bluuebunny/crossref_metadata_2025_split huggingface dataset:
Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)
Authors:   Dmitry Devitt, Kulathunga G. ,   Dmitry Devitt, Devitt D. ,   Dmitry Devitt, Fedorenko R. ,   Dmitry Devitt, Klimchik A. 
Published: January 2022

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.
L2 learning online: Self-directed learning and gender influence in Indonesian university students
Authors: Subekti Adaninggar Septi 
Published: January 2022

The present study was conducted to investigate Indonesian second language (L2) learners’ Self-Directed Learning (SDL) in English during the online classes due to the Covid-19 pandemic and to investigate whether there was a significant difference in SDL between female and male learners. As online learning is implemented nationwide in Indonesia due to the pandemic, it becomes paramount to investigate learners’ SDL, considered very critical in online learning settings where teachers' ability to check learners' progress is not as extensive as it is in the face-to-face mode of instruction. The participants of the study were 187 undergraduate students taking English for Academic Purposes (EAP) classes. Through the data obtained from the online questionnaire, the study found that learners, in general, reported a high level of SDL even though many of them still embraced procrastination behaviours and considered the pragmatic need to pass the EAP class more important than the actual L2 learning. It also found that there was no significant difference between female learners’ SDL and that of male learners. Based on the findings and analysis of the possible factors, possible contributions of the study are presented along with the possible limitations and suggested directions for future research in the field.
HIGHLIGHTS:

Learners, in general, reported a high level of SDL.
Despite that, many of them still embraced procrastination behaviours and considered the pragmatic need to pass the English for Academic Purposes class more important than the actual L2 learning.
Digital fundus image quality assessment
Authors: Starovoitov V. V. , Golub Y. I. , Lukashevich M. M. 
Published: January 2022

Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. We define the task to develop a universal approach for the image quality assessment of a retinal image obtained by an arbitrary fundus camera. It is solved in the first block of any automated retinal image analysis system. The quality assessment procedure is carried out in several stages. At the first stage, it is necessary to perform binarization of the original image and build a retinal mask. Such a mask is individual for each image, even among the images recorded by one camera. For this, a new universal retinal image binarization algorithm is proposed. By analyzing result of the binarization, it is possible to identify and remove imagesoutliers, which show not the retina, but other objects. Further, the problem of no-reference image quality assessment is solved and images are classified into two classes: satisfactory and unsatisfactory for analysis. Contrast, sharpness and possibility of segmentation of the vascular system on the retinal image are evaluated step by step. It is shown that the problem of no-reference image quality assessment of an arbitrary fundus image can be solved.Experiments were performed on a variety of images from the available retinal image databases.
Inequality Kills: The unparalleled action needed to combat unprecedented inequality in the wake of COVID-19
Authors: Ahmed Nabil , Marriott Anna , Dabi Nafkote , Lowthers Megan , Lawson Max , Mugehera Leah 
Published: January 2022

The wealth of the world’s 10 richest men has doubled since the pandemic began. The incomes of 99% of humanity are worse off because of COVID-19. Widening economic, gender, and racial inequalities—as well as the inequality that exists between countries—are tearing our world apart. This is not by chance, but choice: “economic violence” is perpetrated when structural policy choices are made for the richest and most powerful people. This causes direct harm to us all, and to the poorest people, women and girls, and racialized groups most. Inequality contributes to the death of at least one person every four seconds. But we can radically redesign our economies to be centered on equality. We can claw back extreme wealth through progressive taxation; invest in powerful, proven inequality-busting public measures; and boldly shift power in the economy and society. If we are courageous, and listen to the movements demanding change, we can create an economy in which nobody lives in poverty, nor with unimaginable billionaire wealth—in which inequality no longer kills.
Cost-effective Network Disintegration through Targeted Enumeration
Authors: Wang Zhigang , Deng Ye , Holme Petter , Di Zengru , Lu Linyuan , Wu Jun 
Published: January 2022

Abstract
We live in a hyperconnected world---connectivity that can sometimes be detrimental. Finding an optimal subset of nodes or links to disintegrate harmful networks is a fundamental problem in network science, with potential applications to anti-terrorism, epidemic control, and many other fields of study. The challenge of the network disintegration problem is to balance the effectiveness and efficiency of strategies. In this paper, we propose a cost-effective targeted enumeration method for network disintegration. The proposed approach includes two stages: searching candidate objects and identifying an optimal solution. In the first stage, we use rank aggregation to generate a comprehensive node importance ranking, upon which we identify a small-scale candidate set of nodes to remove. In the second stage, we use an enumeration method to find an optimal combination among the candidate nodes. Extensive experimental results on synthetic and real-world networks demonstrate that the proposed method achieves a satisfying trade-off between effectiveness and efficiency. The introduced two-stage targeted enumeration framework can also be applied to other computationally intractable combinational optimization problems, from team assembly, via portfolio investment, to drug design.
Erosion Analysis for the Carcass of Unbonded Flexible Pipes
Authors: Yu Jianxing , Li Haoda , Yu Yang , Liu Xin , Xu Weipeng , Liu Pengfei , Sun Ruoke 
Published: January 2022

Abstract
At present, unbonded flexible pipes (UFPs) are widely used in ocean engineering for oil exploitation. In practice, erosion will lead to premature failure of pipelines. There is a lack of researches on the erosion of interlock carcass of UFPs. As the authority in the field of offshore engineering, DET NORSKE VERITAS(DNV) suggested a way to estimate the erosion rate of pipes, however, it does not study the erosion mechanism of UFPs in detail and the relevant parameters are not specified. This paper modifies erosion prediction of UFPs based on a user defined Fortran subroutine. A series of CFD simulations have been conducted, and three widely used erosion models were used for comparative verification. The effect of geometric shape on erosion rate has been carefully studied. and the effect of velocity, particle size, and concentration are also studied to verify the reliability of the improved model.
WITHDRAWN: Floristic Composition, Diversity and Plant Community Along Elevational Gradients in the Afro-alpine Vegetation of Abohoy Gara Mountain, northern Ethiopia
Authors: Tahir Muhidin , Egzabher Yemane G. , Yonas Meheretu , Giday Kidane , Lemma Hayal , Kidane Leul 
Published: January 2022

Abstract
This research was conducted in Afro-alpine vegetation of Abohoy Gara Mountain in northern Ethiopia with the aim of analyzing floristic composition along elevational gradients, threats and conservation methods. Data were collected from 53 quadrats (15 m x 15 m) laid at 100 m interval along parallel vertical transects spaced at 500m to each other to record shrubs and trees, whereas 1 m x 1 m plots were used to record herbs. Cover abundance data were used to do community analysis using R version 3.1.2 software. Analysis of variance (ANOVA) was employed to determine significant level of species richness; abundance and Shannon diversity index along elevational gradient, whereas pearson’s correlation test was applied to check the relationship between environmental variables with species richness, species abundance and Shannon diversity index (H’) using SPSS v20. Ninety-seven plant species composed of 82 genera under 40 families were recorded. The mean number of plant species showed significant difference along elevational gradient (P < 0.05). Besides, significant (P < 0.05) negative correlation of elevation with the plant richness, abundance as well as Shannon diversity index had been observed. Based on dominant species, a total of five plant communities were identified. The maximum plant diversity occurred in community type III (3.79), whereas the least plant diversity occurred in community type IV and community type V. Vegetation cover and species composition of Abohoy Gara Mountain is being endangered by agricultural expansion, over grazing and logging of wood for construction material. Hence, it needs application of complementary on-site and offsite conservation approaches. This calls for further joint management of the Abohoy Gara Mountain by the local people and administrative bodies in the overall conservation actions that can save and rehabilitate the plant resources and their habitats.
An Electrical Stimulation Method to Control Deep Muscle Contraction using Surface Electrodes
Authors: Ohara Hiroki , Hasegawa Shoichi 
Published: January 2022

Abstract
Conventional EMS technology cannot stimulate deep muscles to induce muscle contraction using surface electrodes. Several treatments use electrical stimulation for various neurological conditions, including stroke and spinal cord injury. One such treatment is functional electrical stimulation (FES), a form of rehabilitation in which electrical muscle stimulation (EMS) is provided while the muscles are being moved. Here, we show whether two interfering electrical stimulation pulses could stimulate the deep muscles of the forearm to control muscle contraction. The results showed that the strongest torques were generated across the subjects when the reference frequency was mid-frequency (4,000 Hz) and the beat frequencies were low (20 Hz, 40 Hz, 80 Hz, 160 Hz and 320 Hz). This study is the first counterexample to demonstrate that it is possible to control muscle contraction in the deep muscles of the forearm using surface electrodes, which was previously thought to be impossible.
Evaluating Climate Change Impact of Rainfed Maize Production Yield in Southern Ethiopia
Authors: Disasa Kinde Negessa , Yan Haofang 
Published: January 2022

Abstract
A developing country like Ethiopia suffers a lot from the effects of climate change due to its limited economic capability to build irrigation projects to combat climate change's impact on crop production. This study evaluates climate change's impact on rainfed maize production in the Southern part of Ethiopia. AquaCrop, developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed maize production in the study area with and without climate change. The Stochastic weather generators model LARS-WG of the latest version is used to simulate local-scale level climate variables based on low-resolution GCM outputs. The expected monthly percentage change of rainfall during these two-time horizons (2040 and 2060) ranges from -23.18 to 20.23% and -14.8 to 36.66 respectively. Moreover, the monthly mean of the minimum and maximum temperature are estimated to increase in the range of 1.296 0C to 2.192 0C and 0.98 0C to 1.84 0C for the first time horizon (2031-2050) and from 1.860C to 3.40C and 1.560C to 3.180C in the second time horizon (2051-2070), respectively. Maize yields are expected to increase with the range of 4.13–7% and 6.36–9.32% for the respective time horizon in the study area provided that all other parameters were kept the same. In conclusion, the study results suggest that rainfed maize yield responds positively to climate change if all field management, soil fertility, and crop variety improve were kept the same to baseline; but since there is intermodal rainfall variability among the seasons planting date should be scheduled well to combat water stress on crops. The authors believe that this study is very likely important for regional development agents (DA) and policymakers to cope up with the climate change phenomenon and take some mitigation and adaptation strategies.
Ezetimibe Prevents IL-1β-induced Inflammatory Reaction in Mouse Chondrocytes via Modulating NF-κB and Nrf2/HO-1 Signaling Crosstalk
Authors: Zhang Yong , Luo Jianning , Weng Qiuyan , Hu Tongzhou , Shen Xiaohan , Han Jinming 
Published: January 2022

Background:Osteoarthritis is a type of age-related, chronic, and degenerative joint disease. Ezetimibe, a cholesterol absorption inhibitor, is widely used for the treatment of various diseases. However, the role of ezetimibe in osteoarthritis remains unclear.Objective:This study aimed to explore the anti-inflammation effect of ezetimibe on mouse chondrocytes.Method:In the present study, ELISA, qPCR and western blot analysis were performed to evaluate the anti-inflammatory effects of ezetimibe. In addition, enzymes that are highly associated with the anabolism and catabolism of the extracellular matrix of the articular cartilage were also evaluated.Results:Treatment with ezetimibe attenuated the IL-1β-induced degradation of the extracellular matrix, including aggrecan and collagen II. Ezetimibe also attenuated the IL-1β-induced expression levels of MMP3, MMP13 and ADAMTS5, thus exerting protective effects against IL-1β- induced extracellular matrix degradation. The complex mechanism of the anti-inflammatory reaction contributed to the activation of the Nrf2/HO-1 pathway and the suppression of the NF-κB pathway.Conclusion:On the whole, the present study demonstrates that ezetimibe may be a promising agent for further osteoarthritis therapy.
Thymoquinone: A Review on its Pharmacological Importance, and its Association with Oxidative Stress, COVID-19, and Radiotherapy
Authors: Taysi Seyithan , Algburi Firas Shawqi , Mohammed Zaid Rakan , Ali Omeed Akbar , Taysi Muhammed Enes 
Published: January 2022

Abstract:
Widely consumed worldwide, Nigella sativa (NS) is a medicinal herb commonly used in various alternative medicine systems, such as Unani and Tibb, Ayurveda, and Siddha. Recommended for regular use in Tibb-e-Nabwi (Prophetic Medicine), NS is considered one of the most notable forms of healing medicine in Islamic literature. Thymoquinone (TQ), the main component of the essential oil of NS, has been reported to have many properties, such as antioxidant, anti-inflammatory, antiviral, and antineoplastic. Its chemical structure indicates antiviral potential against many viruses, including the hepatitis C virus, human immunodeficiency virus, and other coronavirus diseases. Interestingly, molecular docking studies have demonstrated that TQ can potentially inhibit the development of the coronavirus disease 2019 (COVID-19) by binding to the receptor site on the transmembrane serine protease 2 (the activator enzyme that attaches the virus to the cell). In addition, TQ has been shown to be effective against cancer cells due to its inhibitory effect by binding to the different regions of MDM2, according to the proposed molecular docking study. Detailed in this review is the origin of TQ, its significance in alternative medicine, pharmacological value, potential as a cancer antiproliferative agent, use against the coronavirus disease 2019 (COVID-19) and for treatment of other diseases.
Recent Advancement and Novel Application of Organocatalyzed Aldol
Condensation Reactions: A Comprehensive Review
Authors: Verma Ajay Kumar , Kishor Braj Nandan , Prakash Om 
Published: January 2022

Background:
Aldol reactions play an important role in the development of organic synthesis-
owing to their critical importance for the conjugation of carbon-carbon bonds while concurrently,
one or two chiral centers are reported. In the modern scenario, the Aldol condensation reaction
has arisen as perhaps the most significant reaction for the formation of novel medicinal agents,
revealing promising pharmacological activities.

Objective:
The purpose of this study is to present newer synthetic approaches through Aldol condensation
reaction for the synthesis of diverse scaffolds to explore the numerous remarkable biological
activities.

Methods:
Aldol condensation concerns the nucleophilic addition reaction of a ketone enolate to an
aldehyde to form aldol or β- hydroxy ketone. Occasionally, the aldol addition product losing water
molecule yields an α, β-unsaturated ketone.

Results:
Results showed that peptides of amino acids are utilized as chiral catalysts. As of now, the
arrangement of catalysts is intensely one-sided towards proline

Conclusion:
The present study thus provides useful insight concerning the promising coherent way
for the synthesis of prolinamide analogue of proline, through a direct asymmetric aldol condensation
reaction. Thus, the current study summarizes various Aldol condensation reactions for the synthesis
of novel agents as well as their promising pharmacological importance.
Selective Prediction With Long Short-term Memory Using Unit-Wise Batch Standardization for Time Series Health Data Sets: Algorithm Development and Validation
Authors: Nam Borum , Kim Joo Young , Kim In Young , Cho Baek Hwan 
Published: January 2022

BackgroundIn any health care system, both the classification of data and the confidence level of such classifications are important. Therefore, a selective prediction model is required to classify time series health data according to confidence levels of prediction.ObjectiveThis study aims to develop a method using long short-term memory (LSTM) models with a reject option for time series health data classification.MethodsAn existing selective prediction method was adopted to implement an option for rejecting a classification output in LSTM models. However, a conventional selection function approach to LSTM does not achieve acceptable performance during learning stages. To tackle this problem, we proposed a unit-wise batch standardization that attempts to normalize each hidden unit in LSTM to apply the structural characteristics of LSTM models that concern the selection function.ResultsThe ability of our method to approximate the target confidence level was compared by coverage violations for 2 time series of health data sets consisting of human activity and arrhythmia. For both data sets, our approach yielded lower average coverage violations (0.98% and 1.79% for each data set) than those of the conventional approach. In addition, the classification performance when using the reject option was compared with that of other normalization methods. Our method demonstrated superior performance for selective risk (12.63% and 17.82% for each data set), false-positive rates (2.09% and 5.8% for each data set), and false-negative rates (10.58% and 17.24% for each data set).ConclusionsOur normalization approach can help make selective predictions for time series health data. We expect this technique to enhance the confidence of users in classification systems and improve collaborative efforts between humans and artificial intelligence in the medical field through the use of classification that considers confidence.