bluuebunny/crossref_metadata_2025 huggingface dataset:
Does it matter where you invest? The impact of foreign direct investments on domestic job creation and destruction
Authors: Ni Bin , Kato Hayato , Liu Yang
Published: May 2022

This study examines how foreign direct investment (FDI) affects domestic employment by using unique division‐level data of Japanese firms. Contrary to most previous studies focusing on the effect of FDI on net employment growth, we decompose it into job creation (JC) and job destruction (JD) for each individual firm. We find that FDI destination plays an important role: FDI to Asia increases JC, whereas FDI to Europe/North America decreases it; furthermore JD decreases, regardless of FDI destination. A frictional search‐and‐matching model with heterogeneous jobs can explain the differential effects. The model provides additional predictions on JC and JD by job type, which are also empirically confirmed.
Manipulating histone acetylation leads to antitumor effects in hemangiosarcoma cells
Authors: Suzuki Tamami , Aoshima Keisuke , Yamazaki Jumpei , Kobayashi Atsushi , Kimura Takashi
Published: May 2022

Canine hemangiosarcoma (HSA) is a malignant tumour derived from endothelial cells. No effective treatment has yet been developed because of the lack of understanding of its pathogenesis. Histone acetylation, an epigenetic modification, is highly associated with cancer pathogenesis. Manipulating histone acetylation by histone deacetylase inhibitors (HDACi) or bromodomain and extraterminal domain inhibitors (BETi) is one approach to treat various cancers. However, the role of histone acetylation in HSA remains unknown. This study aimed to investigate how histone acetylation functions in HSA pathogenesis using two HDACi, suberanilohydroxamic acid (SAHA) and valproic acid (VPA), and one BETi, JQ1, in vitro and in vivo. Histone acetylation levels were high in cell lines and heterogeneous in clinical cases. SAHA and JQ1 induced apoptosis in HSA cell lines. HSA cell lines treated with SAHA and VPA upregulated inflammatory‐related genes and attracted macrophage cell line RAW264 cells, which suggests that SAHA and VPA can affect immune responses. JQ1 stimulated autophagy and inhibited the cell cycle in HSA cell lines. Finally, we demonstrated that JQ1 suppressed HSA tumour cell proliferation in vivo although SAHA and VPA did not affect tumour growth. These results suggest that BETi can be alternative drugs for HSA treatment. Although further research is required, our study indicated that dysregulation of histone acetylation is likely to be involved in HSA malignancy.
Nasopharyngeal collapse can be identified on radiography in healthy male Beagle dogs without cardiopulmonary diseases
Authors: Na Hyemin , Lee Sang‐Kwon , Choi Hojung , Lee Youngwon , Lee Kija
Published: May 2022

Pharyngeal collapse has been described as a decrease of more than 50% in the diameter of the lumen. However, there has been no study on changes in the pharyngeal lumen in clinically normal dogs. The hypothesis of this prospective, observational, and pilot study was that change in nasopharyngeal lumen would be over 50% in Beagle dogs without cardiopulmonary diseases. Thus, we assessed the nasopharyngeal luminal change using cervical radiography in Beagle dogs without respiratory signs or cardiac abnormalities. The study sample included 42 Beagle dogs without cardiopulmonary diseases. Cervical radiographies were acquired during inspiration and expiration, and nasopharyngeal luminal change (%) was calculated. The median nasopharyngeal luminal change was 16.6%, and nasopharyngeal luminal change over 50% was identified in two dogs. The nasopharyngeal luminal change was significantly associated with body condition score and body weight. There was no correlation between nasopharyngeal luminal change and age. This study revealed that change in nasopharyngeal lumen over 50% can be identified in Beagle dogs without cardiopulmonary diseases and may be over‐diagnosed as partial pharyngeal collapse. Further studies for comparing change in nasopharyngeal lumen between clinically normal dogs and dogs with respiratory signs are warranted.
Fast three‐dimensional contrast‐enhanced magnetic resonance angiography of the canine lumbar spinal cord vascular supply: A feasibility study
Authors: Santifort Koen , Beukers Martijn , Gil Vicente Aige , Pijnenburg Jeanette , Van Soens Iris , Mandigers Paul , Bergknut Niklas
Published: May 2022

Magnetic resonance angiography (MRA) is the noninvasive spinal cord vascular imaging modality of choice in human medicine. The aim of this exploratory, prospective, descriptive study was to assess the feasibility of fast three‐dimensional (3D) contrast‐enhanced (CE) MRA for visualization of spinal vascular structures in the canine lumbar region. Fourteen canine patients weighing > 5 kg were included. The lumbar arteries were consistently visualized (14/14;100%). Spinal arteries, radicular branches, great radicular artery (of Adamkiewicz), ventral spinal artery, and dorsal spinal arteries were not visualized (0/14;0%). The internal vertebral venous plexus was visualized in 11 of 14 (79%) dogs. Overall, the results of this study show that fast 3D CE‐MRA of the lumbar region in dogs is feasible. However, the smaller arteries responsible for the spinal cord vascular supply were not visualized in this study.
National savings in freshwater consumption in Iran by virtual water imports (a case study on cereals)
Authors: Hekmatnia Mehran , Safdari Mehdi , Ahmadi Asaad , Monibi Hamidreza
Published: May 2022

Improper use of water resources for agricultural production has led to a global decline in freshwater resources because more than 80% of the world's freshwater resources are used for agricultural purposes. Most countries can compensate for the water restrictions for food production by importing virtual water through international trade of agricultural commodities by which virtual water is transferred from the production regions to the consumption regions. The present research aimed to investigate the possibility of national savings in freshwater consumption in Iran by importing virtual water of agricultural products (cereals) over the period of 2001–2018. The results show that importing cereals has contributed to national savings on freshwater consumption in Iran, amounting to 21 167 million m3 per year. The total saving is estimated at 381 011 million m3 out of which 53.18% is green water, 34.09% is blue water, and 12.73% is grey water.
Multidrug‐resistant Escherichia coli from free‐living pigeons (Columba livia): Insights into antibiotic environmental contamination and detection of resistance genes
Authors: Bueno Thaís Silveira , Loiko Márcia Regina , Vidaletti Marina Roth , de Oliveira Júlia Alves , Fetzner Tiago , Cerva Cristine , de Moraes Lucas Brunelli , De Carli Silvia , Siqueira Franciele Maboni , Rodrigues Rogério Oliveira , Coppola Mario de Menezes , Callegari‐Jacques Sidia Maria , Mayer Fabiana Quoos
Published: May 2022

Bacterial resistance is a public and one health problem. Free‐living birds can be reservoirs of multidrug‐resistant bacteria and resistance genes. This study aimed to characterize the antimicrobial resistance of Escherichia coli isolated from free‐living urban pigeons (Columba livia) in South Brazil. Ninety‐two animals were sampled, and one isolate was obtained from each one. The isolates were characterized, and the antimicrobial resistance profile and beta‐lactam and colistin resistance genes were investigated. The isolates were classified as phylogroups B1 (35%), B2 (33%), A (16%) and D (16%), and 14% of the strains had the eae virulence gene. All isolates were resistant to at least one antimicrobial, and 63% of them were multidrug‐resistant. Geographical location where the pigeons were captured and presence of the eae gene were associated with multidrug resistance. blaVIM and mcr‐1 genes were detected in one and two isolates, respectively. This is the first report of these genes in E. coli of pigeons. The blaVIM‐positive isolate was classified as Shiga toxin‐producing E. coli, and the isolates with mcr‐1 were classified as Enterohaemorrhagic E. coli and Enteropathogenic E. coli, which raise additional concerns related to public health since these are zoonotic pathotypes. The results reveal that pigeons carry multidrug‐resistant pathogenic E. coli, which may interest public health. Nonetheless, further studies on whether these animals are sources of contamination for humans must be performed to understand their role in spreading antimicrobial resistance.
Slowly‐adapting type II afferents contribute to conscious touch sensation in humans: Evidence from single unit intraneural microstimulation
Authors: Watkins Roger Holmes , Durao de Carvalho Amante Mario , Backlund Wasling Helena , Wessberg Johan , Ackerley Rochelle
Published: May 2022

Slowly‐adapting type II (SA‐II, Ruffini) mechanoreceptive afferents respond well to pressure and stretch, and are regularly encountered in human microneurography studies. Despite an understanding of SA‐II response properties, their role in touch perception remains unclear. Specific roles of different myelinated Aβ mechanoreceptive afferents in tactile perception have been revealed using single unit intraneural microstimulation (INMS), via microneurography, recording from and then electrically stimulating individual afferents. This method directly links single afferent artificial activation to perception, where INMS produces specific ‘quantal’ touch percepts associated with different mechanoreceptive afferent types. However, SA‐II afferent stimulation has been ambiguous, producing inconsistent, vague sensations, or no clear percept. We physiologically characterized hundreds of individual Aβ mechanoreceptive afferents in the glabrous hand skin and examined the subsequent percepts evoked by trains of low amplitude INMS current pulses (<10 μA). We present 18 SA‐II afferents where INMS resulted in a clear, electrically evoked sensation of large (∼36 mm2) diffuse pressure, which was projected precisely to their physiologically‐defined receptive field in the skin. This sensation was felt as natural, distinctive from other afferents, and showed no indications of multi‐afferent stimulation. Stimulus frequency modulated sensation intensity and even brief stimuli (4 pulses, 60 ms) were perceived. These results suggest that SA‐II afferents contribute to perceived tactile sensations, can signal this rapidly and precisely, and are relevant and important for computational models of touch sensation and artificial prosthetic feedback.

Key points

Slowly adapting type II mechanoreceptors (SA‐IIs) are primary sensory neurons in humans that respond to pressure and stretch applied to the skin.

To date, no specific conscious correlate of touch has been linked to SA‐II activation.

Using microneurography and intraneural microstimulation to stimulate single sensory neurons in human subjects, we find a specific sensation linked to the activation of single SA‐II afferents.

This sensation of touch was reported as gentle pressure and subjects could detect this with a high degree of accuracy.

Methods of artificial tactile sensory feedback and computational models of touch should include SA‐IIs as meaningful contributors to the conscious sensation of touch.
Composition of Cosmic Spherules from Ferromanganese Crusts of the Magellan Seamounts
Authors: Savelyev D. P. , Savelyeva O. L. , Moskaleva S. V. , Rashidov V. A.
Published: May 2022

Abstract—

2720 cosmic spherules extracted from ferromanganese crusts sampled at two guyots of the Magellan Seamounts were studied using a scanning electron microscope. In comparison with collections of modern cosmic spherules, our samples are significantly richer in I-type spherules (consisting of Fe oxides, often with a Fe–Ni metal core). The compositions of 406 metal cores were analyzed. Six spherules with cores significantly enriched in Co (>5 wt %) were found; these were the first spherules of this composition ever found worldwide. Such a high Co content in the cores cannot be explained by the evolution of a micrometeorite of chondrite composition at its melting. Some groups of the spherules have either elevated or lower Co concentrations than those expected according to the evolutionary trend of spherules at oxidation during the atmosphere entry. These groups apparently reflect the composition of the initial micrometeorites. It is proposed to use the composition of the cores of cosmic spherules to reveal the time variation in the composition of cosmic dust entering the Earth. Several spherules were found with small cores compared to the oxide shell; these cores are characterized by the presence of a significant amount of platinum group elements (PGE) (up to 2.4 wt % PGE). The averaged composition of PGE in the high-Ni core, which is the richest in PGE, shows a pattern close to that of chondrite. The collection contains 23 spherules that host PGE nuggets. In three spherules, micrometer-sized nuggets with various PGE were found (one nugget is Os + Ir + Ru, and two others contain all PGE). In 20 spherules, nanometer-sized nuggets of rhodium platinum were found. The largest nugget (3 μm) has PGE ratios close to chondritic, with the exception of a significant depletion in Pd.
Electronic Structure of Magnetic Topological Insulators Mn(Bi1 – xSbx)2Te4 with Various Concentration of Sb Atoms
Authors: Glazkova D. A. , Estyunin D. A. , Klimovskikh I. I. , Makarova T. P. , Tereshchenko O. E. , Kokh K. A. , Golyashov V. A. , Koroleva A. V. , Shikin A. M.
Published: May 2022

Intrinsic magnetic topological insulator MnBi2Te4 provides a promising platform to implement the quantum anomalous Hall effect at increased temperatures and other unique topological effects. However, to do this, the energy gap opening at the Dirac point should be located at the Fermi level. One of the widely used methods to shift the Dirac point toward the Fermi level is the partial substitution of Bi atoms for Sb atoms. In this work, the electronic structure of the core levels and valence band of Mn(Bi1 –xSbx)2Te4 compounds with various concentration x of Sb atoms from 0 to 1 has been studied. It has been shown that the Dirac point with an increase in the concentration of Sb atoms is shifted toward the Fermi level and becomes localized at it when x ≈ 0.3. In this case, the “rigid” shift of the valence band, including the Mn 3d level, has been observed without changes in the structure of the valence and conduction bands. The concentration dependence of the shift of the Dirac point is approximated by a square root function, which corresponds to a linear increase in the charge carrier density.
Butterfly Effect in a System of Quantum Dots in the Sachdev–Ye–Kitaev Model
Authors: Lunkin A. V.
Published: May 2022

A method has been proposed to calculate the out-of-time time ordered correlator in the generalization of the Sachdev–Ye–Kitaev model with a nonzero spatial dimension. The result is applicable not only at small times, when the chaotic properties of the system are developed weakly, but also at large times of about the Erenfest time. It has been shown that information on the applied perturbation, which is described by this correlator, propagates ballistically in the form of a front. The velocity of the front for models of this type has been calculated for the first time.
Map of Two-Dimensional Tungsten Chalcogenide Compounds (W–S, W–Se, W–Te) Based on USPEX Evolutionary Search
Authors: Sukhanova E. V. , Kvashnin A. G. , Agamalyan M. A. , Zakaryan H. A. , Popov Z. I.
Published: May 2022

New two-dimensional nanostructures of W–X composition (X = S, Se, Te) are predicted using the evolutionary algorithm implemented in the USPEX software package. Based on the results, two new thermodynamically and dynamically stable two-dimensional W3S5 and W5Te2 structures are proposed. The density functional theory study of the electronic and optical properties of these monolayers is carried out. It is demonstrated that the predicted W3S5 and W5Te2 structures show semiconducting properties with band gaps of 0.62 and 0.40 eV, respectively, and the calculated extinction spectrum indicates a broad absorption band in the visible spectral range, making these materials promising for applications in photovoltaics and solar energy.
Descriptive Complexity of Reversible Languages Having Finitely Many Reduced Automata
Authors: Gelle Kitti , Iván Szabolcs
Published: May 2022

Reversible forms of computations are often interesting from an energy efficiency point of view. When the computation device in question is an automaton, it is known that the minimal reversible automaton recognizing a given language is not necessarily unique, moreover, there are languages having arbitrarily large reversible recognizers possessing no nontrivial “reversible” congruence. Building atop on our earlier result, we show that the corresponding decision problem is [Formula: see text]-complete, and that even in the case when there are only finitely many such reversible recognizers, the largest one among them can be exponentially larger than the minimal automaton. Both results hold for the case of binary alphabets.
Descriptional Complexity of Non-Unary Self-Verifying Symmetric Difference Automata
Authors: Marais Laurette , van Zijl Lynette
Published: May 2022

Unary self-verifying symmetric difference automata have a known tight bound of [Formula: see text] for their state complexity. We now consider the non-unary case and show that, for every [Formula: see text], there is a regular language [Formula: see text] accepted by a non-unary self-verifying symmetric difference nondeterministic automaton with [Formula: see text] states, such that its equivalent minimal deterministic finite automaton has [Formula: see text] states. Furthermore, given any SV-XNFA with [Formula: see text] states, it is possible, up to isomorphism, to find at most another [Formula: see text] equivalent SV-XNFA. Finally, we show that for a certain set of non-unary SV-XNFA, [Formula: see text] is a tight bound on the state complexity.
Isolated Rupture in Thorny Networks
Authors: Yildirim Halil İbrahim , Berberler Zeynep Nihan
Published: May 2022

The robustness evaluates the capability of networks in resisting failures or attacks on some parts of networks. The concept of vulnerability is very important in network analysis. Isolated rupture degree is a novel graph-theoretic concept defined as a measure of network vulnerability. In this paper, the relationships between isolated rupture degree and some other vulnerability parameters such as isolated scattering number and isolated toughness are established. Exact values for isolated rupture degree of thorny networks are obtained.
A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition
Authors: De Lope Javier , Graña Manuel
Published: May 2022

In recent years, speech emotion recognition (SER) has emerged as one of the most active human–machine interaction research areas. Innovative electronic devices, services and applications are increasingly aiming to check the user emotional state either to issue alerts under some predefined conditions or to adapt the system responses to the user emotions. Voice expression is a very rich and noninvasive source of information for emotion assessment. This paper presents a novel SER approach based on that is a hybrid of a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. Mel-frequency log-power spectrograms (MFLPSs) extracted from audio recordings are parsed by a sliding window that selects the input for the TD-CNN. The TD-CNN transforms the input image data into a sequence of high-level features that are feed to the LSTM, which carries out the overall signal interpretation. In order to reduce overfitting, the MFLPS representation allows innovative image data augmentation techniques that have no immediate equivalent on the original audio signal. Validation of the proposed hybrid architecture achieves an average recognition accuracy of 73.98% on the most widely and hardest publicly distributed database for SER benchmarking. A permutation test confirms that this result is significantly different from random classification ([Formula: see text]). The proposed architecture outperforms state-of-the-art deep learning models as well as conventional machine learning techniques evaluated on the same database trying to identify the same number of emotions.
Combining rule-based reasoning and meta-path-based link prediction for heterogeneous target operational network reconstruction
Authors: Qin Changjiang , Yu Lianfei , Feng Yanghe , Huang Jincai , Cheng Guangquan , Fan Changjun
Published: May 2022

Predicting the connections or interactions between nodes in networks remains a fundamental task in network analysis. In target systems, nodes with different types are connected with each other through different relations, as in typical heterogeneous networks. Link prediction can help identify implied information in heterogeneous target operational network (HTON) to boost network reconstruction. In view of the complex network structure of target systems and diversified types of edges alongside the low accuracy and poor interpretability issues of previous methods, we proposed an HTON reconstruction framework (HTONRP) incorporating rule-based reasoning and meta-path-based link prediction. To start with, the link rules are extracted based on operational doctrine, military theory, system formulation, etc., and then they are reasoned with initial nodes and relations (input) and the inferred target relations (output) to improve the target systems architecture. Afterwards, the data are input into the meta-path-based link prediction model of HTON, through which the architecture of target systems is further perfected. The experiments are carried out on real data instances of HTON and results suggest that the proposed method can effectively improve the prediction performance and enhance model interpretability compared to traditional baselines.
Evaluating the effect of different photon beams on intensity modulated radiation therapy for liver cancer
Authors: Hassn S. , El-Shahat Khaled M. , Eissa M. F. , Aly A. H.
Published: May 2022

We aimed to evaluate dosimetrically the effect of 6 MV, 10 MV and dual 6 and 10 MV photon beam energies on the liver of most cancers using steep and shoot IMRT methods, and we investigate a few important parameters, including dose, conformity index (CI) and homogeneity index (HI). The beams were arranged based on different angles. The same optimization constraints were applied for each energy planning and all other parameters, such as number of beams and beam angles, were kept constant.

The dose-volume histograms (DVHs) for the 6 MV, 10 MV and dual energy plans were compared for the planning target volume (PTV) and organs at risk (OAR). Twenty-eight cases ([Formula: see text]) of liver SBRT patients formerly handled the use of steep and shoot IMRT techniques. Three plans with identical beam geometries were created using 6 MV, 10 MV and dual energy. In the central axis, the powerful intensity from each beam was calculated, and the values were averaged.
Comparative study of 1D defective photonic structures composed of single and double defect layers
Authors: Awasthi S. K. , Aly Arafa H. , Mohamed Basma A. , Ameeen A. M. , Matar Z. S. , Mohaseb M. A.
Published: May 2022

In this study, we investigated the sensing capabilities of 1D defective photonic crystal designs composed of single and double defect layers. These structures can detect and distinguish minor refractive index changes of various organic samples infiltrated one by one into defect layer regions of the respective designs. We have used the standard transfer matrix method to analyze the transmission properties of both structures at normal incidence. The normal incidence has been chosen to overcome the experimental difficulties associated with oblique incidence. The performance of the proposed designs has been examined by measuring the shift in the position of the defect modes inside PBGs of respective structures depending upon the change in the refractive index of various organic solvents, called samples. We can also find adulteration in the organic samples by comparing the observed data with the experimentally available standard data of pure organic samples. Moreover, double defect photonic structure possesses pair of two defect modes inside PBG. These pair of defect modes are located on either side of bandgap edges that cannot be found in 1D defective PCs composed of a single defect. This intriguing property of double defect PC structure is due to the quasi Fabry–Perot cavity nature of the structure. Finally, we have compared sensing and detection features of both the designs made up of single and double defect photonic structures. It has been noticed that the double defect photonic structure also improves the transmittance of defect peak to unity compared to the single defect photonic structure. Moreover, the FWHM of two pairs of defect modes in double defect PC are opposite in nature. This remarkable feature of double defect photonic design may be very useful for improving the performance of photonic bio-sensors. It may be very helpful for designing some advanced bio-sensing designs whose defect mode properties are mirror image in nature.
The modified simplest equation procedure for conformable time-fractional Boussinesq equations
Authors: Razzaq Waseem , Zafar Asim , Akbulut Arzu
Published: May 2022

In this paper, we used the traveling wave transformation to find out the exact solutions of the conformable time fractional differential equations with the help of the Modified simplest equation procedure. We applied the given procedure to two different equations which are conformable time-fractional Boussinesq and coupled Boussinesq equations. First, we reduced the given equations to ordinary differential equations using wave transformations. Then, we applied the procedure to the given equations. We obtained hyperbolic, trigonometric and rational solutions. We gave the 3D plots, 2D plots and contour plots for some obtained solutions. According to our results, we can say the given method is effective, powerful and gives simple, reliable solutions compared to the other methods. All obtained solutions were checked with the help of Mathematica.
Robustness of synchronizability in windmill networks with node failures
Authors: Zhang Defu , Xu Dan , Chen Jing , Sun Weigang
Published: May 2022

In this paper, the synchronizability characterized by the Laplacian spectrum is applied to windmill networks where three types of parameters are introduced to control the number of deleted nodes. Using the network’s structures, exact solutions of the Laplacian eigenvalues are obtained and metrics of the synchronizability are correspondingly shown. Relationships between the synchronizability and the introduced parameters are presented. Then, the synchronizability of models with different settings of node failures is compared. The obtained results reveal distinct synchronizabilities originating from intrinsic structures of models and the setting forms of node failures. Finally, numerical examples are provided to verify the effectiveness of theoretical analysis.
Automatic Object Detection and Direction Prediction of Unmanned Vessels Based on Multiple Convolutional Neural Network Technology
Authors: Lin Chuen-Horng , Wang Xin-Cheng
Published: May 2022

This study aims to detect objects quickly and accurately and then start the necessary obstacle avoidance procedure when the uncrewed vessel is at sea. This study uses a multivariate convolutional neural network (CNN) to perform automatic object detection and direction prediction for uncrewed vessels. This study is divided into three parts for processing. The first part of this process uses camera calibration technology to correct the image. Discrete cosine transform (DCT) is then used to detect sea level. Finally, this study uses Kalman filtering and affine transformation to stabilize images taken by uncrewed vessels at sea. The second part of the processing system uses a CNN to detect sea objects automatically. The third part of the process uses the dual-lens camera installed on the vessel to detect the distance and direction of objects at sea. In the experiment, the detection rate can reach more than 91% in this study method. In the experiment on image stabilization, this study’s method can also effectively improve video instability. In the experiment involving the distance and direction of the object, the experimental results show that the distance and direction of the object obtained by this study method have a distance error value of less than 10%, and the prediction results have a good effect no matter whether the object is at a short or long distance. It is hoped that this paper’s method can be applied to the automatic obstacle avoidance of unmanned vessels.
End-to-End Multi-Resolution 3D Capsule Network for People Action Detection
Authors: Fanany Mohamad Ivan , Arinaldi Ahmad
Published: May 2022

In this paper, we propose an end-to-end multi-resolution three-dimensional (3D) capsule network for detecting actions of multiple actors in a video scene. Unlike previous capsule, network-based action recognition does not specifically concern with the individual action of multiple actors in a single scene, our 3D capsule network takes advantage of multi-resolution technique to detect different actions of multiple actors that have different sizes, scales, and aspect ratios. Our 3D capsule network is built on top of 3D convolutional neural network (3DCNN) that extracts spatio-temporal features from video frames inside regions of interest generated by Faster RCNN object detection. We first apply our method to the problem of detecting illegal cheating activities in a classroom examination scene with multiple subjects involved. Second, we test our system on the publicly available and extensively studied UCF-101 dataset. We compare our method with several state-of-the-art 3DCNN-based methods, first the multi-resolution 3DCNN, the single-resolution 3D capsule network, and a combination of both these models. We show that models containing 3D capsule networks have a slight advantage over the conventional 3DCNN and multi-resolution 3DCNN. Our 3D capsule networks not only perform a classification of said actions but also generate videos of single actions. Our experimental results show that the use of multi-resolution pathways in the 3D capsule networks make the result even better. Such findings also hold even when we use pre-trained C3D (convolutional 3D) features to train these networks. We believe that the multiple resolutions capture lower-level features at different scales. At the same time, the 3D capsule layers combine these features in more complex ways than conventional convolutional models.
A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images
Authors: Alex Deepthy Mary , Chandy D. Abraham , Christinal A. Hepzibah , Singh Arvinder , Pushkaran M.
Published: May 2022

Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.
Small-Footprint Keyword Spotting Based on Gated Channel Transformation Sandglass Residual Neural Network
Authors: Zhang Ying , Zhu Shirong , Yu Chao , Zhao Lasheng
Published: May 2022

Keyword spotting plays a crucial role in realizing voice-based user interaction on intelligent equipment terminals and service robots. In this task, it remains challenging to achieve the balance between low memory and high precision. To better satisfy this requirement, we propose an end-to-end neural architecture with sandglass residual blocks embedded with the gated channel-wise attention mechanism. The sandglass residual blocks utilize 1D separable convolutions to extract bottleneck temporal features, which can effectively drive the model to focus more on the speech segment with lower parameters. Especially, the gated attention mechanism helps the model enhance the critical speech temporal features and suppress the useless ones and further focus on the most important part of the human speech region for keyword spotting. The experimental results on Google Speech Commands Dataset show that our proposed model has an accuracy of 97.4[Formula: see text] with only 46K parameters. Compared with the baseline method with the highest accuracy, our model parameters are decreased by 54[Formula: see text] and accuracy is increased by 0.8[Formula: see text]. That makes us take further step in achieving the goal of low memory and high precision.
DMS-SK/BLSTM-CTC Hybrid Network for Gesture/Speech Fusion and Its Application in Lunar Robot–Astronauts Interaction
Authors: Ding Jian , Liu Jin , Ning Xiaolin , Kang Zhiwei
Published: May 2022

In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.
A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy Efficiency
Authors: Sharma Neha , Gupta Vishal
Published: May 2022

Wireless sensor networks’ energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node’s time of death has been delayed by 3% in total.
Gradle-Autofix: An Automatic Resolution Generator for Gradle Build Error
Authors: Kang Mingu , Kim Taeyoung , Kim Suntae , Ryu Duksan
Published: May 2022

Gradle is one of the widely used tools to automatically build a software project. While developers execute the Gradle build for projects, they face various build errors in practice. However, fixing build errors is not easy because developers should manually find out the cause of the build error and its resolution on their project. For this reason, developers spend much time fixing them, and especially it can be worse if a developer lacks the experience of handling build errors. To address this issue, we propose a novel approach named Gradle-AutoFix to automatically fix build errors along with providing their causes and resolutions. In this approach, we collect build errors to group their causes and resolutions and then generate feature vectors from build error messages by applying Bag-of-Word (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Bigram, and an embedding layer. The feature vectors are utilized for training two classification models on cause and resolution. Next, we analyze fixing patterns and define seven resolution rules to fix the build error automatically. Based on our trained models and defined resolution rules, we built Gradle-AutoFix. For the evaluation, we measured how appropriately Gradle-AutoFix provides causes of build errors and resolutions. As a result, we obtained 96% and 91% accuracy, respectively. Also, we assessed how properly Gradle-AutoFix fixes the project’s build error based on the seven resolution rules. The outcome showed a 64.5% build error resolution rate for 231 projects.
Anisotropic scalar field dark energy with a disformally coupled Yang–Mills field
Authors: Gabriel Gómez L. , Rodríguez Yeinzon , Beltrán Almeida Juan P.
Published: May 2022

In the context of scalar–tensor theories, the inclusion of new degrees of freedom coupled nonminimally to the gravitational sector might produce some appealing effects on the cosmic expansion history. We investigate this premise by including a canonical SU(2) Yang–Mills field to the total energy budget of the universe coupled to the standard quintessential field by a disformal transformation.From the dynamical system analysis, we study three cases of cosmological interest that span most of the physical phase space of the model: the uncoupled limit, the isotropic and the Abelian cases. New scaling solutions with a nonvanishing gauge field are found in all cases which can be interesting for early cosmological scenarios. Some of these scaling solutions even exhibit anisotropic features. Also, the background evolution of the universe is studied by means of numerical analysis. As an interesting result, the disformal coupling changes the equation of state of the gauge field from radiation to matter at some stages of the evolution of the universe, thereby the gauge field can contribute to some fraction of the total dark matter. We have also quantified the redshift-dependent contribution of the gauge field in the form of dark radiation during the radiation era to the effective number of relativistic species. This depends essentially on the initial conditions and, more importantly, on the disformal coupling function.
Stabilizing spherical energy shells with angular momentum in gravitational backgrounds
Authors: Antoniou I. , Kazanas D. , Papadopoulos D. , Perivolaropoulos L.
Published: May 2022

Spherical energy shells in General Relativity tend to collapse due to gravitational effects and/or due to tension effects. Shell stabilization may be achieved by modifying the gravitational properties of the background spacetime. Thus, gravastars consist of stiff matter shells with an interior deSitter space and an exterior Schwarzshild spacetime whose attractive gravity balances the interior repulsive gravity of the interior deSitter spacetime leading to a stable stiff matter shell. Similar stabilization effects may be achieved by considering rotating shells. Here we study the stability of slowly rotating fluid shells. We show that the angular velocity of the shell has stabilizing properties analogous to the repulsive deSitter gravity of the interior of a gravastar. We thus use the Israel junction conditions [W. Israel, Nuovo Cim. B 44S10 (1966) 1, Erratum: Nuovo Cim. B 48 (1967) 463; N. Deruelle, M. Sasaki and Y. Sendouda, Prog. Theor. Phys. 119 (2008) 237, arXiv:0711.1150 [gr-qc]] and the fluid equation of state of the rotating shell to construct the dynamical equations that determine the evolution of the rotating shell radius. These dynamical equations depend on the parameters of the background spacetime and on the angular velocity of the shell. Assuming a rotating interior and a Schwarzschild exterior spacetime we show that the angular velocity of the shell has interesting stabilizing properties on the evolution of its radius R. Thus, rotating matter (or vacuum) shells can imitate black holes while avoiding the presence of a singularity and without the presence of an interior deSitter space.
Hybrid Meta-Heuristic Algorithms Based Optimal Antenna Selection for Large Scale MIMO in LTE Network
Authors: Patil Rajashree A. , Kavipriya P. , Patil B. P.
Published: May 2022

MIMO is a type of antenna technology which utilizes multiple antennas to separate signals traveling in various paths as a result of reflections, etc., to be separated and their ability utilized to enhance the throughput of data and the signal to noise ratio which in return enhances the performance of the system. For providing enhanced signal performance and enhanced data rates, MIMO is utilized within LTE. When the quantities of antennas are increased, there is increase in the probability that deep fading is experienced by at least some antennas, which affects the overall efficiency of the MIMO system. To handle these issues, a reliable technique has been presented that involves selection of antenna subset. The proposed technique incorporates combination of SBO and PSO for antenna selection. Antenna selection’s main concept is to utilize a bounded quantity of analog chains that are adaptively switched to subset of the available antennas capable of preserving the selection diversity gains and also can minimize the quantity of radio frequency chains needed. The maximum channel capacity of the channel has been considered as the objective function for selecting optimal antennas. The comparison of the proposed approach’s performance and the existing approaches’ performance is done. From the simulation result, it has been shown that the presented approach’s performance has been better than the performance of the existing approaches in terms of BER, energy efficiency, spectral efficiency and optimal transmit power.
Dynamic Noisy Measurement Aware Localization Model for Wireless Sensor Networks
Authors: Raja Thejaswini N. , Muthupandi G.
Published: May 2022

Localization through received signal strength (RSS) has attained a lot of interest across industries and research organization due to ease of use, high efficiency and low computation complexity; thus, it is widely used in Wireless Sensor Networks (WSNs)-based applications. Existing localization model has been predominantly designed with known transmit power. Recently, few localization approaches have been modeled considering unknown transmit power employing non-convex least squared relative error (LSRE) measurement model. The LSRE optimization problem is solved through semidefinite programming (SDP) using semidefinite relaxation (SDR). However, LSRE-SDP suffers immensely under highly dynamic and noisy environment and induces high computation overhead in meeting convergence. In addressing the aforementioned problem, this paper presents Dynamic Noisy Measurement Aware Localization (DNMAL) model for WSNs using improved least square bounding model. The objective DNMAL is to measure target position by neglecting the collected through noisy (faulty) sensor device. The DNMAL aids in achieving optimal solution using improved least square bounding model through iterative process. The DNMAL is efficient in bounding unknown distribution because of presence of noisy sensor and significantly reduces localization error even with presence of extreme noise.
Health Ratio Optimization of Group Detection-Based Data Network Using Genetic Algorithm
Authors: Suhas A. R. , Manoj Priyatham M.
Published: May 2022

A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance.
Measurement on the National Competitive Advantage Based on Two-Mode MRIO Network: Taking Asian, European and African Countries along the Belt and Road for Example
Authors: Wang Yanni , Chen Wen , Xing Lizhi
Published: May 2022

For the Belt and Road Initiative, “extensive consultation, joint contribution and shared benefits” is the basic principle, and the competitiveness heterogeneity is an important condition for cooperation. Based on the complex network theory, this paper constructs a complex network model of global value chain (GVC) division of labor system by using the Multi-Regional Input-Output (MRIO) table and reveals the variation trend of competitiveness of industrial sectors and economies on the GVC network by the National Competitive Advantage Index (NCAI). The results verify the effectiveness of BRI and help countries along the BRI route to explore their comparative advantages and cooperation prospects with other countries. The research also provides a direction and reference for China to better implement the Initiative.
Commutators in groups of order p7
Authors: Kaushik Rahul , Yadav Manoj K.
Published: May 2022

In this paper, we present a characterization of groups [Formula: see text] of order [Formula: see text], [Formula: see text] prime, in which not all elements of the commutator subgroup [Formula: see text] of [Formula: see text] are commutators in [Formula: see text]. In the way, we obtain several structural results on groups of order [Formula: see text].