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In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

In order to solve the problem that the toughness (impact strength) of the traditional elastomer toughening will decrease its rigidity (modulus) while improving the toughness (impact strength) of the material, the multi-porous nano-cacO3/polymer composite and its application in building plastics were proposed. In this essay, the mechanical properties of nano-caco3/PVC/CPE and nano-caco3/PP/SBS composites are studied. The results showed that the notch impact strength increased by 15.8 % from 46.8 kJ/m2 to 54.2kJ/m2 when two nano-cacO3 was added into the PP/SBS blend system, indicating that nano-cacO3 also toughened PP to some extent. Conclusion: Nano Ca-CO3 has remarkable toughening effect on PVC blending system, and also has certain toughening effect on PP blending system. The profile with nano-cacO3 added can ensure the impact performance of low temperature drop hammer, while the notched impact strength, tensile strength, elongation at break and flexural modulus of the simply supported beam are significantly improved compared with the profile without nano-cacO3 added. The application of nano CaCO3/PVC/CPE composites in the profile of PVC doors and Windows is studied.

With the participation of large quantities of renewable energy in power system operations, their volatility and intermittence increases the difficulties and challenges of power system economic scheduling. Considering the uncertainty of renewable energy generation, based on the distributionally robust optimization method, a two-stage economic dispatch model is proposed to minimize the total operation costs. In this paper, it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set, which is established by utilizing the first-order moment information of available historical data. Furthermore, the theory of conditional value-at-risk is introduced to transform the model into a tractable model, which we call robust counterpart formulation. Based on the stochastic dual dynamic programming method, an improved iterative algorithm is proposed to solve the robust counterpart problem. Specifically, the convergence optimum can be obtained by the improved iterative algorithm, which performs a forward pass and backward pass repeatedly in each iterative process. Finally, by comparing with other methods, the results on the modified IEEE 6-bus, 118-bus, and 300-bus system show the effectiveness and advantages of the proposed model and method.

Human DNA topoisomerase I (Topo I) is an essential enzyme in regulating DNA supercoiling during transcription and replication, and it is an important therapeutic target for anti-tumor agents. Bidens pilosa L. is a medicinal herb that is used as a folk medicine for cancers in China. A new flavonoid (1) and a new polyacetylene (20), along with eighteen flavonoids (2–19) and nine polyacetylenes (21–29), were isolated and identified from the methanol extract of the whole plant of B. pilosa, and some of the compounds (4, 5, 6 and 7) exhibited potent cytotoxicity against a panel of five human cancer cell lines. The DNA relaxation assay revealed that some flavonoids and polyacetylenes exerted inhibitory activities on human DNA Topo I, among them compounds 1, 2, 5, 6, 7, 8, 15, 19, 20, 22, and 24 were the most active ones, with IC50 values of 393.5, 328.98, 145.57, 239.27, 224.38, 189.84, 89.91, 47.5, 301.32, 178.03, and 218.27 μM, respectively. The structure–activity analysis of flavonoids was performed according to the results from the Topo I inhibition assay. The DNA content analysis revealed that 5, 6, and 7 potently arrested cell cycle at the G1/S and G2/M phases in human colon cancer cell DLD-1 depending on the concentration of the inhibitors. The levels of protein expression related to the G1/S and G2/M cell cycle checkpoints were in accordance with the results from the DNA content analysis. These findings suggest that flavonoids are one of the key active ingredients accounting for the anti-tumor effect of B. pilosa.

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

In order to investigate the correlation between respiratory rate and muscle quality in fresh rainbow trout, the changes of muscle quality corresponding to different respiratory rates were analyzed. The contents of lactic acid, glycogen, malondialdehyde (MDA), nucleotide compounds and pressurized water loss rate were measured. The result showed that with the increase of respiratory rate, the content of glycogen decreased, the content of malondialdehyde and nucleotide compounds, the rate of pressure water loss increased. When the respiratory rate increased from 83 to 95 times/min, the lactic acid content increased significantly from 1.46 to 2.37 mmol/g prot (P

In light of growing concerns about the misuse of personal data resulting from the widespread use of artificial intelligence technology, it is necessary to implement robust privacy-protection methods. However, existing methods for protecting facial privacy suffer from issues such as poor visual quality, distortion and limited reusability. To tackle this challenge, we propose a novel approach called Diffusion Models for Face Privacy Protection (DIFP). Our method utilizes a face generator that is conditionally controlled and reality-guided to produce high-resolution encrypted faces that are photorealistic while preserving the naturalness and recoverability of the original facial information. We employ a two-stage training strategy to generate protected faces with guidance on identity and style, followed by an iterative technique for improving latent variables to enhance realism. Additionally, we introduce diffusion model denoising for identity recovery, which facilitates the removal of encryption and restoration of the original face when required. Experimental results demonstrate the effectiveness of our method in qualitative privacy protection, achieving high success rates in evading face-recognition tools and enabling near-perfect restoration of occluded faces.

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

The variability of renewable energy and transmission congestion provide opportunities for arbitrage by merchants in deregulated electricity markets. Merchants strategically invest to maximize their profits. This paper proposes a joint investment framework for renewable energy, transmission lines, and energy storage using the Stackelberg game model. At the upper level, merchants implement investment and operation strategies for deregulated transmission and energy storage to maximize profits. At the middle level, central planners seek to maximize social welfare through investments in centralized renewable energy and energy storage. At the lower level, independent system operators jointly optimize the energy and reserve markets to minimize the total operating costs. Merchants are remunerated through financial rights, which are a settlement method based on locational marginal price. The trilevel optimization problem is reformulated as a tractable single-level one using Karush-Kuhn-Tucker (KKT) conditions and strong duality theory. The interaction between merchants and central planners is studied with an example based on the IEEE 30-bus test system. The assignment of weight coefficients to the corresponding stochastic scenarios can help merchants avoid investment risk, and their effectiveness is verified with the IEEE 118-bus test system.

In order to enhance the security and throughput of medical records storage system, a model using an improved H-Algorand consensus mechanism with credibility assessment is proposed. In this study, the model applies blockchain technology to realize distributed storage and privacy protection of medical data. To ensure the effective operation of the blockchain system, a credibility assessment algorithm is used to obtain the trust value of each medical institution node, and replaces the account balances in the H-Algorand consensus mechanism with the trust value as the node weight. Meanwhile, this paper designs a privacy protection scheme, through the searchable encryption algorithm and asymmetric encryption algorithm to achieve system data storage and sharing. Compared with the existing medical record storage model, the proposed model has higher security and throughput efficiency.

The production season is one of the judgment standards of the green tea quality and spring tea is generally considered of higher quality. Moreover, early spring tea is usually more precious and sells for a higher price. Therefore, a multifaceted strategy that integrates lipidomics and metabolomics, based on UPLC-Triple-TOF/MS coupled with chemometrics, was developed to discriminate early spring green tea (ET) and late spring green tea (LT). Twenty-six lipids and forty-five metabolites were identified as characteristic components. As for characteristic lipids, most of glycerophospholipids and acylglycerolipids have higher contents in ET. By contrast, glycoglycerolipids, sphingolipids and hydroxypheophytin a were shown higher levels in LT samples. Most of the differential metabolites identified were more abundant in ET samples. LT samples have much higher catechin, procyanidin B2, and 3',8-dimethoxyapigenin 7-glucoside contents. Based on the integration of differential lipids and metabolites, the reconstructed orthogonal partial least squares discriminant analysis (OPLS-DA) model displayed 100% correct classification rates for harvest time discrimination of green tea samples. These results demonstrated that the integration of lipidomics and metabolomics approaches is a promising method for the discrimination of tea quality.

Coordination polymers (CPs) constructed by zwitterionic ligands show obvious advantages in the fluorescence sensing of toxic pollutants due to the separated charge centers on the frameworks, where the construction of aqueous-phase stable and multifunctional complexes is crucial for practical applications in environmental or food safety detection. A Cd(II) 2D water-stable porous CPs {[CdL(H2O)2]·(ClO4)·3H2O} (1) (flexible H2LCl = 5-carboxy-1-(4-carboxybenzyl)-2-methylpyridin-1-ium chloride) was solvothermally synthesized from good fluorescent zwitterionic organic linkers and was further characterized by single-crystal X-ray diffraction, Hirshfeld surface analysis, powder X-ray diffraction (PXRD), IR spectra, elemental analysis, and thermogravimetric analysis (TG). Aqueous-phase sensing studies demonstrate that complex 1 can serve as a unique bifunctional luminescent probe for highly selective, quick responsive and multicyclic detection of three noxious high-valent oxo − anions Cr2O72−, CrO42−, MnO4− as well as dimetridazole (DTZ) antibiotic via remarkable fluorescence quenching with low limits of detection (LODs) (Cr2O72− 0.12 μM, CrO42− 0.16 μM, MnO4− 0.29 μM and DTZ 0.09 μM). Moreover, the sensor has a certain practical application potential. It obtains desirable recoveries (96.10–105.35 %) for the determination of oxo-anions and DTZ in milk, respectively. Mechanism for all the turn-off responses between the framework and analytes were elaborately explored by means of the electron transfer analytical methods and density functional theory (DFT) calculations.

In order to help achieve the goal of carbon peak and carbon neutrality, the large-scale development and application of clean renewable energy, like wind generation and solar power, will become an important power source in the future. Large-scale clean renewable energy generation has the uncertain characteristics of intermittency, randomness, and volatility, which brings great challenges to the balance regulation and flexible operation of the power system. In addition, the rapid development of renewable energy has led to strong fluctuations in electricity prices in the power market. To ensure the safe, reliable, and economic operation of the power system, how to improve the power system flexibility in an uncertain environment has become a research hotspot. Considering the uncertainties, this article analyzes and summarizes the research progress related to power system flexibility from the perspective of power system planning, operation, and the electricity market. Aiming at the modeling technology of uncertainty, the related modeling methods including stochastic programming, robust optimization, and distributionally robust optimization are summarized from the perspective of mathematics, and the application of these methods in power system flexibility is discussed.

This study aimed to analyze the impact of different additions (0–1.25%) of yam bean powder (YBP) on myofibrillar protein (MP) gel characteristics such as the structure, water-holding capacity (WHC), chemical interaction strength of grass carp MP, and texture. The results showed that the YBP exhibited a strong water absorption capacity and filled in the protein heat-induced polymerization gel network well, which enabled the gel network to capture and retain water effectively, resulting in MP gels containing YBP with excellent WHC and gel strength (0.75%). In addition, YBP induced the formation of hydrogen and disulfide bonds in proteins and inhibited the conversion of α-helices to β-sheets and β-turn structures, facilitating the formation of high-strength gel networks (p < 0.05). In conclusion, YBP can significantly improve the thermally induced gelling properties of grass carp MP. In particular, the addition of 0.75% YBP had the best effect in terms of filling the gel network of grass carp MP, resulting in the formation of a continuous and dense protein network, leading to the composite gel with the best WHC and texture.

Aiming at the problem of incomplete measurement data in the power system state estimation process, the missing data reconstructed by the residual generative adversarial network (RGAN) was introduced for the power system dynamic estimation. RGAN is a deep learning model based on the idea of a “two-player zero-sum” game, in which two deep neural networks compete with each other to mine the relevant features of the data. Distinguish from existing structure, skip connection and residual network (ResNet) were incorporated into two deep neural networks. And the incomplete measurement data were reconstructed accurately from the remaining measurement data. To lessen the impact of reconstruction errors, the unscented Kalman filter (UKF) method was used to estimate power system state. A case studied in the IEEE 30-bus shows that the UKF based RGAN dynamic estimation can maintain a high accuracy under different proportions of missing data.

ABSTRACT: An environmentally sustainable method to extract phosphatidylcholine (PC) from chicken liver (PCCL) and its functional properties were studied. The extraction times, enzymatic hydrolysis time, the solid-liquid ratio as well as types of enzymes (protamex proteinase and neutral proteinase) were investigated. Furthermore, the content of PCCL, emulsifying properties and solubilities of PCCL were also determined. The optimum conditions of extracting PCCL were found to be: reaction time of 3.75 h, enzymatic hydrolysis time of 85.22 min, 1: 3.15 (w/v) of solid-liquid ratio, using protamex proteinase, and the yield and concentration of PCCL was 88.92% and 0.89 mg/mL, respectively. Solubility and emulsifying properties of PCCL showed that the HLB value of PCCL was 10, and in ethanol and glycerol, the solubility of PCCL was 0.5850 g/mL and 0.0965 g/mL, respectively, which was shown to have good ethanol solubility and lipophilicity. From the perspective of green production and high-value utilization of by-products, PCCL could be used as a potential new lecithin source, providing ideas for the development and application of PC of animal origin.

Large-scale renewable energy integration brings unprecedented challenges to electric power system planning and operation. The paper aims at economic dispatch and the safe operation of high penetration renewable energy power systems. According to the principle of power system dispatchability, the assessment of wind energy accommodation is formulated into a two-stage robust optimization problem with a min-max-min structure. Based on the benders algorithm, the intractable robust optimization problem is transformed into the form of sub-problem and master problem. Strong duality theory and big-M method are used to recast the sub problem into a mixed integer linear programming. The envelope of wind energy accommodation can be obtained by using commercial software to solve the master problem and sub problem alternately. For the realization of arbitrary wind power within the envelope, the amount of wind energy leakage and load shedding in power system operation are acceptable. An example of modified IEEE 39-bus test systems is used to verify the effectiveness and practicability of the evaluation method.

Coordinated planning is an effective method to balance investment costs and benefits in achieving high renewable target under the renewable-driven power system expansion wave. This paper proposes a coordinated planning model to support the efficient achievement of renewable target considering economy of the system by accounting for the interaction among source, grid, and energy storage system. An adaptive two-stage min-max-min robust optimization model is formulated to take into account renewable target as well as the uncertainty associated with renewable production and load demand. To reduce the conservatism of robust optimization, uncertain budget, multiple uncertain sets, and data-driven method are used to design uncertain sets. The resulting model is transformed into a tractable bi-level programming through strong duality theory and big-M method. A customized column-and-constraint generation algorithm is used to solve the bi-level programming. Simulation results presented for the modified IEEE 30-bus test system corroborates the effectiveness of the methodology, which finds siting and sizing of renewable energy sources and energy storage systems as well as transmission expansion schemes. It is capable to provide a flexible planning tool driven by renewable target under a reasonable computational burden.

In this paper, a novel joint planning framework is proposed to coordinate the investment and operation of renewable energy sources and energy storage systems (ESS) in energy and ancillary services markets. Based on this framework, coordinated planning and operation model under centralized and deregulated market mechanism is studied, and multiple factors such as siting and sizing of wind turbine and ESS, the efficiency of ESS, transmission lines constraints are considered. For the centralized market mechanism, the coordinated model aiming at maximizing social welfare is established, which is a tractable single-level optimization problem. For the deregulated market mechanism, the coordinated model aiming at maximizing investment profits is established, which is an intractable bi-level optimization problem as the locational marginal price in the objective function. The bi-level optimization problem is reformulated into a mathematical problem with equilibrium constraints by Karush-Kuhn-Tucker conditions. The big-M method and strong dual theory are used to deal with the nonlinearity in constraints and objective functions, and the problem is transformed into a mixed-integer linear programming, which can be solved by commercial software. Furthermore, the impact of production tax credit and investment tax credit financial incentive policies on investment behavior has been studied, and the evaluation indexes of electrical information and economy have been established. The proposed approach has been implemented on the IEEE 6-bus and the IEEE 30-bus test systems, and results justify the efficiency of the model proposed.

Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus system.

As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

Inter-regional and transnational grid interconnection is necessary for energy development. Xinjiang, which is rich in renewable energy resources, is adjacent to countries in Central Asia and has great potential for interconnection with its neighbors. This paper outlines China’s relevant policies for transnational power interconnection, and introduces the energy structure, load demand endowments, and power supply status of Xinjiang, Pakistan, and five Central Asian countries. Further, it analyzes the advantages of the multinational power interconnection from the aspects of power supply and load complementation. Finally, from the perspective of technical support and practical basis, the feasibility of interconnection between Xinjiang, Pakistan, and five Central Asian countries have been analyzed. This paper provides a theoretical basis for promoting and implementing China’s “Belt and Road” power transnational interconnected development strategy. Keywords: Interconnected power grid, Renewable energy resources, Complementary features, Belt and Road

The Nash equilibrium and competition equilibrium have been widely studied in the electric power market up to now. In this paper, it is explained that the Nash equilibrium can be achieved by using marginal cost pricing and the competition equilibrium can be performed by using accounting cost pricing based on the model of the power market system. The comparison between the Nash equilibrium and competition equilibrium indicates that surplus and unfair allocation of market benefits may be obtained by the Nash equilibrium, and the competition equilibrium realizes the optimization in economics with maximum market efficiency and fairness for market benefit allocations while the optimization in mathematics is achieved by the Nash equilibrium. There is sameness between the Nash equilibrium and competition equilibrium at the point when the power network characteristics are disregarded. The case study is made on a IEEE 30-bus system, and the calculation results indicate that it is the key issue to perform the competition equilibrium by using accounting cost pricing.

Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. Poor network management, low-priced Internet subscriptions, and readily available attack tools can be attributed to their rise. The recently emerged software-defined networking (SDN) and deep learning (DL) concepts promise to revolutionize their respective domains. SDN keeps the global view of the entire managed the network from a single point, i.e., the controller, thus making the network management easier. DL-based approaches improve feature extraction/reduction from a high-dimensional dataset such as network traffic headers. This work proposes a deep learning based multi-vector DDoS detection system in an SDN environment. The detection system is implemented as a network application on top of the SDN controller and can monitor the managed network traÿc. Performance evaluation is based on different metrics by applying the system on traÿc traces collected from different scenarios. A high accuracy with low false-positive rate is observed in attack detection for the proposed system.

Lysozyme (Ly) is a good natural food preservative. However, its enzyme activity is easily influenced by food components, such as tannins. However, the specific mechanism is still unknown. In this study, tannic acid (TA) was used as model tannin to bind with Ly. The interactional properties of Ly-TA complexes were characterized with intrinsic fluorescence intensity, fourier transform infrared spectroscopy, circular dichroism and dynamic light scattering. The enzyme activities of Ly were analyzed by the lysis rate of Micrococcus lysodeikticus. The results indicated that TA can bind well with Ly through hydrophobic, hydrogen and electrostatic interactions, which induced the unfolding of Ly molecule. Consequently, the enzyme activity of Ly was inhibited for conformational changes and masking its active sites by TA, as well as the enzyme stability further declined in pH changes, thermal treatments and high salt concentrations. Our findings shed some light on the possible explanation of negative effects from tannins on the activity of Ly in plant foods.

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

Unmanned systems are of great importance in accomplishing tasks where human lives are at risk. These systems are being deployed in tasks that are time-consuming, expensive or inconclusive if accomplished by human intervention. Design, development and testing of such vehicles using actual hardware could be quite costly and dangerous. Another issue is the limited outdoor usage permitted by Federal Aviation Administration regulations, which makes outdoor testing difficult. An optimal solution to this problem is to have a simulation environment where different operational scenarios, newly developed models, etc., can be studied. In this paper, we propose GNSSim, a Global Navigation Satellite System (GNSS) simulation framework. We demonstrate its effectiveness by integrating it with UAVSim. This allows users to experiment easily by adjusting different satellite as well as UAV parameters. Related tests and evidence of the correctness of the implementation are presented.

Usage of ground, air and underwater unmanned vehicles (UGV, UAV and UUV) has increased exponentially in the recent past with industries producing thousands of these unmanned vehicles every year.With the ongoing discussion of integration of UAVs in the US National Airspace, the need of a cost-effective way to verify the security and resilience of a group of communicating UAVs under attack has become very important. The answer to this need is a simulation testbed which can be used to simulate the UAV Network (UAVNet). One of these attempts is - UAVSim (Unmanned Aerial Vehicle Simulation testbed) developed at the University of Toledo. It has the capability of simulating large UAV networks as well as small UAV networks with large number of attack nodes. In this paper, we analyse the performance of the simulation testbed for two attacks, targeting single and multiple UAVs. Traditional and generic computing resource available in a regular computer laboratory was used. Various evaluation results have been presented and analysed which suggest the suitability of UAVSim for UAVNet attack and swarm simulation applications.