bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of the Spatial Distribution of Forces and Stresses on Wear Surfaces at Optimization of the Excavating Part of an Earthmoving Machine Transverse Profile
Authors: Kyznetsova Victoria N. , Savinkin Vitaliy V. , Ratushnaya Tatyana Yu. , Sandu Andrei Victor , Vizureanu Petrica
Published: February 2021

The relevance of this research lies in the need to develop a scientifically-grounded methodology for designing the optimal profile of the contact frontal surface of an energy-efficient earthmoving machine operating device, which ensures the redistribution of resistance forces along the cutting elements during the excavation of frozen soil when extracting hydrocarbons. The relevance is confirmed by the need to improve domestic methods for designing the profiles of effective operating devices, ensuring the excavation of frozen soils during the laying of oil and gas pipelines and geological exploration in permafrost areas. Increasing the energy efficiency and service life of highly loaded ripper teeth is done by optimizing the geometric profile and designing a spatial shape that provides minimal resistance to cutting the soil. The object of the research is the process of dynamic interaction of the ripper tooth cutting profile with the frozen soil environment. The research method used was kinematic analysis and force calculation. The cyclic process of dynamic loading of the ripper operating device was investigated. The methodology of system analysis, as well as the method of distribution of resistance forces over the geometric elements of the cutting edge, were used. The mathematical apparatus has been effectively applied to establish the dependences of the change in energy and power properties on performance indicators, soil rheology, and dynamic loads of the ripper tooth. The process of interaction of the ripper tooth tip with frozen soil was investigated. A method for substantiating the design parameters of the operating device by using Legendre polynomials was proposed. Separate calculation results subject to the introduced restrictions were presented for the condition for solving the problem. The optimal parameters of the transverse profile of the contact frontal surface of the earth-moving machine operating device were determined taking into account the coefficient of reducing the fracture resistance of frozen soils.
Deposition of Zinc Oxide Coatings on Wood Surfaces Using the Solution Precursor Plasma Spraying Process
Authors: Jnido Ghiath , Ohms Gisela , Viöl Wolfgang
Published: February 2021

In the present work, the solution precursor plasma spray (SPPS) process was used to deposit zinc oxide (ZnO) coatings on wood surfaces using zinc nitrate solution as precursor to improve the hydrophobicity and the color stability of European beech wood under exposure to ultraviolet (UV) light. The surface morphology and topography of the wood samples and the coatings were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The formation of ZnO was detected with the help of X-ray photoelectron spectroscopy (XPS) and by Fourier transform infrared (FTIR) spectroscopy. The FTIR spectra of the coated samples showed the typical Zn–O band at 445 cm−1. According to the XPS analysis, the coatings consist of two different Zn-containing species: ZnO and Zn(OH)2. Variation of the deposition parameters showed that the most significant parameters affecting the microstructure of the coating were the solution concentration, the deposition scan speed, and carrier gas flow rate. The wettability behaviors of the coated wood were evaluated by measuring the water contact angle (WCA). The coatings that completely covered the wood substrates showed hydrophobic behaviors. UV-protection of wood surfaces after an artificial UV light irradiation was evaluated by color measurements and FTIR spectroscopy. The ZnO-coated wood surfaces were more resistant to color change during UV radiation exposure. The total color change decreased up to 60%. Additionally, the FTIR spectra showed that the wood surfaces coated with ZnO had more stability. The carbonyl groups formation and C=C-bonds consumption were significantly lower.
Semiconducting Metal Oxides: SrTiO3, BaTiO3 and BaSrTiO3 in Gas-Sensing Applications: A Review
Authors: Szafraniak Bartłomiej , Fuśnik Łukasz , Xu Jie , Gao Feng , Brudnik Andrzej , Rydosz Artur
Published: February 2021

In this work, a broad overview in the field of strontium titanate (ST, SrTiO3)-, barium titanate (BT, BaTiO3)- and barium strontium titanate (BST, BaSrTiO3)-based gas sensors is presented and discussed. The above-mentioned materials are characterized by a perovskite structure with long-term stability and therefore are very promising materials for commercial gas-sensing applications. Within the last 20 years, the number of papers where ST, BT and BST materials were tested as gas-sensitive materials has ten times increased and therefore an actual review about them in this field has been expected by readers, who are researchers involved in gas-sensing applications and novel materials investigations, as well as industry research and development center members, who are constantly searching for gas-sensing materials exhibiting high 3S parameters (sensitivity, selectivity and stability) that can be adapted for commercial realizations. Finally, the NO2-sensing characteristics of the BST-based gas sensors deposited by the authors with the utilization of magnetron sputtering technology are presented.
An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models
Authors: Tsakalidis George , Georgoulakos Kostas , Paganias Dimitris , Vergidis Kostas
Published: February 2021

Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage.
Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator
Authors: Paolone Gaetanino , Paesani Romolo , Marinelli Martina , Di Felice Paolino
Published: February 2021

Many scholars have reported that the adoption of Model Driven Engineering (MDE) in the industry is still marginal. Real-life case studies, completed with convincing empirical data about the quality of the developed source code, is an effective way to persuade the industry that the adoption of MDE brings an actual added value. This paper reports about the assessment of the quality of the code outputted by xGenerator: a Java technology platform for the development of enterprise Web applications, which implements the MDE paradigm. Two recent papers from Aniche and his colleagues were selected to carry out the measurements. The former study is about metrics and thresholds for MVC Web applications, while the latter presents a catalog of six smells tailored to MVC Web applications. A big merit of both of these proposals is that they fix the metric thresholds by taking into account the MVC software architecture. The results of the empirical assessment, carried out on a real-life project, proved that the quality of the code is high.
Protein Crystal Instance Segmentation Based on Mask R-CNN
Authors: Qin Jiangping , Zhang Yan , Zhou Huan , Yu Feng , Sun Bo , Wang Qisheng
Published: February 2021

Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is introduced for the detection of protein crystals in this paper. Because the protein crystal image is greatly affected by backlight and precipitate, the contrast limit adaptive histogram equalization (CLAHE) is applied with Mask R-CNN. Meanwhile, the Transfer Learning method is used to optimize the parameters in Mask R-CNN. Through the comparison experiments between this combined algorithm and the original algorithm, it shows that the improved algorithm can effectively improve the accuracy of segmentation.
How Canadian Oncologists Use Oncotype DX for Treatment of Breast Cancer Patients
Authors: Zhu Xiaofu , Dent Susan , Paquet Lise , Zhang Tinghua , Tesolin Daniel , Graham Nadine , Aseyev Olexiy , Song Xinni
Published: February 2021

Background: The literature suggests that medical oncologists differ on how they use the Oncotype DX (ODX) genomic assay for making decisions about systemic therapy in breast cancer patients. Given the emergence of data supporting the use of genomic profiling for the prognosis and predicting benefit of chemotherapy, we surveyed medical oncologists in Canada to assess their usage and perception of the ODX assay. Methods: A 34-item survey was distributed to Canadian medical oncologists via the Canadian Association of Medical Oncologists. Data was collected on physician demographics, ODX usage patterns, and physicians’ perception of the impact clinical and pathologic characteristics make on ODX utilization. Results: Response rate was 20.6% with 47 responses received from 228 survey sent. Forty-five responses were eligible for analysis. Sixty-two percent (28/45) of respondents treated predominantly breast cancer, and 60% (27/45) have been in practice for at least 10 years. The most cited reason for using ODX was to avoid giving patients unnecessary chemotherapy (64%; 29/45). Sixty-seven percent (30/45) deferred making treatment decisions until ODX testing was completed. Factors most strongly impacting ODX utilization included: patient request, medical comorbidities and tumor grade. In clinical scenarios, ODX was more frequently selected for patients aged 40–65 (vs. <40 or >65), grade 2 tumors (vs. grade 1 or 3), and Ki-67 index of 10–20% (vs. <10% or >20%). Conclusions: This survey demonstrated that Canadian medical oncologists are preferentially using ODX to avoid giving patients unnecessary chemotherapy. The utilization of ODX is mainly in patients with intermediate clinical and pathologic features.
An Overview of the Helminths of Moor Frog Rana arvalis Nilsson, 1842 (Amphibia: Anura) in the Volga Basin
Authors: Chikhlyaev Igor V. , Ruchin Alexander B.
Published: February 2021

This is the first review of the helminth fauna of the moor frog Rana arvalis Nilsson, 1842 from the Volga river basin (Russia). The article summarizes the authors’ and literature data on the helminthic fauna of this species. The method of complete helminthological dissection was used. Thirthy-eight helminth species were recorded from three classes: Cestoda (1), Trematoda (28), and Chromadorea (9). Nine helminth species are new to the moor frog in Russia: trematodes Gorgodera varsoviensis Sinitzin, 1905, Strigea falconis Szidat, 1928, larvae, Neodiplostomum spathoides Dubois, 1937, larvae, Tylodelphys excavata (Rudolphi, 1803), larvae, Pharyngostomum cordatum (Diesing, 1850), larvae, Astiotrema monticelli Stossich, 1904, larvae and Encyclometra colubrimurorum (Rudolphi, 1819), larvae, nematodes Strongyloides spiralis Grabda-Kazubska, 1978 and Icosiella neglecta (Diesing, 1851). The cestode Spirometra erinacei (Rudolphi, 1918), larvae were observed of this amphibian species in the Volga basin for the first time. The nematodes Rhabdias bufonis, Oswaldocruzia filiformis, Cosmocerca ornata and the trematode Haplometra cylindracea form the core of the helminth fauna of the moor frog. Information on species of helminths includes systematic position, localization, areas of detection, type and scheme of life cycle, geographical distribution, and degree of specificity to host amphibians.
Flock Size Predicts Niche Breadth and Focal Wintering Regions for a Rapidly Declining Boreal-Breeding Passerine, the Rusty Blackbird
Authors: Evans Brian S. , Powell Luke L. , Demarest Dean W. , Borchert Sinéad M. , Greenberg Russell S.
Published: February 2021

Once exceptionally abundant, the Rusty Blackbird (Euphagus carolinus) has declined precipitously over at least the last century. The species breeds across the Boreal forest, where it is so thinly distributed across such remote areas that it is extremely challenging to monitor or research, hindering informed conservation. As such, we employed a targeted citizen science effort on the species’ wintering grounds in the more (human) populated southeast United States: the Rusty Blackbird Winter Blitz. Using a MaxEnt machine learning framework, we modeled patterns of occurrence of small, medium, and large flocks (<20, 20–99, and >99 individuals, respectively) in environmental space using both Blitz and eBird data. Our primary objective was to determine environmental variables that best predict Rusty Blackbird occurrence, with emphasis on (1) examining differences in key environmental predictors across flock sizes, (2) testing whether environmental niche breadth decreased with flock size, and (3) identifying regions with higher predicted occurrence (hotspots). The distribution of flocks varied across environmental predictors, with average minimum temperature (~2 °C for medium and large flocks) and proportional coverage of floodplain forest having the largest influence on occurrence. Environmental niche breadth decreased with increasing flock size, suggesting an increasingly restrictive range of environmental conditions capable of supporting larger flocks. We identified large hotspots in floodplain forests in the Lower Mississippi Alluvial Valley, the South Atlantic Coastal Plain, and the Black Belt Prairie.
Functional Diversity of Soil Nematodes in Relation to the Impact of Agriculture—A Review
Authors: Lazarova Stela , Coyne Danny , G. Rodríguez Mayra G. , Peteira Belkis , Ciancio Aurelio
Published: February 2021

The analysis of the functional diversity of soil nematodes requires detailed knowledge on theoretical aspects of the biodiversity–ecosystem functioning relationship in natural and managed terrestrial ecosystems. Basic approaches applied are reviewed, focusing on the impact and value of soil nematode diversity in crop production and on the most consistent external drivers affecting their stability. The role of nematode trophic guilds in two intensively cultivated crops are examined in more detail, as representative of agriculture from tropical/subtropical (banana) and temperate (apple) climates. The multiple facets of nematode network analysis, for management of multitrophic interactions and restoration purposes, represent complex tasks that require the integration of different interdisciplinary expertise. Understanding the evolutionary basis of nematode diversity at the field level, and its response to current changes, will help to explain the observed community shifts. Integrating approaches based on evolutionary biology, population genetics and ecology can quantify the contribution of nematode fauna to fundamental soil functions. These include carbon transformation, nutrient cycling, pest control and disease transmission. In conclusion, different facets of nematode diversity such as trophic groups, life history traits, variability in body size and/or taxa identities in combination with DNA-based techniques are needed in order to disclose nematode–soil–ecosystem functioning relationships. Further experimental studies are required to define locally adapted and sustainable management practices, through ecosystem-based approaches and nature-based solutions.
Using Blood Eosinophil Count as a Biomarker to Guide Corticosteroid Treatment for Chronic Obstructive Pulmonary Disease
Authors: Sivapalan Pradeesh , Bikov András , Jensen Jens-Ulrik
Published: February 2021

Treating patients hospitalised with acute exacerbations of chronic obstructive pulmonary disease (COPD) usually involves administering systemic corticosteroids. The many unwanted side effects associated with this treatment have led to increased interest in minimising the accumulated corticosteroid dose necessary to treat exacerbations. Studies have shown that short-term treatment with corticosteroids is preferred, and recent trials have shown that biomarkers can be used to further reduce exposure to corticosteroids. Interestingly, high eosinophil counts in patients with acute exacerbations of COPD are indicative of an eosinophilic phenotype with a distinct response to treatment with corticosteroids. In addition, post-hoc analysis of randomised control trials have shown that higher blood eosinophil counts at the start of the study predict a greater response to inhaled corticosteroids in stable COPD. In this review, we examine the studies on this topic, describe how blood eosinophil cell count may be used as a biomarker to guide treatment with corticosteroids, and identify some relevant challenges.
Interreader Reliability of Liver Imaging Reporting and Data System Treatment Response: A Systematic Review and Meta-Analysis
Authors: Kim Dong Wook , Choi Sang Hyun , Lee Ji Sung , Kim So Yeon , Lee So Jung , Byun Jae Ho
Published: February 2021

Background: For a proper management strategy in patients with locoregionally treated hepatocellular carcinoma (HCC), it is essential that the Liver Imaging Reporting and Data System (LI-RADS) treatment response algorithm (LR-TR) has high interreader reliability. We aimed to systematically evaluate the interreader reliability of LR-TR and sources of any study heterogeneity. Methods: Original studies reporting the interreader reliability of LR-TR were identified in MEDLINE and EMBASE up to 20 September 2020. The pooled kappa coefficient (κ) was calculated using the DerSimonian–Laird random effects model. Subgroup analyses were performed according to imaging modality (magnetic resonance imaging (MRI) or computed tomography (CT)). Meta-regression analyses were performed to explore study heterogeneity. Results: Eight studies with 851 HCCs were finally included. Pooled κ was 0.70 (95% CI, 0.58–0.82) for CT/MRI LR-TR, and those of MRI and CT were 0.71 (95% CI, 0.53–0.89) and 0.71 (95% CI, 0.65–0.78), respectively. Study design (p < 0.001) and type of treatment (p = 0.02) were significantly associated with substantial study heterogeneity. Conclusion: LR-TR showed substantial interreader reliability regardless of the imaging modality. Because of substantial study heterogeneity, which was significantly associated with study design and type of treatment, published values for the interreader reliability of LR-TR should be interpreted with care.
Role of Endoscopic Ultrasonography and Endoscopic Retrograde Cholangiopancreatography in the Diagnosis of Pancreatic Cancer
Authors: Ishii Yasutaka , Serikawa Masahiro , Tsuboi Tomofumi , Kawamura Ryota , Tsushima Ken , Nakamura Shinya , Hirano Tetsuro , Fukiage Ayami , Mori Takeshi , Ikemoto Juri , Kiyoshita Yusuke , Saeki Sho , Tamura Yosuke , Miyamoto Sayaka , Chayama Kazuaki
Published: February 2021

Pancreatic cancer has the poorest prognosis among all cancers, and early diagnosis is essential for improving the prognosis. Along with radiologic modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), endoscopic modalities play an important role in the diagnosis of pancreatic cancer. This review evaluates the roles of two of those modalities, endoscopic ultrasonography (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), in the diagnosis of pancreatic cancer. EUS can detect pancreatic cancer with higher sensitivity and has excellent sensitivity for the diagnosis of small pancreatic cancer that cannot be detected by other imaging modalities. EUS may be useful for the surveillance of pancreatic cancer in high-risk individuals. Contrast-enhanced EUS and EUS elastography are also useful for differentiating solid pancreatic tumors. In addition, EUS-guided fine needle aspiration shows excellent sensitivity and specificity, even for small pancreatic cancer, and is an essential examination method for the definitive pathological diagnosis and treatment decision strategy. On the other hand, ERCP is invasive and performed less frequently for the purpose of diagnosing pancreatic cancer. However, ERCP is essential in cases that require evaluation of pancreatic duct stricture that may be early pancreatic cancer or those that require differentiation from focal autoimmune pancreatitis.
Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine
Authors: Lahoura Vivek , Singh Harpreet , Aggarwal Ashutosh , Sharma Bhisham , Mohammed Mazin Abed , Damaševičius Robertas , Kadry Seifedine , Cengiz Korhan
Published: February 2021

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.
Endoscopic Ultrasound Quality Metrics in Clinical Practice
Authors: Ku Lawrence , Hou Linda A. , Eysselein Viktor E. , Reicher Sofiya
Published: February 2021

Recent advances in endoscopic ultrasound (EUS), particularly EUS-guided tissue acquisition, may have affected EUS procedural performance as measured by current American Society for Gastrointestinal Endoscopy (ASGE)/American College of Gastroenterology (ACG) quality indicators. Our study aims to assess how these quality metrics are met in clinical practice. We retrospectively analyzed 732 EUS procedures; data collected were procedural indications, technical aspects and outcomes, completeness of documentation, and malignancy staging. EUS was performed in 660 patients for a variety of indications. All ASGE/ACG EUS procedural quality metrics were met or exceeded. Intervention was successful in 97.7% (715/732) of cases, with complication rate of 0.4% (3/732). EUS outcomes changed clinical management in 58.7% of all cases and in 91.2% of malignancy work-up cases; in 26.0% of suspected choledocholithiasis cases, endoscopic retrograde cholangiopancreatography (ERCP) was avoided after EUS. Locoregional EUS staging was accurate in 61/65 (93.8%) cases of non-metastatic disease and in 15/22 (68.2%) cases of metastatic disease. Pancreatic mass malignancy detection rate with EUS-guided fine needle aspiration (FNA) or fine needle biopsy (FNB) was 75.8%, with a sensitivity of 96.2%; a significant increase in detection rate from 46.2% (6/13) to 95.0% (19/20) (p = 0.0026) was observed with a transition to the predominant use of FNB for tissue acquisition. All ASGE/ACG EUS quality metrics were met or exceeded for EUS procedures performed for a wide variety of indications in a diverse patient population. EUS was instrumental in changing clinical management, with a low complication rate. The malignancy detection rate in pancreatic masses significantly increased with FNB use.
Impact of Lymphovascular Invasion on Prognosis in the Patients with Bladder Cancer—Comparison of Transurethral Resection and Radical Cystectomy
Authors: Yoneda Kei , Kamiya Naoto , Utsumi Takanobu , Wakai Ken , Oka Ryo , Endo Takumi , Yano Masashi , Hiruta Nobuyuki , Ichikawa Tomohiko , Suzuki Hiroyoshi
Published: February 2021

(1) Background: This study aimed to evaluate the associations of lymphovascular invasion (LVI) at first transurethral resection of bladder (TURBT) and radical cystectomy (RC) with survival outcomes, and to evaluate the concordance between LVI at first TURBT and RC. (2) Methods: We analyzed 216 patients who underwent first TURBT and 64 patients who underwent RC at Toho University Sakura Medical Center. (3) Results: LVI was identified in 22.7% of patients who underwent first TURBT, and in 32.8% of patients who underwent RC. Univariate analysis identified ≥cT3, metastasis and LVI at first TURBT as factors significantly associated with overall survival (OS) and cancer-specific survival (CSS). Multivariate analysis identified metastasis (hazard ratio (HR) 6.560, p = 0.009) and LVI at first TURBT (HR 9.205, p = 0.003) as significant predictors of CSS. On the other hand, in patients who underwent RC, ≥pT3, presence of G3 and LVI was significantly associated with OS and CSS in univariate analysis. Multivariate analysis identified inclusion of G3 as a significant predictor of OS and CSS. The concordance rate between LVI at first TURBT and RC was 48.0%. Patients with positive results for LVI at first TURBT and RC displayed poorer prognosis than other patients (p < 0.05). (4) Conclusions: We found that the combination of LVI at first TURBT and RC was likely to provide a more significant prognostic factor.
Measuring and Controlling Bias for Some Bayesian Inferences and the Relation to Frequentist Criteria
Authors: Evans Michael , Guo Yang
Published: February 2021

A common concern with Bayesian methodology in scientific contexts is that inferences can be heavily influenced by subjective biases. As presented here, there are two types of bias for some quantity of interest: bias against and bias in favor. Based upon the principle of evidence, it is shown how to measure and control these biases for both hypothesis assessment and estimation problems. Optimality results are established for the principle of evidence as the basis of the approach to these problems. A close relationship is established between measuring bias in Bayesian inferences and frequentist properties that hold for any proper prior. This leads to a possible resolution to an apparent conflict between these approaches to statistical reasoning. Frequentism is seen as establishing figures of merit for a statistical study, while Bayes determines the inferences based upon statistical evidence.
Linkage between Leaders’ Behaviour in Performance Management, Organisational Justice and Work Engagement in Public Sector
Authors: Stankevičiūtė Živilė , Savanevičienė Asta
Published: February 2021

In the last decade, leaders’ behaviour in performance management has been gaining increasing attention, arguing that it is beneficial in terms of improved employee attitudes, behaviour, and performance in the public sector. However, empirical support for such claim is still scant. Given the relevance of work engagement and organisational justice in the public sector and acknowledging a worldwide employee engagement crisis, the paper aims at revealing the linkage between leaders’ behaviour in performance management, organisational justice, and employee engagement in the public sector. In doing this, quantitative data were collected in a survey from employees working in the public sector in Lithuania (299 responses). The findings showed that goal setting and feedback had a significant and positive effect on employee engagement, supporting the theoretical notion that leaders’ behaviour in performance management was crucial in engaging people. As it was expected, goal setting and feedback had a positive effect on organisational justice; meanwhile, organisational justice significantly and positively predicted employee engagement. Turning to the mechanism by which leaders’ behaviour influences work engagement, it seems that organisational justice partly mediated the relationships between goal setting and employee engagement and fully mediated the relationships between feedback and work engagement. These findings affirm that public sector should strive for improving the leaders’ behaviour in performance management as it in turn might impact overall organisational performance.
Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms
Authors: Consiglio Arianna , Casalino Gabriella , Castellano Giovanna , Grillo Giorgio , Perlino Elda , Vessio Gennaro , Licciulli Flavio
Published: February 2021

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.
An Industrial Quadrotor UAV Control Method Based on Fuzzy Adaptive Linear Active Disturbance Rejection Control
Authors: Sun Changhao , Liu Mengqi , Liu Chang’an , Feng Xueling , Wu Hua
Published: February 2021

In this paper, a fuzzy adaptive linear active disturbance rejection control (Fuzzy-LADRC) is proposed for strong coupling and nonlinear quadrotor unmanned aerial vehicle (UAV). At present, UAV conveys new opportunities in the industry, such as power line inspection, petroleum conduit patrolling, and defects detection for the wind turbine, because of its advantages in flexibility, high efficiency, and economy. Usually, the scene of the UAV mission has a high risk, and there are internal sensor noise and unknown external disturbance. Thus, the attitude stability and anti-interference ability of UAV are especially essential. To solve the strong coupling problem of UAV, the dynamics model of UAV is established via the Newton-Euler method, and the coupling part of dynamics is modeled as an internal disturbance. According to the function of linear active disturbance rejection control (LADRC) parameters, a Fuzzy-LADRC is proposed to improve the dynamic performance of the system. The proposed control method makes full use of the adaptive ability of the fuzzy controller and the anti-interference ability of LADRC to the nonlinear and strong coupling systems. As we know, this is the first time that Fuzzy-LADRC has been used in UAV control. In the simulation, the performance indicators of four controllers, including Fuzzy-LADRC, LADRC, PID, and Fuzzy-PID are compared and analyzed. The results indicate that the average response speed of Fuzzy-LADRC is 12.65% faster than LADRC, and it is 29.25% faster than PID. The average overshoot of Fuzzy-LADRC is 17% less than LADRC and 77.75% less than PID. The proposed control method can significantly improve the response speed and anti-interference ability of UAV.
Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications
Authors: Roupin Olivier , Fradet Matthieu , Baillard Caroline , Moreau Guillaume
Published: February 2021

Precise knowledge of the real environment is a prerequisite for the integration of the real and virtual worlds in mixed-reality applications. However, real-time updating of a real environment model is a costly and difficult process; therefore, hybrid approaches have been developed: An updated world model can be inferred from an offline acquisition of the 3D world, which is then updated online using live image sequences under the condition of developing fast and robust change detection algorithms. Current algorithms are biased toward object insertion and often fail in object removal detection; in an environment where there is uniformity in the background—in color and intensity—the disappearances of foreground objects between the 3D scan of a scene and the capture of several new pictures of said scene are difficult to detect. The novelty of our approach is that we circumvent this issue by focusing on areas of least change in parts of the scene that should be occluded by the foreground. Through experimentation on realistic datasets, we show that this approach results in better detection and localization of removed objects. This technique can be paired with an insertion detection algorithm to provide a complete change detection framework.
The Vulnerability of the Production Line Using Industrial IoT Systems under DDoS Attack
Authors: Horak Tibor , Strelec Peter , Huraj Ladislav , Tanuska Pavol , Vaclavova Andrea , Kebisek Michal
Published: February 2021

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.
Highly Reliable MAC Protocol Based on Associative Acknowledgement for Vehicular Network
Authors: Urmonov Odilbek , Kim HyungWon
Published: February 2021

IEEE 1609/802.11p standard obligates each vehicle to broadcast a periodic basic safety message (BSM). The BSM message comprises a positional and kinematic information of a transmitting vehicle. It also contains emergency information that is to be delivered to all the target receivers. In broadcast communication, however, existing carrier sense multiple access (CSMA) medium access control (MAC) protocol cannot guarantee a high reliability as it suffers from two chronic problems, namely, access collision and hidden terminal interference. To resolve these problems of CSMA MAC, we propose a novel enhancement algorithm called a neighbor association-based MAC (NA-MAC) protocol. NA-MAC utilizes a time division multiple access (TDMA) to distribute channel resource into short time-intervals called slots. Each slot is further divided into three parts to conduct channel sensing, slot acquisition, and data transmission. To avoid a duplicate slot allocation among multiple vehicles, NA-MAC introduces a three-way handshake process during slot acquisition. Our simulation results revealed that NA-MAC improved packet reception ratio (PRR) by 19% and successful transmission by 30% over the reference protocols. In addition, NA-MAC reduced the packet collisions by a factor of 4. Using the real on-board units (OBUs), we conducted an experiment where our protocol outperformed in terms of PRR and average transmission interval by 82% and 49%, respectively.
An Improved Multi-Exposure Image Fusion Method for Intelligent Transportation System
Authors: Gao Mingyu , Wang Junfan , Chen Yi , Du Chenjie , Chen Chao , Zeng Yu
Published: February 2021

In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Considering the weather factor and environmental noise, the source image is preprocessed by bilateral filtering and dehazing algorithm. Further, this paper uses adaptive optimization to improve the quality of the output image of the fusion model. The qualitative and quantitative experiments on the new dataset show that the multi-exposure image fusion algorithm proposed in this paper is effective and practical in the ITS.
A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle
Authors: Jabbarpour Mohammad Reza , Saghiri Ali Mohammad , Sookhak Mehdi
Published: February 2021

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.
Development and Realization of an Experimental Bench Test for Synchronized Small Angle Light Scattering and Biaxial Traction Analysis of Tissues
Authors: Vignali Emanuele , Gasparotti Emanuele , Landini Luigi , Celi Simona
Published: February 2021

Insights into the mechanical and microstructural status of biological soft tissues are fundamental in analyzing diseases. Biaxial traction is the gold standard approach for mechanical characterization. The state of the art methods for microstructural assessment have different advantages and drawbacks. Small angle light scattering (SALS) represents a valuable low energy technique for soft tissue assessment. The objective of the current work was to develop a bench test integrating mechanical and microstructural characterization capabilities for tissue specimens. The setup’s principle is based on the integration of biaxial traction and SALS analysis. A dedicated control application was developed with the objective of managing the test procedure. The different components of the setup are described and discussed, both in terms of hardware and software. The realization of the system and the corresponding performances are then presented.
An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm
Authors: Xiang Yongbing , Yang Xiaomin
Published: February 2021

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.
Impact of Clean Energy on CO2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries
Authors: Piłatowska Mariola , Geise Andrzej
Published: February 2021

This study explores the impact of clean energy and non-renewable energy consumption on CO2 emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO2 emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.
Optimal Power Investment and Pandemics: A Micro-Economic Analysis
Authors: Detemple Jerome , Kitapbayev Yerkin
Published: February 2021

This paper derives the optimal investment policy of an electricity producer during a pandemic. We consider three problems: (1) investing in a gas-fired plant, (2) investing in a wind plant, and (3) investing in the best of a gas plant and a wind plant. Optimal investment boundaries are characterized and valuation formulas derived. For single technology projects, a pandemic postpones wind investment, but can accelerate gas investment when the relative price of gas decreases. For choices between the two technologies, a substitution effect can reinforce the single technology effects, accelerating gas investment under certain conditions. The paper examines the impact of pandemic parameters, economic parameters and policy parameters on the investment boundaries, the values of projects and the premium for green energy.
Dynamic Performance of Planetary Gear Joint for Satellite Antenna Driving Mechanism Considering Multi-Clearance Coupling
Authors: Han Jianchao , Liang Lei , Zhao Yang
Published: February 2021

Dynamic pointing and tracking accuracy are the most relevant indicators of dynamic performance for the satellite antenna driving mechanism. Multi-clearance coupling in the joints will incur high-frequency vibration and dynamic errors of the system. Joints of existing analytical models are generally oversimplified as planar revolute hinges, which ignore the coupling effect of multi-clearance. It cannot proficiently predict the dynamic behavior of the driving mechanism with multi-clearance on the orbit. To address this problem, a typical 2K-H planetary gear joint model with multi-clearance coupling has been developed by considering radial clearance, backlash, tooth profile error, time-varying meshing stiffness, and damping. A dynamic model of a typical dual-axis driving mechanism is established to analyze the dynamic characteristics of multibody systems with planetary gear joints. The effects of rotational speed, radial clearance, backlash, and their coupling on the dynamic performance of the dual-axis driving mechanism under different driving modes are explored by numerical simulations. The results show that the coupling of radial clearance and backlash in joints have a significant influence on the dynamic performance of the system. Appropriate clearance design avails the dynamic pointing accuracy and tracking accuracy of the dual-axis driving mechanism.