bluuebunny/crossref_metadata_2025_split huggingface dataset:
Greece and Turkey Shaken by African tectonic retreat
Authors: Meng Jiannan , Sinoplu Ozan , Zhou Zhipeng , Tokay Bulent , Kusky Timothy , Bozkurt Erdin , Wang Lu 
Published: March 2021

AbstractEarthquakes are a consequence of the motions of the planet’s tectonic plates, yet predicting when and where they may occur, and how to prepare remain some of the shortcomings of using scientific knowledge to protect human life. A devastating Mw 7.0 earthquake on October 30, 2020, offshore Samos Island, Greece was a consequence of the Aegean and Anatolian upper crust being pulled apart by north–south extensional stresses resulting from slab rollback, where the African plate is subducting northwards beneath Eurasia, while the slab is sinking by gravitational forces, causing it to retreat southwards. Since the retreating African slab is coupled with the overriding plate, it tears the upper plate apart as it retreats, breaking it into numerous small plates with frequent earthquakes along their boundaries. Historical earthquake swarms and deformation of the upper plate in the Aegean have been associated with massive volcanism and cataclysmic devastation, such as the Mw 7.7 Amorgos earthquake in July 1956 between the islands of Naxos and Santorini (Thera). Even more notable was the eruption of Santorini 3650 years ago, which contributed to the fall of the Minoan civilization. The Samos earthquake highlights the long historical lack of appreciation of links between deep tectonic processes and upper crustal deformation and geological hazards, and is a harbinger of future earthquakes and volcanic eruptions, establishing a basis for studies to institute better protection of infrastructure and upper plate cultures in the region.
Gathering, processing, and interpreting information about COVID-19
Authors: Boot Arnout B. , Eerland Anita , Jongerling Joran , Verkoeijen Peter P. J. L. , Zwaan Rolf A. 
Published: March 2021

AbstractDoes cognitive motivation influence how people gather and interpret information about COVID-19 and their adherence to measures? To address these questions, we conducted a longitudinal survey among European and American respondents. Wave 1 (N = 501) was conducted on March 27, 2020 and Wave 2 (N = 326) on July 1, 2020. We assessed COVID-19 knowledge, endorsement of COVID-19 conspiracy theories, media use, Need for Cognition (NC), Need for Cognitive Closure (NCC), and self-reported adherence to governmental measures taken. Results showed that nearly three-quarters of our respondents actively searched for information about COVID-19. Most at least once a day. Information seeking behaviour was not influenced by cognitive motivation (i.e., NC and NCC). However, cognitive motivation was related to (1) knowledge about COVID-19, (2) conspiracy rejection, and (3) change in knowledge over time. Respondents with more knowledge on COVID-19 also indicated to adhere more often to measures taken by their government. Self-reported adherence to measures was not influenced by cognitive motivation. Implications of these findings will be discussed.
Rate of Communicative Gestures and Developmental Outcomes in Toddlers With and Without Autism Spectrum Disorder During a Home Observation
Authors: Delehanty Abigail D. , Wetherby Amy M. 
Published: March 2021

Purpose
Most toddlers with autism spectrum disorder and other developmental delays receive early intervention at home and may not participate in a clinic-based communication evaluation. However, there is limited research that has prospectively examined communication in very young children with and without autism in a home-based setting. This study used granular observational coding to document the communicative acts performed by toddlers with autism, developmental delay, and typical development in the home environment.

Method

Children were selected from the archival database of the FIRST WORDS Project (
N
= 211). At approximately 20 months of age, each child participated in everyday activities with a caregiver during an hour-long, video-recorded, naturalistic home observation. Inventories of unique gestures, rates per minute, and proportions of types of communicative acts and communicative functions were coded and compared using a one-way analysis of variance. Concurrent and prospective relationships between rate of communication and measures of social communication, language development, and autism symptoms were examined.

Results
A total of 40,738 communicative acts were coded. Children with autism, developmental delay, and typical development used eight, nine, and 12 unique gestures on average, respectively. Children with autism used deictic gestures, vocalizations, and communicative acts for behavior regulation at significantly lower rates than the other groups. Statistically significant correlations were observed between rate of communication and several outcome measures.

Conclusion
Observation of social communication in the natural environment may improve early identification of children with autism and communication delays, complement clinic-based assessments, and provide useful information about a child's social communication profile and the family's preferred activities and intervention priorities.

Supplemental Material

https://doi.org/10.23641/asha.14204522
A quantum‐based diagnostics approach for additive manufacturing machine
Authors: Sharma Vishal , Gupta Shantanu , Mehta Gaurav , Lad Bhupesh K. 
Published: March 2021

AbstractCurrent Additive Manufacturing machines have limited techniques to observe process conditions and to decrease process errors. In order to overcome these limitations and increase the level and accuracy of machine intelligence, machine conditions need to be monitored more meticulously. A novel method for the condition monitoring of a 3D‐printer is proposed in this paper. Quantum support vector machine (QSVM) is compiled for recognizing the health condition of the 3D‐printer. The proposed quantum machine learning approach helps in monitoring the health state of the machine and classifies the same as healthy or aberrant. Classical machine learning approaches are inefficient to process the large amount of experimental data in real time. For better decision‐making on such big data, quantum machine learning approaches are deployed which are much more efficient due to their exponential speed and parallel operation on complex sensor data, they show speedups in both the dimensionality and number of experimental data deployed to train the algorithm. The simulation results show that the proposed method has higher accuracy in fault diagnosis than the traditional Support Vector Machine. All the numerical simulations and experiments have been carried out on a real quantum hardware provided by the IBM Quantum computing over the cloud.
A survey on adversarial attacks and defences
Authors: Chakraborty Anirban , Alam Manaar , Dey Vishal , Chattopadhyay Anupam , Mukhopadhyay Debdeep 
Published: March 2021

AbstractDeep learning has evolved as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. The advancement of deep learning has been so radical that today it can surpass human‐level performance. As a consequence, deep learning is being extensively used in most of the recent day‐to‐day applications. However, efficient deep learning systems can be jeopardised by using crafted adversarial samples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. Herein, the authors attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate on the efficiency and challenges of recent countermeasures against them.
A combined survey on distribution system state estimation and false data injection in cyber‐physical power distribution networks
Authors: Bhattar Poornachandratejasvi Laxman , Pindoriya Naran M. , Sharma Anurag 
Published: March 2021

AbstractThe penetration of renewable energy sources (RES) in distribution systems has entailed heavy deployment of monitoring and control infrastructure in the distribution system. This deployment of sensors and communication infrastructure has led to the emergence of a modern grid with a complex cyber‐physical network. On one hand, the cyber‐physical system can play a pivotal role in coordination and control of distribution systems through distribution system state estimation (DSSE). On the other hand, the risk caused by cyber‐attack has emerged as a major challenge for grid operation. The modelling aspects, namely DC loads, plug‐in‐vehicle and distributed generators (DGs) in DSSE and security aspects in cyber‐physical distribution systems, are highlighted herein. This paper is divided into different sections emphasising on the problems and challenges associated with DSSE, cyber‐attack focussing primarily on impact of false data injection (FDI) attack and co‐simulation platform for investigation of vulnerability of cyber‐physical systems. A comparative study among various DSSE and challenges namely, pseudo‐measurement, time synchronisation and communication issues are discussed. The potential investigation of FDI and need for a co‐simulation platform in distribution systems for risk analysis are also addressed. The opportunities and future research in the field of cyber‐physical distribution systems are discussed in depth.
Tracking control of a robotic system with deferred constraints and actuator faults
Authors: Zhang Yu , Kong Linghuan , Zhang Shuang , Yu Xinbo , Yang Pengxin 
Published: March 2021

AbstractThis paper proposes an adaptive sliding mode tracking control for a robotic manipulator to guarantee a deferred performance under input non‐linearities and external perturbations. Control inputs of the robotic manipulator are entrapped into saturation and failures simultaneously. A common method to deal with output constraints is based on the barrier Lyapunov function, which requires the system states to be within the prescribed bounds initially; an error‐shifting transformation is utilized and barrier functions are integrated into sliding model control to remove the unreasonable requirement and to realize a finite time convergence result collectively. Furthermore, the actuator faults are accommodated without any prior knowledge. Simulation examples are provided to verify the effectiveness of the proposed scheme.
Improved multiple vector model predictive torque control of permanent magnet synchronous motor for reducing torque ripple
Authors: Qu Jianzhen , Jatskevich Juri , Zhang Chengning , Zhang Shuo 
Published: March 2021

AbstractThe two‐voltage‐vector (2VV) model predictive torque control (MPTC) has been widely discussed and compared to the conventional single‐voltage‐vector (1VV) MPTC in permanent magnet synchronous motor drive applications, where it was shown to have quick response and low torque ripple. An improved 2VV‐MPTC is set forth based on extended control set. In the proposed approach, the reference voltage vector is calculated using the established torque and flux deadbeat control and optimal calculation of the duty cycles. Moreover, to further improve the torque performance, the proposed algorithm is extended to three‐voltage vector (3VV), which is shown to achieve even better performance. Experimental results demonstrate that the proposed 2VV and 3VV‐MPTC algorithms have better computation efficiency and can significantly reduce torque ripple compared to the previous methods.
New transformer‐less DC–DC converter topologies with reduced voltage stress on capacitors and increased voltage conversion ratio
Authors: Abbasi Maysam , Abbasi Ehsan , Li Li 
Published: March 2021

AbstractThis article proposes new transformer‐less step‐up/down and step‐up DC–DC topologies providing numerous merits such as less voltage stress on capacitors, lower duty‐cycle, and higher voltage conversion ratio compared to other DC–DC converters. The proposed converters are extendible by benefiting from several switched‐capacitors, making it possible to transfer more power from the converter. The proposed structures are more suitable for modern applications in which it is desirable to achieve high voltage gains by using non‐extreme duty‐cycles. For proving the analysis and claims, the detailed comparison and experimental results are presented. In the experiments, the dynamic performance of the proposed converters is also validated.
A non‐isolated DC‐DC converter with low voltage stress and high step‐down voltage conversion ratio
Authors: Syrigos Stylianos P. , Christidis Georgios C. , Mouselinos Theodoros P. , Tatakis Emmanuel C. 
Published: March 2021

AbstractThis work focuses on a new non‐isolated interleaved DC‐DC converter with very high step‐down voltage conversion ratio. By employing the switched/series capacitor concept, along with the proper component interconnection, the converter features a high step‐down voltage conversion ratio where, in the private case of equal duty ratios, it is six times higher than the conventional buck converter. This high step‐down voltage conversion ratio is achieved with the lowest number of components compared to other similar topologies of the same conversion ratio. Also, due to the blocking/series capacitors, the voltage stress on the switches is reduced, improving the converter efficiency. One of the key features of the proposed converter is the inherent automatic current sharing between the interleaved phases, which in the case of equal duty ratios, is uniformly distributed. To add on, due to the use of only two interleaved phases, the converter has a wide output voltage range, since the duty cycle can be extended to 0.5. To validate the proposed converter operation, a 250 W wide‐input/wide‐output experimental prototype was built, achieving a peak efficiency of 94%.
Experimental validation of synergetic approach based MPPT controller for an autonomous PV system
Authors: Ayat Rahma , Bouafia Abdelouahab , Gaubert Jean‐Paul 
Published: March 2021

AbstractA novel nonlinear maximum power point tracking (MPPT) controller for autonomous photovoltaic systems based on synergetic control theory is presented in this paper as a solution to eliminate the chattering drawback provided by sliding mode controller, where the proposed strategy allows to generate continuous control law instead of a switching term. A DC/DC boost converter is introduced in the content of this work as an interface between a photovoltaic array and a resistive load. The developed MPPT controller was tested both in simulations using Matlab/Simulink tool and experimentally using dSPACE RTI 1104 real‐time platform and compared with the sliding mode‐based MPPT controller. Furthermore, the EN 50530 standard test with different ramp gradients values is used to calculate the MPPT efficiency under irradiance changes from the slow to the very fast. Illustrative results that prove the effectiveness and the robustness of the proposed MPPT controller even the non‐uniform conditions (temperatures and solar irradiances) are presented here. The fast response and the accurate tracking to the maximum power point (MPP) with considerable reduction in the oscillations are successfully reached with the developed controller which is much better than sliding mode controller.
A hybrid energy storage strategy based on multivariable fuzzy coordinated control of photovoltaic grid‐connected power fluctuations
Authors: Xu Dong , Cen Honglei 
Published: March 2021

AbstractAiming at the problem that the grid‐connected power fluctuation of the photovoltaic power system affects the stability of grid operation, a multivariable fuzzy coordinated control strategy for photovoltaic grid‐connected power fluctuation based on super‐capacitor and battery is proposed. First, a multivariable fuzzy controller is established based on the grid‐connected estimated fluctuation power of the photovoltaic power system and the current energy state of charge of super‐capacitor and battery. The fuzzy relational matrix is used to introduce interaction effects of inputs into the fuzzy control, the fuzzy relation matrix is established by multiplying with weights, and the time constant of the low‐pass filter is adjusted to coordinate the distribution of fluctuating power between different energy storage in real‐time. Second, according to the SOC variation of HESS, a super‐capacitor fuzzy controller and battery fuzzy controller are established to modify the charging and discharging instructions of battery and super‐capacitor. The charging and discharging power of the energy storage are constraints to optimize the charging and discharging power of the energy storage, and the energy state of the HESS is updated in real‐time. Finally, an overall example analysis of fuzzy coordinated control is performed to verify the effectiveness of the proposed control strategy.
A robust refined training sample reweighting space–time adaptive processing method for airborne radar in heterogeneous environment
Authors: Xiao Hao , Wang Tong , Zhang Shuguang , Wen Cai 
Published: March 2021

AbstractTo improve the clutter suppression performance of airborne radar in heterogeneous environment, a robust refined training sample reweighting space–time adaptive processing (STAP) method called RRSRW is proposed here. First, some target‐free training samples around the cell under test (CUT) are selected and the corresponding clutter dictionary matrices are constructed using the radar system parameters. Then, the clutter patch amplitudes and array error for selected training samples are simultaneously estimated through the formulated constrained least squares problem. Subsequently, based on the covariance matching estimation criterion, the local weighting coefficients for selected training samples are estimated by the redesigned convex optimisation problem. Finally, the STAP weight vector is calculated to process the CUT data. The proposed method is robust to array error and can effectively protect the moving targets in CUT data, which is free of hyper‐parameters and has global convergence properties. Simulation results demonstrate that the proposed RRSRW method can effectively suppress the strong ground clutter and greatly improve the detection performance of moving targets in heterogeneous environment.
Autograft diameter in ACL reconstruction: size does matter
Authors: Alkhalaf Fahad N.A. , Hanna Sager , Alkhaldi Mohammed Saleh Hattab , Alenezi Fares , Khaja Aliaa 
Published: March 2021

Background: Anterior cruciate ligament injuries are commonly seen in orthopedic surgery practice. Although anterior cruciate ligament reconstruction (ACLR) has come a long way, the causes of failure have yet to be fully understood.Objective: The aim of this study was to investigate whether or not the intraoperative 4-strand hamstring autograft diameter does in fact influence the failure rates of ACLR.Methods: Retrospective intraoperative data were collected from ACLR patients from the only tertiary center available in Kuwait. Patients who underwent ACLR from 2012 to 2018 for isolated ACL injuries were included in this study, allowing for a 24 month follow-up period The cohorts were categorized into 3 groups: patients with graft size≤8mm, 2, patients with graft sizes≥8mm with 4-strands and patients with graft sizes≥8mm with 4-strands or more. ANOVA analysis was applied to address group differences between mean graft size and strand numbers and subsequently the failure rates for each group. In addition, the Mann–WhitneyUtest was used to investigate the relationship between revision and initial ACL graft size.Results: Out of the 711 out of 782 patients were included in this study. Only 42.6% of the patients did not need more than 4-strands to achieve an 8mm sized autograft. The patients who had autografts≤8mm in this study accounted for 17.1% of the population. About 7.2% of these patients required revision surgery. Patients with a 4-strand autograft size that was less than 8mm were 7.2 times more at risk for ACLR failure (RR=7.2, 95% CI: 6.02; 8.35,p=0.007).Conclusions: There is a significant correlation between 4-strand autograft diameter size and the need for ACLR revision surgery.Level of evidence: IV case series
Recent Developments Towards Synthesis of (Het)arylbenzimidazoles
Authors: Mamedov Vakhid A. , Zhukova Nataliya A. 
Published: March 2021

AbstractBenzimidazole is an important heterocycle that is widely researched and utilized by the pharmaceutical industry and is one of the five most commonly used five-membered aromatic heterocyclic compounds approved by the US Food and Drug Administration. In view of their wide-ranging bioactivities, systems containing benzimidazole as one of the moieties occupy a special place among other benzimidazole derivatives. Since 2010, many improved synthetic strategies have been developed for the construction of hetaryl- and arylbenzimidazole molecular scaffolds under environmentally benign conditions. This review emphasizes the recent trends and modifications frequently used in the synthesis of derivatives of benzimidazole such as the Phillips–Ladenburg and Weidenhagen reactions, as well as entirely new methods of synthesis, involving oxidative cyclization, cross-coupling, ring distortion strategy, and rearrangements carried out under environmentally benign conditions.1	Introduction2	From 1,2-Diaminobenzenes with Various One-Carbon Unit Suppliers2.1	Phillips–Ladenburg Reaction2.1.1	With (Het)arenecarboxylic Acids2.2.2	With (Het)arenecarboxylic Acid Derivatives2.2	Weidenhagen Reaction2.2.1	With (Het)arenecarbaldehydes or (Het)aryl Methyl Ketones2.2.2	With Primary Alcohols2.2.3	With Primary Alkylamines2.2.4	With 2-Methylazaarenes2.2.5	With Other One-Carbon Fragment Suppliers3	From 2-Haloacetanilides and Amines4	From Amidines5	From Tetrahydroquinazolines6	Mamedov Rearrangement7	Conclusions and Outlook
Retrospective Analysis of Factors Affecting Recurrence in Borderline Ovarian Tumors
Authors: Ifthikar Mariam Anjum , Rajanbabu Anupama , Nair Indu R. , Murali Vinita , Nair Anjaly S. 
Published: March 2021

Abstract
Background Borderline ovarian tumors (BOTs) are an intermediate form of neoplasia, between benign and malignant. The aim of this retrospective analysis is to evaluate the clinicopathological characteristic profile of BOTs and to determine the predictors of recurrence in BOTs.
Methods A retrospective review of all patients diagnosed, treated, and followed up for BOTs between 2010 and 2017 at Amrita Institute of Medical Sciences, Kerala, India, was conducted. Clinicopathological details and details of management, outcome, and survival were retrieved, and data were analyzed descriptively and for survival.
Results A total of 103 patients were identified. During the median follow-up of 46.0 months, 15 (14.6%) patients developed recurrent disease, 6 (5.82%) had recurrence with progression to invasive carcinoma, and 9 had recurrent disease with borderline or benign histology. Mucinous tumors were found to have more recurrences than serous BOT (17.8 vs. 12.3%). Disease-related deaths (5/103 [4.9%]) were observed only in patients with progression to invasive carcinoma. Univariate analysis indicated that staging surgery was the most important prognostic factor that affected the disease-free survival ([DFS] 103 vs. 97 vs. 71 months, respectively, for complete staging vs. fertility-preserving staging vs. conservative surgery; p < 0.05).
Conclusions Conservative surgery was associated with a higher risk of recurrence. Fertility-preserving staging surgery is an acceptable option in younger patients. The overall survival is not affected by the mode of surgery.