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A novel fluorescent probe, amino-pillar[5]arene (APA), was prepared via a green, effective, and convenient synthetic method, which was characterized by nuclear magnetic resonance (NMR), infrared (IR), and high-resolution mass spectrometry. The fluorescence sensing behavior of the APA probe toward 22 metal ions in aqueous solutions were studied by fluorescence spectroscopy. The results showed that APA could be used as a selective fluorescent probe for the specificity detection of Au3+ ions. Moreover, the detection characteristics were investigated by fluorescence spectral titration, pH effect, fluorescence competitive experiments, Job's plot analysis, 1H NMR, and IR. The results indicated that detection of Au3+ ions by the APA probe could be achieved in the range of pH 1-13.5 and that other coexisting metal ions did not cause any marked interference. The titration analysis results indicated that the fluorescence intensity decreased as the concentration of Au3+ ions increased, with an excellent correlation (R 2 = 0.9942). The detection limit was as low as 7.59 × 10-8 mol·L-1, and the binding ratio of the APA probe with Au3+ ions was 2:1. Therefore, the APA probe has potential applications for detecting Au3+ ions in the environment and in living organisms.

The ever-expanding power system is developed into an interconnected pattern of power grids. Zone partitioning is an essential technique for the operation and management of such an interconnected power system. Owing to the transmission capacity limitation, transmission congestion may occur with a regional influence on power system. If transmission congestion is considered when the system is decomposed into several regions, the power consumption structure can be optimized and power system planning can be more reasonable. At the same time, power resources can be properly allocated and system safety can be improved. In this paper, we propose a power system zone partitioning method where the potential congested branches are identified and the spectral clustering algorithm is improved. We transform the zone partitioning problem into a graph segmentation problem by constructing an undirected weighted graph of power system where the similarities between buses are measured by the power transfer distribution factor (PTDF) corresponding to the potential congested branches. Zone partitioning results show that the locational marginal price (LMP) in the same zone is similar, which can represent regional price signals and provide regional auxiliary decisions.

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&amp, CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

Static characteristics of supervisory control and data acquisition (SCADA) system are often exploited to perform malicious activities on smart grids. Most of the time, the success of cyberattacks begins with the profiling of the target system and follows by the analysis of the limited resources. To alleviate the asymmetry between attack and defense, network-based moving target defense (MTD) techniques have been applied in the network system to defend against cyberattacks by constructing a dynamic attack surface to the adversary. In this paper, we propose a novel MTD technique based on adaptive forwarding path migration (AFPM) that focuses on improving the defense capability and optimizing the network performance of path mutation. Considering the transient problems present in path mutation caused by the dynamic switching of the forwarding path, we formalize the mutation constraints based on the satisfiability modulo theory (SMT) to select the mutation path. Considering the limited defense capability of path mutation owing to the traditional mutation selection mechanism, we design the mutation path generation algorithm based on the network security capacity matrix to obtain an optimal combination of mutation path and mutation period. Finally, we compare and analyze various cyber defense techniques used in the SCADA network and demonstrate experimentally that our MTD technique can prevent more than 92% of passive monitoring under specified conditions while ensuring the quality of service (QoS) to be almost the same as the static network.

In smart grid, a malicious entity can launch a direct load altering attack by injecting false commands into aggregators responsible for direct load control. It may remotely manipulate load, causing deviation in the operating frequency, and consequently lead to disruption in the system. In this paper, we mainly focus on the successive direct load altering attack, with which the attacker can continuously manipulate aggregators to achieve the larger impact. In addition to resulting in a larger impact, it is difficult for the controllers to detect such attacks as the attackers can inject false data to contaminate feedback data from aggregators to controllers. We present an attack model, and our analysis in this paper is from an attacker’s perspective. Our model and analysis can serve as an important component also in the future for designing the counter strategies to such attacks. We propose a new frequency response model, which shows changes of the frequency undergoing a successive direct load altering attack. Attackers can utilize this model to analyze the impact of an attack sequence. Considering that attack sequences with different false commands can result in different levels of impact, we develop a three-step optimization method to analyze and find the optimal attack sequence. Our simulation results validate the feasibility and effectiveness of the successive direct load altering attacks.

Cyber-physical system (CPS) like smart grids deeply integrated with communication networks are often subjected to sophisticated cyber-attacks, such as false data injection attack (FDIA) with a strong capability of strategic reconnaissance required to learn the environment, where the static characteristics of the system enable an easier profiling of the critical infrastructure resources by the adversary. In this paper, we propose a cyber-physical moving target defense (CPMTD) technique that focuses on both attack prevention and detection to mitigate such static vulnerabilities and provide a combination of defense strategies for power system. For attack prevention, we design the Cyber-MTD strategy to mislead and disrupt attack preparation by randomizing the data acquisition with controlled change across multiple system dimensions based on the network programmability of protocol oblivious forwarding (POF). For attack detection, we design the Physical-MTD strategy to improve the detection probability of FDIA by periodically changing the measurement matrix of state estimation based on the D-FACTS devices’ capability of perturbing the transmission line susceptance. Simulations on IEEE 14 bus and 57 bus systems demonstrate the effectiveness of CPMTD against FDIA with small overhead. The probability of cyber-attacks in two cases can be reduced by more than 90%; FDIA introduces little operation cost as most of them are detected. Network throughput barely changes and network latency increases by less than 9%.

Carefully-crafted attackers usually collect sufficient information regarding cyber-physical system (CPS) resources from accessible network communications before performing destructive activities. If these sophisticated cyberattacks like false data injection attack (FDIA) are developed, they are typically stealthy and barely detectable through general passive defense approaches. In this paper, we propose a network-based multidimensional moving target defense (NMMTD) mechanism for power system, which focuses on disrupting the development of FDIA in the preparation stage. We extend the attack space into multiple dimensions by randomizing data acquisitions in multiple rounds. In each round, we increase apparent complexity and uncertainty in network communications with different controlled changes. To reduce the traffic burden, we include the packet dropping policy in NMMTD for transparent network transmission. We demonstrate the effectiveness of NMMTD in preventing the attacker from intercepting data packets and securing power system against FDIA with little impact on both network and system performances.

False data injection (FDI) attack is a hot topic in large-scale Cyber-Physical Systems (CPSs), which can cause bad state estimation of controllers. In this paper, we focus on FDI detection on transmission lines of the smart grid. We propose a novel and effective detection framework to identify FDI attacks. Different from the previous methods, there are multi-tier detectors which utilize edge nodes such as the programmable logic controllers (PLCs) instead of the central controller to detect attacks. The proposed framework can decrease the transmission time of data to reduce the latency of decisions because many sensory data need not be transmitted to the central controller for detection. We also develop a detection algorithm which utilizes classifiers based on machine learning to identify FDI. The training process is split from every edge node and is placed on the central node. The detectors are lightweight and are properly adopted in our detection framework. Our simulation experiments show that the proposed detection framework can provide better detection results than the existing detection approaches.

The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a great impact on the safe and stable operation of the smart grid, many effective detection methods have been proposed, such as an FDI detector based on the support vector machine (SVM). In this paper, we first analyze the problem existing in the detector based on SVM. Then, we propose a new attack method to reduce the detection effect of the FDI detector based on SVM and give a proof. The core of the method is that the FDI detector based on SVM cannot detect the attack vectors which are specially constructed and can replace the attack vectors into the training set when it is updated. Therefore, the training set is changed and then the next training result will be affected. With the increase of the number of the attack vectors which are injected into the positive space, the hyperplane moves to the side of the negative space, and the detection effect of the FDI detector based on SVM is reduced. Finally, we analyze the impact of different data injection modes for training results. Simulation experiments show that this attack method can impact the effectiveness of the FDI detector based on SVM.

The phase transition law of Fe-N system is very important for efficiently synthesizing single-phase γ'-Fe4N thin films. The γ"-FeN thin films are deposited on silicon wafers via DC reactive magnetron sputtering; some of them are stripped from the silicon wafers and measured by using the synchronous thermal analysis (TG-DSC) for studying the phase transition law of Fe-N system. The results of TG-DSC show that at a heating rate of 10 ℃/min, the Fe-N system has five phase transitions in a temperature range between room temperature (RT) and 800 ℃, i.e. I (330−415 ℃): γ''-FeN→ξ-Fe2N with an endothermic value of 133.8 J/g; II (415−490 ℃): ξ-Fe2N→ε-Fe3N with no obvious latent heat of phase change; III (510−562 ℃): ε-Fe3N→γ'-Fe4N with an exotherm value of 29.3 J/g; IV (590−636 ℃): γ'-Fe4N→γ-Fe with an exotherm value of 42.6 J/g; V (636−690 ℃): γ-Fe→α-Fe with an endothermic value of 14.4 J/g. According to the phase transition law of Fe-N system, the crystal phase of iron nitride thin film is effectively regulated by vacuum annealing. The x-ray diffraction pattern (XRD) results show that the iron nitride thin film obtained by direct-sputtering in pure N2 is a single-phase γ"-FeN film, and it becomes a single-phase ξ-Fe2N film after being annealed at 350 ℃ for 2 h, a single-phase ε-Fe3N film after being annealed at 380 ℃ for 2 h, and a single-phase γ'-Fe4N film after being annealed at 430 ℃ for 7 h. The annealing temperature for the phase transition of Fe-N thin film is generally lower than that predicted by the TG-DSC experimental results, because it is affected by the annealing time too, that is, prolonging the annealing time at a lower temperature is also effective for regulating the crystal phase of Fe-N thin film. The magnetic properties of the Fe-N thin film are also studied via vibrating sample magnetometer (VSM) at room temperature. The γ'-Fe4N polycrystalline thin film shows an easy-magnetized hysteresis loop for the isotropic in-plane one, but a hard-magnetized hysteresis loop with a large demagnetizing field for the out-of-plane one, which belongs to the typical magnetic shape anisotropy. However, their saturation magnetizations are really the same (about 950 emu/cm3) both in the plane and out of the plane.

In this paper, a failure model of Cyber-Physical systems and an attack model are proposed. We divide the attacks into three kinds: simultaneous attack, sequential attack and composite attack. Through numerical simulations, we find that: (1) the sequential attack may bring more damage in single physical systems, (2) the coupling process of cyber system and physical systems makes it possible that sequential attack causes more damage than simultaneous attacks when the attackers only attack the cyber system, (3) with some target sets, composite attack leads to more failures than both simultaneous attack and sequential attack. The above results suggest that defenders should take all the three kinds of attacks into account when they select the critical nodes.

The feasibility of transcatheter arterial chemoembolization (TACE) combined with intratumoral injection of cisplatin as treatment for hepatocellular carcinoma. 30 cases receiving TACE were denoted the TACE group, another 30 cases receiving TACE combined with an intratumoral multi-point injection of cisplatin were denoted the TACE/cisplatin group. Cases with partial remission/complete remission (PR/CR) were analyzed using 2 tests; alpha fetoprotein (AFP), aspartate amino transferase (AST), total bilirubin (TBIL), erythrocyte, and platelet levels were detected and the differences between two groups were analyzed using the Student’s t-test; cases with complications, including intrahepatic metastasis (IM), upper gastrointestinal bleeding (UGB), and liver failure were also counted. The correlation of clinical parameters with PR/CR was analyzed using multifactorial correlation analysis. Cases with PR/CR in the TACE/cisplatin group were significantly more than in TACE group, accompanied by significant declination in FAP. There were no significant differences of AST, ALT, TBIL, blood urea nitrogen (BUN), white blood cells (WBC), red blood cells (RBC), and platelets (PLT) between two groups; 3 cases with IM, one case with UGB and one case with LF were found in the TACE group, but only 1 case with IM was found in the TACE/cisplatin group. In addition, tumor stage was correlated with PR/CR. We concluded that TACE combined with intratumoral injection of cisplatin was more effective than TACE, and with fewer complications and side effects.

Amorphous carbon nanodot arrays were successfully prepared through filtered cathodic arc plasma (FCAP) technique, and via a special widen process, the negative effect of the anion impurities distributed in the anodic aluminum oxide (AAO) template was avoided during carbon ions deposition. The morphology of the samples was studied by a field emission scanning electron microscopy (FESEM). The AAO template prepared by multi-steps combining anodization process and pore-widening process has special pore openings. The nanodot arrays are uniformly distributed and in good agreement with the AAO pore arrays. The diameter of the as-prepared nanodot is about 100 nm, and the density was estimated to be 1010 cm-2. Field emission properties of the nanodot arrays were investigated and a low threshold field of 37 V/mm at 10 mA/cm2 was obtained.

Ni nanopowders with various grain sizes were successfully prepared by anodic arc discharging plasma method.The samples were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM) and the corresponding selected-area electron diffraction (SAED) to determine the crystal structure, lattice parameter, morphology and particle size. The experiment results indicate that the samples have fcc crystal structure as the bulk materials. The lattice parameter of Ni nanopowders is always larger than the equilibrium value of the perfect single crystal lattice, so the lattice expansion has taken place. The lattice parameter and the value of the unit cell volume increase significantly with the decrease of the grain size, and the increase in lattice parameter is inversely proportional to the grain size. The lattice expansion is the result of the interfacial energy and surface tension induced mutual attraction of Ni grains, and this phenomenon can be explained according to the thermodynamic theory.

Copper nitride(Cu33N) thin films with nano-crystalline were deposite d on glass substrates at a temperature of 100℃ by DC magnetron sputtering with a columnar target. The structure of the Cu33N thin films was characterized by x-ray diffraction, x-ray photoelectron spectroscopy and atomic force microsco py. The results indicate that Cu33N thin films are composed of nanome ter grains with a cu bic crystal structure of anti-ReO33 type. The films have a uniform s mooth surf ace with only a roughness(Raa) of 17nm. The binding energy peaks of Cu2 p3/23/2, Cu2p1/21/2 and N1s were at 9327, 9527 and 3999eV respecti vely, and spin-orbit coupling energy gap of Cu2p was 20eV. The film thickness was determined by a profile step scanner, and its resistivity was measured usin g the four-probe method. The deposition rate and resistivity were found to chan ge with the nitrogen content.