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Dexmedetomidine (Dex) is an alpha-2 agonist used for sedation during various procedures. Dex activates 2-adrenoceptors, and causes the decrease of sympathetic tone, with attenuation of the neuroendocrine and hemodynamic responses to anesthesia and surgery; it reduces anesthetic and opioid requirements; and causes sedation and analgesia. Objective: it was to compare the perioperative effects of different doses of Dex at different timing in patients using Dex during the perioperative period adopting a medical data classification algorithm based on optimized semi-supervised collaborative training (Tri-training). Methods: 495 patients requiring surgical treatment in Xingtai People’s Hospital were randomly selected as the study subjects. The patients were divided into group A (used before induction), group B (used during induction), and group C (used after induction) according to different induction timing, with 165 cases in each group. Then, groups A, B, and C were divided into groups A1, B1, and C1 (0.4 μg/(kg·h) rate), groups A2, B2, and C2 (0.6 μg/(kg·h) rate), and groups A3, B3, and C3 (0.8 μg/(kg·h) rate) according to the dose used, with 55 cases in each group. Intraoperative anesthesia and postoperative adverse reactions were compared among the 9 groups. Results: the similarity between the Tri-training algorithm optimized by Naive Bayes (NB) classification algorithm and the actual classification (93.49%) was clearly higher than that by decision tree (DT) and K-nearest neighbor (kNN) classification algorithm (76.21%, 74.31%); heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) values decreased obviously after Dex used in groups B1, C1, B2, C2, B3, and C3 (P 0.05); the proportion of patients with satisfactory Ramsay score in group A3 was distinctly superior than that in groups A1, A2, B1, B2, B3, C1, C2, and C3 (P

The drying and heating system (DHS) is the core component of automatic stirring equipment, so analyzing its thermodynamics is crucial for improving the efficiency of the equipment. However, existing empirical thermodynamic formulas generally take certain working conditions as prerequisites, and substituting relevant parameters of DHS into calculation can cause large errors. In view of this problem, this paper attempts to perform thermodynamic analysis and calculation on the DHS of automatic stirring equipment. At first, the material ratio of the automatic stirring equipment was analyzed in the text, and the drying and heating process of mixture was divided into three stages: mixture preheating, mixture drying, and heating mixture to the working temperature. Then, the heat consumption of DHS, the heat consumption of fuel combustion, the consumption rate of the fuel, and the heat efficiency of DHS were measured and calculated. At last, the thermodynamic analysis results of DHS of automatic stirring equipment were given, and the measurement and calculation methods adopted in the paper were proved to be effective.

Let$G$be a finite abelian group and$A\subseteq G$. For$n\in G$, denote by$r_{A}(n)$the number of ordered pairs$(a_{1},a_{2})\in A^{2}$such that$a_{1}+a_{2}=n$. Among other things, we prove that for any odd number$t\geq 3$, it is not possible to partition$G$into$t$disjoint sets$A_{1},A_{2},\dots ,A_{t}$with$r_{A_{1}}=r_{A_{2}}=\cdots =r_{A_{t}}$.

The pig renal cell was used as the target to evaluate the damage induced by CdTe quantum dots (QDs). By microcalorimetry, the difference of metabolism of the native renal cell and the treated cells was measured and analyzed. With addition of Wnt ligands, the heat flow increased significantly, and then the heat power decade gradually, which is was caused by binding of Wnt ligands with the receptors. However, the exothermic peak induced by Wnt ligands disappeared in the presence of QDs. The results of attenuated total reflection Fourier transform infrared spectra of LLC-PK1 cell suggested that the –NH, –COOH, and PO2 − groups on the cell surface were damaged. Besides, the increase of permeability was observed, and Cd2+ could be detected in cells after QDs treatment.

Audio recordings should be authenticated before they are used as evidence. Although audio watermarking and signature are widely applied for authentication, these two techniques require accessing the original audio before it is published. Passive authentication is necessary for digital audio, especially for the most popular audio format: MP3. In this article, we propose a passive approach to detect forgeries of MP3 audio. During the process of MP3 encoding the audio samples are divided into frames, and thus each frame has its own frame offset after encoding. Forgeries lead to the breaking of framing grids. So the frame offset is a good indication for locating forgeries, and it can be retrieved by the identification of the quantization characteristic. In this way, the doctored positions can be automatically located. Experimental results demonstrate that the proposed approach is effective in detecting some common forgeries, such as deletion, insertion, substitution, and splicing. Even when the bit rate is as low as 32 kbps, the detection rate is above 99%.

Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital cameras. However, SPN can be contaminated largely in the frequency domain by image content and nonunique artefacts of JPEG compression, on-sensor signal transfer, sensor design, color interpolation. The source camera identification (CI) performance based on SPN needs to be improved for small sizes of images and especially in resisting JPEG compression. Because the SPN is modelled as an additive white Gaussian noise (AWGN) in its extraction process from an image, it is reasonable to assume the camera reference SPN to be a white noise signal in order to remove the interference mentioned above. The noise residues (SPN) extracted from the original images are whitened first, then they are averaged to generate the camera reference SPN. Motivated by Goljan 's test statistic peak to correlation energy (PCE), we propose to use correlation to circular correlation norm (CCN) as the test statistic, which can lower the false positive rate to be a half of that with PCE. Theoretical analysis shows that the proposed CI method can remove the interference and raise the CCN value of a positive sample and thus achieve greater CI performance, CCN values of the negative sample class with the proposed method follow the normal distribution N (0,1) and the false positive rate can be calculated. Compared with the existing state of the art on seven cameras, 1400 photos totally (200 for each camera), the experimental results show that the proposed CI method achieves the best receiver operating characteristic (ROC) performance among all CI methods in all cases and especially achieves much better resistance to JPEG compression than all of the existing state-of-the-art CI methods.

Let $G$ be a finite abelian group, and $k_1,k_2$ be two integers. For any subset $A\subset G$, let $r_{k_1,k_2}(A,n)$ denote the number of solutions of $n=k_1a_1+k_2a_2$ with $a_1,a_2\in A$. In this paper, we generalize a result of Q.-H. Yang and Y.-G. Chen to finite abelian groups. More precisely, we characterize all subsets $A\subset G$ such that $r_{k_1,k_2}(A,n)=r_{k_1,k_2}(G\backslash A,n)$ for all $n\in G$.

Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital camera. However, sensor pattern noise can be contaminated largely in frequency domain by image detail from scene according to Li's work and non-unique artifacts of on-sensor signal transfer, sensor design, color interpolation according to Chen et al's work, the source camera identification performance based on SPN needs to be improved especially for small image block. Motivated by their works, in order to lessen the effect of these contaminations, the unique SPN fingerprint for identifying one specific camera is assumed to be a white noise which has a flat frequency spectrum, so the SPN extracted from an image is whitened first to have a flat frequency spectrum, then inputted to the mixed correlation detector. Source camera identification is the detection of the existence of the camera reference SPN in the SPN extracted from a single image. Compared with the correlation detection approach and Li's model based approaches on 7 cameras, 1400 photos totally, each camera is responsible for 200, the experimental results show that the proposed mixed correlation detection enhances the receiver operating characteristic (ROC) performance of source camera identification, especially greatly raises the detection rate (true positive rate) in the case of trustworthy identification which is with a low false positive rate. For example, the proposed mixed correlation detection raises the true positive rate from 78% to 93% at zero false positive rate on image blocks of 256x256 pixels cropped from the center of the 1400 photos. The proposed mixed correlation detection also has large advantage to resist JPEG compression with low quality factor. Fridrich's group has proposed two reference SPN extraction methods which are the noise residues averaging and the maximum likelihood estimation method. They are compared from the aspect of ROC performance associated with the correlation detection and mixed correlation detection respectively. It is observed that the combination of mixed correlation detection and reference SPN extraction method of noise residues averaging achieves the best performance. We also demonstrate an image management application of the proposed SPN detection method for the news agent. It shows that the detection method discriminates the positive samples from a large number of negative samples very well on image bock size of 512×512 pixels.

Image splicing detection has been considered as one of the most challenging problems in passive image authentication. In this paper, we propose an automatic detection framework to identify a spliced image. Distinguishing from existing methods, the proposed system is based on a human visual system (HVS) model in which visual saliency and fixation are used to guide the feature extraction mechanism. An interesting and important insight of this work is that there is a high correlation between the splicing borders and the first few fixation points predicted by a visual attention model using edge sharpness as visual cues. We exploit this idea to develope a digital image splicing detection system with high performance. We present experimental results which show that the proposed system outperforms the prior arts. An additional advantage offered by the proposed system is that it provides a convenient way of localizing the splicing boundaries.

Simple binary patterns have been successfully used for extracting feature representations for visual object classification. In this paper, we present a method to learn a set of discriminative tri-value patterns for projecting high dimensional raw visual inputs into a low dimensional subspace for tasks such as face detection. Unlike previous methods that use predefined simple transform bases to generate tens of thousands features first and then use machine learning to select the most useful features, our method attempts to learn discriminative transform bases directly. Since it would be extremely hard to develop analytical solutions, we define an objective function that can be solved using simulated annealing. To reduce the search space, we impose sparseness and smoothness constraints on the transform bases. Experimental results demonstrate that our method is effective and provides an alternative approach to effective visual object classification.

MP3 is the most popular compressed audio format in our daily life but it can be doctored very easily by pervasive audio editing software. Thus it is necessary to develop authentication methods for MP3. Different from JPEG compression for image, MP3 compression has its own characteristics. Thus existing forensics methods for JPEG compression is unable to be applied to MP3 compression directly. In this paper, we propose a passive approach to detect doctored MP3 audio by checking frame offsets. As the audio samples are divided into frames to encode, each frame has its own frame offset after encoding. Forgeries lead to the broken of frame grids. So the frame offsets are good indication for locating forgeries, and the frame offsets can be detected by the identification of quantization characteristic. In this way, the doctored positions can be automatically located. Experimental results demonstrate the validity of the proposed approach on detecting some common forgeries, such as deletion, insertion, substitution and splicing. Under different bitrates, the detection ratios are above 94%. To the best of our knowledge, this piece of work is the first one to investigate digital forensics on MP3 format.

One of the most common practices in image tampering involves cropping a patch from a source and pasting it onto a target. In this paper, we present a novel method for the detection of such tampering operations in JPEG images. The lossy JPEG compression introduces inherent blocking artifacts into the image and our method exploits such artifacts to serve as a 'watermark' for the detection of image tampering. We develop the blocking artifact characteristics matrix (BACM) and show that, for the original JPEG images, the BACM exhibits regular symmetrical shape; for images that are cropped from another JPEG image and re-saved as JPEG images, the regular symmetrical property of the BACM is destroyed. We fully exploit this property of the BACM and derive representation features from the BACM to train a support vector machine (SVM) classifier for recognizing whether an image is an original JPEG image or it has been cropped from another JPEG image and re-saved as a JPEG image. We present experiment results to show the efficacy of our method.