bluuebunny/crossref_metadata_2025 huggingface dataset:
Probing the early Universe from deep underground
Authors: Cavanna Francesca ,   for the LUNA collaboration
Published: February 2022

Big Bang Nucleosynthesis (BBN) occurs during the first minutes of cosmological time in a rapidly expanding hot and dense Universe, where a fraction of protons and nearly all free neutrons end up bound in 4He, while D, 3H, 3He, 6Li, 7Li and 7Be nuclei form in trace quantities. Among these elements, deuterium is an excellent indicator of cosmological parameters because its abundance is highly sensitive to the primordial baryon density and to the number of relativistic species. Although astronomical observations of primordial deuterium abundance have reached percent accuracy, theoretical predictions based on BBN were hampered by large uncertainties on the cross-section of the deuterium burning D(p,γ)3He reaction, before the LUNA measurement. In the following, I will report the results of the experimental campaign carried on at LUNA and its cosmological implications.
Probing Massive Star Nucleosynthesis with Data on Metal-Poor Stars and the Solar System
Authors: Qian Yong-Zhong
Published: February 2022

Metal-poor stars were formed during the early epochs when only massive stars had time to evolve and contribute to the chemical enrichment. Low-mass metal-poor stars survive until the present and provide fossil records of the nucleosynthesis of early massive stars. On the other hand, short-lived radionuclides (SLRs) in the early solar system (ESS) reﬂect the nucleosynthesis of sources that occurred close to the proto-solar cloud in both space and time. Both the ubiquity of Sr and Ba and the diversity of heavy-element abundance patterns observed in single metal-poor stars suggest that some neutron-capture mechanisms other than the r-process might have operated in early massive stars. Three such mechanisms are discussed: the weak s-process in non-rotating models with initial carbon enhancement, a new s-process induced by rapid rotation in models with normal initial composition, and neutron-capture processes induced by proton ingestion in non-rotating models. In addition, meteoritic data are discussed to constrain the core-collapse supernova (CCSN) that might have triggered the formation of the solar system and provided some of the SLRs in the ESS. If there was a CCSN trigger, the data point to a low-mass CCSN as the most likely candidate. An 11.8 M⊙ CCSN trigger is discussed. Its nucleosynthesis, the evolution of its remnant, and the interaction of the remnant with the proto-solar cloud appear to satisfy the meteoritic constraints and can account for the abundances of the SLRs 41Ca, 53Mn, and 60Fe in the ESS.
Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study
Authors: Abdulrahman Saifuldeen H , Salim Mohammad
Published: February 2022

Emails have become the most economical and fastest communication forms. However, during the past few years, the increment of email users has dramatically increased spam emails. Various anti-spam techniques have been developed to minimize if not eliminate the spam problem. In this paper, we study the disparity in the effectiveness of using different decision tree algorithms in email classification and combat spam problems. For that, we have chosen Universiti Utara Malaysia emails as a case study. To achieve the best possible classification accuracy, we compared all chosen algorithms’ performance, which are Random Forest, LMT, Decision Stump, J48, Random Tree, and REP Tree. The experimental results showed that the Decision Stump algorithm is more effective to be used in classifying the emails, and the F-measures, Precision, and recall score for the Decision Stump algorithm are higher than the other comparison algorithms.
SDN for IoT Environment: A Survey and Research Challenges
Authors: Manguri Kamaran H. , Omer Saman M.
Published: February 2022

Recently, it has emerged that both Internet of Things (IoT), and Software Defined Network (SDN) are becoming popular technologies. The main goal of IoT is to link electronic devices via the internet, meanwhile SDN facilitates network arrangement for management of a network by distinguishing the control layer and the data layer from each other. The number of electronic devices over the internet is increasing constantly, therefore it is a complicated process to manage and control especially over the huge distributed network. IoT network can be reasonably flexible and programmable through The SDN without introducing any trouble to the previously implemented network infrastructure. This paper reviews various IoT domains and applications such as cellular network, wireless Sensor, IoT management, security and smart city framework and common IoT SDN solutions. Moreover, The IoT and SDN notion has been explored critically, with assessing the current contributions in the research field. Lastly, analyzing current available solutions for SDN-based IoT implementations comparatively helps easily understanding the emerging trends view.
Giardia purification from fecal samples using rigid spiral inertial microfluidics
Authors: Ding Lin , Razavi Bazaz Sajad , Hall Timothy , Vesey Graham , Ebrahimi Warkiani Majid
Published: February 2022

Giardia is one of the most common waterborne pathogens causing around 200 × 106 diarrheal infections annually. It is of great interest to microbiological research as it is among the oldest known eukaryotic cells. Purifying Giardia from fecal samples for both research and diagnostic purposes presents one of the most difficult challenges. Traditional purification methods rely on density gradient centrifugation, membrane-based filtration, and sedimentation methods, which suffer from low recovery rates, high costs, and poor efficiency. Here, we report on the use of microfluidics to purify Giardia cysts from mouse feces. We propose a rigid spiral microfluidic device with a trapezoidal cross section to effectively separate Giardia from surrounding debris. Our characterizations reveal that the recovery rate is concentration-dependent, and our proposed device can achieve recovery rates as high as 75% with 0.75 ml/min throughput. Moreover, this device can purify Giardia from extremely turbid samples to a level where cysts are visually distinguishable with just one round of purification. This highly scalable and versatile 3D printed microfluidic device is then capable of further purifying or enhancing the recovery rate of the samples by recirculation. This device also has the potential to purify other gastrointestinal pathogens of similar size, and throughput can be significantly increased by parallelization.
Importance of dynamical electron correlation in diabatic couplings of electron-exchange processes
Authors: Nishio Soichiro , Kurashige Yuki
Published: February 2022

We demonstrate the importance of the dynamical electron correlation effect in diabatic couplings of electron-exchange processes in molecular aggregates. To perform a multireference perturbation theory with large active space of molecular aggregates, an efficient low-rank approximation is applied to the complete active space self-consistent field reference functions. It is known that kinetic rates of electron-exchange processes, such as singlet fission, triplet–triplet annihilation, and triplet exciton transfer, are not sufficiently explained by the direct term of the diabatic couplings but efficiently mediated by the low-lying charge transfer states if the two molecules are in close proximity. It is presented in this paper, however, that regardless of the distance of the molecules, the direct term is considerably underestimated by up to three orders of magnitude without the dynamical electron correlation, i.e., the diabatic states expressed in the active space are not adequate to quantitatively reproduce the electron-exchange processes.
Performance of in situ oxidized platinum/iridium alloy Schottky contacts on (001), (2¯01), and (010)
β
-Ga2O3
Authors: Dela Cruz Z. , Hou C. , Martinez-Gazoni R. F. , Reeves R. J. , Allen M. W.
Published: February 2022

The performance of β-Ga2O3 Schottky contacts (SCs) fabricated using amorphous, intentionally oxidized platinum–iridium alloys was investigated as a function of alloy composition and β-Ga2O3 crystal orientation. PtyIr(1−y)Ox SCs with Pt fractions of y = 0.8, 0.5, and 0.3 were deposited on (001), (2¯01), and (010) single-crystal β-Ga2O3 substrates via the reactive rf and dc co-sputtering of Pt and Ir targets using oxygen–argon plasmas. In each case, the PtyIr(1−y)Ox SCs were highly rectifying with current rectification ratios (at ±3 V) of 11/10/9 orders of magnitude (at 300 K) for the (001)/(2¯01)/(010) β-Ga2O3 substrates. Current–voltage (I–V) and capacitance–voltage (C–V) measurements revealed that the Pt0.5Ir0.5Ox SCs contained the highest Schottky barriers on all β-Ga2O3 crystal faces, with the best contacts having ideality factors of 1.05 and image-force-corrected I–V and C–V determined barrier heights of 2.10 and 2.20 eV, respectively. These were consistently higher by ∼0.2 eV than the corresponding barriers for the Pt0.8Ir0.2Ox and Pt0.3Ir0.7Ox SCs, with the Pt0.5Ir0.5Ox SCs also having significantly lower reverse leakage currents (in the 0 to −100 V range). In comparison, the barrier heights of the best unoxidized plain-metal Pt0.5Ir0.5 SCs were only ∼1.2 eV, illustrating the effectiveness of in situ oxidation in improving the performance of PtIr SCs. All PtyIr(1−y)Ox SCs on (2¯01) β-Ga2O3 showed excellent high-temperature performance with rectification ratios (at ±3 V) of 109 at 300 °C and of 106 at 500 °C.
Pressure driven phase transitions in honeycomb Fe4Nb2O9: A possible re-entrant multiferroic behavior
Authors: Sahu Mrinmay , Ghosh Bishnupada , Jana Rajesh , Cheng Jinguang , Dev Mukherjee Goutam
Published: February 2022

A detailed high-pressure investigation is carried out on Fe4Nb2O9 using angle resolved x-ray diffraction and Raman spectroscopy measurements. We find a structural transition from the ambient trigonal phase to a monoclinic phase above 8.8 GPa. The structural transition is assumed to be driven by a large distortion of Nb–O6 octahedra as seen from x-ray diffraction analysis and a large pressure dependence of an Nb–O6 octahedra breathing Raman mode. Anomalous behavior of Raman modes and an increase in the phonon lifetime at the phase transition pressure indicate a possible change in the magnetic property of the sample above 8.8 GPa. A decrease in the diffusive scattering rate of a low-frequency electronic contribution contradicts the results of a decrease in the intensity of a high-frequency electronic response and excludes the phenomenon of an insulator to metal transition. On the contrary, the enhancement of the intensity of the Raman modes up to about 8.8 GPa indicates a large change in ferroelectric polarization of the sample, indicating a possible pressure induced re-entrant multiferroic behavior in Fe4Nb2O9.
Utopia, war, and justice
Authors: Mooney Tara , Price Gareth
Published: February 2022

Between 2014 and 2017, Islamic State in Iraq and Syria (ISIS) morphed from a terrorist organization into a proto-state that held a large swathe of territory, within which its strict interpretation of sharia law was enforced. We argue that ISIS discursively constructed itself politically by appealing to elements of both a traditional Islamic caliphate and a modern legitimate state. Linguistic and semiotic data is presented from political communications through social media that revolve around three key themes, specifically the construction of a utopian state founded on justice and engaged in righteous war, which all rely fundamentally on a conception of the modern state as holding a “monopoly on the legitimate use of force”, in Weber’s definition. We analyze this data from a critical discourse analysis (CDA) perspective, within a theoretical framework that draws on the political sociology of the state and political communications in asymmetric conflicts.
Optimizing sprint interval exercise for post‐exercise hypotension: A randomized crossover trial
Authors: Ketelhut Sascha , Möhle Martin , Gürlich Tina , Hottenrott Laura , Hottenrott Kuno
Published: February 2022

This study aimed to examine the effects of manipulating the rest intervals during sprint interval training (SIT) on post‐exercise hypotension and within‐session oxygen consumption.

Thirty healthy, trained adults (aged 30.9 ± 8.7 years; 14 males, 16 females; BMI 22.1 ± 2.3 kg/m2; VO2max 50.7 ± 7.8 ml/kg/min) completed two different SIT protocols (4x 30‐seconds all‐out cycling sprints) with a one‐week washout period. Sprint bouts were separated by either 1 (R1) or 3 (R3) minutes of active recovery. Both before and throughout the 45 min after the training, peripheral systolic (pSBP) and diastolic (pDBP) blood pressure, central systolic (cSBP) and diastolic (cDBP) blood pressure, aortic pulse wave velocity (aPWV), stroke volume (SV), and heart rate (HR) were assessed. Throughout the SIT protocols, oxygen consumption (VO2) was monitored.

There were no significant differences in time spent at 75%, 85%, 95%, and 100% of maximal VO2 between R1 and R3. After R3, there was a significant reduction in pSBP, pDBP, cSBP, cDBP, and aPWV. After R1, there were no changes in the respective parameters. There were significant interaction effects in pSBD (p < 0.001), pDBP (p < 0.001), cSBP (p < 0.001), cDBP (p = 0.001), and aPWV (p = 0.033). HR significantly increased after both conditions. Only R1 resulted in a significant reduction in SV.

Longer resting intervals during SIT bouts seem to result in more substantial post‐exercise hypotension effects. Time spent at a high percentage of maximal VO2 was not affected by rest interval manipulation.
Extracting white blood cells from blood on microfluidics platform: a review of isolation techniques and working mechanisms
Authors: Laxmi Vijai , Joshi Suhas S , Agrawal Amit
Published: February 2022

Selective isolation of human blood cells has numerous applications in disease diagnostic, prognostics, drug discovery, and drug delivery. In particular, isolation of white blood cells (WBCs) is required for the detection of various diseases such as leukemia, human immunodeficiency virus infection, Epstein–Barr virus (EBV), and cancers. Although the conventional methods of centrifugation and flow cytometry are broadly employed to isolate WBCs in clinical practice, they experience several limitations such as the requirement of the large volume of samples and reagents, trained personnel, large setup, and have an adverse effect on the quality of cells. In contrast, microfluidics-based methods have appeared as a superior approach of cells isolation with advantages such as low cost, ease to operate, compact in size, and requiring a lower sample volume. This review focuses on various microfluidics techniques for isolating WBCs from blood. Here, we have discussed the working mechanism of different microfluidics techniques, microdevice designs, and their performance parameters to isolate WBCs. In addition, a brief description of the numerous advantages and limitations of the existing microdevices and their future prospects aiming to develop an affordable, user-friendly point-of-care solution is provided.
Unconventional superconductivity in UTe2
Authors: Aoki D , Brison J-P , Flouquet J , Ishida K , Knebel G , Tokunaga Y , Yanase Y
Published: February 2022

The novel spin-triplet superconductor candidate UTe2 was discovered only recently at the end of 2018 and already attracted enormous attention. We review key experimental and theoretical progress which has been achieved in different laboratories. UTe2 is a heavy-fermion paramagnet, but following the discovery of superconductivity, it has been expected to be close to a ferromagnetic instability, showing many similarities to the U-based ferromagnetic superconductors, URhGe and UCoGe. This view might be too simplistic. The competition between different types of magnetic interactions and the duality between the local and itinerant character of the 5f Uranium electrons, as well as the shift of the U valence appear as key parameters in the rich phase diagrams discovered recently under extreme conditions like low temperature, high magnetic field, and pressure. We discuss macroscopic and microscopic experiments at low temperature to clarify the normal phase properties at ambient pressure for field applied along the three axis of this orthorhombic structure. Special attention will be given to the occurrence of a metamagnetic transition at H
m = 35 T for a magnetic field applied along the hard magnetic axis b. Adding external pressure leads to strong changes in the magnetic and electronic properties with a direct feedback on superconductivity. Attention is paid on the possible evolution of the Fermi surface as a function of magnetic field and pressure. Superconductivity in UTe2 is extremely rich, exhibiting various unconventional behaviors which will be highlighted. It shows an exceptionally huge superconducting upper critical field with a re-entrant behavior under magnetic field and the occurrence of multiple superconducting phases in the temperature-field-pressure phase diagrams. There is evidence for spin-triplet pairing. Experimental indications exist for chiral superconductivity and spontaneous time reversal symmetry breaking in the superconducting state. Different theoretical approaches will be described. Notably we discuss that UTe2 is a possible example for the realization of a fascinating topological superconductor. Exploring superconductivity in UTe2 reemphasizes that U-based heavy fermion compounds give unique examples to study and understand the strong interplay between the normal and superconducting properties in strongly correlated electron systems.
Classification of macular abnormalities using a lightweight CNN-SVM framework
Authors: Wang Xuqian , Gu Yu
Published: February 2022

Macular abnormalities are the main reason for central vision loss, especially in elderly people. Due to global population aging, a heavy burden will be placed on the health care system. Therefore, it is urgent and necessary to develop an automatic and intelligent tool to identify macular abnormalities. Optical coherence tomography is a non-invasive rapid imaging technique to diagnose macular abnormalities. We propose a lightweight convolutional neural network–support vector machine (CNN-SVM) framework consisting of a novel lightweight CNN backbone and an SVM classifier for the accurate detection of macular abnormalities. The CNN-SVM framework achieves excellent performance based on various metrics (precision, recall, F1-score, and accuracy) with an accuracy of 99.8% and demonstrates strong interpretability using heatmap visualization, outperforming several state-of-the-art models (Joint-Attention Network, OpticNet, MobileNet-V3, DenseNet-169, ResNet-50, lesion-aware CNN, Atten-ResNet, least-squares generative adversarial network and others). The proposed CNN-SVM framework is a feasible and reliable tool for the classification of macular abnormalities and shows potential for diagnostic ophthalmology in clinical practice.
Properties and applications of boron nitride nanotubes
Authors: Turhan Emine Ayşe , Pazarçeviren Ahmet Engin , Evis Zafer , Tezcaner Ayşen
Published: February 2022

Nanomaterials have received increasing attention due to their controllable physical and chemical properties and their improved performance over their bulk structures during the last years. Carbon nanostructures are one of the most widely searched materials for use in different applications ranging from electronic to biomedical because of their exceptional physical and chemical properties. However, BN nanostructures surpassed the attention of the carbon-based nanostructure because of their enhanced thermal and chemical stabilities in addition to structural similarity with the carbon nanomaterials. Among these nanostructures, one dimensional-BN nanostructures are on the verge of development as new materials to fulfill some necessities for different application areas based on their excellent and unique properties including their tunable surface and bandgap, electronic, optical, mechanical, thermal, and chemical stability. Synthesis of high-quality boron nitride nanotubes (BNNTs) in large quantities with novel techniques provided greater access, and increased their potential use in nanocomposites, biomedical fields, and nanodevices as well as hydrogen uptake applications. In this review, properties and applications of one-dimensional BN (1D) nanotubes, nanofibers, and nanorods in hydrogen uptake, biomedical field, and nanodevices are discussed in depth. Additionally, research on native and modified forms of BNNTs and also their composites with different materials to further improve electronic, optical, structural, mechanical, chemical, and biological properties are also reviewed. BNNTs find many applications in different areas, however, they still need to be further studied for improving the synthesis methods and finding new possible future applications.
Preface for International Conference on Recent Trends in Applied Research (ICoRTAR2021) Proceedings
Authors: Chakraverty S , Edeki SO , Adinya I , González-Gaxiola O , Hammouch Z , Fotso TJ , Owino JN , Jena RM , Bhattacharya T , Zelibe SC , Animasaun IL , Jena SK , Andam Boiquaye PS , Athavale VA
Published: February 2022

The 2nd International Conference on Recent Trends in Applied Research (ICoRTAR2021) is a virtual conference based on enabling platforms to present research results relating to global issues. ICoRTAR2021 is hosted by ResearchAcad HUB with channels and human resources in India, Nigeria, Kenya, Mexico, Morocco, Ghana, and Cameroon. Thus, the conference was held virtually from October 08-09, 2021.The Theme of this second series (ICoRTAR2021) is ‘Effective Research Practices Amidst Covid-19 Pandemic’. ICoRTAR202 aims to bring together faculty members, leading scientists, academicians, research & graduate scholars, industrial professionals, and decision-makers to discuss current trends in research with implications on global phenomena such as COVID-19, economic growth & development, and so on. Basically, the ICORTAR2021 participants discussed extensively vital topics, recent trends, and issues in Applied Science, Mathematical Sciences, Computational Science, Engineering Science, Physics and Nanoscience, Financial Modeling, Artificial Intelligence, and Stochastic Dynamics. Five plenary sessions with different aspects of the ICORTAR2021 Theme were held successfully. The first keynote speaker, Professor Snehashish Chakraverty from the National Institute of Technology, Rourkela, INDIA, spoke on ‘Estimating Corona Positive Cases via Uncertainty Modeling.’ The second keynote speaker, Professor/Dr. Sannan khan, from the University Putra Malaysia, spoke on ‘Online Learning: A Panacea in the Time of COVID-19 Crisis.’

List of editorial board/organizing committee are available in the pdf.
Direct Solution of the Black-Scholes PDE Models with Non-Integer Order
Authors: Ogunyebi SN , Fadugba SE , Ogunlade TO , Adebayo KJ , Babalola BT , Faweya O , Emeka HO
Published: February 2022

This paper presents a direct solution of Black-Scholes PDE models with non-integer order via the non-integer iterative method. The Black-Scholes PDE models of non-integer order became popular and accepted globally by option traders for the valuation of financial derivatives over the period of time and the study of its numerical approaches has a wide range of applications in the real world.. The performance of the method has been confirmed and measured.
Iterative Method for Approximate-Analytical Solutions of Linear Schrödinger Equation
Authors: Akinlabi G.O. , Braimah J.A. , Abolarinwa A. , Edeki S.O.
Published: February 2022

In this study, the modified Picard Iterative Method (MPIM) is used to provide analytic and numerical solutions to linear Schrödinger Equations. These approximate-analytical solutions for the examples under consideration are easily computed. The suggested method is employed without any transformation, discretization, linearization, or limiting assumptions. The obtained results are similar to their exact forms. As a result, the approach is highly suggested for both linear and non-linear time-space fractional partial differential models with applications in various applied disciplines.
Direct Visual Odometry by Fusing Luminosity and Depth Information
Authors: Yin Jinming , Zhou Haibo , Zhang Haoxin , Li Chenming , Sun Guoqing
Published: February 2022

In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. The algorithm proposed in this paper uses Kinect 2 to collect RGB images with the corresponding depth information, and selects points with large changes of gray gradient to construct a luminosity error function and uses the corresponding depth information to construct a depth error function. The two error functions are merged into one function and converted into the least squares function of the pose of camera, the Levenberg-Marquardt algorithm is used to solve the camera pose. Finally, the Graph optimization theory and the g2o library are used to optimize the initial pose. Experiments show that the algorithm can reduce the error to a certain extent and reduce the drift caused by illumination changes.
Analysis on Influence Degree of Static and Dynamic Kinematics Error of EMA Pitch Servo Mechanism
Authors: Wan Ziping , Xie Xin , He Aihua , Fan Dapeng
Published: February 2022

Aiming at the problem that the influence degree of static and dynamic accuracy of EMA pitch servo mechanism is not clear, this paper analyzes the influence degree of static and dynamic error of EMA pitch servo mechanism based on high-precision pointing system. In terms of static error, based on the static error kinematics model, by establishing the static error probability model and solving the static error model, it is pointed out that the geometric error is the largest factor affecting the static kinematics error. In terms of the influence degree of dynamic error, based on the dynamic error kinematics model, through the establishment of the dynamic error separation process, and based on the test and mechanical experiment platform, it is pointed out that the general trend error is the largest factor affecting the dynamic kinematics error. Through the analysis of the influence degree of static and dynamic kinematic errors, the accuracy improvement direction of the EMA pitch servo mechanism is defined.
The Premise of Automatic Four-wheel Alignment: Intelligent Identification and Processing of Rim Targets
Authors: Wang Yifan , Yao Meibao
Published: February 2022

With high demand of automatic development of four-wheel alignment technology, this paper explores the core technology of automatic detection of four-wheel alignment instrument: intelligent identification and processing of rim target. According to the working process of 3D vision four-wheel aligner, starting from the target positioning, this paper proposes a method based on matching idea to realize the target recognition and positioning; Then the target area image is segmented to obtain the center coordinates on it, which provides a basis for the subsequent calculation of four-wheel positioning parameters. Simulation results demonstrate the effectiveness of our method.
Design and Analysis of a Three-DOF Assembly Robot
Authors: Wang Wei , Zhao Jinzhi , Zhu Chunyi , Ma ZiMeng , Sun WenPei , Wang Bin
Published: February 2022

The paper designs a Three-DOF assembly robot based on the planar quadrilateral mechanism. It is mainly used for the handling and assembly of parts, featuring small size, low cost and simple transmission principle. The robot combined with the characteristics of the planar quadrilateral mechanism, a detailed kinematic analysis is carried out and the forward and inverse solutions are obtained. The paper use MATLAB to analyze its end working space and motion trajectory according to the constraint equation. Finally, the motion simulation is carried out using ADAMS, which verifies the effectiveness of the design and provides a theoretical basis for the kinematics research and structural optimization of the assembly robot.
Energy-aware Distributed Formation Control for Multiple Unmanned Vehicles
Authors: Xie Qifeng , Xia Yaoxin , Wang Zhe , Zhang Jianjie , Kong Delei , Li Heng
Published: February 2022

Multi-agent system is an important branch of distributed artificial intelligence. As a new field of engineering control science, it has developed vigorously in recent years. From a practical point of view, most existing agents, such as unmanned vehicles and unmanned aerial vehicles, are powered by mobile power supply. Therefore, it is of practical significance to consider the battery state and explore how to improve the overall battery life when designing the multi-agent control strategy. In this paper, we first introduced a classical algorithm based on the consensus theory, and on this basis, the overtaking action in the formation is realized by changing the topology, so as to simulate the individuals (action individuals) who consume more energy in the formation; then we discussed how to improve the endurance of high-energy-consuming individuals in formation through average energy consumption in strategy design, and proposed an algorithm based on energy-aware. Finally, the MATLAB simulation results show that compared with the classical algorithm, the proposed algorithm can improve the endurance of formation.
Dynamics Modeling and Vibration Analysis of Planar 3-RRR Flexible Parallel Robot
Authors: Chen Xinyu , Wang Leilei , Chen Yayu , Guo Fei
Published: February 2022

To address the issue of complicated dynamics modeling and difficult solution of 3-RRR flexible parallel robot, we propose a method of building a flexible parallel robot dynamics model using screw bond graph. Firstly, the structure of the flexible parallel robot is analyzed; secondly, the multi-energy domain dynamics model of the robot is built using screw bond graph and vector bond graph considering the elastomeric transformation in the middle flexible linkage; finally, based on the model, the closed-loop control strategy for suppressing harmful vibration of the system, and the speed loop error compensation for different control parameters is also studied. Author establishes the vector bond and spin bond conversion junction type based on the power conservation transfer principle, which realizes the combination of rigid and flexible system in this paper. And we construct the dynamical model of multi-energy domain flexible parallel robot system.
A Fusion Feature for the Oestrous Sow Sound Identification Based on Convolutional Neural Networks
Authors: Chen Peng , Yin Dan , Yang Bo , Tang Wensheng
Published: February 2022

Affected by expert experience and individual differences in sows, artificial identification of sows in estrus is prone to various problems leading to low efficiency. A reliable audio classifier helps to identify the sows for estrus. In this work we propose a novel approach for estrus sound recognition using a fusion of two representative features as inputs and Convolutional Neural Networks (CNN) as training model. We demonstrate that the fusion feature generalizes better on the CNN model with training data under a real farm environment and gives remarkably higher test performance. Our model is also compared with some works that are published recently and achieve state-of-the-art performance.
Research on Object Detection in Campus Scene Based on Faster R-CNN
Authors: Tong Yan , Ma Hang , Zhang Shizhuo , Wu Xibao , Chen Wenbai
Published: February 2022

This paper focuses on the research of object detection technology in Smart Campus scene. Firstly, an object detection algorithm is constructed based on Faster R-CNN two-stage detector, which is trained on COCO2017 and VOC0712 public datasets, and the performance of the trained detector is evaluated. On this basis, the actual image data is collected from the campus scene, and divided into different groups according to the shooting angle, lighting conditions, occlusion, etc. After using the algorithm to detect, the results are analyzed systematically. In general, occlusion between objects is more likely to lead to unsatisfactory detection results. In the future, we still need to find a non-maximum suppression method which can effectively distinguish the same object repeated detection and occlusion between objects.
Sparse Enhancement Fuzzy Broad Learning System Based on Multiple Clustering Methods
Authors: Wang Jing , Lyu Shubin , Duan Junwei , Lin Zhengchun
Published: February 2022

The fuzzy broad learning system (FBLS) is a novel, neuro-fuzzy model. Different from other neuro-fuzzy models with low efficiency, FBLS can obtain better performance using less computation time. However, the clustering-based fuzzy rule generation approach makes the performance of FBLS limited. Meanwhile, it is unknown how the enhancement layers from FBLS contribute to the model performance, which hinders the further extension of the model structure. To solve these problems, we propose a sparse enhancement fuzzy broad learning system (SEFBLS). It uses only a sparse set of enhancement nodes to replace the original enhancement node groups. To obtain a better representation, the designed principal component-based sparse autoencoder is used for feature reconstruction and information removal. In addition, to explore the optimal model structure and performance, multiple clustering methods (fuzzy and non-fuzzy) are used to improve SEFBLS. The results on 10 UCI classification datasets show that the proposed SEFBLS obtains competitive accuracy using fewer fuzzy rules.
A Collaborative Optimization Algorithm for Ship Damage Stability Design
Authors: Huang Xiaohao , Yang Genke , Yang Chunlei , Sheng Qingwu , Pan Changchun
Published: February 2022

The damage stability of ship is a key performance metric in ship design. The challenge of damage stability optimization is a complex calculation process with multi scene dynamic inflow simulation relying on the cooperation of ship design software such as NAPA. Our work is to establish a collaborative optimization framework to effectively coordinate reinforcement learning (RL) with particle swarm optimization (PSO) and NAPA. The collaboration of RL and PSO realizes the update direction selection of watertight bulkhead position scheme. The collaboration of PSO and Napa promotes the iterative process of watertight bulkhead position and damage stability value A. The experimental results show that compared with the traditional damage stability optimization method, the collaborative optimization method improves the damage stability by 2.36% and reduces the calculation time significantly.
Penetration Depth Prediction Based on Data Fusion
Authors: Wang Jimin , Jiang Mingwei , Ji Changzheng , Zhang Lei
Published: February 2022

This paper presents a penetration depth prediction model based on data fusion. The parameters of the penetration analysis are divided into different evaluation spaces, and then empirical algorithms are evaluated and the better algorithm is selected in each evaluation space. A large number of simulation data is generated to solve the problem of lack of experimental data. Two BP neural network prediction models are built based on experiment data and simulation data, respectively, and the genetic algorithm is used for parameter optimization. Finally, the attention mechanism is used to fuse the two models to generate the final dimensionless penetration depth. The experiment results show that the data fusion model has good prediction accuracy both in the whole parameter space and each evaluation space.
Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learning
Authors: Ai P. , Deng Z. , Wang Y. , Shen C.
Published: February 2022

Characterizing uncertainty is a common issue in nuclear
measurement and has important implications for reliable physical
discovery. Traditional methods are either insufficient to cope with
the heterogeneous nature of uncertainty or inadequate to perform
well with unknown mathematical models. In this paper, we propose
using multi-layer convolutional neural networks for empirical
uncertainty estimation and feature extraction of nuclear pulse
signals. This method is based on deep learning, a recent development
of machine learning techniques, which learns the desired mapping
function from training data and generalizes to unseen test
data. Furthermore, ensemble learning is utilized to estimate the
uncertainty originating from trainable parameters of the network and
to improve the robustness of the whole model. To evaluate the
performance of the proposed method, simulation studies, in
comparison with curve fitting, investigate extensive conditions and
show its universal applicability. Finally, a case study is made
using data from a NICA-MPD electromagnetic calorimeter module
exposed to a test beam at DESY, Germany. The uncertainty estimation
method successfully detected out-of-distribution samples and also
achieved good accuracy in time and energy measurements.
Fluence map analyzer reduces low dose volume in locally advanced post mastectomy breast cancer patients
Authors: Uğurlu Berat Tuğrul , Türk Ali , Celasun Mustafa Gürol , Hekimoglu Azamat
Published: February 2022

Multi-leaf collimator dose leakage in intensity-modulated radiotherapy (IMRT) plans causes higher low dose volume which increases the long-term risks of radiotherapy. We have developed Fluence Map Analyzer (FMA) program that suggests the ideal field geometry to reduce low dose volume in locally advanced breast cancer IMRT plans. In this comparative experimental study, FMA has been applied to standard IMRT plans (STD-IMRT) of randomly selected 15 left and 15 right-sided locally advanced breast cancer patients. All patients underwent a modified radical mastectomy. The chest wall, IMN, axillary, and supraclavicular lymph nodes are included in planning target volume (PTV). The heart, lungs, contralateral breast, and medulla spinalis were delineated as organs at risk (OARs). Two sets of plans, namely STD-IMRT and FMA-IMRT, were generated for each patient. The dosimetric analysis was performed using dose-volume histogram (DVH) and standard evaluation parameters of PTV and OARs. No differences could be observed among the two techniques for PTV coverage. However, FMA-IMRT plans achieved significantly lower V5 volumes and mean doses of the heart, lungs, contralateral breast, and body contours. FMA-IMRT used a smaller number of sub-fields and fewer monitor units (MU). FMA automizes the field geometry determination process for locally advanced breast cancer IMRT planning while reducing low dose volume significantly.
Schatten class Hankel operators on the Segal-Bargmann space and the Berger-Coburn phenomenon
Authors: Hu Zhangjian , Virtanen Jani
Published: February 2022

We give a complete characterization of Schatten class Hankel operatorsHfH_facting on weighted Segal-Bargmann spacesF2(φ)F^2(\varphi )using the notion of integral distance to analytic functions inCn\mathbb {C}^nand Hörmander’s∂¯\bar \partial-theory. Using our characterization, forf∈L∞f\in L^\inftyand1>p>∞1>p>\infty, we prove thatHfH_fis in the Schatten classSpS_pif and only ifHf¯∈SpH_{\bar {f}}\in S_p, which was previously known only for the Hilbert-Schmidt classS2S_2of the standard Segal-Bargmann spaceF2(φ)F^2(\varphi )withφ(z)=α|z|2\varphi (z) = \alpha |z|^2.
TheJungle BookIs Not a Book. Adaptation, Intertextuality, and the Hegemonic Text
Authors: Oulton Harry
Published: February 2022

In this paper, Rudyard Kipling’s The Jungle Book is viewed in its role as a source text. The paper considers how genre and structure are affected across a range of adaptations, both novel to film/TV and novel to novel. This process allows for the consideration of two linked but discrete outcomes of adaptation; firstly, an exploration of what is currently understood by the signifier ‘the Jungle Book’, and secondly, what is transferred when very different acts of adaptation are performed on the same text. In the same way that the hermeneutic circle shows the impossibility of interpreting any individual part without reference to the whole, there is a particular type of text, which I will call hegemonic, which can only be fully explicated when it is considered as being composed both of itself and its adaptive iterations.
A Systematic Review of Childhood Adversity and Executive Functions Outcomes among Adults
Authors: Lund Jessie I , Boles Kara , Radford Abbey , Toombs Elaine , Mushquash Christopher J
Published: February 2022

Objective

Exposure to adverse childhood experiences (ACEs) is linked to disruptions in typical neurodevelopment of brain structures and functioning, including changes in executive functions. Although the relationships among ACEs, executive functions, and psychopathology are well documented in pediatric samples, a systematic review is needed to examine these relationships in adulthood.

Method

A systematic review examining the link between ACEs and executive functions among adult clinical and nonclinical samples was conducted across 33 scientific and grey literature databases. Among reviewed studies, 17 sources met review criteria, with 11 involving clinical samples and six involving nonclinical samples.

Results

Among clinical samples, evidence suggested that ACEs increased risk for executive function difficulties among those diagnosed with bipolar disorder, schizophrenia, post-traumatic stress disorder, and those experiencing a first episode of psychosis, however not within those diagnosed with major depressive disorder. Among nonclinical samples, executive function difficulties associated with ACEs were found among those in early and middle adulthood. Not all retrieved sources showed consistent findings, and two studies described better executive function outcomes among those who experienced childhood sexual abuse and emotional abuse.

Conclusions

Executive function difficulties associated with ACEs appear to persist into adulthood, though inconsistently. Future research may further explore distinct differences among specific ACEs and executive function difficulties to further inform ongoing prevention and treatment efforts.
Integrated unsupervised–supervised modeling and prediction of protein–peptide affinities at structural level
Authors: Zhou Peng , Wen Li , Lin Jing , Mei Li , Liu Qian , Shang Shuyong , Li Juelin , Shu Jianping
Published: February 2022

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein–protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein–peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom–residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised–supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised–supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson’s correlation coefficients Rp = 0.635 and 0.406, respectively.
Mapping the developing human cardiac endothelium at single-cell resolution identifies MECOM as a regulator of arteriovenous gene expression
Authors: McCracken Ian R , Dobie Ross , Bennett Matthew , Passi Rainha , Beqqali Abdelaziz , Henderson Neil C , Mountford Joanne C , Riley Paul R , Ponting Chris P , Smart Nicola , Brittan Mairi , Baker Andrew H
Published: February 2022

Aims

Coronary vasculature formation is a critical event during cardiac development, essential for heart function throughout perinatal and adult life. However, current understanding of coronary vascular development has largely been derived from transgenic mouse models. The aim of this study was to characterize the transcriptome of the human foetal cardiac endothelium using single-cell RNA sequencing (scRNA-seq) to provide critical new insights into the cellular heterogeneity and transcriptional dynamics that underpin endothelial specification within the vasculature of the developing heart.

Methods and results

We acquired scRNA-seq data of over 10 000 foetal cardiac endothelial cells (ECs), revealing divergent EC subtypes including endocardial, capillary, venous, arterial, and lymphatic populations. Gene regulatory network analyses predicted roles for SMAD1 and MECOM in determining the identity of capillary and arterial populations, respectively. Trajectory inference analysis suggested an endocardial contribution to the coronary vasculature and subsequent arterialization of capillary endothelium accompanied by increasing MECOM expression. Comparative analysis of equivalent data from murine cardiac development demonstrated that transcriptional signatures defining endothelial subpopulations are largely conserved between human and mouse. Comprehensive characterization of the transcriptional response to MECOM knockdown in human embryonic stem cell-derived EC (hESC-EC) demonstrated an increase in the expression of non-arterial markers, including those enriched in venous EC.

Conclusions

scRNA-seq of the human foetal cardiac endothelium identified distinct EC populations. A predicted endocardial contribution to the developing coronary vasculature was identified, as well as subsequent arterial specification of capillary EC. Loss of MECOM in hESC-EC increased expression of non-arterial markers, suggesting a role in maintaining arterial EC identity.