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Oxidative effects of the harmful algal blooms on primary organisms of the food web
No full textDegraded water quality from nutrient pollution, physical, biological, and other chemical factors contributes
to the development and persistence of many harmful algal blooms (HABs). The complex dynamics of the HABs is
a challenge to marine ecosystems for the toxic effects reported. The consequences include fish, bird, and mammal
mortality, respiratory or digestive tract problems, memory loss, seizures, lesions and skin irritation in many organisms.
This review is intended to briefly summarize the recent reported information on harmful marine toxin deleterious
effects over the primary organisms of the food web, namely algae, zooplankton and invertebrates. Special focus is made
on oxidative stress status of cells and tissues. Even though in situ field research is less controlled than laboratory studies,
in which the organisms are directly exposed to the toxins under consideration, both types of approaches are required to
fully understand such a complex scenario. On top of that, the contribution of the increasing water temperatures in the
sea, as a consequence of the global climate change, will be addressed as a topic for further studies, to evaluate the effect
on regulating algal growth, species composition, trophic structure, metabolic stress and function of aquatic ecosystems
Mesenchymal stem cells are more effective than captopril in reverting cisplatin-induced nephropathy
No full textCisplatin is a powerful anticancer drug but its nephrotoxic effects limit its clinical use. We aimed to
evaluate the effect of mesenchymal stem cells (MSCs) injection or of captopril to counteract the cisplatin-induction
of nephropathy. MSCs isolation, preparation and tracking, transforming growth factor-β (TGF-β) and interleukin-10
(IL-10) expressions, kidney function tests, oxidative stress state, and histological examinations were done. Cisplatininduced nephropathy was indicated biochemically and confirmed histopathologically. MSCs treatment showed normal
kidney architecture, and significantly decreased oxidative stress and TGF-β while increased IL-10 and improved kidney
function tests. Rats treated with cisplatin + captopril showed noticeable kidney histopathological changes. Superior
positive impact of MSCs in amelioration of cisplatin-induced nephropathy via their ability to motivate functional and
structural renal repair is evidenced
Research on Architecture of Risk Assessment System Based on Block Chain
No full textThe risk assessment system has been applied to the information security, energy, medical and other industries. Through the risk assessment system, it is possible to quantify the possibility of the impact or loss caused by an event before or after an event, thereby avoiding the risk or reducing the loss. However, the existing risk assessment system architecture is mostly a centralized architecture, which could lead to problems such as data leakage, tampering, and central cheating. Combined with the technology of block chain, which has the characteristics of decentralization, security and credibility, collective maintenance, and untamperability. This paper proposes a new block chain-based risk assessment system architecture and a consensus mechanism algorithm based on DPOS improvement. This architecture uses an improved consensus mechanism to achieve a safe and efficient risk assessment solving the problem of data tampering in the risk assessment process, avoiding data leakage caused by improper data storage. A convenient, safe and fast risk assessment is achieved in conjunction with the improved consensus mechanism. In addition, by comparing existing risk assessment architecture, the advantages and impacts of the new block chain-based risk assessment system architecture are analyzed
GPR Wave Propagation Model in a Complex Geoelectric Structure Using Conformal First-Order Symplectic Euler Algorithm
No full textPossessing advantages such as high computing efficiency and ease of programming, the Symplectic Euler algorithm can be applied to construct a ground-penetrating radar (GPR) wave propagation numerical model for complex geoelectric structures. However, the Symplectic Euler algorithm is still a difference algorithm, and for a complicated boundary, ladder grids are needed to perform an approximation process, which results in a certain amount of error. Further, grids that are too dense will seriously decrease computing efficiency. This paper proposes a conformal Symplectic Euler algorithm based on the conformal grid technique, amends the electric/magnetic field-updating equations of the Symplectic Euler algorithm by introducing the effective dielectric constant and effective permeability coefficient, and reduces the computing error caused by the ladder approximation of rectangular grids. Moreover, three surface boundary models (the underground circular void model, the undulating stratum model, and actual measurement model) are introduced. By comparing reflection waveforms simulated by the traditional Symplectic Euler algorithm, the conformal Symplectic Euler algorithm and the conformal finite difference time domain (CFDTD), the conformal Symplectic Euler algorithm achieves almost the same level of accuracy as the CFDTD method, but the conformal Symplectic Euler algorithm improves the computational efficiency compared with the CFDTD method dramatically. When the dielectric constants of the two materials vary greatly, the conformal Symplectic Euler algorithm can reduce the pseudo-waves almost by 80% compared with the traditional Symplectic Euler algorithm on average
A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes
Full text linkNode location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance
Design of Feedback Shift Register of Against Power Analysis Attack
Full text linkStream ciphers based on linear feedback shift register (LFSR) are suitable for constrained environments, such as satellite communications, radio frequency identification devices tag, sensor networks and Internet of Things, due to its simple hardware structures, high speed encryption and lower power consumption. LFSR, as a cryptographic primitive, has been used to generate a maximum period sequence. Because the switching of the status bits is regular, the power consumption of the LFSR is correlated in a linear way. As a result, the power consumption characteristics of stream cipher based on LFSR are vulnerable to leaking initialization vectors under the power attacks. In this paper, a new design of LFSR against power attacks is proposed. The power consumption characteristics of LFSR can be masked by using an additional LFSR and confused by adding a new filter Boolean function and a flip-flop. The design method has been implemented easily by circuits in this new design in comparison with the others
Controlled Secure Direct Communication Protocol via the Three-Qubit Partially Entangled Set of States
Full text linkIn this paper, we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme, which was found insecure under two kinds of attacks, fake entangled particles attack and disentanglement attack. Then, by changing the party of the preparation of cluster states and using unitary operations, we present an improved protocol which can avoid these two kinds of attacks. Moreover, the protocol is proposed using the three-qubit partially entangled set of states. It is more efficient by only using three particles rather than four or even more to transmit one bit secret information. Given our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource, it makes this protocol more convenient from an applied point of view
MalDetect: A Structure of Encrypted Malware Traffic Detection
Full text linkRecently, TLS protocol has been widely used to secure the application data carried in network traffic. It becomes more difficult for attackers to decipher messages through capturing the traffic generated from communications of hosts. On the other hand, malwares adopt TLS protocol when accessing to internet, which makes most malware traffic detection methods, such as DPI (Deep Packet Inspection), ineffective. Some literatures use statistical method with extracting the observable data fields exposed in TLS connections to train machine learning classifiers so as to infer whether a traffic flow is malware or not. However, most of them adopt the features based on the complete flow, such as flow duration, but seldom consider that the detection result should be given out as soon as possible. In this paper, we propose MalDetect, a structure of encrypted malware traffic detection. MalDetect only extracts features from approximately 8 packets (the number varies in different flows) at the beginning of traffic flows, which makes it capable of detecting malware traffic before the malware behaviors take practical impacts. In addition, observing that it is inefficient and time-consuming to re-train the offline classifier when new flow samples arrive, we deploy Online Random Forest in MalDetect. This enables the classifier to update its parameters in online mode and gets rid of the re-training process. MalDetect is coded in C++ language and open in Github. Furthermore, MalDetect is thoroughly evaluated from three aspects: effectiveness, timeliness and performance
Research on Public Opinion Propagation Model in Social Network Based on Blockchain
Full text linkWith the emergence and development of blockchain technology, a new type of social networks based on blockchain had emerged. In these social networks high quality content creators, filters and propagators can all be reasonably motivated. Due to the transparency and traceability brought by blockchain technology, the public opinion propagation in such social networks presents new characteristics and laws. Based on the theory of network propagation and blockchain, a new public opinion propagation model for this kind of social network based on blockchain technology is proposed in this paper. The model considers the effect of incentive mechanism produced by reasonably quantifying value contribution on the propagation of information in such social networks, and the income-risk matrix under different propagation behaviors is constructed. Furthermore, the transformation process and transfer probability among different states in the propagation model are defined on the basis of income-risk matrix. The model is helpful to break the bottleneck of network public opinion management by using blockchain technology. The propagation of false network public opinion can be contained, and a good ecological environment of network public opinion propagation would be realized
Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition
Full text linkGait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches