V and 17-4 PH alloys due to its smoother pore pathways. Ti-6Al-4V, on the other hand, demonstrates the highest specific surface areas due to powder sticking along the pore pathways and led to the lowest permeability among the three alloys.
Tool Condition Monitoring Using Machine Tool Spindle Electric Current and Multiscale Analysis while Milling Steel Alloy
Authors: Jamshidi Maryam , Chatelain Jean-François , Rimpault Xavier , Balazinski Marek
Published: October 2022

In the metal cutting process, the tool condition directly affects the quality of the machined component. To control the quality of the cutting tool and avoid equipment downtime, it is essential to monitor its condition during the machining process. The primary purpose is to send a warning before tool wear reaches a certain level, which could influence product quality. In this paper, tool condition is monitored using fractal analysis of the spindle electric current signal. The current study analyzes the monitoring of the cutting tool when milling AISI 5140 steel with a four-flute solid carbide end mill cutter to develop monitoring techniques for wear classification of metal cutting processes. The spindle electric current signal is acquired using the machine tool internal sensor, which meets industrial constraints in their operating conditions. As a new approach, the fractal theory is referred to analyze the spindle electric current signal and then assess the tool wear condition during the metal cutting process. Fractal parameters were defined to extract significant characteristic features of the signal. This research provides a proof of concept for the use of fractal analysis as a decision-making strategy in monitoring tool condition.
The Post-Processing of Additive Manufactured Polymeric and Metallic Parts
Authors: Syrlybayev Daniyar , Seisekulova Aidana , Talamona Didier , Perveen Asma
Published: October 2022

The traditional manufacturing industry has been revolutionized with the introduction of additive manufacturing which is based on layer-by-layer manufacturing. Due to these tool-free techniques, complex shape manufacturing becomes much more convenient in comparison to traditional machining. However, additive manufacturing comes with its inherent process characteristics of high surface roughness, which in turn effect fatigue strength as well as residual stresses. Therefore, in this paper, common post-processing techniques for additive manufactured (AM) parts were examined. The main objective was to analyze the finishing processes in terms of their ability to finish complicated surfaces and their performance were expressed as average surface roughness (Sa and Ra). The techniques were divided according to the materials they applied to and the material removal mechanism. It was found that chemical finishing significantly reduces surface roughness and can be used to finish parts with complicated geometry. Laser finishing, on the other hand, cannot be used to finish intricate internal surfaces. Among the mechanical abrasion methods, abrasive flow finishing shows optimum results in terms of its ability to finish complicated freeform cavities with improved accuracy for both polymer and metal parts. However, it was found that, in general, most mechanical abrasion processes lack the ability to finish complex parts. Moreover, although most of post-processing methods are conducted using single finishing processes, AM parts can be finished with hybrid successive processes to reap the benefits of different post-processing techniques and overcome the limitation of individual process.
Heat Dissipation Plays Critical Role for Longevity of Polymer-Based 3D-Printed Inserts for Plastics Injection Moulding
Authors: Moritz Vicente F. , Bezerra Gilberto S. N. , Hopkins Jnr Michael , Fuenmayor Evert , Günbay Suzan , Hayes Conor , Lyons John G. , Devine Declan M.
Published: October 2022

Injection moulding is a polymer processing method of choice for making plastic parts on industrial scale, but its traditional mould is made from tooling steel with time-consuming and costly production. Additive manufacturing technologies arise as an alternative for creating mould inserts at lower costs and shorter lead times. In this context, this study describes a series of stereolithography (SLA)-printed injection mould inserts fabricated from two photopolymer resins, utilised to mould standard tensile specimens of a commercial-grade polypropylene, aiming to evaluate effects on the polymer’s thermal and mechanical properties. Our results demonstrated that the glass fibre-filled resin inserts withstood more moulding cycles before failure, had superior mechanical properties, higher Tg and greater thermal conductivity. Calorimetric data revealed that PP thermal properties and degree of crystallinity were little affected, while mechanical testing suggests a significant effect in the elongation at break. Thus, these findings highlight the importance of adequate heat extraction during injection moulding and endorse further application of SLA mould inserts for the manufacturing of injection-moulded plastic parts in the case of prototypes or small batches, provided suitable cooling is made available, contributing to the feasibility and affordability of employing this approach for an industrial setting.
The Comparison of Mutational Progression in SARS-CoV-2: A Short Updated Overview
Authors: Asif Abeer , Ilyas Iqra , Abdullah Mohammad , Sarfraz Sadaf , Mustafa Muhammad , Mahmood Arif
Published: October 2022

The COVID-19 pandemic has impacted the world population adversely, posing a threat to human health. In the past few years, various strains of SARS-CoV-2, each with different mutations in its structure, have impacted human health in negative ways. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations influence the virulence, antibody evasion, and Angiotensin-converting enzyme 2 (ACE2) affinity of the virus. These mutations are essential to understanding how a new strain of SARS-CoV-2 has changed and its possible effects on the human body. This review provides an insight into the spike mutations of SARS-CoV-2 variants. As the current scientific data offer a scattered outlook on the various type of mutations, we aimed to categorize the mutations of Beta (B.1.351), Gamma (P.1), Delta (B.1.612.2), and Omicron (B.1.1.529) systematically according to their location in the subunit 1 (S1) and subunit 2 (S2) domains and summarized their consequences as a result. We also compared the miscellany of mutations that have emerged in all four variants to date. The comparison shows that mutations such as D614G and N501Y have emerged in all four variants of concern and that all four variants have multiple mutations within the N-terminal domain (NTD), as in the case of the Delta variant. Other mutations are scattered in the receptor binding domain (RBD) and subdomain 2 (SD2) of the S1 domain. Mutations in RBD or NTD are often associated with antibody evasion. Few mutations lie in the S2 domain in the Beta, Gamma, and Delta variants. However, in the Omicron variant many mutations occupy the S2 domain, hinting towards a much more evasive virus.
Progress on Offshore Wind Farm Dynamic Wake Management for Energy
Authors: Zhao Liye , Xue Lei , Li Zhiqian , Wang Jundong , Yang Zhichao , Xue Yu
Published: October 2022

The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage.
Morphometric Analyses of Phenotypic Plasticity in Habitat Use in Two Caspian Sea Mullets
Authors: Bakhshalizadeh Shima , Abbasi Keyvan , Rostamzadeh Liafuie Adeleh , Bani Ali , Pavithran Anu , Tiralongo Francesco
Published: October 2022

To understand the functional meaning of morphological traits in the exploitation of natural resources, it is necessary to develop a quantitative, meaningful scheme for understanding ecophenotypes; this will facilitate management and conservation, which are the most pressing challenges in vulnerable aquatic environments. In this context, the management of cryptic and very similar species is more challenging, because of the difficulty of distinguishing them and determining their frequency in sympatry, even though they do not necessarily have the same ecologies. As such, in order to understand how morphological similarities are associated with their ecology, thirteen morphometric characteristics related to body landmark-based geometric morphometries, sagittal otolith morphology, and shape were examined in mature Chelon auratus and Chelon saliens, which were collected from the coastal waters of the southwest Caspian Sea between October 2020 and April 2021. Univariate and multivariate analysis of variance were conducted to evaluate the potential morphological differences between the species. The analyses highlight the morphological differences between C. auratus and C. saliens, and identify potentially helpful traits for using body and otolith shape for the interspecific distinction of these very similar species of Caspian mullet, which can reflect functional similarity and are an important component of community ecology.
An Optimal BP Neural Network Track Prediction Method Based on a GA–ACO Hybrid Algorithm
Authors: Zheng Yuanzhou , Lv Xuemeng , Qian Long , Liu Xinyu
Published: October 2022

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship’s future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments.
Study on Elastic Response of Double-Rotor VAWTs
Authors: Iwamatsu Saika , Suzuki Hideyuki , Nihei Yasunori
Published: October 2022

This study investigates the elastic response characteristics of a floating wind turbine (FOWT) with two vertical-axis wind turbines (VAWTs), called double-rotor VAWTs. The model consists of two VAWTs mounted on a single semi-submersible floating structure and employs a single point mooring, which allows the FOWT to always self-align with the wind. Usually, a coupled analysis of the wind turbine and floating structure is used in the design of FOWTs; however, there is no coupled analysis available for VAWTs. In this study, we attempted to combine the wind turbine design software “QBlade” and the coupled wind turbine/floating body analysis code “UTWind” as one of the methods of coupled analysis of a VAWT and a floating body. Numerical simulation results were compared with experimental results using an elastic model scaled down to 1/100 of its actual model to determine the motion response and cross-sectional bending moments. The experimental results showed that the thrust of the VAWT had a particular influence on the cross-sectional forces and motion response between the two VAWTs. For cross-sectional forces, all results showed similar trends. Overall, the results of UTWind for double-rotor VAWTs are reasonable. It was also found that the pitch motion must be accurately reproduced to improve the accuracy.
Palytoxin-Analogues Accumulation in Natural Mussel Banks during an Ostreopsis cf. ovata Bloom
Authors: Accoroni Stefano , Ubaldi Marika , Bacchiocchi Simone , Neri Francesca , Siracusa Melania , Buonomo Maria Giovanna , Campanelli Alessandra , Totti Cecilia
Published: October 2022

Intense blooms of the toxic dinoflagellate Ostreopsis producing palytoxin (PlTX) analogs, mainly ovatoxins (OVTXs), have been a recurrent phenomenon along several Mediterranean coasts. Although there is evidence of seafood contamination by these toxins, the dynamics of their bioaccumulation during Ostreopsis blooms is not yet clear. Toxin concentrations in wild mussels at two sites in the Conero Riviera, along the northern Adriatic Sea (Portonovo and Passetto), were analyzed from August to October 2021, throughout an Ostreopsis cf. ovata bloom, to investigate their relationships with the bloom pattern and abundances. Contaminated mussels showed the typical toxin profile dominated by OVTX-a and -b, with levels lower than the threshold considered unsafe for human consumption (30 µg PlTX-equivalent kg−1 soft tissue). The toxin content reached 36.4 µg PlTX kg−1 soft tissue only in a single sampling date. A clear correlation between the bioaccumulation of OVTXs in mussels and the abundance of Ostreopsis was observed. Our results highlighted, however, that bioaccumulation in the mussels is not affected merely by the abundance of toxic microalgae, since higher toxin levels were recorded at Portonovo, where the cell abundances were lower than at Passetto. The results from this study indicate that the Italian guidelines for the management of Ostreopsis blooms in bathing waters are also useful in managing the risks of human intoxication through ingestion, as mussel contamination was detected only during the alert phase (10,000–30,000 cells L−1).
Study on the Cumulative Effects of Using a High-Efficiency Turbocharger and Biodiesel B20 Fuelling on Performance and Emissions of a Large Marine Diesel Engine
Authors: Visan Nicolae Adrian , Carlanescu Razvan , Niculescu Dan Catalin , Chiriac Radu
Published: October 2022

The marine sector represents probably the most powerful segment of international transport. Most ships use diesel engines for propulsion. Pollutant emission regulations with their continuous decline of acceptable limits put huge pressure on engine manufacturers. The use of low-quality fuels makes the marine sector a significant contributor to global pollution. The present study shows how turbocharger operating parameters and replacing diesel fuel with biodiesel B20 (20% oil and 80% diesel volumetric fractions) affect the performance, efficiency and pollutant emissions of a four-stroke diesel engine ALCO V16 251F for marine application. A combustion model developed with the AVL BOOST software was used to perform calculations using diesel fuel and biodiesel B20 for different turbocharger characteristics and injection timings. The model was calibrated against experimental data measured on a tested engine at the application site using diesel fuel and operating in a stationary condition of full load at 600, 700 and 800 rpm engine speeds. The results show that the cumulative effects of using an improved turbocharger associated with B20 fuelling under optimized injection timings could provide reductions of 45% for soot and 5% for NOx, while maintaining the same engine performance obtained with diesel fuel operation.
Dynamic Target Tracking of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning
Authors: Shi Jiaxiang , Fang Jianer , Zhang Qizhong , Wu Qiuxuan , Zhang Botao , Gao Farong
Published: October 2022

Due to the unknown motion model and the complexity of the environment, the problem of target tracking for autonomous underwater vehicles (AUVs) became one of the major difficulties in model-based controllers. Therefore, the target tracking task of AUV is modeled as a Markov decision process (MDP) with unknown state transition probabilities. Based on actor–critic framework and experience replay technique, a model-free reinforcement learning algorithm is proposed to realize the dynamic target tracking of AUVs. In order to improve the performance of the algorithm, an adaptive experience replay scheme is further proposed. Specifically, the proposed algorithm utilizes the experience replay buffer to store and disrupt the samples, so that the time series samples can be used for training the neural network. Then, the sample priority is arranged according to the temporal difference error, while the adaptive parameters are introduced in the sample priority calculation, thus improving the experience replay rules. The results confirm the quick and stable learning of the proposed algorithm, when tracking the dynamic targets in various motion states. Additionally, the results also demonstrate good control performance regarding both stability and computational complexity, thus indicating the effectiveness of the proposed algorithm in target tracking tasks.