bluuebunny/crossref_metadata_2025 huggingface dataset:
Privacy-Preserving Mechanisms for Multi-Label Image Recognition
Authors: Xu Honghui , Cai Zhipeng , Li Wei
Published: January 2022

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this gap, two privacy-preserving models, Privacy-Preserving Multi-label Graph Convolutional Networks (P2-ML-GCN) and Robust P2-ML-GCN (RP2-ML-GCN), are developed in this article, where differential privacy mechanism is implemented on the model’s outputs so as to defend black-box attack and avoid large aggregated noise simultaneously. In particular, a regularization term is exploited in the loss function of RP2-ML-GCN to increase the model prediction accuracy and robustness. After that, a proper differential privacy mechanism is designed with the intention of decreasing the bias of loss function in P2-ML-GCN and increasing prediction accuracy. Besides, we analyze that a bounded global sensitivity can mitigate excessive noise’s side effect and obtain a performance improvement for multi-label image recognition in our models. Theoretical proof shows that our two models can guarantee differential privacy for model’s outputs, weights and input features while preserving model robustness. Finally, comprehensive experiments are conducted to validate the advantages of our proposed models, including the implementation of differential privacy on model’s outputs, the incorporation of regularization term into loss function, and the adoption of bounded global sensitivity for multi-label image recognition.
Multiple Graphs and Low-Rank Embedding for Multi-Source Heterogeneous Domain Adaptation
Authors: Wu Hanrui , Ng Michael K.
Published: January 2022

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take advantage of the knowledge extracted from multiple sources as well as bridge the heterogeneous spaces for handling the MHDA paradigm. This article proposes a novel method named Multiple Graphs and Low-rank Embedding (MGLE), which models the local structure information of multiple domains using multiple graphs and learns the low-rank embedding of the target domain. Then, MGLE augments the learned embedding with the original target data. Specifically, we introduce the modules of both domain discrepancy and domain relevance into the multiple graphs and low-rank embedding learning procedure. Subsequently, we develop an iterative optimization algorithm to solve the resulting problem. We evaluate the effectiveness of the proposed method on several real-world datasets. Promising results show that the performance of MGLE is better than that of the baseline methods in terms of several metrics, such as AUC, MAE, accuracy, precision, F1 score, and MCC, demonstrating the effectiveness of the proposed method.
Adaptive Model Scheduling for Resource-efficient Data Labeling
Authors: Yuan Mu , Zhang Lan , Li Xiang-Yang , Yang Lin-Zhuo , Xiong Hui
Published: January 2022

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an
Adaptive Model Scheduling
framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.
Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual Semantics
Authors: Abulaish Muhammad , Fazil Mohd , Zaki Mohammed J.
Published: January 2022

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than
domain-specific
keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user-generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.
Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream
Authors: Yang Xu , Song Chao , Yu Mengdi , Gu Jiqing , Liu Ming
Published: January 2022

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a graph stream has been widely studied, and numerous triangle counting steaming algorithms have emerged. To improve the throughput and scalability of streaming algorithms, many researches of distributed streaming algorithms on multiple machines are studied. In this article, we first propose a framework of distributed streaming algorithm based on the Master-Worker-Aggregator architecture. The two core parts of this framework are an edge distribution strategy, which plays a key role to affect the performance, including the communication overhead and workload balance, and aggregation method, which is critical to obtain the unbiased estimations of the global and local triangle counts in a graph stream. Then, we extend the state-of-the-art centralized algorithm TRIÈST into four distributed algorithms under our framework. Compared to their competitors, experimental results show that DVHT-i is excellent in accuracy and speed, performing better than the best existing distributed streaming algorithm. DEHT-b is the fastest algorithm and has the least communication overhead. What’s more, it almost achieves absolute workload balance.
Real-Time Anomaly Detection in Edge Streams
Authors: Bhatia Siddharth , Liu Rui , Hooi Bryan , Yoon Minji , Shin Kijung , Faloutsos Christos
Published: January 2022

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect
individually surprising
edges.

In this work, we propose
Midas
, which focuses on detecting
microcluster anomalies
, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. We further propose
Midas
-F, to solve the problem by which anomalies are incorporated into the algorithm’s internal states, creating a “poisoning” effect that can allow future anomalies to slip through undetected.
Midas
-F introduces two modifications: (1) we modify the anomaly scoring function, aiming to reduce the “poisoning” effect of newly arriving edges; (2) we introduce a conditional merge step, which updates the algorithm’s data structures after each time tick, but only if the anomaly score is below a threshold value, also to reduce the “poisoning” effect. Experiments show that
Midas
-F has significantly higher accuracy than
Midas
.

In general, the algorithms proposed in this work have the following properties: (a) they detects microcluster anomalies while providing theoretical guarantees about the false positive probability; (b) they are online, thus processing each edge in constant time and constant memory, and also processes the data orders-of-magnitude faster than state-of-the-art approaches; and (c) they provides up to 62% higher area under the receiver operating characteristic curve than state-of-the-art approaches.
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction
Authors: Zhang Min-Ling , Wu Jing-Han , Bao Wei-Xuan
Published: January 2022

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple
candidate
labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named
Delin
is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to
k
NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of
Delin
in improving the generalization performance of well-established partial label learning algorithms.
Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification
Authors: Wu Hanrui , Ng Michael K.
Published: January 2022

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of these deep learning approaches do not take full consideration of either the hyperedge information or the original relationships among nodes and hyperedges. In this article, we present a simple yet effective semi-supervised node classification method named Hypergraph Convolution on Nodes-Hyperedges network, which performs filtering on both nodes and hyperedges as well as recovers the original hypergraph with the least information loss. Instead of only reducing the cross-entropy loss over the labeled samples as most previous approaches do, we additionally consider the hypergraph reconstruction loss as prior information to improve prediction accuracy. As a result, by taking both the cross-entropy loss on the labeled samples and the hypergraph reconstruction loss into consideration, we are able to achieve discriminative latent data representations for training a classifier. We perform extensive experiments on the semi-supervised node classification problem and compare the proposed method with state-of-the-art algorithms. The promising results demonstrate the effectiveness of the proposed method.
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
Authors: Yuan Junkun , Wu Anpeng , Kuang Kun , Li Bo , Wu Runze , Wu Fei , Lin Lanfen
Published: January 2022

Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it’s an art rather than science to find valid IVs in many real-world scenes. Moreover, the predefined hand-made IVs could be weak or erroneous by violating the conditions of valid IVs. These thorny facts hinder the application of the IV-based counterfactual prediction methods. In this article, we propose a novel Automatic Instrumental Variable decomposition (AutoIV) algorithm to automatically generate representations serving the role of IVs from observed variables (IV candidates). Specifically, we let the learned IV representations satisfy the relevance condition with the treatment and exclusion condition with the outcome via mutual information maximization and minimization constraints, respectively. We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome. The IV and confounder representations compete for the information with their constraints in an adversarial game, which allows us to get valid IV representations for IV-based counterfactual prediction. Extensive experiments demonstrate that our method generates valid IV representations for accurate IV-based counterfactual prediction.
Advanced Deep Learning Human Herpes Virus 6 (HHV-6) Molecular Detection in Understanding Human Infertility
Authors: Alazzam Malik Bader , Al-Radaideh Ahmad Tawfig , Binsaif Nasser , AlGhamdi Ahmed S. , Rahman Md Adnan
Published: January 2022

To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively); endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA; this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur’s infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility.
Analysis on the Difference of Energy Evolution in the Process of Energy Storage Failure of Strong Coal Rush Rock Samples under Different Adaptive Modification Regulation Measures
Authors: Zhang Fangfang
Published: January 2022

Current energy to “release” after accumulating + first for the mechanism of rock bursts occurred in analysis of the strategy is accepted by many scholars, based on the existing means of prevention and control of percussive ground pressure, from the angle of the prevention and control of design of the mechanism of impact ground pressure energy regulation, namely, “weakened after the first release +” softened water injection measures and “lead after the first release +” drilling pressure relief measures, for the study of mining under the action of a strong shock tendentiousness rock energy regulatory mechanism; based on rock mechanics experiment, the analysis under different modification measures should be variant energy storage mechanisms of induced damage evolution of rock energy. The mechanism of energy evolution in the modification of strong bursting liability roof rock is revealed. The results show that different modification regulation measures can effectively change the physical and mechanical parameters of target rock samples and realize “hard rock softening or soft rock hardening.” Samples under different modification measures are classified as initial consolidation stage, elastic stage, stage of plastic deformation, yield failure stage, and late stage, the energy evolution is roughly the same as the sample complete natural condition, but the yield failure stage and the destruction of late stage have an obvious difference, which provides favorable conditions for impact ground pressure to prevent. With the help of three characteristic energy indexes of total strain, elastic strain energy, and dissipative strain energy of rock samples, the evolution law of energy indexes under different modification control measures is analyzed. The index of elastic energy consumption ratio is introduced as a precursor feature of rock instability and failure, which indicates the rock impact tendency to a certain extent. The energy regulation mechanisms of “first release+then weakening” water injection softening measures and “first release+then guidance” drilling pressure relief measures are explained theoretically, respectively. However, we should focus on the defects of the corresponding control measures and finally try to make a reasonable combination of different modification measures. Finally, the gradient pressure relief scheme should be considered in order to avoid large stress drop caused by large-scale pressure relief in the region and aggravate the instability of rock mass. The instability of rock mass is further aggravated.
DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective
Authors: Li Zhaobin , Yang Bin , Zhang Xinyu , Guo Chao
Published: January 2022

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.
Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment
Authors: Qi Ping
Published: January 2022

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.
Antioxidant Effects of Irisin in Liver Diseases: Mechanistic Insights
Authors: Zhao Junzhou , Qiao Linlan , Dong Jian , Wu Rongqian
Published: January 2022

Oxidative stress is a crucial factor in the development of various liver diseases. Irisin, a metabolic hormone discovered in 2012, is mainly produced by proteolytic cleavage of fibronectin type III domain containing 5 (FNDC5) in skeletal muscles. Irisin is induced by physical exercise, and a rapidly growing body of literature suggests that irisin is, at least partially, responsible for the beneficial effects of regular exercise. The major biological function of irisin is believed to be involved in the maintenance of metabolic homeostasis. However, recent studies have suggested the therapeutic potential of irisin against a variety of liver diseases involving its antioxidative function. In this review, we aim to summarize the accumulating evidence demonstrating the antioxidative effects of irisin in liver diseases, with an emphasis on the current understanding of the potential molecular mechanisms.
A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images
Authors: Nasiri Hamid , Alavi Seyed Ali
Published: January 2022

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus’s transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method’s precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.
Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing
Authors: Arif Muhammad , Ajesh F. , Shamsudheen Shermin , Shahzad Muhammad
Published: January 2022

The use of application media, gamming, entertainment, and healthcare engineering has expanded as a result of the rapid growth of mobile technologies. This technology overcomes the traditional computing methods in terms of communication delay and energy consumption, thereby providing high reliability and bandwidth for devices. In today’s world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. So, this paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks is done using the LSTM algorithm, the strategy for computation offloading of mobile devices is based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. Experiments show that our proposed architecture, which consists of an LSTM-based offloading technique and routing (LSTMOTR) algorithm, can efficiently decrease total task delay with growing data and subtasks, reduce energy consumption, and bring much security to the devices due to the firewall nature of LSTM.
Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
Authors: Hasan Tayyabah , Ahmad Fahad , Rizwan Muhammad , Alshammari Nasser , Alanazi Saad Awadh , Hussain Iftikhar , Naseem Shahid
Published: January 2022

Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the prioritized contents are stored in QMM using a Two-Level Spin Quantum Phenomenon (TLSQP). After selecting the most appropriate lattice map (32 × 32) in 750,000 iterations using SOMs, the data points below the dark blue region are mapped onto the data frame to get the videos. These videos are considered a high priority for trending according to the input parameters provided in the dataset. Similarly, the light-blue color region is also mapped to get medium-prioritized content. After the SOMs algorithm’s training, the topographic error (TE) value together with quantization error (QE) value (i.e., 0.0000235) plotted the most appropriate map after 750,000 iterations. In addition, the power of QC is due to the inherent quantum parallelism (QP) associated with the superposition and entanglement principles. A quantum computer taking “n” qubits that can be stored and execute 2n presumable combinations of qubits simultaneously reduces the utilization of resources compared to conventional computing. It can be analyzed that the cache hit ratio will be improved by ranking the content, removing redundant and least important content, storing the content having high and medium prioritization using QP efficiently, and delivering precise results. The experiments for content prioritization are conducted using Google Colab, and IBM’s Quantum Experience is considered to simulate the quantum phenomena.
Assessing Seismic Performance of Gantry Crane Subjected to Near-Field Ground Motions Using Incremental Dynamic and Endurance Time Analysis Methods
Authors: Peng Qihui , Cheng Wen-ming , Guo Peng , Jia Hongyu
Published: January 2022

Assessing the seismic performance of the gantry crane is significant since the structure is more vulnerable to earthquakes with the increase in size and lifting weight capacity. This paper aims to investigate the seismic response of the gantry crane incorporating near-field ground motions using incremental dynamic and endurance time analysis (IDA and ETA) methods. To model the structure accurately, a nonlinear finite element model of the gantry crane considering the viscoelastic effect is developed in the OpenSees platform. Then, the IDA method is also carried out for a comparison with the ETA method. The results of the two methods are consistent with a correlation of 93.9% while the computational demand of the ETA method is much less than those of the IDA method. To study further, both the seismic incident angle and the application of viscous dampers using the Maxwell model are analyzed and discussed in detail. The results show that seismic incident angle has a distinct influence on the maximum seismic displacement and viscous dampers can significantly reduce the seismic demand of the gantry crane. These findings support the seismic design of gantry cranes and evaluate the structural seismic performance efficiently.
Macrophage Extracellular Traps: Current Opinions and the State of Research regarding Various Diseases
Authors: Weng Weizhen , Hu Zuoyu , Pan Yunfeng
Published: January 2022

Macrophages are an important component of the human immune system and play a key role in the immune response, which can protect the body against infection and regulate the development of tissue inflammation. Some studies found that macrophages can produce extracellular traps (ETs) under various conditions of stimulation. ETs are web-like structures that consist of proteins and DNA. ETs are thought to immobilize and kill microorganisms, as well as play an important role in tissue damage, inflammatory progression, and autoimmune diseases. In this review, the structure, identification, mechanism, and research progress of macrophage extracellular traps (METs) in related diseases are reviewed.
Effects of Polyphenols on Oxidative Stress, Inflammation, and Interconnected Pathways during Spinal Cord Injury
Authors: Fakhri Sajad , Abbaszadeh Fatemeh , Moradi Seyed Zachariah , Cao Hui , Khan Haroon , Xiao Jianbo
Published: January 2022

Despite the progression in targeting the complex pathophysiological mechanisms of neurodegenerative diseases (NDDs) and spinal cord injury (SCI), there is a lack of effective treatments. Moreover, conventional therapies suffer from associated side effects and low efficacy, raising the need for finding potential alternative therapies. In this regard, a comprehensive review was done regarding revealing the main neurological dysregulated pathways and providing alternative therapeutic agents following SCI. From the mechanistic point, oxidative stress and inflammatory pathways are major upstream orchestras of cross-linked dysregulated pathways (e.g., apoptosis, autophagy, and extrinsic mechanisms) following SCI. It urges the need for developing multitarget therapies against SCI complications. Polyphenols, as plant-derived secondary metabolites, have the potential of being introduced as alternative therapeutic agents to pave the way for treating SCI. Such secondary metabolites presented modulatory effects on neuronal oxidative stress, neuroinflammatory, and extrinsic axonal dysregulated pathways in the onset and progression of SCI. In the present review, the potential role of phenolic compounds as critical phytochemicals has also been revealed in regulating upstream dysregulated oxidative stress/inflammatory signaling mediators and extrinsic mechanisms of axonal regeneration after SCI in preclinical and clinical studies. Additionally, the coadministration of polyphenols and stem cells has shown a promising strategy for improving post-SCI complications.
Smart Garden Planning and Design Based on the Agricultural Internet of Things
Authors: Xun Yi , Ren Guangpei
Published: January 2022

To improve the effect of urban agricultural garden landscape planning and design, this paper combines the agricultural Internet of Things technology to construct a smart garden planning and design system. Moreover, this paper selects the LEACH protocol that can support monitoring for a long time according to actual application needs, introduces the latest swarm intelligence optimization algorithm, the gray wolf algorithm, to optimize some of the problems in the LEACH protocol, and conducts simulation experiments on the improved algorithm. The simulation experiment results show that the improved algorithm has obvious advantages in cluster head selection, data transmission within the cluster, and route maintenance. After constructing a smart garden planning system based on the agricultural Internet of Things, the effect of the agricultural Internet of Things data processing in this paper is evaluated. Finally, this paper constructs a garden simulation system and analyzes the performance of the system. The results verify that the agricultural Internet of Things has a good effect in the planning and design of smart gardens.
A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms
Authors: Zhou Wei
Published: January 2022

In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource‐sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.
Magnetic Induction Technology-Based Wireless Sensor Network for Underground Infrastructure, Monitoring Soil Conditions, and Environmental Observation Applications: Challenges and Future Aspects
Authors: Singh Pratap , Singh Rishi Pal , Singh Yudhvir , Chohan Jasgurpreet Singh , Sharma Shubham , Sadeghzadeh Milad , Issakhov Alibek
Published: January 2022

Wireless sensor networks (WSNs) especially with sensor nodes communicating with each other in medium other than air have been naive area of research since the last few years. In comparison to underwater communication, wireless underground sensor networks (WUSNs) are now being used in a large number of applications ranging from environmental observation, estimating chances of earthquake, communicating in underground tunnels or mines, and infrastructure monitoring to soil monitoring for agricultural purposes. In spite of all such promising applications, due to harsh and dynamically changing soil characteristics including soil type, water content in soil, and soil temperature, underground communication with conventional electromagnetic (EM) wave-based technology could not prove to be feasible for long-distance communication. Alternatively, due to magnetic permeability of soil being similar to air, magnetic induction- (MI-) based approach was adopted using magnetic coils as antenna for sensor nodes. Subsequently, MI waveguide and 3D coil mechanisms were considered to improve the system efficiency. Attributing to different characteristics of underlying transmission channels, communication protocols as well as architecture of MI-based WUSNS (MI-WUSNs) have been developed with different approaches. In this review paper, in addition to the latest advancements made for MI-WUSNs, closely associated areas of MI-WUSNs have also been explored. Additionally, research areas which are still open to be worked upon have been detailed out.
Auxiliary Teaching of Badminton Basic Movements Based on Wireless Network Communication and Kinect
Authors: Liang Fusheng
Published: January 2022

Badminton is a sport with relatively delicate and complicated technical movements, which is widely welcomed in China. In order to study the specific impact of the sports game teaching method and traditional teaching method on the three aspects of the badminton learning technology mastery level, interest level, and subjective experience, with the reference of the wireless network to the students and Kinect on the students, in a three-month group teaching of students without foundation in a medical school in this city, the badminton action is broken down and the doubling of the badminton action is detected by WiFi and Kinect, to compare the traditional pedagogy and the method with this paper before and the lesson and to draw out conclusions. The results of the study found that badminton-assisted teaching based on wireless network communication and Kinect can achieve good results. The learning efficiency of students is significantly higher than that of traditional teaching methods, and badminton teaching based on wireless network communication and Kinect can effectively improve students’ interest in learning, 24% higher than the traditional method. This shows that wireless network communication and Kinect technology can play an important role in badminton teaching.
Persistent Fetal SVT in a COVID-19 Positive Pregnancy
Authors: Wang Gloria , Stapley Eric , Peterson Sara , Parrott Jessica , Clark-Ganheart Cecily
Published: January 2022

Background. Rapid introduction and spread of SARS-CoV-2 have posed unique challenges in understanding the disease, role in vertical transmission, and in developing management. We present a case of a patient with COVID-19 infection and fetus with new-onset fetal SVT. Case. A 26-year-old gravida 4 para 2012 with third trimester COVID-19 infection was diagnosed with new onset fetal SVT. Successful cardioversion was achieved with flecainide. The patient was followed outpatient until induction of labor at 39 and 3/7 weeks of gestational age resulting in an uncomplicated vaginal delivery. Postpartum course was uncomplicated. Conclusion. Fetal SVT is a potential complication of maternal COVID-19 infection. The use of transplacental therapy with flecainide is an appropriate alternative to digoxin in these cases.
A ilusão dos relógios
Authors: Pedrazzoli Mario
Published: January 2022

A mecanicidade ou digitalidade dos relógios representa a imutabilidade da duração de frações de tempo. A contagem das 24h de um dia teve como referência, a princípio, as pistas ambientais associadas às condições do dia e da noite que são diferentes em diferentes locais da terra e portanto mutáveis. A emergência de uma sub-área da Biologia, a Cronobiologia, em meados do século XX permitiu a interpretação de que a apreensão do tempo de um dia como regularidade mecânica aliena os seres humanos da percepção da temporalidade diária como integração entre temporalidade ambiental e temporalidade biológica. Pretendo demonstrar que esse equívoco perceptual da duração do tempo de um dia pode ter como consequência uma desorganização temporal fisiológica que é a origem ou está associada a origem de muitas doenças modernas.
Slow movement
Authors: Trigo Luiz Gonzaga Godoi , Panosso Netto Alexandre , Bauer Rafael Chequer
Published: January 2022

Este artigo discute o descompasso entre os ritmos biológicos e os ritmos sociais emergentes a partir da Revolução Industrial. Para tal, são apresentados indícios de mudanças rítmicas nas últimas décadas, acarretando um processo contínuo e profundo de aceleração e mecanização sociocultural, predominante nas estruturas societárias capitalistas. Em seguida, discute-se a relação entre ritmos sociais e ritmos biológicos, com a contribuição conceitual advinda da Cronobiologia. Por fim, destaca-se o processo de surgimento e consolidação do Slow Movement nas últimas décadas, tornando-se mais um indício da desarticulação temporal vivenciada nos dias atuais.
Resenha do livro aceleração e alienação
Authors: Silveira Rafael H.
Published: January 2022

Em Aceleração e alienação: Esboço de uma teoria crítica da temporalidade na Modernidade tardia, Hartmut Rosa recapitula resumidamente e amplia sua Teoria da Aceleração Social. A ampliação da teoria se dá em primeiro lugar através da análise de elementos desaceleradores da tendência aceleratória e, em seguida, da análise das consequências da aceleração para a Teoria Crítica social atual, cujos questionamentos levantados e respostas dadas até o presente momento não apresentariam uma solução para a perda da credibilidade do projeto da Modernidade, uma vez que a aceleração social teria sucumbido e instrumentalizado a possibilidade de autonomia prometida. Partindo da busca de uma resposta à questão de o que seria uma vida plena, Rosa retraça, assim, o contexto do surgimento de diferentes categorias de alienação, retratando em sua teoria uma tendência social crescente extremamente relevante e em crescimento na era moderna.
Os tempos da vida
Authors: Menna-Barreto Luiz
Published: January 2022

O tema do tempo tem atraído bastante atenção no ambiente acadêmico contemporâneo. Apresentarei uma abordagem na qual são associados os conceitos de condicionamento reflexo clássico com a cronobiologia, área na qual a dimensão temporal da matéria viva é explorada. O conceito de antecipação é proposto como elo central dessa associação. Discuto a seguir os níveis de determinação que podem ser propostos a partir da observação de fenômenos temporais nos organismos. Concluo com as noções de desafios e armadilhas temporais que parecem caracterizar fortemente os dilemas humanos num mundo globalizado, conduzindo a diferentes processos de adaptação resultantes desses desafios e armadilhas.
Ordem e progresso, aceleração e alienação
Authors: Silveira Rafael H.
Published: January 2022

Como diversos exemplos dados em Aceleração e alienação [1] confirmam, a condição de especialista no campo da aceleração social muitas vezes não exime o próprio autor da ação dos fenômenos por ele analisados – sobretudo por se tratar de uma das personalidades acadêmicas mais conhecidas, citadas e requisitadas na imprensa alemã atualmente. Como minha resenha da análise de Hartmut Rosa mostra, a obra está longe de ser die Entdeckung der Langsamkeit ou um éloge de la lenteur, como interpretado por alguns. No diálogo, conduzido em 23/10/2014 na cidade de Jena, Alemanha, originalmente em alemão, transcrito, editado e traduzido para o português por Rafael H. Silveira, são abordados pontos que complementam o entendimento da Teoria da Aceleração através de uma perspectiva voltada para a realidade brasileira.
Literatura Paraguay/guaraní - transversalidades
Authors: Cabrera Damián
Published: January 2022

Passando por trabalhos compilatórios dos escritores paraguaios Augusto Roa Bastos e Rubén Bareiro Saguier, e a partir de discursos literários e não literários, analisa-se a ambiguidade fundada na palavra guarani; que designa, indistintamente, uma língua, uma cultura, uma etnia; e que, por metonímia, constitui-se em apelido-gentílico dos paraguaios. Relações entre literatura paraguaia e literatura Guarani são exploradas, desde a perspectiva dos autores citados; tanto conhecedores e divulgadores da mesma, como dois dos poucos paraguaios capazes de ultrapassar um cerco de isolamento cultural graças, em parte, ao exílio político; sob a luz de uma tradição crítica latino-americana hispanizante que, enquanto invisibiliza a literatura paraguaia, contribui com uma mistificação dela, fundada em sua peculiaridade linguística, seja ela real ou inventada.
Investigación feminista, historia de las mujeres y mujeres en la historia en los estudios sobre próximo oriente antiguo
Authors: Ventura Agnès García
Published: January 2022

Suele decirse que el estudio del pasado siempre tiene relación con el presente y con el futuro, bien porque presente y futuro se construyen a su imagen y semejanza, bien porque no podemos imaginar un pasado sin los referentes de nuestro presente. Por este motivo, ocuparse de la historia de las mujeres en la Antigüedad y de cómo incluir a las mujeres en la historia, nos permite reflexionar acerca de la situación de las mujeres en el mundo presente en el que vivimos y en el mundo futuro en el que querríamos vivir. En este artículo propongo aproximarnos a este tema con las herramientas críticas de la investigación feminista, ilustrando la propuesta con algunos ejemplos acerca de cómo algunos sesgos pueden afectar al modo en que se aborda el estudio de las vidas de las mujeres en el Próximo Oriente Antiguo.
A higienização do século XIX e o “contra a corrupção” do século XXI
Authors: Borba André Vitor Brandão Kfuri
Published: January 2022

Cada momento histórico é único, mas carrega em si tensões permanentes, num paradoxo entre o novo e o velho, valendo-se de novas experiências sem, entretanto, negar toda a bagagem cultural adquirida. Assim, este trabalho busca relacionar dois momentos distintos da história do Brasil, mas com características em comum: a higienização do início da República e o momento recente, em que estava em jogo o mandato da presidente Dilma Roussef . Por ser o Brasil um país com pouca mobilidade social e sem alterações substanciais no seu controle político, veremos como os interesses das camadas superiores da sociedade se reproduzem e se perpetuam, no intuito de fazer a população aderir a essa ideologia em favor de seus interesses privados.
O cansaço como nova categoria de análise para os estudos críticos da deficiência
Authors: Freitas Marcos Cezar De
Published: January 2022

O artigo retoma as contribuições que os estudos culturais, os estudos feministas e a argumentação foucaultiana ofereceram à educação inclusiva, especialmente aos estudos críticos da deficiência. Acrescenta, também as contribuições de Byung-Chul Han que, com as categorias cansaço, positividade e performance, ampliou, com novas possibilidades críticas, o universo analítico desse campo.