In this paper, we present the concept of a novel diagnostic device for on-site analyses, based on the use of advanced bio-sensing and photonics technologies to tackle emerging and endemic viruses causing swine epidemics and significant economic damage in farms. The device is currently under development in the framework of the EU Commission co-funded project. The overall concept behind the project is to develop a method for an early and fast on field detection of selected swine viruses by non-specialized personnel. The technology is able to detect pathogens in different types of biological samples, such as oral fluids, faeces, blood or nasal swabs. The device will allow for an immediate on-site threat assessment. In this work, we present the overall concept of the device, its architecture with the technical requirements, and all the used innovative technologies that contribute to the advancements of the current state of the art. In response to the increasing human population (estimated 9.15 billion by 2050) [During the last decades a reduction in the direct burden of livestock diseases has been observed, as a result of more effective treatments, vaccination and improvements in diagnostic technologies and veterinary services in developing countries  which arTraditionally, virus detection in the laboratory relies on two different approaches: direct detection of the pathogen by polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA) or cell culture-based virus isolation, and/or detection of circulatory antibodies in blood samples. The first approach is time consuming and need at least 2–3 h to be performed, while the later one although fast, simple and often informative, neither coincides with the timing of infection nor it necessarily correlates robustly with clinical signs, and the phase of the clinical disease.Currently rapid diagnostics systems are designed to detect the nucleic acid or protein of the infectious agents. The vast majority of these systems rely on nucleic acid extraction, purification, and PCR-based amplification and detection. PCR amplification methods are very sensitive but often produce false positives from trace contamination of the specimen/equipment. Additionally, nucleic acid-based tests do not provide information on the viability/infectivity of the detected microorganism and often cannot be multiplexed to more than few targets.In recent years, silicon based Photonic Integrated Circuits (PIC) have been demonstrated as a powerful platform for bio/sensing based on monoclonal antibodies. PIC can provide portable multiplex detection of proteins, with sensitivity and specificity previously unknown ,10,11.PICs are devices, on which different optical components and electrical parts are integrated thought the integrated optics technology and their main application is in optical fiber communications.The method used for PICs fabrication is based on the wafer-scale technology, that including lithography technique, using as substrates silicon, silicon nitride or silica. In all cases, the employed substrate material determines features and limitations of the technology.PICs can host large arrays of identical structures or contain complex circuit configurations. However, nowadays the achievable complexity is still not as high as for electronic integrated circuits. Due to these characteristics, PICs are proposed as an emerging technology that may yield novel applications in the field label-free sensor for chemical and biological detection. In fact, the tight confinement of light in integrated structures leads to an increased light-matter interaction that can be exploited to implement the detection of the analytes through the change of the optical properties of the PIC.www.genalyte.com).At the moment, the first commercial products, for different applications, were been introduced on the market (www.swinostics.eu) [The SWINOSTICS (swine diseases field diagnostics toolbox) project (tics.eu)  aims to tics.eu) .The SWINOSTICS approach is based on the combination of photonic integrated circuit (PIC) technology and nano-deposition technology to detect six different swine viruses.The aim of the present paper is to describe the overall concept with particular attention to the target diseases, the state of the art in pathogen detection and the innovation of the SWINOSTICS approach. In The SWINOSTICS diagnostics targets are important emerging and endemic swine viruses, namely: African swine fever (ASF) , ClassicThe majority of existing and widely used diagnostic technologies rely on the following: (1) growing the infectious agent in cell culture ; (2) obsThe SWINOSTICS device  will proPhotonic Integrated Circuit (PIC) technology, as the basis for the development of the biosensors;Label-free optical detection, based on refractive index sensing through ring resonators;Nano-deposition technology ,51, usedThe main technical advantage is the application of three novel and already validated technologies:The SWINOSTICS device will be the only field-use device capable of the detection of six important swine viruses of particular interest for the European and global economy.The device will use oral fluids as a sample for the analysis. This simplifies the sampling and minimizes the time needed for the test (no sample treatment is needed) allowing also the analysis of wild boar samples collected through adequate lures. The device will anyway be compatible with the use of other types of samples, such as faeces, blood or nasal swabs.The sample analysis will require less than 30 min. Additionally, five sensors, working in parallel, will be available within the device, to process up to five samples from five different animals simultaneously.The detection of the infectious agents is made using novel biosensors. The patented, nano-deposition technology used to develop the bio-sensing surface allows the specific determination of the presence of the biomarkers of interest.The PIC sensors that will be used in the device do not require the use of a fluorescent label for the detection (label-free detection).The increased sensitivity of the sensors is supported by the use of commercial monoclonal antibodies against the targeted viruses, which will be immobilized in an oriented manner on the PIC surface.The expected sensitivity and specificity levels are about 95% and 90%, respectively.Cost-effective and mass production suitable fabrication process of the PIC sensors. The fabrication technology will be CMOS -compatible.The device will integrate a biosensor regeneration mechanism, to make each PIC reusable for at least up to 100 times.The SWINOSTICS innovation is at various levels. The key SWINOSTICS features are summarized below:In this section, we present the overall design of the SWINOSTICS device. The system requirements were identified during the initial months of the project and according to the stakeholders and the end-users’ requirements. The device is composed of five separate functional modules as shown in The analytical core module is the biosensor. It is made up of four main components: the microfluidic subsystem, the bio-sensing surface, the photonic transducer system, and the optical analysis module. These main building blocks will be controlled through the appropriate process control and communication module.The microfluidic subsystem is responsible for the delivery of the sample and buffer liquids to the bio-sensing surface . It deals with the flow and distribution of fluids that are constrained to sub-millimeter channels in the dimension about 600 μm wide and 300 μm deep. The flow will be 30 μL/min. The microfluidic subsystem (chip) will be made of PMMA (Poly Methyl Methacrylate). It will consist of one single fluidic inlet and one single corresponding outlet, which is connected via tubing with a peristaltic micro-pump and allows the insertion of the bio-sensing surface sensor chip underneath .It contains the bio-sensing core. The transducer will be constructed using highly compact PIC-based detectors for biochemical sensing. CMOS technology will be used for the implementation of the PIC and in this case the underlying target design also consists of high-Q filtering structures. In each circuit there are three rings: one to compensate derivatives, another for the negative control of the detection and a third one for the measurement. 3N4) over a BOX substrate. The full layer stack is built on a silicon substrate, with a deposition of 300 nm of nitride, remaining a buried oxide layer (BOX) of 3.25 μm. PIC structures are fabricated at silicon nitride layer then an extra layer of silicon oxide is deposited as top cladding. In SWINOSTICS project, it is planned to use vertical coupling by means of focal grating couplers (GC) due to the versatility it provides to the design. Then, due to the required resolution, fabrication techniques as deep UV or electron beam are required on PIC fabrication. Each ring resonator will have its own output finished with a grating coupler. In this way, the response of each ring will be treated separately to maximize the dynamic response of the sensors and to simplify the algorithm for tracking sensor response.Ring resonators are manufactured on Silicon Nitride (SiPIC fabrication process will be based on standard photolithography techniques (electron beam and UV mask photolithography). A direct writing photolithography process will be optimized to transfer the patterns into a positive resist. The resist will be deposited by spin coating using an EVG101 system on the silicon nitride substrate prior to an optimized photolithography process.After exposing the patterns, the resist will be developed. The developed resist could be used directly as etching mask or to create a metal mask by an evaporation/lift-off process. Finally, the silicon nitride layer will be etched by an ICP-RIE tool, which uses fluoride gases. After that, a PECVD process will be used to cover the sample/wafer with a silica upper cladding of 1 micron thick. Finally, a second photolithography step as described will be used to open windows in the sensing areas. The The mentioned fabrication process can be used on complete 4 or 6 inch wafers or for smaller samples, which can be employed to adjust building blocks and fabrication processes.The approach used for the functionalized PIC active-surface preparation will be to first perform derivatization with the specific reactive-group (amino and/or carboxylic group) and then functionalize it with commercial monoclonal antibodies, selected as molecular recognition elements (MREs), against the targeted viruses. As shown in During the measurement, the sample will be conducted by the microfluidic system to the PIC transducer. In the transducer, if the binding of the viruses to the MRE takes place, it is translated in a positive optical signal of detection. 3N4 waveguide has a lower refractive index contrast and shows reduced sensitivity to temperature changes with respect to Silicon on Insulator (SoI).The sensor system will be based on fabricating cost-effective and mass production sensors with multi-analyte capabilities consisting of an array of nano-rings. Each ring in the PIC will be functionalized with a specific probe, achieving multi-analyte sensing in a single chip. In addition, PICs will be fabricated at wafer scale with CMOS compatible technologies resulting to cost-effective sensor chips. Silicon-based nanofabrication is now a mature technology with several companies already placing silicon photonic devices on the market. Of course, there is still room for R&D in terms of more complex and higher-functionality devices. Silicon photonics is in fact being touted as the most likely successor to electronics in the interconnections between microprocessors in data centers and high-end computers. The sensors developed in SWINOSTICS will certainly benefit from both already achieved technological advances in the field and from the mass market, which will reduce the production costs due to economies of scale. To this end, the exclusive use of silicon-based materials, such as silicon nitride and silicon dioxide for the fabrication of the photonic nano-devices employed means that the fabrication technology will be CMOS-compatible. The SiThe optical analysis module is responsible for “reading” the biosensors output. The module includes the fiber optic interfaces between the sensor cell and the optical components, such as the laser module and the detectors. The optical analysis module will operate in close conjunction with the temperature-conditioning module and with the microfluidic interface. The optical subsystem will be based on a transfer function measurement of the chip, using a tunable laser source and a photodetector connected with a fiber optic. A reference wavelength-sweeping signal from the laser will be measured continuously (power and wavelength), and the power transmitted from the circuit will be simultaneously measured.To provide accurate and consistent results during the analytic procedure, the developed PIC biosensors require a stable temperature. For this reason, an analysis chamber will be available within the SWINOSTICS device. A temperature of about 20 °C will be used in the analysis chamber, even though the final value will be selected after optimizing the analytic protocol. The temperature-conditioning module will be responsible for maintaining the temperature within the analysis chamber, as long as the instrument is powered. A temperature control loop will be developed, based on a temperature sensor and a Peltier actuator (for both heating and cooling) .v/v will be used to sanitize the tubes a microfluidic system before each measurement. Our concept foresees the reuse of the bio-sensing surface after each measurement. In order to achieve this, post-testing, the bio-sensing surface, will be washed extensively for 5 min with a regeneration buffer to free the antibodies from the antigens. Different buffers will be tested such as glycine/HCL 50 mM pH 3.0 or ethanolamine 50 mM at pH 9.0. After this step, the surface will be washed again with a PBS buffer pH 7.4 for 10 min. Thereby the biosensor will be ready for immediate or future re-use. This will allow the reuse of the same PIC for at least up to 100 times. After this number of assays, the user will have to replace the sensor. For this purpose, a dedicated module and a specific algorithm will be developed for delivering the buffers to the PIC biosensor and controlling the regeneration procedure. Also, in order to prevent possible contamination between two measurements, due to use clinical sample, a sanitation step will be performed. For this purpose, ethanol at 70% The SWINOSTICS device will include its own processing unit for controlling the device operation , acquiriThe device, biosensors and overall approach will be validated and verified through intra-laboratory studies using both laboratory reference samples and field samples from pigs and wild boars. Validation will be done first with reference samples and then with field samples from four EU countries  most at risk for epidemics along the geographical path of the epidemic’s diffusion. The SWINOSTICS device will be an innovative, quicker and cheaper alternative to the current laboratory testing performed using sophisticated laboratory equipment. The proposed technical solutions will reduce the costs and the time required for testing and will make it possible to perform accurate analysis directly on the field for a broad panel of important pathogens of swine . The use of oral fluids as the main input diminishes the time needed for the analysis and simplifies the sample collection, allowing also the collection of wild boar samples through adequate licking/chewing-lures. Moreover, oral fluids are the easiest and most convenient samples to collect for farmers, veterinarians, but also for non-expert potential end-users of the device, such as hunters and the sampling includes absolutely non-invasive techniques.Nevertheless, if the virus concentration in oral fluids proves to be too low for certain diseases, the device will be able to receive as an input pre-processed samples of different type such as nasal swabs, faeces or blood. The possibility to use different type of samples as an input is an added value, since viruses might not always be detectable in oral fluids in adequate concentration for all the targeted diseases.In this paper, we describe the preliminary architecture and design of the SWINOSTICS portable device. The approach takes advantage of three novel and validated technologies: (a) Photonic Integrated Circuit (PIC) technology, (b) Label-free optical detection, based on refractive index sensing through ring resonators, (c) a nano-deposition technology used for the biosensors developing to detect in viruses in oral fluid such as saliva.


The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH With the rapid development of modern science and technology, sensor technology and pattern recognition methods continue to improve, promoting the development of machine olfaction. Machine olfaction is a bionic detection technology that uses electronic devices to simulate biological olfactory systems. The system is widely used in gas/odour qualitative identification and quantitative detection. Unlike machine vision technology, which shows mature development and wide application, machine olfaction technology is still in the stage of research and development. This technology shows a wide range of application prospects in such fields as environmental monitoring , medicalThe machine olfactory system includes three main components: (1) a gas sensor array; (2) a signal acquisition and processing device; (3) a pattern recognition algorithm, as shown in Traditional machine olfactory systems rely on MOS gas sensor array technology and pattern recognition algorithms to detect and analyse mixed gases ,21. The Pattern recognition can be defined as the identification or classification of complex signal samples. The current machine olfactory system mainly includes two parts: gas qualitative recognition and quantitative detection . TherefoAlthough the above-mentioned methods have effectively achieved mixed gas identification and concentration detection to some extent, both PCA and ICA are linear feature extraction methods , and the(1)This paper proposes a KPCA-KNN gas identification method aiming at the low identification rate of binary mixed gas in the existing machine olfactory system. The method uses KPCA to extract the nonlinear characteristics of a binary mixed gas with different concentration ratios, composes the mixed gas feature set, and then uses a KNN classifier to identify the gases.(2)To improve binary mixture gas detection accuracy, this paper proposes to use MVRVM’s multi-input multi-output feature, with the MOS gas sensor array’s response signal as the input and the two target gas concentrations as the output, to achieve binary mixed gas concentration detection.(3)4 mixed gases. The experimental results show that the proposed method has better resolution accuracy for binary mixed signals than other methods do.The accuracy of the proposed method is verified by qualitative analysis and quantitative detection of CO and CHTo resolve the nonlinear characteristics of MOS gas sensor responses to mixed gas signals, we present KPCA as a feature extraction method for mixed gas signals. KPCA addresses the limitations of PCA for extracting nonlinear data features. Through kernel functions, nonlinear data in a low-dimensional space are mapped to a high-dimensional space for analysis to achieve nonlinear feature extraction . The K-n4 gas detection. Validation experiments are also presented. The major findings of this work are summarized in The rest of this article is organized as follows: the signal feature extraction method KPCA and the classification algorithm KNN are described in Kernel principal component analysis (KPCA) maps nonlinear raw data from input space to high-dimensional space i-th Assumption Then, the covariance matrix of the original observation sample in the feature space can be expressed as:The eigenvalue solving equation of the covariance matrix By substituting Equation (4) into Equation (3), we obtain:An By combining Equations (4)–(6), the eigenvalue solving problem can be further transformed into the following:k-th principal component of the new observation sample The It is worth noting that when the observed sample does not meet the requirements of Formula (1), the nuclear matrix The K-nearest neighbour algorithm calculates the distance between a sample to be classified and a training sample of a known class and obtains the K training samples closest to the sample to be classified. If the K samples closest to the sample to be classified belong to the same category, then the sample to be classified also belongs to this category. If the K samples closest to the sample to be classified do not belong to the same category, it is determined that the sample to be classified belongs to the category with the highest number of K samples.i-th class. The total number of training samples is The simplest case is that in which K = 1, wherein the resulting training sample point is the closest training sample to the input sample. The hypothetical training sample is The rules of judgement are expressed as:1NN uses the nearest training sample as the determination condition. Obviously, this is a simple, intuitive method of classification. However, when the number of samples in the training dataset is large, the method of using this distance from a single sample as a classification criterion has a certain probability of producing an error, resulting in a low classification accuracy. To improve the accuracy of the classification, the number of training samples examined is extended to Let The judgement rule is:When designing the nearest neighbour classifier, a metric function is needed to measure the distance between samples, which gives the size of the scalar distance between two samples. Euclidean distance is the most common distance metric function. In the supervised classification problem, two samples containing Although the Euclidean distance formula can always be used to calculate the distance between two vectors, the resulting distance value is not always meaningful. For example, if the coordinates are transformed and each coordinate axis is multiplied by an arbitrary constant, the actual transformation of this coordinate simply changes the unit of each attribute. However, the relationship between the Euclidean distance in the transformed space and the distance in the original space may be completely different.The metric in the more general Such a distance metric is also called the The Multivariable Relevance Vector Machine (MVRVM) is a supplement and extension to the Relevance Vector Machine (RVM), which can realize the simultaneous regression of multiple variables. MVRVM still exhibits good generalization ability under small-sample conditions and can guarantee the accuracy of regression. The model is sparse, and the complexity is not high, which is conducive to confirming the real-time output of the measured value. As a kernel learning method, MVRVM maps the complicated input-output relationship of a gas sensor to a linear high-dimensional space, which can help solve the corresponding nonlinear problem. This method is suitable for solving concentration estimation problems based on MOS gas sensor arrays.nth set of sample data sets. Given training sample set The solution process of the multiple regression model based on MVRVM is as follows:Assume that the weight matrix m-th estimated output, and m-th row and the n-th column in the weight matrix In (22), the element The prior probability distribution of the weight matrix Finally, by maximizing the maximum edge likelihood function of the objective function, the optimal hyperparameter and noise parameters are obtained, as indicated in Equations (27) and (28):The resulting averaged vector The optimized variance matrix Correspondingly, if the latest test sample is denoted as The error vector is represented by the diagonal elements of the matrix When calculating the optimal hyperparameters, as the number of iterations increases, many hyperparameters will tend to infinity. As most of the corresponding weights tend to zero, an increasing number of sample vectors in the training data set will be rejected, and fewer correlation vectors will be preserved, thus sparsifying the model.Step 1:Use the MOS gas sensor array to collect the response signals of mixed gas samples of different compositions. To remove the influence of the baseline, subject the collected data to a baseline reduction process.Step 2:By constructing a kernel matrix Step 3:Use the feature vector of the training sample set obtained by KPCA to obtain the characteristics of the test sample.Step 4:Identify the characteristics of the test sample using the KNN algorithm, select the K points with the smallest distance, and count the number of occurrences of the category to which the K-point belongs the most. The category corresponding to the most frequent point is the category of the measured point.KPCA possess a powerful ability to extract useful features from nonlinear signals, mapping the extracted feature data into a space that facilitates classification. In the classification algorithm, KNN algorithm offers higher accuracy and lower training time complexity. Therefore, this paper proposes a new hybrid gas type identification method based on the KPCA and KNN algorithms. A flow chart of the proposed method is presented in Step 1:Collect the response signals of the mixed gas samples with different concentrations through the MOS gas sensor array. To remove the influence brought by the baseline, subtract the baseline data from the collected data signals.Step 2:For the training sample set, select the kernel function K, establish the relevant MVRVM model, obtain the optimal hyperparameter, and determine the number of related vectors to obtain the mean vector and the weight matrix.Step 3:Calculate the estimated gas concentration by calculating the mean value vector and the weight matrix.For the gas concentration estimation problem, the Multivariate Relevance Vector Machine (MVRVM) has the characteristics of strong generalization ability for small sample data, high regression accuracy, and sparse model. In this paper, a new mixed gas concentration estimation method is proposed based on MVRVM. A flow chart of the proposed method is presented in To verify the feasibility and effectiveness of the binary mixed gas detection method in the machine olfactory system, a binary gas detection experiment system was designed to analyse the performance of the method. The experimental system block diagram is presented in 4 and CO) is prepared, and mixed gas of various concentrations is injected into the gas chamber. Before each different concentration of gas is injected into the gas chamber, 300 s of pure air is injected first, and then the next concentration of mixed gas is injected. The response output value of the sensor array is recorded as an experimental sample.The experimental system is mainly composed of a gas sensor array, an AD acquisition board, a PXI chassis, a host computer, and a DC power supply. The MOS gas sensor array consists of five Figaro sensors with different sensitivities: TGS2600, TGS2610, TGS2611, TGS2602, and TGS2620. To improve the reliability of the sensor array under experimental conditions, five types of gas sensors are selected for each type to form a 5 × 4 array. The array consisting of multiple sensors has a certain universality. Each sensor selects four, which ensures that the sensor array has a certain fault-tolerant ability. When one or several sensors have problems, other sensors can be used instead. On the other hand, it can eliminate the problem that the same sensor has different response to gas due to manufacturing process problems. The AD acquisition board uses an independently designed P105 function board, and this board is based on the acquisition function of DSP and FPGA, with 32 channels. In this project, only the first 20 channels are used to collect the voltage signal output by the sensor in real time, and the signal is saved in the txt file. The sampling rate of the board is set to 10 Hz, the input range of the signal is −9 V~+9 V, the A/D resolution is 16 bits, and the full-scale accuracy is 0.5%. The AD acquisition board uses the CPCI interface to communicate with the host computer. The PXI chassis uses a PXI-1042 produced by NI Corporation. The DC power supply provides a +5 V supply voltage and heating voltage. Because the response characteristics of the sensor array are susceptible to temperature and humidity, the selected sensor has the best gas selectivity at 15 °C and relative humidity of 20%. Therefore, signal acquisition must be performed in a room with constant temperature and humidity, where the constant temperature and humidity are ensured by a fan and a humidifier . The exp− and O2−, etc., resulting in a decrease in the number of electrons on the surface of the material and an increase in the surface resistance of the gas-sensitive material. When reducing gases  occur in the environment, the oxidation-reduction reaction on the surface of the semiconductor material will cause the electrons in O− and O2− to return to the semiconductor material, causing the surface resistance value of the gas sensitive material to decrease. It can be observed that the sensitivity of MOS gas sensors is based on complex physical and chemical reactions. The MOS gas sensor detects the target gas through the abovementioned process. The chemical reaction equations of the detection principle are presented as Equations (34)–(36):The detection principle of the target gas by the MOS gas sensor is such that, at a certain heating temperature, the surface of the gas sensor can adsorb oxygen molecules O in the environment. Oxygen molecules obtain electrons from the surface of the gas-sensitive material and form charged particles OTo investigate the sensitivity to methane gas and carbon monoxide gas of the MOS gas sensors produced by the five commercially available Figaro companies, this paper analyses the sensitivity characteristics of each sensor under different concentrations of methane gas and carbon monoxide gas. The MOS gas sensor array’s sensitivity curve corresponding to the different target gases is presented in As presented in Therefore, regarding the characteristics of the MOS gas-sensitive materials, it is not possible to obtain exact information about the target gas species or concentration through the sensor output. The incorporation of the subsequent signal processing method can reveal the target gas information via its response signal and then select an appropriate pattern recognition method for analysis.4, mixed gas, and average recognition rate.The kernel function in the KPCA algorithm employed the most commonly used Gaussian radial kernel function The quantitative analysis of the mixed gas concentration was based on the results of a qualitative analysis. The gas concentrations were estimated using the MVRVM method for a single gas and a mixed gas, respectively. The kernel functions all employed the most commonly used Gaussian radial basis function, and the optimal kernel parameters were solved by a 5-fold cross-validation method. The binary mixed gas concentration estimation results are shown in 4 were 0.76 and 0.25, and the average relative errors were 2.36% and 2.01%, respectively. The prediction result for mixed gas was an optimal kernel parameter value of 0.67, and the average relative errors of CO and CH4 were 9.01% and 8.79%, respectively.For single gas predictions, the optimal kernel parameters for CO and CHTo illustrate the performance of the MVRVM binary mixed concentration detection method proposed in this paper, (1)KPCA was verified as a feature extraction method for processing nonlinear signals. Compared with PCA and ICA, KPCA exhibits a good signal feature extraction capability. Using the KNN classification algorithm to construct a gas identification model, the recognition accuracy rate exceeds 98%.(2)This study also examined the detection of mixed gas concentrations and proposed an MVRVM algorithm that is different from the ANN and requires many training cycles. The average relative error of gas concentration monitoring is within 6%, and the detection time is short, which is more suitable than other methods for real-time detection of mixed gas.(3)The method for qualitative identification and quantitative detection of the binary mixed gas proposed in this paper was verified via experiments, and the accuracy of detection and the detection of a mixed gas by the machine olfactory system was improved. It is worth expanding the application of the system to the identification and detection of multiple gas mixtures.Based on the metal oxide gas sensor array, the detection accuracy of mixed gas in the machine olfactory system is low. This paper proposes a feature extraction method based on KPCA. Combined with the binary mixed gas identification model of the KNN classification algorithm, qualitative identification of mixed gas is realized. For the qualitative identification results, a regression method based on MVRVM was proposed to achieve quantitative detection of gas concentration. The major findings of this work can be summarized as follows:


In this work, we adopt a quantum mechanical approach based on time-dependent density functional theory (TDDFT) to study the optical and electronic properties of alizarin supported on TiO Converting the endless supply of freely available solar energy into electric power appears to be one of the most economically viable strategies to generate current from alternative energy sources in the long term, although concerns were raised about the efficiency of the process. In this context, Dye Sensitized Solar Cells (DSSC) have attracted significant interest since they can directly convert sunlight into electrical energy at low cost and with comparatively high efficiency ,2,3,4,5.ct steps ,7 (1) theen them , and theeen them ,10. The et al. 2 clusterThe isosurfaces of selected MOs involved in the simulated transitions of the alizarin-P^V  the short-time population evolution of the two carbonyl groups is strongly affected by the coherence when considered separately, but not when considered as a whole (TC+BC). The loss of population in the different ring fragments  does not surpass the ∼10%, being almost equal for all the fragments in the incoherent propagation. As one would expect from Equation , and then experiencing a loss of population until ∼3 fs to return to its original value (zero charge transfer). This process is then repeated until ∼5 fs but with a lower gain at ∼9%. Two further oscillations are then observed in the BC population before 10 fs, after which the population starts to oscillate around negative values. The net electron loss amounts to about 5% by the end of the propagation. The oscillatory effect is not observed in the total population of the alizarin and, therefore, should not be observed in the charge transfer toward the TiOThe coherent case reveals a slight asymmetry among the ring fragment contributions, where the loss of the population is larger in the OT ring, followed by the OB ring, the IT ring, and, finally, the IB ring. Inspection of the oscillatory patterns reveals that the motion within the outer in inner rings, i.e., one has an effective charge transfer between one carbonyl group towards the other through the rings, which ultimately serve as a bridge between the two carbonyl groups. This analysis of the population suggests that a strong alternating electron flux density should exist between the two carbonyl groups, while the entire alizarin experiences a continuous charge transfer toward the cluster. This behavior can be simply explained in terms of a Rabi oscillation between the two dominant contributions to the initial conditions. The oscillation period of about 3 fs correlates perfectly with the inverse of the transition energy between the two excited electronic states, Interestingly, the top carbonyl group (TC) experiences the same oscillation pattern as the BC fragment but in opposite phase, while the charge transfer remains negative (loss of electronic population) during the entire dynamics. Note that the gain-loss processes in the two carbonyl groups are mutually correlated, as can be seen from the simple exponential behavior of the TC+BC curve, To gain a qualitative understanding of the dynamics, it is instructive to have a look at the evolution of the one-electron density. i.e., how the electron probability density flows from one of the carbonyl group to the other through the rings, we analyse the electronic flux density etc.) [along the bonds building the bridge between the dye and the cluster, and not through empty space. It is important to note that, while the computation and the representation of the flux density maps may appear cumbersome, they yield valuable information about the electron flow with both spatial and temporal resolution, while alleviating the requirement of arbitrary partitioning schemes.In order to study how this alternating charge process between the two carbonyl groups happens, quations , 10) an ani.e., quations ). A greas (etc.)  may yielivatives . MeanwhiIn conclusion, we have studied the ultrafast electron transfer dynamics in alizarin adsorbed to a colloidal titanium oxide nano-crystallite using a one-electron, time-dependent density functional theory ansatz. The optical spectra were simulated in linear response, revealing the local character of the excitation for the first few excited states. Our optical spectral for isolated alizarin shows a low energy band at 2.82 eV calculated with B3LYP functional, which is in agreement with the experimental value (2.88 eV), while the value obtained with PBE functional underestimated the transition energy. The superiority of the B3LYP over the PBE functional was underlined by the semi-quantitative agreement of the spectrum with experiment. Qualitative analysis of the orbitals involved in the low-lying bands revealed that, apart from the important contributions from the orbitals localized on the dye, many orbitals localized on the nano-crystallite and other delocalized over the complex play a significant role.Accordingly, the dynamical simulations were performed using a large basis of virtual orbitals, instantaneously excited via a semi-classical dipole mechanism. The injected electron was absorbed at the end of the numerical grid using a simple local imaginary potential. To reveal the details of the ultrafast injection process, a variety of quantum mechanical analysis tools based on the Voronoi projector formalism and derived from the electronic flux density were introduced. Integrated quantities such as the electronic flux and the electron yield proved useful to extract quantitative information about the flow of electrons and the relative contribution of different fragments to the injection. Incoherent propagation of the density was found to behave much more symmetrically than the coherent propagation of the electronic wave packet, for which Rabi oscillations between the two carbonyl groups were observed.To avoid resorting to the projector formalism and thereby alleviating their ambiguous definition, we investigated the flux density maps during the reaction. In particular, it was found that the carbonyl groups play an important role in the charge transfer process, while the aromatic rings remain mostly spectator, merely mediating the electron transfer from one carbonyl to the other. Further, although the environment surrounding the two aromatic rings is quite different, the Rabi oscillations within the dye-molecule complex were found to be symmetric and synchronous at all times.We hope that the formalism presented, namely the analysis of the probability density


Electrochemical chloride removal (ECR) from reinforced concrete can be considered as an environment-friendly technique since it can reduce the environmental issues arising from demolition and reconstruction. In this study, we used ultrasonic waves to promote the ECR efficiency without increasing the current density so as to shorten the overall power-on time, lowering the power consumption and electricity-induced material damage. Rebar-embedded cement mortar specimens were prepared and a set of ultrasonic-assisted ECR test devices was designed. For obtaining the optimal parameters, different ultrasonic frequencies and powers were adopted to conduct the ECR test. After that, the discharged and residual chloride ion amounts were detected to characterize the ECR efficiency. The corrosion behavior of rebar was characterized by electrochemical method. It was found that ultrasonic waves can not only promote the discharge of chloride ions, but also promote the passivation process of steel bar. For this investigation, the ultrasonic waves with a frequency of 40 Hz and a power of 60 W had the best auxiliary effect and could reduce the work time by 64%. It is considered that the ultrasound-assisted method has potential to promote the application possibilities of the ECR technique. Reinforced concrete structures are the most common and widely-used construction forms in the modern world. However, reinforced concrete is vulnerable to chloride-induced corrosion in coastal, salt lakes and deicing salts environments ,2,3. WitThe traditional rehabilitation method is to knock out the polluted concrete and replace it with new mortar or concrete. Although this method is simple and relatively fast, due to the difference of chloride ion content and alkalinity between the old and new materials, it is possible to form new corrosive galvanic cells at the alternation of the old and the new, resulting in re-corrosion of steel bars ,9. In adRecently, Elsener et al.  and VeluCement was P·II 42.5 Portland cement produced by China Cement Plant. Its main chemical composition is shown in 2 through the surface area of the steel bar. At the same time, the current was monitored every day and stabilized by adjusting the resistor. The saturated Ca(OH)2 solution was used as anolyte, which was refreshed every 3 days. The cooling liquid was tap water.An electrolytic cell with a diameter of 150 mm and a height of 150 mm was designed and manufactured with polymethyl methacrylate. In the cell, a ring-shaped titanium mesh, 80 mm in diameter and 100 mm in height, was employed as the anode to connect the power supply. The embedded steel bar was used as the cathode. The ultrasonic generator was set at the bottom of the electrolytic cell. Owing to the small size of the electrolytic cell, the temperature of the solution will be uncontrollable under the thermal effect of long-term ultrasound, which is inconsistent with the actual application scenario. Thus, the cell wall was designed as double-decked, the cooling liquid could pass the interlayer to control the temperature of the system, and the ultrasonic generator worked ten seconds an hour. An actual ECR device is shown in Before each solution refresh, the chloride ion quantity of the anolyte solution was determined via potentiometric titration and then recorded. The total discharged chloride ion quantity can be calculated by summing the recorded chloride ion quantities. 3) as titrant. The free chloride ion content was calculated according to Equation (1):AgNOC3 is the concentration of silver nitrate solution, G is the mass of mortar specimen, V1 is the volume of deionized water when soaking mortar, V2 is the volume of filter solution when titrating, and V3 is the volume of silver nitrate consumed in titration. In the case of calculating the total chloride ion quantity, Equation (1) is still applicable but the V1 becomes the volume of the dilute nitric acid used for soaking.The specimens with and without ECR were drilled to collect powder to measure the chloride ion distribution. The location of powder sampling was shown in For characterizing the repassivation behavior of the rebar, open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) were conducted via a PARSTAT 2273 multifunctional workstation. The three-electrode system was used for the electrochemical test, including the steel bar as working electrode, saturated calomel electrode as reference electrode, and platinum electrode as auxiliary electrode ,17.corrE). The EIS scanning was carried out by applying a sinusoidal potential perturbation of 10 mV with a frequency scan from 100 kHz to 0.01Hz. Using ZSimpWin software, the measured spectra were fitted and sorted out, and the values of the polarization resistances (pR) of steel bar were obtained. Then the corrosion current (corrI) of steel bar can be calculated by Equation (2)  [corrI is related to the exposed surface area of the steel electrode, the corrosion current density (corri) is usually used to normalize the corrosion rate, which can be calculated by Equation (3).A five-minute OCP test was implemented to measure the corrosion potential (quation) . Since IS is the side surface area of steel (25.13 cm2 in this study), B is the Stern–Geary constant . According to the literature, B can be approximately equal to 26 mV when the steel bar is activated and 52 mV when the steel bar is passivated [Among them, ssivated ,20.The discharged chloride ion quantity is an important value to intuitively present the ECR efficiency. As can be seen from From Generally, it can be found that the distribution of residual chloride ion content varies with ultrasonic parameters. It should be noted that the residual chloride ion content in the middle layer was the highest, while the chloride ion content near the steel bar was the lowest. This was mainly because the chloride ion near the cathode region tends to migrate to the area away from the reinforcing steel under the action of electric field force. The residual chloride ion content of the outer layer also became slightly smaller. As we know, the anolyte was frequently refreshed, and the chloride ion content was maintained in very low value. Thus, the chloride ions in the outer layer tended to diffuse into the anolyte under the action of concentration gradient, leading to a content drop in the outer layer.corrE is one of the important indicators used to characterize the state of a steel bar [corrE evolution curves of the embedding steel bars after ECR treatments assisted by different ultrasonic frequencies and powers, respectively. After ECR treatment, the corrosion potentials of steel bars in the specimens were generally lower than –1000 mV, which was consistent with the findings of many previous researchers [corrE after ECR is probably due to the polarization of the steel bar connected with the negative pole of the power supply. In the following 42 days after ECR treatment, the corrE of steel bar first tended to decrease rapidly and then reached a stable and relatively positive level, which can be seen as a result of repassivation. With the increase of depolarization time, oxygen diffuses to the surface of steel bar and produces hydroxide ions, which will make the corrosion potential of steel bar move forward. Comparing with the repassivation process without ultrasonic assistance, it can be obviously found that ultrasonic waves can make the rehabilitation of corrosion potential of reinforcing steel bar faster. This is attributed to the fact that the ultrasonic cavitation could effectively promote the diffusion rate of oxygen to accelerate the repassivation. Besides, as mentioned previously, the ultrasonic waves could further reduce the residual chloride ion content, which provided a better chemical environment for repassivation.teel bar . Figure earchers ,25. As e2+ into the specimen during ECR. Ca2+ will gradually deposit in the pores of cement mortars, resulting in the densification of mortars [In order to further reveal the effect of ultrasonic-assisted ECR treatment on the repassivation behavior, EIS was used to detect the electrochemical characteristics of steel bars.  mortars . The NyqRo(C1R1) (dC(pZwR)). In the circuit, the left part, i.e., the terms of Ro(C1R1) represents the response of cement mortar. The left part can be considered as a simplified equivalent model for Song’s three-path ECM [CCPR is the resistance of the continuously connected micro-pores in the concrete; CPR is the resistance of the discontinuously connected micro-pores; matC is the capacitance across the concrete matrix; DPC is the capacitance of the cement paste layers blocking the discontinuously connected micro-pores in the concrete. According to this, R0 + R1 is equivalent to RCCP, whose value is approximately equal to the abscissa value of the connection point of high-frequency arcs and low-frequency arc in the Nyquist plot, as previously mentioned. The right part of the used ECM, i.e., the terms of (dC(pZwR), represents the response of steel bar, where, dC is the double layer capacitance of concrete/steel interface; pR is the reactive charge transfer resistance of steel surface electrode; wZ is the Warburg impedance related to diffusion process. For quantitatively analyzing the impedance spectra, an electrical equivalent circuit mode (ECM) was adopted to fit the response signal as shown in path ECM .(4)R0=Rcorri of steel bar were obtained. corri after ECR assisted by ultrasonic waves with different frequencies and powers. It can be seen from the figure that the corri of steel bar had exceeded 3 μA/cm2 within a short time after ECR treatment. This current increase is mainly due to the polarization voltage of the steel bar connected with the negative pole of the power supply. In the following 42 days, the corri of steel bar first decreased rapidly and then reached a relatively stable and small level, which declared that the corrosion has been effectively controlled due to the repassivation. As previously mentioned, as depolarization time developed, oxygen diffused to the surface of steel bar to produce hydroxide ion, and meanwhile chloride ions were pushed away from the steel surface, which provided a good environment for repassivation. Comparing with the repassivation process without ultrasonic assistance, it can be obviously found that ultrasonic waves can make the decline of the corrosion current density faster. As mentioned previously, this is attributed to the fact that the ultrasonic cavitation could effectively promote the diffusion rate of oxygen and discharge rate of chloride ions. Besides, as reported [Using the ZSimpWin software, the measured spectra were fitted and sorted out, and the values of the reported , the wat(1).Ultrasonic waves could effectively promote the ECR efficiency. When frequency is located in 17–49 Hz and power is in 40–60 W, the promotion effect has a weak positive correlation with the ultrasonic frequency and a relatively strong positive correlation with the ultrasonic power.(2).The promotion of ultrasonic waves on ECR efficiency was more reflected in the reduction of residual bound chloride ions, which might be a merit of ultrasonic-assisted ECR technique since it would reduce the potential risk from the prospective re-conversion of bound chloride to free ones after ECR treatment.(3).Electrochemical tests demonstrated that ultrasonic-assisted ECR treatment could accelerate the repassivation process of the embedding steel bar.In this study, we used ultrasonic waves to promote the ECR efficiency without increasing the current density. On the basis of the results and discussion above, the following conclusions can be drawn:It is considered that the ultrasound-assisted method has the potential to promote the application possibilities of the ECR technique, and the applications of this technique in practical engineering may be one of the urgent topics for follow-up research.


The formability of aluminum alloy sheet in electromagnetic impaction deformation has attracted the attention of numerous researchers for the past decades. However, the influences of die geometry and high-speed impaction electromagnetic deformation on formability have not been well established, thereby resulting in the formability of the sheet not being developed fully. In this study, the influence of die geometry on the formability of 5052 aluminum alloy in electromagnetic deformation was investigated by comparing the formability of 5052 aluminum alloys formed using a hemispherical die and a cylindrical die. The intriguing finding is that the formability of the 5052 aluminum alloy formed using a cylindrical die is considerably higher than that formed using a hemispherical die. Therefore, die geometry significantly influences the formability of 5052 aluminum alloy. The influence of die geometry on the formability of 5052 aluminum alloy in high-speed impaction electromagnetic deformation was explained in terms of strain rate, pressure stress, and stress state. This investigation enhances insight into the interaction between sheets and dies, and provides a reference for the studying influence of dies on the forming limit of sheets in high-speed impaction deformation. Aluminum alloys have been investigated to verify their high potential for the aerospace field to improve fuel economy and reduce environmental pollution . HoweverMany researchers have established that the formability of aluminum alloys can be improved to a certain extent via electromagnetic free forming. Tamhane et al.  investigThe manufacturing of an aluminum alloy thin-walled structure part such as a hemispherical shape workpiece used as a rocket storage tank has become a challenge due to the low forming limit of aluminum alloy. The formability of aluminum alloy is an intrinsic property which could also be affected by external process conditions, for example, the friction, strain rate, temperature, and the die geometry ,9. In thIn this paper, an interesting phenomenon was observed in that the high-speed impaction was not always beneficial for the increase in forming limit. The boundary conditions (die geometry) would affect the formability of 5052 aluminum alloy sheet in high-speed impaction. Therefore, this article focuses on reporting the occurrence and the explanation of this phenomenon. The reasons for this phenomenon will be explained in terms of strain rate, pressure stress, and stress state. This investigation provides a deeper insight into the influence of boundary conditions on formability in high-speed impaction electromagnetic deformation. It will provide theoretical guidance to further increase the forming limit of aluminum alloys high-speed impaction electromagnetic forming.The as-received 5052 aluminum alloy sheet with a thickness of 1.0 mm was obtained via the tandem rolling. The chemical composition of the 5052 aluminum alloy sheet is listed in 2), According to Maxwell’s equation, the magnetic field equations are as follows:The magnetic vector potential usually chooses Thus, the equation obtained isThe magnetic force density is expressed as follows:Hence, the magnetic forces can be expressed as p and m are the C-S strain rate parameters. For aluminum alloy, p = 6500 s−1 and m = 0.25 are the specific parameters according to Liu et al. [E) is assumed to be constant for the elastoplastic stage. The material volume is assumed to be incompressible during the plastic deformation, the poisson ratio is considered as a constant. By fitting the quasi-static constitutive model (Equation (11)), the model parameters k = 376.8 MPa and n = 0.3 can be obtained. The k and n are the strain hardening coefficient and hardening exponent, respectively. The fitted curve of the true stress vs. strain is highly consistent with the experimental data, when the plastic strain is ≥0.02 , whereas the boundary element method (BEM) is used to solve the electromagnetic field of the air. The shell elements of the BEM are generated by the surface of finite elements. The established numerical model consists of a holder, a coil, a workpiece, and a die. All the parts were meshed into eight-node hexahedral elements, and the workpiece was meshed with a higher mesh density to improve the accuracy of the numerical model. The five-layer griddings were distributed along the sheet thickness direction. Coils were meshed with an element size of 0.5 mm in the cross section. The finite element model of high-speed impaction electromagnetic deformation with a hemispherical die was shown in The evolution of the shape and major strain of the alloy sheet in electromagnetic impaction deformation with the cylindrical die is shown in −1 when the sheet impacted with the bottom of the cylindrical die at high speed.−1 when the sheet impacted with the sidewall and the bottom of the hemispherical die. However, the maximum strain rate of element A was more than 8000 s−1 when electromagnetic discharging was completed during the inertia stage. Therefore, the strain rate was different for sheet high-speed impaction with different die geometries at the same discharge energy. The value of strain rate with the cylindrical die was almost four times higher than that with the hemispherical die.The radial stress state of the sheet is highly complicated during a continuous impaction process. −1, and the major strain’s relative improvement was approximately 26.67% in formability. Consequently, in the electromagnetic high-speed impact with the hemispherical die, the strain rate did not achieve a higher value compared with impact with the cylindrical die due to die restriction. Moreover, the maximum strain rate was only approximately 1700 s−1 when the sheet impacted with the bottom of the hemispherical die. In the electromagnetic high-speed impaction with the hemispherical die, the maximum strain rate was recorded when electromagnetic discharging was completed, which differed from the time that the sheet’s center region impacted with the hemispherical die. The high-speed impaction formability observed in the specimen was dependent on die geometry, which is related to strain rate, pressure stress, and stress state. The strain rate of impacting with the cylindrical die increased considerably compared with the hemispherical die. The strain rate reached nearly 30,000 sAxial pressure stress underwent a “whipping” and increased formability when the sheet impacted with the bottom of the cylindrical die. Axial impaction pressure suddenly increased dramatically (approximately 160 MPa), which exceeded the sheet’s yield strength when the sheet impacted with the cylindrical die at high speed. A high impaction pressure can promote the plastic flow of the material. Therefore, the formability of the sheet can be improved to some extent. However, when the sheet impacted with the hemispherical die, the sheet’s center was subjected to impaction pressure stress of approximately 100 MPa, and impaction compressive stress did not reach the sheet’s yield strength (101.4 MPa). Plastic deformation did not evidently occur during the impaction process. Thus, this condition may be one of the reasons why the sheet’s electromagnetic impaction with the hemispherical die did not increase formability. The deformation of sheet’s peripheral and center regions was severely constrained by the die when the sheet impacted with the hemispherical die at high speed. The sheet’s bottom region was subjected to radial compressive stress at least thrice the axial impaction pressure stress, which resulted in the radial compression state during impaction. Moreover, when the center region of the sheet impacted and rebounded upward, the apex region suffered from tensile stress while the sidewall region was simultaneously subjected to increased circumferential extrusion stress, which accelerated the onset of fracture in the center region. Consequently, the combination effect of upward “extrusion-stretching” led to the premature rupture of the sheet’s center region, which also resulted in formability decrease in the electromagnetic impact with the hemispherical die.(1)The influence of boundary conditions on formability was investigated in sheet high-speed electromagnetic impaction deformation. When the sheet impacted with the cylindrical die cavity, the major strain of 5052 aluminum alloy improved by approximately 26.67% compared with that of the conventional forming limit. However, when the sheet impacted at high speed with the hemispherical die, the formability of 5052 aluminum alloy was not increased significantly, but instead, decreased by approximately 25% compared with that of the conventional forming limit. Therefore, the high-speed impaction electromagnetic deformation may not improve formability when the deformation of workpiece’s sidewall and bottom was severely constrained by die geometry. Our findings will provide a reference in electromagnetic forming in the future to manufacture ideal hemispherical parts.(2)−1 when the sheet impacted to the cylindrical die at high speed. However, this rate was only 1700 s−1 when the specimen impacted to the hemispherical die at the same energy level. The plastic flow of sheet’s center region was inhibited, and the strain state changed from biaxial stretching to plane strain. The fracture or necking locations of the sheet evidently differed between the hemispherical die and cylindrical die in electromagnetic impaction deformation.The maximum effective strain rate was approximately 30,000 s(3)The impaction pressure was strongly dependent on the discharge energy and the die geometry in sheet electromagnetic impaction deformation. The stress state effect of “extrusion-stretching” resulted in the fracture of the sheet’s center region, which caused the formability decrease with the hemispherical die. In order to improve formability in the high-speed impaction electromagnetic deformation, it was necessary to get the higher strain rate and impaction pressure between the sheet and the die.In the forming of a hemispherical-shaped part, an interesting phenomenon was observed that the high-speed impaction was not always beneficial for the increase of forming limit, and the die geometry could affect the formability of 5052 aluminum alloy sheet with sheet high-speed impaction. This article focusses on reporting the occurrence and the explanation of this phenomenon. The following conclusions could be drawn:


With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems. In recent years, with the extensive applications of the Internet of Things (IoT) and the continuous development of machine learning algorithms, many researchers have proposed a number of IoT malicious traffic detection techniques based on machine learning algorithms ,3,4,5,6.At present, most adversarial sample generation methods are designed for image classification models ,16,17. HThis paper proposes a testing framework for learning-based IoT traffic detection system (TLTD). The main challenge of TLTD is the method of generating adversarial samples for the IoT traffic detection model based on a genetic algorithm. Without having to know the model parameters, TLTD uses the original data as the input of the algorithm, and produces the counter sample of the specific tag, and the only information used is the classification probability of the tag’s output by the model.We migrate the application scenarios of adversarial samples from the image recognition domain to the IoT malicious traffic detection field. This migration cannot be achieved simply by replacing the model’s training data from pictures to traffic. We need to do specific technical processing on the traffic data to ensure the validity of the adversarial sample.We introduce the genetic algorithm into the method of generating the adversarial sample and realize the black-box attack against the machine learning model.Our approach is equally valid for networks that have difficulty computing gradients or expressing mathematically.We hope TLTD can be a benchmark learning-based IoT traffic detection model. Our contribution is mainly reflected as follows:The rest of the paper is organized as follows. X), a target tag (t) and Given a primitive input The input samples in the image are all pixels and the values of the pixels are continuous. However, in the field of network security, input characteristics are usually discrete and the range of values of different features is usually different.(2)The pixels in the image can be freely changed within the value range. The restrictions on the modification of traffic or software are much more demanding. Arbitrary modifications may result in traffic or software not working properly.Compared to image data, there are the following differences in adversarial samples against malicious traffic or software:TLTD can detect the captured IoT traffic. The overview of TLTD is shown in X), the classifier returns a two-dimensional vector X) is less than The goal of the adversarial sample generation algorithm is to add a slight disturbance to the captured malicious IoT traffic so that the previously trained detection model misjudges it as normal traffic. Similar to the aforementioned adversarial sample for the malware detection model, for one input sample  is actually divided into two sections, as shown in Step 1.At this time, the adversarial sample cannot successfully mislead the classifier. Individuals at the top of section A gradually approach the bottom through crossover and mutation operators.Step 2.The individuals move from Section A to Section B, indicating that Step 3.Individuals at the top of Section B gradually approach the bottom, indicating the improvement of the similarity between the adversarial traffic and the original traffic.Through the preceding fitness function, the population . Each sample in KDD99 contains 41 features. The 41 features can be divided into three categories:The hardware environment and software environment for all experiments are shown in First, the detection model is trained to determine whether the traffic generated by the IoT device is malicious traffic. When the detection model reaches a certain accuracy, it combines with the method proposed in this paper to generate an adversarial sample and test the security of the model. Because the amount of data between different categories in KDD99 has changed significantly, we selected 4 categories with a large amount of data for perturbation, as shown in Neural network models are established for each of satan, ipsweep, portsweep, and nmap to find the recognition results. The detection model uses a fully connected network with a network structure of Finally, the detection rate of the detection model is as shown in the We conducted a total of three sets of simulation experiments. There were slight differences in the technical implementation methods for generating adversarial samples in the simulation experiments. In each set of simulation experiments, we tested the four types of traffic detection models respectively. We hoped to select the best technical implementation by comparing the test results.The experimental parameters are shown in STEP 1.Determine the number of individuals selected each time;STEP 2.Choose individuals randomly from the population and select the individuals with the best fitness values to enter the offspring population;STEP 3.Repeat STEP 2 for several times and the resulting individuals constitute a new generation of the population.The tournament selection strategy takes a certain number of individuals from the population each time, and then selects the best one of them into the offspring population. This operation is repeated until the new population size reaches the original population size. The specific steps are as follows:(A) Preprocessinghot, num_failed_logins, logged_in, num_compromised, root_shell, su_attempted, num_root, num_file_creations, num_shells, num_access_files, num_outbound_cmds, is_hot_login, is_guest_login.The numerical distribution of 13 features in the data set is more concentrated and less variable. If we disturb these features, we will destroy the inherent distribution of traffic characteristics. This makes the generated adversarial sample clearly distinguishable from normal traffic. In order to ensure the effectiveness of the countermeasures, we chose to eliminate these 13 data features; that is, the values of these 13 features were not changed when disturbed. The 13 features which were not used in TLTD-I are as follows: (B) Fitness FunctionThe fitness function in TLTD-I is shown in Equation(12). We used the Euclidean distance to describe the degree of difference between the original sample and the adversarial sample:(C) Experimental ResultsThe experimental results are shown in the (A) Data PreprocessingIn TLTD-II, a zero-mean normalization method was used to normalize all original data sets to a data set with a mean of 0 and a variance of 1.z is the normalized value, The standardized formula is(B) Fitness FunctionIn TLTD-II, we improved Equation  as the fWhen the original sample is quite different from the disturbed sample, the left part has a larger value. In order to reduce the value of (C) Experimental ResultsThe experimental results of TLTD-II are shown in In TLTD-III, the data pre-processing process and the fitness function were all those conceived in TLTD-II. We believe that the very low success rate of TLTD-II may be related to the large range of feature disturbances. In addition, in order to make the adversarial samples more similar to the original data, we defined the maximum range of variation for each type of feature as . The experimental results are shown in From the experimental results, it can be seen that the success rate of TLTD-III was almost 100% of the average of We compared the average success rate of adversarial samples and the perturbation size in three experiments. The results re shown in To address the challenge of a lack of the testing framework for learning-based IoT traffic detection systems, we developed TLTD. Our experimental results show that our approach generates high-quality adversarial samples with the success rate closed to 100%. In the technical implementation of the TLTD algorithm, the selection of normalization method, features, and the range of disturbance are the keys to a good result. We hope TLTD can be a benchmark learning-based IoT traffic detection model. Our future work includes designing an effective defense approach to reinforce the traffic detection model.


Background. The concept of fluorescence-guided navigation surgery based on indocyanine green (ICG) is a developing interest in many fields of surgical oncology. The technique seems to be promising also during hepatic resection. Case Presentation. We reported our experience of ICG-fluorescence-guided liver resection of metastasis located at VIII Couinaud's segment from colon squamous cell carcinoma of a 74-year-old male patient. Results. After laparotomy, the fluorescing tumour has been clearly identified on the liver surface. We have also identified that a large area of fluorescent parenchyma that gets from the peripheral of the lesion up to the portal pedicle such as the neoplasia would interest the right biliary tree in the form of neoplastic lymphangitis. This datum was not preoperatively known. Conclusion. Fluorescent imaging navigation liver resection could be a feasible and safe technique helpful in identifying additional characteristics of lesion. It could be a powerful tool but further studies are required. Indocyanine green (ICG) is a water-soluble, anionic, amphiphilic tricarbocyanine molecule that emits fluorescence upon illumination with near infrared light , 2.For several years, it has been used in ophthalmic angiography and for determining cardiac output and hepatic function . HoweverFocusing on liver metastases, many studies showed how near infrared fluorescence imaging using indocyanine green is a promising technique to intraoperatively visualize the contrast between hepatic lesions and normal liver tissue in real time and how it could be considered a powerful tool to help surgery in the “fluorescent-guided surgery” –12.Referring to the surgical treatment of colon cancer metastatic disease, liver resection is allowed in about 20% of patients . The proIn this case the “ICG-fluorescence-based navigation” can be a useful tool in order to locate supplementary lesions that could have been missed with current imaging techniques, such as computer tomography (CT), magnetic resonance imaging, and ultrasonography (US) .Based on recent studies that suggest the effective utility of Photodynamic-Eye (PDE) assessment of ICG fluorescence (PDE + ICG) combined with Intraoperative Ultrasound (IOUS) for ensuring the complete surgical removal of liver metastases from colorectal cancer –16, we rA 74-year-old male patient was admitted to our hospital in March 2017 to undergo liver resection to treat a malignant hepatic lesion diagnosed with CT and PET and a fine-needle biopsy positive for squamous carcinoma. The hepatic tumour discovered during follow-up for a previous bladder cancer submitted to endoscopic surgery three years before measured 22 mm in diameter and was located in the VIII Couinaud's segment [The case is discussed with radiologists, oncologists, and pathologists of our hospital. Even if the lesion had been the single site of disease; due to the proximity/doubtful infiltration of the lesion to the biliary tree, we decided to submit the patient to an explorative staging laparotomy and possible palliative surgery.Our internal protocol states that during the preadmission every patient who is a candidate for a liver resection is subjected to a routine liver function test with ICG to determinate the most appropriate surgical procedures : 0,5 mg/Thanks to the ICG property of being fluorescent with the light emitted from the photodynamic eye of the laparoscopic system in our possession, it is possible to visualize the lesion during the surgical procedure. To this target, timing of administration and dose of ICG are key points.Several studies have demonstrated that the effective dose of ICG depends on the timing of injection; in particular, if the function liver test had been performed more than 7 days before surgery it would have been necessary to administer an adjunctive dose  the day before . In thisA right hepatectomy would have been the oncologically correct surgical procedure due to the infiltration of right biliary duct. Considering the probable metastatic nature of the lesion, the absence of a clearly primary lesion, the age, the comorbidities, and the small size of residual liver, we have decided to perform an atypical segmental resection of S8 associated with cholecystectomy and lymphadenectomy of the hepatic pedicle nodes, including the area of impaired biliary excretion.At the histological examination, the lesion, the lymph nodes of the hepatic pedicle region, and the right biliary branch, respectively, resulted in hepatic metastases from squamous cell carcinoma and sites of metastatic location. As expected the resection margin was interested by neoplasia.Macroscopical exam: the neoplasia, in a site, appears to be in contact with the resection marginMicroscopical exam: parenchymal hepatic section that showed metastatic localization of squamous carcinoma moderately differentiated. The neoplasia interest the surgery resection margin.In particular, the histological examination showed the following: In this case, fluorescent imaging has revealed a fluorescing ring around the hepatic metastasis . The fluICG-fluorescent imaging techniques have begun to be used in hepatobiliary procedures in the last few years. These techniques are expected to complement conventional imaging techniques without having the purpose to replace all of the roles of conventional techniques. It provides simultaneous, real time, and high resolution identification of many type of different structures and, particularly during the navigation surgery application, could be a helpful tool to discover adjunctive lesions and identify segmental staining and biliary tree visualization , 19, 20.The major advantage of this system is that it overlays fluorescent images on the background colour images. This property can make it easier for a surgeon to understand the exact location of the fluorescing lesions (liver cancer/or bile ducts) on the surrounding structures as compared with that in the conventional fluorescent imaging system , 21.The fluorescent imaging of segmental cholestasis region, caused by the duct tumour invasion or thrombi and demonstrated at the intraoperative PDE-visualization, could be explained by the fact that ICG has been retained in the noncancerous liver parenchyma due to bile ducts obstruction and the consecutive altered biliary excretion in this tissue .Our case report shows how feasibility and safety of this procedure could help to identify additional characteristics of the lesion, such as the cholestasis area around the lesion itself and infiltration of the biliary tree too with minimal adjunctive costs –25. So iThe major advantages of fluorescent imaging are its feasibility and safety. Once ICG is preoperatively administered for liver function testing, we can obtain fluorescent images of tumour and cholestasis regions in real time by simply placing the camera imaging on the liver surface before the resection, during the liver resection, and over surgical specimens.Further and ongoing studies are required to better define the feasibility and usefulness of this technique.


Examining the behavior of a single cell within its natural environment is valuable for understanding both the biological processes that control the function of cells and how injury or disease lead to pathological change of their function. Single-cell analysis can reveal information regarding the causes of genetic changes, and it can contribute to studies on the molecular basis of cell transformation and proliferation. By contrast, whole tissue biopsies can only yield information on a statistical average of several processes occurring in a population of different cells. Electrowetting within a nanopipette provides a nanobiopsy platform for the extraction of cellular material from single living cells. Additionally, functionalized nanopipette sensing probes can differentiate analytes based on their size, shape or charge density, making the technology uniquely suited to sensing changes in single-cell dynamics. In this review, we highlight the potential of nanopipette technology as a non-destructive analytical tool to monitor single living cells, with particular attention to integration into applications in molecular biology. Nanopipettes are of scientific interest due to their application potential in several arenas, from biomedical diagnostics to cellular biology. Nanopipettes are characterized by the submicron to nanoscale size of the pore opening at the tip, which serves as a suitable surface to fabricate functional tools for delivery to and/or aspiration from a single living cell, or for probing the cell’s contents. The hollow structure enables the dispensation of fluid from one region to the next, with their cavity acting as passage . In viewThe fundamental understanding of the molecular biology of single living cells in heterogeneous cell populations is of the utmost importance in assessing changes in cellular functions in tissues. Whole tissue biopsies can provide information on many events that are occurring in different cells, but difficulties not always suitable for drawing conclusions regarding the progression of some diseases. For example, malignant tumors are heterogeneous in most cases and can include cells at different stages of transformation . BecausePipettes have been employed to transfer specified volumes of liquids in science and medicine for centuries . The useRecently, information illuminating the behavior of single cells has received a great deal of attention ,19. To aOur group has presented the development of a fully electrical system that makes it possible to inject a controlled amount of material (~50 fL) into a single cell . We demoVarious molecules can be used to track a cell, such as genetically encoded fluorescent proteins , quantumA single-cell nanobiopsy platform was developed for continuous sampling of intracellular content from individual cells and has been described in detail elsewhere . BecauseAside from carbohydrates and nucleic acids, proteins are one of the most common macromolecules in cells. Among these molecules, proteins are the most diverse molecules, playing a variety of biological roles: communication of information within and among cells, protection of cells against infection, catalysts for chemical reactions, and as structural components, to name but a few . TherefoThe Human Genome Project pioneered research to identify the genetic entities behind conditions such as genetic disorders and drug resistance . This reExtraction of molecules from a single cell by means of the nanobiopsy platform relies on electrowetting within a nanopipette. In brief, when an organic solution fills a nanopipette and the device is inserted into an aqueous solution, a liquid-liquid interface is created at the tip. Once voltage is applied between these two solutions, a force is produced at the interface that causes the solution to enter into or leave from the nanopipette ,52. UndeThe nanopipette can also be employed to aspirate cell contents from the same single cell multiple times during its lifetime to study molecular dynamics. This platform was previously validated to isolate molecules such as RNA for cDNA synthesis and qPCR, and our group became one of only a few to have performed next-generation sequencing (NGS) from the species extracted with nanopipettes. Nashimoto’s research group has shown device automation in the ZYX axis for isolation of mRNA molecules . Guillau2+ agent Fluo4 AM, and fluorescent microscopy was used to show the localization of Ca2+ ions before, during and after nanopipette biopsy [2+ during nanobiopsy. The cell recovered a few seconds after the process, reaching Ca2+ concentrations that matched pre-aspiration levels. By contrast, Actis et al. demonstrated that micropipette aspiration caused dramatic changes in the concentration of Ca2+ ions in the cell [To show that nanopipettes did not affect in the function of cells upon piercing the cell membrane, human BJ fibroblasts were treated with Cae biopsy . Optic mthe cell . The lowIt is important to note that nanopipette aspiration is based on a voltage-controlled influx of material and not adsorption of molecules to the walls of nanopipette. PCR amplification of DNA templates was not observed if negative voltage was not applied to the nanopipette during single-cell interrogation and when aspiration was performed in the bulk solution. This is the critical element that differentiates the nanobiopsy technology from AFM-based platforms. Both Wickramasinghe’s and Osada’s groups used AFM probes to extract RNA from cells in culture, either based on physisorption or hybridization of complementary RNA immobilized onto the probe ,60. We cThe nanopipette platform also allows subcellular interrogation. By using different dyes in the cellular nucleus or by staining the cytoplasm, enabling the isolation of the nucleus, it is possible to target the two compartments differentially. The following pictures in To control the sequencing process of nanopipette aspiration downstream, we implemented the addition of External RNA Controls Consortium (ERCC) spike-in controls with samples collected from the cells. ERCC controls are a set of RNA standards for use in microarray, qPCR and sequencing applications . These mAfter separation of the ERCC counts from reads proceeding from cellular content, reads mapped to the human reference genome were plotted as Principal Component Analysis (PCA) results of cellular expression, showing the clustering pattern of the nuclear aspirations of single cells. The PCA of gene expression in the nuclear nanobiopsy samples, using both non-processed and pre-processed gene counts, are shown in It was not clear to what extent the MDA-MB-231 cells and MCF-7 cells were distinguishable using PCA in To determine the identity of more abundant genes in the MDA-MB-231 and MCF-7 cells, we extracted RefSeq IDs with more than 200 reads in at least one of the 39 sequenced libraries from the dataset, and checked the presence of the genes in both MDA-MB-231 and MCF-7 cells. The identification and quantification of molecules in the single cell play a crucial role in diagnostics and fundamental molecular biology. The ability to dynamically monitor the presence and amount of any molecule and/or biomarkers in the single cell aids in understanding the relationship of these molecules to several diseases, and contributes to drug discovery research. The sensing region or the nanopipette tip surface responds to changes in the ionic current flowing through the pore, which can be brought about by electrostatic, biotin-streptavidin, or antibody-antigen interactions. Specific antigen-antibody interaction changed the current amplitude and showed reasonable promise for future applications in biomolecular diagnosis. The successful implementation of nanopipette technology in biosensing enabled the identification of a variety of molecules from glucose to proteins. In the following section, we review the commonly used immobilization techniques, techniques to generate signal, and recognition probes on the nanopipette. Specific examples from the literature are given subsequently.N-hydroxysulfosuccinimide (NHS) were then deposited to create a link between PLL and the subsequent layer [The idea of running voltage through the nanopore and using the resultant current as feedback originated in 2002 . Beginnint layer . Proteinnt layer . Finallynt layer . During nt layer .Nanopipettes have not always been functionalized into biosensors as they are today. Beginning in 2002, the Korchev and Klenerman labs began using nanopipettes as a new SICM probe for cellular structures and substructures ,72,79,80At approximately this time, research began using nanopipettes for SICM; the labs cited above as well as others began to use nanopipettes to capture and transport DNA, metal ions, and other molecules ,82,83,84In 2004, the Stanford Genome Technology Center began to use a nanopipette as an electrochemical biosensor . Unlike d-glucose to d-gluconic acid. This led to a drop in pH, which caused a measurable change in current rectification [The recognition element is one of the major factors affecting nanosensor performance. The specificity of a nanopipette-based biosensor is restricted by the molecule deployed as the recognition element. Receptors, enzymes, antibodies, and nucleic acids can be employed in the sensor design to recognize the target of interest . Initialfication .The differences between glucose levels of individual cells may be indicative of diseases such as cancer . These cCytoplasmic ions and molecules can be used in the prediction of cell heterogeneity. Previously, it was found that the accumulation of metal ions  can be iBecause changes in ROS levels in cancerous cells had been observed previously, the development of ROS single-cell monitoring systems will benefit studies of tumor progression . PreviouSingle DNA molecules labeled with nanoparticles were detectible using glass nanopipettes by monitoring the ionic current blockade events caused by translocation of these molecules . This te2+ presence in a concentration-dependent manner. The ability to temporally direct the binding of molecules to a target contributes not only to the fabrication of sensing devices, but also to studies on the thermodynamics and kinetic properties of analyte-receptor interaction. The immobilization of calmodulin, a calcium-binding protein, enhanced the calcium ion response of the nanopipette and made possible the development of a sensor that presents selective and reversible binding of calcium at neutral pH [2+ was successfully released from the nanopipette surface by cycling the nanopipettes between solutions with different pH levels [Functionalization of a solid-state nanopore with chitosan and polyacrylic acid (PAA) made it possible to detect metal ions based on reversible binding of metal ions to polyelectrolytes  Figure . The senutral pH . In anotH levels . In the last decade, much effort has been concentrated on applications of nanopipettes as single-cell surgical tools both for injection and for aspiration of various materials. The combination of nanopipettes as surgical tools and selective sensing tools enabled detection of biologically relevant compounds in single living cells and in well-defined regions of the cell compartment. The advances in nanopipette technology have also benefited various areas of molecular biology research, including but not limited to proteomics and genomics. In the future, the technology presented here can be applied to automated cellular collection systems, which could allow the researcher to perform several tasks at the same time. As mentioned above, when a pipette is used as a biosensor the shape and the diameter of the tip is of greater interest than the overall volume. Therefore, there is significant interest in producing nanopipettes with precisely identical geometrical parameters, such as size and shape of the tip. Post-processing of nanoparticle production can help fine-tune these parameters by applying different approaches. Development of precise tip parameters is of scientific interest not only for sensing applications but would also be of interest to researchers dealing with dielectric etching. We believe that with increased attention to nanopores, nanogating, and ion channels, nanopipettes will find even broader application in a variety of fields, from electrophysiological to medical research, and will become a fundamental tool for single-cell studies.