bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance of a Wet Electrostatic Precipitator in Marine Applications
Authors: Järvinen Anssi , Lehtoranta Kati , Aakko-Saksa Päivi , Karppanen Mikko , Murtonen Timo , Martikainen Jarno , Kuusisto Jarmo , Nyyssönen Sami , Koponen Päivi , Piimäkorpi Pekka , Friman Eero , Orasuo Varpu , Rintanen Jaakko , Jokiluoma Juha , Kuittinen Niina , Rönkkö Topi
Published: February 2023

Emissions of marine traffic can be lowered by switching to less polluting fuels or by investing in exhaust aftertreatment. Electrostatic precipitation is a widely used method for particle removal but it is not currently used in combination with marine engines. This study presents the particle filtration characteristics of an emission reduction system designed for marine applications and consisting of a scrubber and a Wet Electrostatic Precipitator (WESP) in series. Partial flow of exhaust from a 1.6 MW marine engine, operated with light and heavy fuel oil, was led to the system. Particle concentrations were measured before the system, after the scrubber and after the WESP. Particle removal characteristics were determined for different engine loads. The scrubber alone removed 15–55% of non-volatile particle number, 30–40% of particle mass and 30–40% of black carbon mass depending on engine load, when HFO fuel was used. By studying particle size distributions, scrubber was found also to generate particles seen as an additional mode in 20–40 nm size range. The system combining the scrubber and WESP removed over 98.5% of particles in number, mass and black carbon metrics when HFO fuel was used. With MDO fuel, 96.5% of PN and 99% of black carbon were removed.
In Candida glabrata, ERMES Component GEM1 Controls Mitochondrial Morphology, mtROS, and Drug Efflux Pump Expression, Resulting in Azole Susceptibility
Authors: Okamoto Michiyo , Nakano Keiko , Takahashi-Nakaguchi Azusa , Sasamoto Kaname , Yamaguchi Masashi , Teixeira Miguel Cacho , Chibana Hiroji
Published: February 2023

Mitochondrial dysfunction or morphological abnormalities in human pathogenic fungi are known to contribute to azole resistance; however, the underlying molecular mechanisms are unknown. In this study, we investigated the link between mitochondrial morphology and azole resistance in Candida glabrata, which is the second most common cause of human candidiasis worldwide. The ER-mitochondrial encounter structure (ERMES) complex is thought to play an important role in the mitochondrial dynamics necessary for mitochondria to maintain their function. Of the five components of the ERMES complex, deletion of GEM1 increased azole resistance. Gem1 is a GTPase that regulates the ERMES complex activity. Point mutations in GEM1 GTPase domains were sufficient to confer azole resistance. The cells lacking GEM1 displayed abnormalities in mitochondrial morphology, increased mtROS levels, and increased expression of azole drug efflux pumps encoded by CDR1 and CDR2. Interestingly, treatment with N-acetylcysteine (NAC), an antioxidant, reduced ROS production and the expression of CDR1 in Δgem1 cells. Altogether, the absence of Gem1 activity caused an increase in mitochondrial ROS concentration, leading to Pdr1-dependent upregulation of the drug efflux pump Cdr1, resulting in azole resistance.
Scoring Systems to Evaluate the Mortality Risk of Patients with Emphysematous Cystitis: A Retrospective Observational Study
Authors: Chen Yi-Hsuan , Hsieh Ming-Shun , Hu Sung-Yuan , Huang Shih-Che , Tsai Che-An , Tsai Yi-Chun
Published: February 2023

Background: Emphysematous cystitis (EC) is a complicated urinary tract infection (UTI) characterized by gas formation within the bladder wall and lumen. Immunocompetent people are less likely to suffer from complicated UTIs, but EC usually occurs in women with poorly controlled diabetes mellitus (DM). Other risk factors of EC include recurrent UTI, neurogenic bladder disorder, blood supply disorders, and prolonged catheterization, but DM is still the most important of all aspects. Our study investigated clinical scores in predicting clinical outcomes of patients with EC. Our analysis is unique in predicting EC clinical outcomes by using scoring system performance. Materials and Methods: We retrospectively collected EC patient data from the electronic clinical database of Taichung Veterans General Hospital between January 2007 and December 2020. Urinary cultures and computerized tomography confirmed EC. In addition, we investigated the demographics, clinical characteristics, and laboratory data for analysis. Finally, we used a variety of clinical scoring systems as a predictor of clinical outcomes. Results: A total of 35 patients had confirmed EC, including 11 males (31.4%) and 24 females (68.6%), with a mean age of 69.1 ± 11.4 years. Their hospital stay averaged 19.9 ± 15.5 days. The in-hospital mortality rate was 22.9%. The Mortality in Emergency Department Sepsis (MEDS) score was 5.4 ± 4.7 for survivors and 11.8 ± 5.3 for non-survivors (p = 0.005). For mortality risk prediction, the AUC of ROC was 0.819 for MEDS and 0.685 for Rapid Emergency Medicine Score (REMS). The hazard ratio of univariate and multivariate logistic regression analyses of REMS for EC patients was1.457 (p = 0.011) and 1.374 (p = 0.025), respectively. Conclusion: Physicians must pay attention to high-risk patients according to clinical clues and arrange imaging studies as soon as possible to confirm the diagnosis of EC. MEDS and REMS are helpful for clinical staff in predicting the clinical outcome of EC patients. If EC patients feature higher scores of MEDS (≥12) and REMS (≥10), they will have higher mortality.
Analysis of Frailty Syndrome in Men with Metastatic Prostate Cancer: A Scoping Review
Authors: Mafla-España Mayra Alejandra , Torregrosa María Dolores , Cauli Omar
Published: February 2023

Most patients with metastatic prostate cancer (mPCa) are older. In addition, current geriatric oncology guidelines suggest that all cancer patients aged over 70 years should undergo a comprehensive geriatric assessment (CGA), with the identification of frailty syndrome being crucial for clinical decisions. Frailty can be associated with lower quality of life (QoL) and interfere with the feasibility or side effects of oncology treatments. Methods: We performed a systematic literature search to evaluate frailty syndrome and associated alterations related to CGA impairment by searching in different academic databases (PubMed, Embase, and Scopus). The identified articles were reviewed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Of the 165 articles consulted, 7 met our inclusion criteria. Analysis of data related to frailty syndrome in patients with mPCa showed a prevalence between 30–70% depending on the tool used. Additionally, frailty was associated with other CGA assessments and QoL evaluation outcomes. In general terms, CGA scores for patients with mPCa were lower than those for patients without metastasis. Furthermore, functional QoL appeared to be worse for patients with metastasis, and global QoL (burden) was more strongly associated with frailty. Conclusion: Frailty syndrome was related to a poorer QoL in patients with mPCa and its evaluation should be considered in clinical decision-making and when choosing the most appropriate active treatment, if any, to increase survival.
Dynamic Conditional Correlation and Volatility Spillover between Conventional and Islamic Stock Markets: Evidence from Developed and Emerging Countries
Authors: Sahabuddin Mohammad , Islam Md. Aminul , Tabash Mosab I. , Alam Md. Kausar , Daniel Linda Nalini , Mostafa Imad Ibraheem
Published: February 2023

This study aims to investigate the dynamic conditional correlation and volatility spillover between the conventional and Islamic stock markets in developed and emerging countries in order to develop better portfolio and asset allocation strategies. We used both multivariate GARCH (MGARCH) and multi-scales-based maximal overlap discrete wavelet transform (MODWT) approaches to investigate dynamic conditional correlation and volatility spillover between conventional and Islamic stock markets in developed and emerging countries. The results show that conventional and Islamic markets move together in the long run for a specific time horizon and present time-varying volatility and dynamic conditional correlation, while volatility movement changes due to financial catastrophes and market conditions. Further, the findings point out that Chinese conventional and Islamic stock indexes showed higher volatility, whereas Malaysian conventional and Islamic stock indexes showed comparatively lower volatility during the global financial crisis. This study provides fresh insights and practical implications for risk management, asset allocation, and portfolio diversification strategies that evaluate stock market reactions to the crisis in the international avenues of finance literature.
Losing the Way or Running Off? An Unprecedented Major Movement of Asian Elephants in Yunnan, China
Authors: Jiang Luguang , Liu Ye , Xu Haixia
Published: February 2023

In 2021, an unprecedented major movement of Asian elephants in China aroused the curiosity of hundreds of millions of people around the world. For research objectives, we firstly reproduce the movement route of Asian elephants and reveal their geographical spatial characteristics and landscape characteristics using multisource data; secondly, we reveal the reason for this Asian elephant movement. We found Asian elephants went far beyond the northernmost movement boundary from past years. Most of the areas along the movement route fell within the higher accessibility to road traffic. Over the past 20 years, the rubber and tea areas of Xishuangbanna and Pu’er have increased by 91.1% and 120.1%, respectively, from 2005 to 2019. Asian elephants spent 18 days in areas with suitable food, but relatively low vegetation coverage. The 2021 movement was most likely a “purposeful” trip rather than a “detour”. The elephants chose the most rewarding way to move forward, which showed they are far smarter than we thought. They may have left to find food due to exhausted food supply. The expansion of rubber and tea plantations has caused the habitat of Asian elephants to shrink, which was one of the reasons for the northward movement of them.
Image-Building and Place Perception of the Subway Station’s Cultural Landscape: A Case Study in Xi’an, China
Authors: Zhang Qian , Yan Jianwei , Sun Ting , Liu Juan
Published: February 2023

In the new phase of urbanization in China, the collective cultural landscapes of subway stations in many metropolises are flourishing, providing a powerful way to coordinate urban cultural development and display the image and identity of the city. This study focuses on the image-building cultural landscapes of subway stations. In the theoretical analysis section, it clarifies the construction logic with supporting theory, highlighting the key concepts involved: environmental design, cultural image perception and place perception, and setting out a structural framework of hypotheses concerning the relationships between these three concepts. In the empirical section, six stations in the urban historical center of Xi’an city were selected as the objects, and evaluation indexes of the three variables based on the perspective of the individual were constructed. Using a questionnaire and a combination of factor analysis and structural equation modeling methods, the data from 480 samples were then analyzed. The results were as follows: the cultural image and place perception presented by the environmental design of subway stations are universal to different categories of people; the structural model results showed that environmental design positively affects cultural image perception and place perception; the mediating effect results showed that environmental design affects place perception through cultural image perception. The empirical results confirmed the necessity and rationality of building the image of the cultural landscape of subway stations. Finally, the study makes suggestions for the optimization of current subway landscape development practices. The contribution of this study lies in the construction of a vertical analysis framework from “encoding: construction” to “decoding: perception”, which could provide a reference for the integration of theory and practice for the cultural landscapes of subway stations in Chinese metropolises. However, the innovative definitions and methods used to examine some of the concepts also have a certain subjectivity and therefore require further evidence-based investigation.
The Relationship between Language Educators’ Perceptions and Assessment Practices during the COVID-19 Crisis
Authors: Maaoui Asma , Tsagari Dina , Dammak Hanen
Published: February 2023

This study investigated language educators’ readiness in coping with language assessment during the global outbreak of the COVID-19 pandemic and the enforced move to Emergency Remote Teaching (ERT). This pandemic prompted debates on language assessors’ roles in adjusting to a New Normal. While a good body of research has investigated the role of teacher assessment perceptions versus assessment behavior for many decades, little is known about the factors that may have impacted language educators’ assessment perceptions and practices during the recent crisis. To address this issue, an online survey was administered to 256 language educators. Pearson correlations and simple linear regression were utilized to determine if the language educators’ perceptions of (1) the official assessment measures, (2) purposes, and (3) their assessment self-efficacy were predictors of their assessment practices during this crisis. The results revealed a total absence of any correlations between these variables. The findings suggest that the assessment accommodations adopted by the teachers were not determined by their assessment perceptions. Other factors such as assessment policy and the assessment culture may have shaped their practices during this crisis.
Interplay Impact of Exogenous Application of Abscisic Acid (ABA) and Brassinosteroids (BRs) in Rice Growth, Physiology, and Resistance under Sodium Chloride Stress
Authors: Hussain Sajid , Nanda Satyabrata , Ashraf Muhammad , Siddiqui Ali , Masood Sajid , Khaskheli Maqsood , Suleman Muhammad , Zhu Lianfeng , Zhu Chunquan , Cao Xiaochuang , Kong Yali , Jin Qianyu , Zhang Junhua
Published: February 2023

The hormonal imbalances, including abscisic acid (ABA) and brassinosteroid (BR) levels, caused by salinity constitute a key factor in hindering spikelet development in rice and in reducing rice yield. However, the effects of ABA and BRs on spikelet development in plants subjected to salinity stress have been explored to only a limited extent. In this research, the effect of ABA and BRs on rice growth characteristics and the development of spikelets under different salinity levels were investigated. The rice seedlings were subjected to three different salt stress levels: 0.0875 dS m−1 (Control, CK), low salt stress (1.878 dS m−1, LS), and heavy salt stress (4.09 dS m−1, HS). Additionally, independent (ABA or BR) and combined (ABA+BR) exogenous treatments of ABA (at 0 and 25 μM concentration) and BR (at 0 and 5 μM concentration) onto the rice seedlings were performed. The results showed that the exogenous application of ABA, BRs, and ABA+BRs triggered changes in physiological and agronomic characteristics, including photosynthesis rate (Pn), SPAD value, pollen viability, 1000-grain weight (g), and rice grain yield per plant. In addition, spikelet sterility under different salt stress levels (CK, LS, and HS) was decreased significantly through the use of both the single phytohormone and the cocktail, as compared to the controls. The outcome of this study reveals new insights about rice spikelet development in plants subjected to salt stress and the effects on this of ABA and BR. Additionally, it provides information on the use of plant hormones to improve rice yield under salt stress and on the enhancement of effective utilization of salt-affected soils.
Stem Cell Therapy for Erectile Dysfunction: A Step towards a Future Treatment
Authors: Pérez-Aizpurua Xabier , Garranzo-Ibarrola María , Simón-Rodríguez Carlos , García-Cardoso Juan Vicente , Chávez-Roa César , López-Martín Leticia , Tufet i Jaumot Jaime Jorge , Alonso-Román Josué , Maqueda-Arellano Jesús , Gómez-Jordana Blanca , Ruiz de Castroviejo-Blanco Joaquín , Osorio-Ospina Felipe , González-Enguita Carmen , García-Arranz Mariano
Published: February 2023

Background: The improvement of absent or partial response in the medical treatment of erectile dysfunction (ED) has led to the development of minimally invasive new treatment modalities in the field of regenerative medicine. Methods: A literature review on stem cell therapy for the treatment of ED was performed. We searched for the terms “erectile dysfunction” and “stem cell therapy” in PubMed and Clinicaltrials.gov. Literature searching was conducted in English and included articles from 2010 to 2022. Results: New treatment modalities for ED involving stem cell therapy are not only conceived with a curative intent but also aim to avoid unnecessary adverse effects. Several sources of stem cells have been described, each with unique characteristics and potential applications, and different delivery methods have been explored. A limited number of interventional studies over the past recent years have provided evidence of a safety profile in their use and promising results for the treatment of ED, although there are not enough studies to generate an appropriate protocol, dose or cell lineage, or to determine a mechanism of action. Conclusions: Stem cell therapy is a novel treatment for ED with potential future applications. However, most urological societies agree that further research is required to conclusively prove its potential benefit.
Beyond the Risk of Biofilms: An Up-and-Coming Battleground of Bacterial Life and Potential Antibiofilm Agents
Authors: Zeineldin Mohamed , Esmael Ahmed , Al-Hindi Rashad R. , Alharbi Mona G. , Ashenafi Bekele Debebe , Teklemariam Addisu D.
Published: February 2023

Microbial pathogens and their virulence factors like biofilms are one of the major factors which influence the disease process and its outcomes. Biofilms are a complex microbial network that is produced by bacteria on any devices and/or biotic surfaces to escape harsh environmental conditions and antimicrobial effects. Due to the natural protective nature of biofilms and the associated multidrug resistance issues, researchers evaluated several natural anti-biofilm agents, including bacteriophages and their derivatives, honey, plant extracts, and surfactants for better destruction of biofilm and planktonic cells. This review discusses some of these natural agents that are being put into practice to prevent biofilm formation. In addition, we highlight bacterial biofilm formation and the mechanism of resistance to antibiotics.
Lung Transplant Rehabilitation—A Review
Authors: Abidi Yafet , Kovats Zsuzsanna , Bohacs Aniko , Fekete Monika , Naas Saoussen , Madurka Ildiko , Torok Klara , Bogyo Levente , Varga Janos Tamas
Published: February 2023

Background: Both lung transplant recipients and candidates are characterised by reduced training capacity and low average quality of life (QoL). This review investigates the impact of training on exercise ability and QoL in patients before and after lung transplant. Methods: Searches were conducted from the beginning to 7 March 2022 using the terms “exercise,” “rehabilitation,” “lung transplant,” “exercise ability,” “survival,” “quality of life” and “telerehabilitation” in six databases, including Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, CINAHL, Nursing and Allied Health, and Scopus. The inclusion criteria were studies evaluating the effects of an exercise training programme concurrent with lung transplantation as well as patients and candidates (>18 years old) through any lung diseases. The term “lung transplant rehabilitation” was used to refer to all carefully thought-out physical activities with the ultimate or intermediate objective of improving or maintaining physical health. Results: Out of 1422 articles, 10 clinical- and 3 telerehabilitation studies, candidates (n = 420) and recipients (n = 116) were related to the criteria and included in this review. The main outcome significantly improved in all studies. The 6-min walk distance, maximum exercise capacity, peak oxygen uptake, or endurance for constant load rate cycling improved measuring physical activity [aerobic exercises, breathing training, and aerobic and inspiratory muscle training sessions (IMT)]. Overall scores for dyspnoea improved after exercise training. Furthermore, health-related quality of life (HRQOL) also improved after aerobic exercise training, which was performed unsupervised or accompanied by breathing sessions. Aerobic training alone rather than combined with inspiratory muscle- (IMT) or breathing training enhanced exercise capacity. Conclusion: In conclusion, rehabilitation programmes seem to be beneficial to patients both preceding and following lung transplantation. More studies are required to determine the best training settings in terms of time scale, frequency, and work intensity in terms of improving exercise ability, dyspnoea, and HRQOL.
Artificial Intelligence in Cardiovascular CT and MR Imaging
Authors: Lanzafame Ludovica R. M. , Bucolo Giuseppe M. , Muscogiuri Giuseppe , Sironi Sandro , Gaeta Michele , Ascenti Giorgio , Booz Christian , Vogl Thomas J. , Blandino Alfredo , Mazziotti Silvio , D’Angelo Tommaso
Published: February 2023

The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists’ workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.
Involvement of Lipids in the Pathogenesis of Amyotrophic Lateral Sclerosis
Authors: Alessenko Alisa V. , Gutner Uliana A. , Shupik Maria A.
Published: February 2023

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of upper and lower motor neurons. To study its underlying mechanisms, a variety of models are currently used at the cellular level and in animals with mutations in multiple ALS associated genes, including SOD1, C9ORF72, TDP-43, and FUS. Key mechanisms involved in the disease include excitotoxicity, oxidative stress, mitochondrial dysfunction, neuroinflammatory, and immune reactions. In addition, significant metabolism alterations of various lipids classes, including phospholipids, fatty acids, sphingolipids, and others have been increasingly recognized. Recently, the mechanisms of programmed cell death (apoptosis), which may be responsible for the degeneration of motor neurons observed in the disease, have been intensively studied. In this context, sphingolipids, which are the most important sources of secondary messengers transmitting signals for cell proliferation, differentiation, and apoptosis, are gaining increasing attention in the context of ALS pathogenesis given their role in the development of neuroinflammatory and immune responses. This review describes changes in lipids content and activity of enzymes involved in their metabolism in ALS, both summarizing current evidence from animal models and clinical studies and discussing the potential of new drugs among modulators of lipid metabolism enzymes.
Characterization and Quantification of Arsenic Species in Foodstuffs of Plant Origin by HPLC/ICP-MS
Authors: D’Amore Teresa , Miedico Oto , Pompa Ciro , Preite Chiara , Iammarino Marco , Nardelli Valeria
Published: February 2023

Arsenic is a well-known carcinogenic, mutagenic and toxic element and occurs in the environment both as inorganic arsenic (iAs) and organoarsenical compounds (oAsCs). Since the toxicity of arsenic compounds depends on their chemical form, the identification and determination of arsenic species are essential. Recently, the European Food Safety Authority, following the European Commission request, published a report on chronic dietary exposure to iAs and recommended the development and validation of analytical methods with adequate sensitivity and refined extraction procedures for this determination. Moreover, the authority called upon new arsenic speciation data for complex food matrices such as seaweeds, grains and grain-based products. Looking at this context, an optimized, sensitive and fast analytical method using high performance liquid chromatography followed by inductively coupled plasma—mass spectrometry (HPLC/ICP-MS) was developed for the determination of iAs (sum of arsenite—AsIII and arsenate—AsV) and the most relevant oAsCs, arsenobetaine, dimethylarsinic acid and monomethylarsonic acid. The method was validated with satisfactory results in terms of linearity, sensitivity, selectivity, precision, recovery, uncertainty, ruggedness and matrix effect, and then successfully applied for the analysis of several matrices, i.e., processed and unprocessed cereal and cereal products, fruits, vegetables, legumes, seaweeds, nuts and seeds. The results obtained indicate that not only seaweed and rice matrices but also many cereals, legumes and plant-based foods for infants and young children contain significant concentrations of iAs and oAsCs. These findings contribute to the data collection necessary to assess the role of these matrices in the total arsenic exposure and if specific maximum limits have to be established.
Landscapes of Biochemical Warfare: Spatial Self-Organization Woven from Allelopathic Interactions
Authors: Carvalho Sylvestre , Mota Henrique , Martins Marcelo
Published: February 2023

Evidence shows that diversity and spatial distributions of biological communities are largely driven by the race of living organisms in their adaptation to chemicals synthesized by their neighbors. In this report, the emergence of mathematical models on pure spatial self-organization induced by biochemical suppression (allelopathy) and competition between species were investigated through numerical analysis. For both random and patched initial spatial distributions of species, we demonstrate that warfare survivors are self-organized on the landscape in Turing-like patterns driven by diffusive instabilities of allelochemicals. These patterns are simple; either all species coexist at low diffusion rates or are massively extinct, except for a few at high diffusivities, but they are complex and biodiversity-sustained at intermediate diffusion rates. “Defensive alliances” and ecotones seem to be basic mechanisms that sustain great biodiversity in our hybrid cellular automata model. Moreover, species coexistence and extinction exhibit multi-stationarity.
Predictor Factors for the Detection of Positive Nodes in Patients Undergoing Radical Prostatectomy and Lymph Node Dissection
Authors: Muñoz Guillermo Victoria , Rosino Sánchez Antonio , Pardo Martínez Arancha , Barceló Bayonas Isabel , Carrillo George Carlos , Fernández Aparicio Tomás
Published: February 2023

Background: The detection of positive lymph nodes after a lymph node dissection changes the clinical prognosis; therefore, we evaluated what factors help us predict the presence of positive lymph nodes. Methods: A retrospective analysis of all radical prostatectomies and extended lymph node dissection performed from January 2010 to October 2018 in our centre was conducted. The variables included in the Briganti nomogram (preoperative PSA, Gleason biopsy, percentage of cores, and clinical stage) were considered, as well as perineural invasion and involvement of the seminal vesicles in the prostate biopsy; Results: A total of 110 lymph node dissections are obtained. Patient mean age is 64.18 years (46.55–75.91). Of the 110 lymphadenectomies performed, 16 patients (14.5%) presented positive nodes. Presenting infiltrated seminal vesicles, perineural invasion, higher PSA, higher clinical stage, higher Gleason biopsy and percentage of cores is more likely to have statistically significant lymph node involvement (p < 0.05). In the multivariate analysis, the percentage of positive core, together with the involvement of the seminal vesicles and Gleason ≥ 4 in the majority are predictor factors for positive nodes (p < 0.01); Conclusions: The percentage of positive cores, the involvement of the seminal vesicles, and the majority Gleason ≥ 4 are independent predictors of lymph node involvement in prostate cancer.
Interleaving Modulation Schemes in Asymmetrical Dual Three-Phase Machines for the DC-Link Stress Reduction
Authors: DeMarcos Ander , Robles Endika , Ugalde Unai , Martinez de Alegria Inigo , Andreu Jon
Published: February 2023

The DC-Link capacitor plays a crucial role as far as power density and reliability are concerned: it occupies approximately 40% of the inverter, and causes approximately 30% of its failures. Asymmetrical dual three-phase (ADTP) multiphase arrangements are gaining relevance in the automotive sector for powertrain applications. This work focuses on reducing the impact that the widely used double zero sequence injection (DZSI) family of PWM techniques have on such a bulky and failure-prone component in an ADTP arrangement by means of interleaving techniques. By using the double Fourier integral formalism, the input current spectra and the overall performance of these PWM techniques have been derived, in terms of current rms value and voltage ripple in the DC-Link capacitor. Simulations have shown that choosing an adequate interleaving scheme and angle considerably relieves both current and voltage stresses on the DC-Link capacitor compared to noninterleaved operation. Reductions of 84% current rms and 86% voltage ripple have been achieved at static operating points. Finally, by averaging the rms current over WLTP standard driving cycle, reductions up to 26% have been obtained under more realistic conditions. All this would enhance the reliability and reduce the size of the onboard capacitors in future electric vehicles.
Robot Navigation in Crowded Environments: A Reinforcement Learning Approach
Authors: Caruso Matteo , Regolin Enrico , Camerota Verdù Federico Julian , Russo Stefano Alberto , Bortolussi Luca , Seriani Stefano
Published: February 2023

For a mobile robot, navigation in a densely crowded space can be a challenging and sometimes impossible task, especially with traditional techniques. In this paper, we present a framework to train neural controllers for differential drive mobile robots that must safely navigate a crowded environment while trying to reach a target location. To learn the robot’s policy, we train a convolutional neural network using two Reinforcement Learning algorithms, Deep Q-Networks (DQN) and Asynchronous Advantage Actor Critic (A3C) and develop a training pipeline that allows to scale the process to several compute nodes. We show that the asynchronous training procedure in A3C can be leveraged to quickly train neural controllers and test them on a real robot in a crowded environment.
Structural Design and Theoretical Analysis of Jetting Spin Type Lotus Root Digging Machine Based on CFD
Authors: Wang Yingjie , Wu Hao , Jiang Yajun , Hu Zhigang
Published: February 2023

In order to solve the problem that digging lotus roots manually was high in labor intensity, low in efficiency and easy to damage lotus roots, and, in view of the defects of the high cost of existing digging lotus roots equipment and the cumbersome operating process needing a certain experience in technology, a jetting spin type digging lotus root machine is designed. Combined with the mechanism of hydraulic digging lotus root, a rotation pipeline is designed, so as to simplify the whole machine structure and increase the digging width. It takes only manpower or vehicles to push forward to perform digging lotus root work, with simple operation and low manufacturing cost. Pro/Engineer 3D design software and CAD 2D drawing software are used to design key working parts such as rotation pipeline, spray-head, nozzle, etc. In addition, key parameters of nozzle structure are designed. Through validation, the intensity of jet flow impact force produced by the diameter 17 mm nozzle to lotus root surface is 97.22 N, and the pressure is 0.20 MPa, which produces no damage to lotus root. By means of a mechanical analysis method, combined with CFD flow field analysis and fluid dynamics analysis, the rotational speed mathematical model of rotation pipeline in water is established through MATLAB software solving and calculating. In addition, the influence of nozzle structural parameters on rotational speed of rotation pipeline is made clear. By using the kinematic analysis method, the rotational speed mathematical model of rotation pipeline associated with the jetting impact frequency of single point and the time of each impact is established, and from which the restricting factors of the working speed of jetting spin type digging lotus root machine is obtained, so as to improve the efficiency of digging lotus roots machine under the premise of ensuring the digging depth and the quality of lotus roots. Through the CFD flow field analysis and dynamic analysis, the mathematical model of loss power of rotating pipeline is established. Through comprehensive analysis, the mathematical model of working speed associated with digging time and speed of rotary pipe and effective range radius of jet impact for digging lotus root machine is obtained, which provides a theoretical basis for adjustment of working parameters of digging lotus root machine.
Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes
Authors: Rodriguez Pablo Cosa , Marti-Puig Pere , Caiafa Cesar F. , Serra-Serra Moisès , Cusidó Jordi , Solé-Casals Jordi
Published: February 2023

Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are also responsible for the delivery of greenhouse gases. Industrial maintenance is a set of measures of a technical-organizational nature whose purpose is to sustain the functionality of the equipment and guarantee an optimal state of the machines over time, with the aim of saving costs, extending the useful life of the machines, saving energy, maximising production and availability, ensuring the quality of the product obtained, providing job security for technicians, preserving the environment, and reducing emissions as much as possible. Machine learning techniques can be used to detect or predict faults in wind turbines. However, labelled data suffers from many problems in this application because alarms are usually not clearly associated with a specific fault, some labels are wrongly associated with a problem, and the imbalance between labels is evident. To avoid using labelled data, we investigate here the use of the clustering technique, more specifically K-means, and boxplot representations of the variables for a set of six different tests. Experimental results show that in some cases, the clustering and boxplot techniques allow us to determine outliers or identify erroneous behaviours of the wind turbines. These cases can then be investigated in detail by a specialist so that more efficient predictive maintenance can be carried out.
Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach
Authors: Chen Ya-Ling , Cai Yan-Rou , Cheng Ming-Yang
Published: February 2023

This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety production, and has a high success rate in object grasping/pick-and-place tasks. The proposed approach consists of a computer vision-based object detection algorithm and a deep reinforcement learning algorithm with self-learning capability. In particular, the You Only Look Once (YOLO) algorithm is employed to detect and classify all objects of interest within the field of view of a camera. Based on the detection/localization and classification results provided by YOLO, the Soft Actor-Critic deep reinforcement learning algorithm is employed to provide a desired grasp pose for the robot manipulator (i.e., learning agent) to perform object grasping. In order to speed up the training process and reduce the cost of training data collection, this paper employs the Sim-to-Real technique so as to reduce the likelihood of damaging the robot manipulator due to improper actions during the training process. The V-REP platform is used to construct a simulation environment for training the deep reinforcement learning neural network. Several experiments have been conducted and experimental results indicate that the 6-DOF industrial manipulator successfully performs object grasping with the proposed approach, even for the case of previously unseen objects.
Substantiating and Implementing Concept of Digital Twins for Virtual Commissioning of Industrial Mechatronic Complexes Exemplified by Rolling Mill Coilers
Authors: Gasiyarov Vadim R. , Bovshik Pavel A. , Loginov Boris M. , Karandaev Alexander S. , Khramshin Vadim R. , Radionov Andrey A.
Published: February 2023

Implementing digital technologies and digital twins (DT) in operating industrial units is one of the key problems in smart production. Metallurgical plants require a solution to implement in the rolling mill retrofitting. A conceptual approach to the virtual configuration of industrial mechatronic complexes based on object-oriented digital twins-prototypes and twins-instances of electromechanical and hydraulic systems is justified. Alternative options for virtual commissioning (VC) were considered, and the Hardware-In-The-Loop (HIL) option was adopted, involving the implementation of DTs in the hardware and software of a programmable logic controller (PLC). Virtual models were built, and control algorithms were tested in the Matlab Simulink software; the prospect of using Simscape domains was noted. The paper’s contribution is the description of a methodology for the development of object-oriented DTs, interlinked in the process, exemplified by a rolling mill coiler. The results of the control over the ‘virtual reeling’ mode are provided; the adequacy of real and virtual processes is confirmed. The problem of developing DTs for a coiler group, solved herein, is a fragment of a large-scale scientific and practical problem of developing DTAs (digital twin aggregates) for interconnected mechatronic rolling mill complexes. The developed VC methodology and proposed DTs are recommended for the commercial implementation in various industrial units. Developing frameworks of mechatronic systems and multi-domain DTE (Digital Twin Environment) applications are shown as prospects for future research.
Off-Diagonal Magnetoimpedance in Annealed Amorphous Microwires with Positive Magnetostriction: Effect of External Stresses
Authors: Buznikov Nikita A.
Published: February 2023

It was observed recently that the giant magnetoimpedance (GMI) effect in Fe-rich glass-coated amorphous microwires with positive magnetostriction can be improved significantly by means of post-annealing. The increase in the GMI is attributed to the induced helical magnetic anisotropy in the surface layer of the microwire, which appears after the annealing. The application of external stresses to the microwire may result in changes in its magnetic structure and affect the GMI response. In this work, we study theoretically the influence of the tensile and torsional stresses on the off-diagonal magnetoimpedance in annealed amorphous microwires with positive magnetostriction. The static magnetization distribution is analyzed in terms of the core–shell magnetic structure. The surface impedance tensor is obtained taking into account the magnetoelastic anisotropy induced by the external stresses. It is shown that the off-diagonal magnetoimpedance response exhibits strong sensitivity to the magnitude of the applied stress. The obtained results may be useful for sensor applications of amorphous microwires.
Blockchain-Driven Real-Time Incentive Approach for Energy Management System
Authors: Kumari Aparna , Kakkar Riya , Gupta Rajesh , Agrawal Smita , Tanwar Sudeep , Alqahtani Fayez , Tolba Amr , Raboaca Maria Simona , Manea Daniela Lucia
Published: February 2023

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.
Modified DTS Iteration Methods for Spatial Fractional Diffusion Equations
Authors: Shao Xin-Hui , Kang Chong-Bo
Published: February 2023

For the discretized linear systems of the spatial fractional diffusion equations, we construct a class of a modified DTS iteration method and give its asymptotic convergence conditions. Then, we design a fast modified DTS preconditioner by replacing Toeplitz matrix T with the τ matrix to accelerate the convergence rates of GMRES method. Theoretically, we show that the spectrum of fast modified DTS preconditioned matrix is clustered around one. Numerical experiments verify the validity of the constructed fast modified DTS preconditioner for GMRES method.
Transfer Learning-Based Intelligent Fault Detection Approach for the Industrial Robotic System
Authors: Raouf Izaz , Kumar Prashant , Lee Hyewon , Kim Heung Soo
Published: February 2023

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on smart factories. In smart factories, robotic components are vulnerable to failure due to various industrial operations such as assembly, manufacturing, and product handling. Timely fault detection and diagnosis (FDD) is important to keep the industrial operation smooth. Previously, only the unloaded-based FDD algorithms were considered for the industrial robotic system. In the industrial environment, the robot is working under various working conditions such as speeds, loads, and motions. Hence, to reduce the domain discrepancy between the lab scale and the real working environment, we conducted experimentations under various working conditions. For that purpose, an extensive experimental setup is prepared to perform a series of various experiments mimicking the real environmental condition. In addition, in previous research work, various machine learning (ML) and deep learning (DL) approaches were proposed for robotic arm component fault detection. However, various issues are related to the DL and ML approaches. The ML models are problem-specific, and complex in computations. The DL model needs a huge amount of data. The DL model is composed of various layers that have not been thoroughly explored; as a result, the fault detection model lacks a comprehensive explanation. To overcome these issues, the transfer learning (TL) model is considered with the diverse experimental scenarios. The main contribution is to increase the generalization capabilities of the robotic PHM in the context of previously available research work. For that purpose, the VGG16 model is used because of its autonomous feature extractions for fault classification. The data are collected under a variety of different operating conditions such as loadings, speeds, and motion patterns. The 1D signal is converted to a 2D signal (scalogram) to perform the TL model. The proposed approach shows effective fault detection performance and has the capabilities of generalization under variable working conditions.
Mining Significant Utility Discriminative Patterns in Quantitative Databases
Authors: Tang Huijun , Wang Jufeng , Wang Le
Published: February 2023

Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations.