bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of Anti-Stripping Measures to Improve the Adhesion of Asphalt to Granite Aggregates
Authors: Shu Shiqi , Zhuang Chuanyi , Zhao Shuqian , Hao Yan , Guo Hao , Ye Yali
Published: December 2022

Granite is very widely distributed in the world, but granite is an acidic aggregate with poor adhesion to asphalt. In untreated asphalt mixtures, asphalt pavement water damage and loose disease are more prominent, affecting the service life of the pavement and service level. Enhancing the road performance of granite asphalt mixture generally improves the adhesion properties of asphalt and granite. Qingchuan rock asphalt, anti-stripping agent KH5, aliphatic amine anti-stripping agent AJ-1 and two groups of the composite anti-stripping agents were used to modify the asphalt and analyze the effect of different anti-stripping agents on the improvement of asphalt-granite aggregate adhesion properties based on the surface free energy (SFE) theory. Second, the effect of anti-stripping agents on the rheological properties of asphalt were analyzed by rheological tests, and the modification effect of granite asphalt mixture road performance was evaluated by Marshall tests. The aging of asphalt and asphalt mixture was simulated by thermal oxygen aging, and the durability of different anti-stripping agents was investigated by comparing the performance of asphalt and asphalt mixtures before and after aging. The results showed that Qingchuan rock asphalt can effectively enhance the high-temperature stability and anti-aging properties of asphalt mixes, but the low temperature performance was relatively poor. KH5 and AJ-1 can make up for the lack of low temperature performance of rock asphalt, but the water stability of asphalt mixes decreases after aging. All five groups of anti-stripping agents improve the adhesion of granite-asphalt and the water stability of the asphalt mixture to some extent. Considering the aging effect, the order of granite-asphalt mixture water stability is: KH/RAMA > AJ/RAMA > RAMA > KHMA > AJMA.
Functional Parametric Elasto-Dynamics for Efficient Multicomponent Design
Authors: Wu Jiajun , Ghnatios Chady , Mordillat Philippe , Tourbier Yves , Chinesta Francisco
Published: December 2022

In industrial settings, engineering products are often divided into separate components for detailed conception. They often require iterative corrections between different designers/teams to optimize the final product with all components assembled into a system. This article proposes a surrogate modeling approach with functional descriptions of parts in the model and aims to accelerate the design and optimization phase in real projects. The approach is applied to a vibration problem of a two-component plate structure, where the model estimates the dynamic behavior of the assembled system when only the properties of each individual part are available. A database is built using high-fidelity numerical simulations, and neural-network-based regressions provide reliable predictions on unseen data.
Effect of Growth Temperature on Crystallization of Ge1−xSnx Films by Magnetron Sputtering
Authors: Huang Hongjuan , Zhao Desheng , Qi Chengjian , Huang Jingfa , Zeng Zhongming , Zhang Baoshun , Lu Shulong
Published: December 2022

Ge1−xSnx film with Sn content (at%) as high as 13% was grown on Si (100) substrate with Ge buffer layer by magnetron sputtering epitaxy. According to the analysis of HRXRD and Raman spectrum, the quality of the Ge1−xSnx crystal was strongly dependent on the growth temperature. Among them, the GeSn (400) diffraction peak of the Ge1−xSnx film grown at 240 °C was the lowest, which is consistent with the Raman result. According to the transmission electron microscope image, some dislocations appeared at the interface between the Ge buffer layer and the Si substrate due to the large lattice mismatch, but a highly ordered atomic arrangement was observed at the interface between the Ge buffer layer and the Ge1−xSnx layer. The Ge1−xSnx film prepared by magnetron sputtering is expected to be a cost-effective fabrication method for Si-based infrared devices.
Immunotherapy Use Prior to Liver Transplant in Patients with Hepatocellular Carcinoma
Authors: Woo Stephanie M. , Kimchy Alexandra V. , Sequeira Lynette M. , Dorris Charles S. , He Aiwu R. , Rangnekar Amol S.
Published: December 2022

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related mortality worldwide, and its incidence has increased rapidly in the United States over the past two decades. Liver transplant is considered curative, but is not always possible, and pre-transplant immunotherapy is of great interest as a modality for downstaging the tumor burden. We present a review of the literature on pre-liver transplant immunotherapy use in patients with HCC. Our literature search queried publications in Ovid MEDLINE, Ovid Embase, and Web of Science, and ultimately identified 24 original research publications to be included for analysis. We found that the role of PD-1 and PD-L1 in risk stratification for rejection is of special interest to researchers, and ongoing randomized clinical trials PLENTY and Dulect 2020-1 will provide insight into the role of PD-1 and PD-L1 in liver transplant management in the future. This literature search and the resulting review represents the most thorough collection, analysis, and presentation of the literature on the subject to date.
Thermal Tolerance of Cyclops bohater (Crustacea: Copepoda); Selection of Optimal and Avoided Conditions in Experimental Conditions
Authors: Lazareva Valentina , Mayor Tatyana , Malysheva Olga , Medyantseva Elena , Zhdanova Svetlana , Grishanin Andrey , Verbitsky Vladimir
Published: December 2022

Temperature adaptations of ectothermic species as well as the plasticity of their thermal strategies are important for survival during temperature fluctuations, in particular, caused by global warming. The critical thermal maximum (CTM)—the values of the water temperature at which heat shock was noted (loss of motor activity in case of copepods) was determined under laboratory conditions. The “chronic” method was used to identify the temperature preferences of the copepods in which a group of test organisms are placed into a thermogradient apparatus. The main result is that in the experiment for individuals of the summer generation C. bohater, the optimal thermal conditions (FTP) were within 6–11 °C. Summer generation of this copepod in natural water bodies develops at a temperature of 5–12 °C, which is close to FTP in the experiment. At the same time, the thermal resistance of C. bohater (CTM 31.5 °C) was found to be the lowest among the species of the genus Cyclops. The differences between the thermal preferences of the winter and summer generations expand the temperature of normal performance (TNP) range and indicate a high physiological plasticity of the C. bohater population. This property is likely to allow C. bohater to survive as the climate continues to warm.
How Many Abalone Species Live in the Mediterranean Sea?
Authors: Chiappa Giacomo , Fassio Giulia , Corso Andrea , Crocetta Fabio , Modica Maria Vittoria , Oliverio Marco
Published: December 2022

Morphological traits in Haliotidae may be highly variable and not consistently diagnostic for species identification, highlighting the need for an integrative approach to the taxonomy of the family, including genetic data. Four species of the genus Haliotis are currently reported for the Mediterranean Sea and the neighboring Atlantic Ocean: Haliotis tuberculata, the common European abalone with the widest Atlanto-Mediterranean range; Haliotis mykonosensis, from the Aegean, the Tyrrhenian, and the Adriatic; Haliotis stomatiaeformis, from Malta, Lampedusa, and southeastern Sicily; and the Lessepsian Haliotis pustulata, only known on the basis of few samples from the Levant. However, their taxonomic status still relies only on shell morphology. Here, sequences of two fragments of the mitochondrial molecular marker COI were obtained from 84 abalone specimens collected in the Mediterranean Sea and the neighboring Atlantic and analyzed in order to provide for the first time a genetic framework for species delimitation. This study’s results prove that H. mykonosensis is genetically identical to H. tuberculata, whereas H. stomatiaeformis is a distinct species, endemic to a restricted area of the southern Mediterranean Sea. Finally, Haliotis tuberculata coccinea from Macaronesia may deserve its status as a subspecies of H. tuberculata, with genetic signature of a limited gene flow found in specimens of the nominal subspecies (H. t. tuberculata) in both the Atlantic and the Mediterranean Sea.
Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma
Authors: Guo Yinghong , Wu Jiangfeng , Wang Yunlai , Jin Yun
Published: December 2022

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2− breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2− cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann–Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2− groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.
Imaging Modalities Used for Frameless and Fiducial-Less Deep Brain Stimulation: A Single Centre Exploratory Study among Parkinson’s Disease Cases
Authors: Krahulik David , Blazek Filip , Nevrly Martin , Otruba Pavel , Hrabalek Lumir , Kanovsky Petr , Valosek Jan
Published: December 2022

Deep brain stimulation (DBS) is a beneficial procedure for treating idiopathic Parkinson’s disease (PD), essential tremor, and dystonia. The authors describe their set of imaging modalities used for a frameless and fiducial-less method of DBS. CT and MRI scans are obtained preoperatively, and STN parcellation is done based on diffusion tractography. During the surgery, an intraoperative cone-beam computed tomography scan is obtained and merged with the preoperatively-acquired images to place electrodes using a frameless and fiducial-less system. Accuracy is evaluated prospectively. The described sequence of imaging methods shows excellent accuracy compared to the frame-based techniques.
Pathogenic Copy Number Variations Involved in the Genetic Etiology of Syndromic and Non-Syndromic Intellectual Disability—Data from a Romanian Cohort
Authors: Streață Ioana , Caramizaru Alexandru , Riza Anca-Lelia , Șerban-Sosoi Simona , Pîrvu Andrei , Cara Monica-Laura , Cucu Mihai-Gabriel , Dobrescu Amelia Mihaela ,   Ro-NMCA-ID Group,   CExBR Pediatric Neurology Obregia Group,   CExBR Pediatric Neurology “V. Gomoiu” Hospital Group, Shelby Elena-Silvia , Albeanu Adriana , Burada Florin , Ioana Mihai
Published: December 2022

The investigation of unexplained global developmental delay (GDD)/intellectual disability (ID) is challenging. In low resource settings, patients may not follow a standardized diagnostic process that makes use of the benefits of advanced technologies. Our study aims to explore the contribution of chromosome microarray analysis (CMA) in identifying the genetic etiology of GDD/ID. A total of 371 Romanian patients with syndromic or non-syndromic GDD/ID, without epilepsy, were routinely evaluated in tertiary clinics. A total of 234 males (63.07%) and 137 (36.93%) females, with ages ranging from 6 months to 40 years (median age of 5.5 years), were referred for genetic diagnosis between 2015 and 2022; testing options included CMA and/or karyotyping. Agilent Technologies and Oxford Gene Technology CMA workflows were used. Pathogenic/likely pathogenic copy number variations (pCNVs) were identified in 79 patients (21.29%). Diagnosis yield was comparable between mild ID (17.05%, 22/129) and moderate/severe ID 23.55% (57/242). Higher rates were found in cases where facial dysmorphism (22.97%, 71/309), autism spectrum disorder (ASD) (19.11%, 26/136) and finger anomalies (20%, 27/96) were associated with GDD/ID. GDD/ID plus multiple congenital anomalies (MCA) account for the highest detection rates at 27.42% (17/62). pCNVs represent a significant proportion of the genetic causes of GDD/ID. Our study confirms the utility of CMA in assessing GDD/ID with an uncertain etiology, especially in patients with associated comorbidities.
Sudden Sensorineural Hearing Loss in the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Authors: Frosolini Andrea , Franz Leonardo , Daloiso Antonio , de Filippis Cosimo , Marioni Gino
Published: December 2022

Introduction: Accumulating data indicate that patients with COVID-19 could be affected by sudden sensorineural hearing loss (SSNHL). The aim of the study was to analyze the epidemiological and clinical trend of SSNHL occurrence during the COVID-19 pandemic by applying a systematic review and meta-analysis approach. Methods: PubMed, Scopus, Web of Science, ScienceDirect, and Cochrane databases were searched. Results: The seven included studies had adequate relevance to the topic and the quality was fair. The mean age at SSNHL onset ranged from 39.23 to 62.18 years during the pandemic year period (PYP); a meta-analysis of four studies comparing these data with those of previous periods in the same institutions found a younger age during the PYP (pooled mean −0.2848). The heterogeneity was high (76.1935%) and no frank asymmetry was observed in the funnel plot. The SARS-CoV-2 positivity rate of SSNHL patients ranged from 0% to 57.53%. Standard steroid treatments were applied without significant adverse effects. Comprehensively, hearing improvement was achieved for more than half of the cases. No studies reported long-term follow-up data. Conclusions: Further prospective analyses on large series and a long-term follow up on COVID-related SSNHL cases are necessary to address the open questions regarding the causative link between COVID-19 infection and SSNHL.
Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms
Authors: Papageorgiou Ismini , Valous Nektarios A. , Hadjidemetriou Stathis , Teichgräber Ulf , Malich Ansgar
Published: December 2022

Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0–180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov–Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies.
Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis
Authors: Liu Peiling , Chai Jing , Dai Liyi , Chen Beidi , Zhao Jinxia , Lu Ming , Zeng Lin , Xia Zhiwei , Mu Rong
Published: December 2022

Objective. Esophageal dysmotility is a common and neglected complication of systemic sclerosis (SSc) associated with poor prognosis, while the assessment remains a challenge. We aimed to develop a diagnostic model for esophageal dysmotility in SSc patients that provides individualized risk estimates. Methods. Seventy-five SSc patients who underwent high-resolution manometry (HRM) were included in the study. Esophageal widest diameter (WED) was measured on a chest CT scan. Esophageal parameters between patients with and without esophageal dysmotility were compared. Multivariate logistic regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to fit the model. The diagnostic model was evaluated by discrimination and calibration. Internal validation was estimated using the enhanced bootstrap method with 1000 repetitions. Results. Sixty-one systemic sclerosis patients (81.3%) were diagnosed with esophageal dysmotility according to the Chicago Classification v 3.0. The diagnostic model for evaluating the probability of esophageal dysmotility integrated clinical and imaging features, including disease duration, ILD, and WED. The model displayed good discrimination with an area under the curve (AUC) of 0.923 (95% CI: 0.837–1.000), a Brier score of 0.083, and good calibration. A high AUC value of 0.911 could still be achieved in the internal validation. Conclusion. The diagnostic model, which combines the disease duration, ILD, and imaging feature (WED), is an effective and noninvasive method for predicting esophageal dysmotility in SSc patients.
Texture Parameters Measured by UHF-MRI and CT Scan Provide Information on Bone Quality in Addition to BMD: A Biomechanical Ex Vivo Study
Authors: Knoepflin Paul , Pithioux Martine , Bendahan David , Poullain François , Le Corroller Thomas , Fabre Cyprien , Pauly Vanessa , Creze Maud , Soldati Enrico , Champsaur Pierre , Guenoun Daphne
Published: December 2022

The current definition of osteoporosis includes alteration of bone quality. The assessment of bone quality is improved by the development of new texture analysis softwares. Our objectives were to assess if proximal femoral trabecular bone texture measured in Ultra high field (UHF) 7 Tesla MRI and CT scan were related to biomechanical parameters, and if the combination of texture parameters and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry provided a better prediction of femoral failure than aBMD alone. The aBMD of 16 proximal femur ends from eight cadavers were investigated. Nineteen textural parameters were computed in three regions or volumes of interest for each specimen on UHF MRI and CT scan. Then, the corresponding failure load and failure stress were calculated thanks to mechanical compression test. aBMD was not correlated to failure load (R2 = 0.206) and stress (R2 = 0.153). The failure load was significantly correlated with ten parameters in the greater trochanter using UHF MRI, and with one parameter in the neck and the greater trochanter using CT scan. Eight parameters in the greater trochanter using UHF MRI combined with aBMD improved the failure load prediction, and seven parameters improved the failure stress prediction. Our results suggest that textural parameters provide additional information on the fracture risk of the proximal femur when aBMD is not contributive.
Maxillary Sinus Dimensions in Skeletal Class I Chinese Population with Different Vertical Skeletal Patterns: A Cone-Beam Computed Tomography Study
Authors: Wang Jingyi , Zou Min , Syverson Abby , Zheng Zhong , Li Chenshuang
Published: December 2022

Due to the close relationship between the maxillary posterior teeth roots and the maxillary sinus floor, the maxillary sinus can significantly impact various dental treatments, including endodontic procedures and surgical apicectomy, periodontal flap surgery, surgical tooth extraction, dental implantation, and orthognathic surgeries. Specifically, in orthodontics, the location of the maxillary sinus floor may affect tooth movement and insertion of temporary anchorage devices (TADs). This study aims to evaluate the dimensions and location of the maxillary sinus in the Chinese orthodontic patient population with skeletal class I. Using cone-beam computed tomography (CBCT), the volumetric size, height, width, and depth of the sinus and the amount of alveolar bone below the sinus floor and buccal/palatal to the sinus wall were compared between patients of different genders and different vertical skeletal patterns. Unlike the previously reported skeletal class II population, the skeletal class I patients with different vertical patterns do not have significantly different size sinuses. On the other hand, males have larger maxillary sinuses in all parameters than females in the testing population. In addition, no significant correlation was noticed between the SN-MP angle and sinus dimensions or between the ANB angle and sinus dimensions. Nevertheless, the distance from the sinus floor to the alveolar bone crest is not correlated with skeletally sagittal or vertical parameters in females but negatively correlated with the skeletal sagittal parameter in males. In summary, different from the skeletal class II population, there is no significant difference in maxillary sinus size and location among different vertical skeletal patterns in the skeletal class I population. Compared to the skeletal class II population, a higher percentage of the skeletal class I population has an alveolar bone with less than 5 mm thickness, representing a narrowed safe zone of TADs placements.
Optimization Criteria and Efficiency of a Thermoelectric Generator
Authors: Juárez-Huerta V. H. , Sánchez-Salas N. , Chimal-Eguía J. C.
Published: December 2022

The efficiency of a thermoelectric generator model under maximum conditions is presented for two optimization criteria proposed under the context of finite-time thermodynamics, namely, the efficient power criterion and the Omega function, where this last function represents a trade-off between useful and lost energy. The results are compared with the performance of the device at maximum power output. A macroscopic thermoelectric generator (TEG) model with three possible sources of irreversibilities is considered: (i) the electric resistance R for the Joule heating, (ii) the thermal conductances Kh and Kc of the heat exchangers between the thermal baths and the TEG, and (iii) the internal thermal conductance K for heat leakage. In particular, two configurations of the macroscopic TEG are studied: the so-called exoreversible case and the endoreversible limit. It shows that for both TEG configurations, the efficiency at maximum Omega function is always greater than that obtained in conditions of maximum efficient power, and this in turn is greater than that of the maximum power regime.
Regional Wage Differences and Agglomeration Externalities: Micro Evidence from Thai Manufacturing Workers
Authors: Prasertsoong Nutchapon , Puttanapong Nattapong
Published: December 2022

The large and persistent wage gap between the Bangkok Metropolitan Region and its peripheries remains a major concern for policymakers and civil society. Theoretically, these regional disparities exist due to differences in workforce skills and the local characteristics of the regions. This study empirically investigates the sources of spatial wage disparity in Thailand using data sets from the Labor Force Survey, the Industrial Census, geospatial data, and satellite imagery for the years 2007, 2012, and 2017. The two-stage estimation method was applied, and the soil clay content was used as the instrumental variable for correcting endogeneity and variable bias omission. The results show that workers’ education and experience affect the wage differential. Other than individual skills, workers also benefited from the agglomeration externalities of large cities. Specifically, the effect of agglomeration externalities on wages in Thailand was found to be statistically significant. To overcome the paradox of a low urbanization rate and high urban primacy in Thailand, this study suggests the establishment of multiple regional cities that create high agglomeration externalities.
Income-Related Inequality in Health Care Utilization and Out-of-Pocket Payments in China: Evidence from a Longitudinal Household Survey from 2000 to 2015
Authors: Yang Miaoqing , Erreygers Guido
Published: December 2022

In recent decades, China has experienced rapid economic growth and rising health inequality. The government has introduced a nationwide health care reform aimed at achieving affordable and equitable basic health care for all. This paper investigates income-related inequality in health care utilization and out-of-pocket (OOP) payments and explores the underlying factors that drive the inequalities. Using data running from 2000 to 2015 and covering nine of thirty-one provinces in China, we calculate indices to measure income-related inequality and adopt a regression-based decomposition approach to explore the sources of inequality. We find pro-rich inequality in the use of preventive care and pro-poor inequality in the use of folk doctors. In addition, the better-off have preferential access to higher level hospitals, while the use of primary care facilities is more concentrated among the poor. The poor are also found to face a heavier financial burden since they tend to spend a larger share of their income on OOP payments. Education, employment and geographic regions all appear to contribute to the total inequality. Our results indicate that affordability remains a common barrier for the poor to access health care, and that the inequality is largely driven by socio-economic factors.
Embracing Thinking Diversity in Higher Education to Achieve a Lifelong Learning Culture
Authors: Mejía-Manzano Luis Alberto , Sirkis Gabriela , Rojas Juan-Carlos , Gallardo Katherina , Vázquez-Villegas Patricia , Camacho-Zuñiga Claudia , Membrillo-Hernández Jorge , Caratozzolo Patricia
Published: December 2022

The Fourth Industrial Revolution requires global, structural, and technological changes in all economic and production fields. The most apparent adaptation requirements fall on Generation Z individuals, who will have more and more responsibilities within organizations. The international reports of the World Economic Forum and the Organization for Economic Co-operation and Development indicate that this training will mainly require a strong development of thinking diversity and lifelong learning skills. This study presents an analysis of the education in these future skills that these individuals will need to work successfully throughout their careers, taking into account the evolution of conditions toward the changes expected in the following years. The methodology used in this research belongs to the field of futures studies. Through strategies in the field of foresight, various scenarios are proposed that involve the expected changes in the short, medium, and long term, both in higher education institutions and in industries and companies, to ensure the adaptation of Generation Z individuals to changes in work, workplace, and workforce. The findings show the strong influence that other skills and attitudes, such as self-esteem, self-efficacy, initiative, emotions, and motivation, have on the capacity for self-managed development throughout professional life.
Smart Random Walk Distributed Secured Edge Algorithm Using Multi-Regression for Green Network
Authors: Saba Tanzila , Haseeb Khalid , Rehman Amjad , Damaševičius Robertas , Bahaj Saeed Ali
Published: December 2022

Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes’ batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes’ communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work.
A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security
Authors: Rabie Osama , Balachandran Praveen , Khojah Mohammed , Selvarajan Shitharth
Published: December 2022

Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems.
Fabrication and Characterization of Si/PEDOT: PSS-Based Heterojunction Solar Cells
Authors: Venkatesan Ragavendran , Sheik Kadar Maideen Sheik Moideen Thaha , Chandhiran Saravanan , Kushvaha Sunil Singh , Sagadevan Suresh , Venkatachalapathy Vishnukanthan , Mayandi Jeyanthinath
Published: December 2022

In this study, we fabricated a planar Si/PEDOT: PSS heterojunction solar cell using three different solvents—ethylene glycol, acetonitrile, and dimethyl sulfoxide—to find the best one. The fabricated samples were characterized by diffuse reflectance spectroscopy, scanning electron microscopy, X-ray diffraction, and current–voltage. Diffused reflectance spectrum analysis showed reduced reflectance compared to the bare silicon wafers. The absorbance spectrum shows the change in absorption of the Si-coated PEDOT: PSS which was more than a 50% increase in the UV region, and for the EG sample, there was a 20% increase in the entire visible spectrum. This indicates that the solvent plays a major role in the bandgap between the Si and Si/PEDOT: PSS. Scanning electron microscope (SEM) was used to examine the surface morphology of Si/PEDOT: PSS as agglomerated, island-formed surfaces and carbon-layered Si-PEDOT: PSS. Cross-sectional images show the thickness of the PEDOT: PSS layer on the silicon wafer surface. The X-ray diffraction (XRD) pattern shows the characteristic peaks for silicon (69.5°), and Si/PEDOT: PSS shows a forbidden Si (200) peak at 32°. Current–voltage measurements have shown the characteristic diode curve for all fabricated cells. This characteristic diode curve indicated the presence of a heterojunction. Ethylene glycol-containing cells showed current output as 0.2 μA with Voc of 0.2 V.
Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment
Authors: Khan Abdur Rehman , Yasin Amanullah , Usman Syed Muhammad , Hussain Saddam , Khalid Shehzad , Ullah Syed Sajid
Published: December 2022

The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.
Multi-Static Multi-Band Synthetic Aperture Radar (SAR) Constellation Based on Integrated Photonic Circuits
Authors: Reza Manuel , Amir Malik Muhammad Haris , Imran Muhammad , Pandey Gaurav , Camponeschi Federico , Maresca Salvatore , Scotti Filippo , Serafino Giovanni , Malacarne Antonio , Porzi Claudio , Ghelfi Paolo , Bogoni Antonella , Scaffardi Mirco
Published: December 2022

Multi-static SARs from LEO orbits allow the single-pass high-resolution imaging and detection of moving targets. A coherent MIMO approach requires the generation of multi-band, thus orthogonal, signals, the fusion of which increases the system resolution. Up to now the synchronization capability of SAR signals of different satellites is critical. Here, we propose the use of photonics to generate, receive and distribute the radar signals in a coherent multi-static SAR constellation. Photonics overcomes issues in the implementation of MIMO SAR, allowing for the flexible generation of multi-band signals and centralized generation in a primary satellite with coherent distribution to all the secondary satellites of the SAR signals over FSO links. The numerical analysis shows the proposed system has a NESZ < −29.6 dB, satisfying the SAR system requirements. An experimental proof of concept based on COTS, for both signal up- and down-conversion, is implemented to demonstrate the system functionality, showing performance similar to the simulations. The implementation of the proposed systems with integrated technologies could reduce the system SWaP and increase robustness to vibrations. A design based on the consolidated SOI platform with the transfer printing-based hybrid integration of InP semiconductor optical amplifiers is proposed. The amplifiers compensate for the losses of the passive SOI waveguides, decreasing the overall conversion loss. The polarization multiplexing of the modulated and unmodulated combs to be sent from (to) the primary to (from) the secondary satellite over the FSO links avoids complex space-consuming optical filters requiring several control signals.
A Virtual Reality Whiteboard System for Remote Collaboration Using Natural Handwriting
Authors: Wang Jiangkun , Jing Lei
Published: December 2022

The COVID-19 pandemic has increased the significance of remote collaboration. This study proposes a virtual reality (VR) whiteboard system that enables remote collaboration among multiple participants using natural handwriting. In total, three experiments were conducted to investigate, respectively, collaboration efficiency, user experience, and system delay. First, we compared the collaboration efficiency of the traditional whiteboard, the electronic whiteboard, and the proposed virtual reality whiteboard in a series of controlled experiments. It was discovered that the VR whiteboard significantly improves collaboration efficiency in comparison to the mouse-based electronic whiteboard and is comparable to the traditional whiteboard. Second, we assessed the user experience with a survey scale (questionnaires). The subsequent results demonstrate that the VR whiteboard provides a superior user experience in terms of efficiency, usability, etc., compared to the traditional whiteboard. We also measured an end-to-end latency of approximately 115 milliseconds, which is sufficient for remote collaboration.
Modeling Stochastic Data Using Copulas for Applications in the Validation of Autonomous Driving
Authors: Lotto Katrin , Nagler Thomas , Radic Mladjan
Published: December 2022

The verification and validation processes of fully automated vehicles are linked to an almost intractable challenge of reflecting the real world with all its interactions in a virtual environment. Influential stochastic parameters need to be extracted from real-world measurements and real-time data, capturing all interdependencies, for an accurate simulation of reality. A copula is a probability model that represents a multivariate distribution, examining the dependence between the underlying variables. This model is used on drone measurement data from a roundabout containing dependent stochastic parameters. With the help of the copula model, samples are generated that reflect the real-time data. The resulting applications and possible extensions are discussed and explored.
Modeling of Electrodynamic Phenomena in an Ultra-Rapid Inductive–Dynamic Actuator as Applied to Hybrid Short-Circuit Breakers—A Review Study
Authors: Hallmann Damian , Jankowski Piotr , Mindykowski Janusz , Jakubiuk Kazimierz , Nowak Mikołaj , Woloszyn Mirosław
Published: December 2022

This article is a study of the research development of electrodynamic phenomena occurring in ultra-fast electrodynamic drives. These types of linear drives are among the fastest, not only because of the huge accelerations achieved, but, above all, because of the extremely short reaction time. For this reason, electrodynamic drives are used in hybrid short-circuit breakers. The phenomena occurring in this type of drive are actually magneto-thermo-elastic in nature, but the coupling of these phenomena should be considered weak if the criteria for repeatable operation in a hybrid circuit breaker system are met. The authors have been researching this type of drive for many years through not only experimental studies, but also primarily simulation studies developing models of such drives. The authors present the history of the development starting from the first works of Thomson, and Kesserling and ending with the most current models, including mainly their own. This article presents mainly works studying electrodynamic phenomena. Thermal and mechanical phenomena were comprehensively presented by the authors in previous papers.
Interaction Analysis among PMSG-Based Wind Turbines Based on Self- and En-Stabilizing Functions
Authors: Kui Meihan , Zheng Wanning
Published: December 2022

Sub-synchronous oscillation is one of the most challenging issues in modern power systems with a high proportion of wind power. Interactions among the wind turbines are key factors causing the oscillations. This paper aims to propose a method to quantify the interactions among N permanent-magnet-synchronous-generator (PMSG)-based wind turbines by transfer functions and analyze the influence of the interactions on the system stability. We derived different terms of the self- and en-stabilizing functions to describe different interactions caused by different wind turbines analytically. Meanwhile, the parameter sensitivity of this method is proposed to analyze the impact of the system and control parameters on the interactions and the system stability. Examples and simulations are given to show the effectiveness of this method.
Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models
Authors: Ryu Ju-Yeol , Lee Bora , Park Sungho , Hwang Seonghyeon , Park Hyemin , Lee Changhyeong , Kwon Dohyeon
Published: December 2022

The rising share of renewable energy in the energy mix brings with it new challenges such as power curtailment and lack of reliable large-scale energy grid. The forecasting of wind power generation for provision of flexibility, defined as the ability to absorb and manage fluctuations in the demand and supply by storing energy at times of surplus and releasing it when needed, is important. In this study, short-term forecasting models of wind power generation were developed using the conventional time-series method and hybrid models using support vector regression (SVR) based on rolling origin recalibration. For the application of the methodology, the meteorological database from Korea Meteorological Administration and actual operating data of a wind power turbine (2.3 MW) from 1 January to 31 December 2015 were used. The results showed that the proposed SVR model has higher forecasting accuracy than the existing time-series methods. In addition, the conventional time-series model has high accuracy under proper curation of wind turbine operation data. Therefore, the analysis results reveal that data curation and weather information are as important as the model for wind power forecasting.
Solar Energy Production Planning in Antikythera: Adequacy Scenarios and the Effect of the Atmospheric Parameters
Authors: Kosmopoulos Panagiotis , Mechilis Marios , Kaoura Panagiota
Published: December 2022

The National Observatory of Athens intends to operate a European Climate Change Observatory (ECCO) on the island of Antikythera, which meets the criteria to become a first-class research infrastructure. This project requires electricity that is unprofitable to get from the thermal units of this small island (20 km2). Solar energy is the subject that was examined in case it can give an environmentally and economically viable solution, both for the observatory and for the whole island. Specifically, observational and modeled data were utilized relevant to solar dynamic and atmospheric parameters in order to simulate the solar energy production by photovoltaics (PV) and Concentrated Solar Power (CSP) plant technologies. To this direction, a synergy of aerosol and cloud optical properties from the Copernicus Atmosphere Monitoring Service (CAMS) and the Eumetsat’s support to nowcasting and very short range forecasting (NWC SAF) with Radiative Transfer Model (RTM) techniques was used in order to quantify the solar radiation and energy production as well as the effect of the atmospheric parameters and to demonstrate energy adequacy scenarios and financial analysis. The ultimate goal is to highlight the opportunity for energy transition and autonomy for both the island itself and the rest of the community with the operation of ECCO, and hence to tackle climate change.
Reliability Analysis and Life Prediction of Aging LNG Unloading Arms Based on Non-Destructive Test Data
Authors: Ngo Duc-Vu , Lim Jong-Kwon , Kim Dong-Hyawn
Published: December 2022

Unloading arms (ULAs) among seaport infrastructures are susceptible to deterioration posed by the effects of harsh marine environmental conditions. During infrastructure’s service life, the deterioration of structural integrity may increase the risk of failure of infrastructure, and should be taken into account during structural reliability assessment. In this study, a simple non-destructive test (NDT) was employed to examine the structural deterioration of ULAs which were installed over 30 years ago. Then, these aging ULAs were modeled by the finite-element program, using non-destructive test data to update the thickness dimensions of structural members. Next, a reliability assessment was conducted based on the stress distribution of the main structural components under external loads, which are calculated by their relation to wind speed. Moreover, the time-dependent reliability index curve was also built by considering the deterioration function to predict the failure probability of the particular components during the remaining lifetime. The study revealed that the present condition of the ULA system was satisfactory for current loading conditions. A reliability index predicted with deteriorations factors may be a rational and appropriate approach for the assessment of aging structures needed for efficient infrastructure management.