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The induction magnetometers are widely applied for magnetotelluric detection due to the characteristics of wide frequency band, large detection depth range and small size. However, the key part of the induction magnetometers – the magnetic core has eddy current loss and hysteresis loss, which significantly affects the sensitivity of the induction magnetometers. In order to improve the sensitivity of the induction magnetometers at high frequencies, this paper investigates various parameters related to the performance of the induction magnetometers working at 10 kHz to 100 kHz. Moreover, optimization method is proposed to realize the development of a wide-band, high-sensitivity, and low-noise induction magnetometer. First of all, the parameters related to the sensitivity of the sensor are investigated according to the law of electromagnetic induction. A three-dimensional finite element (3D-FE) simulation model was established to study the influence of various parameters of induction magnetometers. In addition, an analysis method combining orthogonal experiment and response surface method is adopted to reduce the quantity of computations and improve the efficiency of analysis. The orthogonal experiment is able to obtain preliminary optimal parameters with only a small amount of computation results. Based on the results of the orthogonal experiment, the response surface method is used to illustrate the relationship between the sensor parameters and losses, and hence the optimal sensor parameters can be obtained. Finally, the model is verified by other sets of simulations, and the results show the regression coefficient of the model $R^{2}=0.9735$ , indicating the effectiveness of the proposed model.

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of life. The network-based centrality methods are a common way to identify essential proteins for protein interaction networks. Due to the differences between the existing centrality methods, it is a feasible approach to improve the identification accuracy of essential proteins by combining centrality methods. In this paper, we propose a centrality combination method based on feature selection. First, the measure values of the 14 classical centrality methods are viewed as feature data. Then, a subset of the relevant features is selected according to the importance of features. Finally, the centrality methods corresponding to the selected features are combined by using the geometric mean method for the identification of essential proteins. To verify the effectiveness of the combination method, we apply the combination method on the original static protein interaction network (SPIN), the dynamic protein interaction network (DPIN) and the refined dynamic protein interaction network (RDPIN), and compare the result with those by each single centrality method (LAC, DC, DMNC, NC, TP, CLC, BC, LC, CC, KC, CR, EC, PR, LR). The experimental results on the identification of essential proteins shows that the combination method achieves better results in prediction performance than the 14 centrality mehtods in terms of the prediction precision, sensitivity, specificity, positive predictive value, negative predictive value, F-measure and accuracy rate. It has been illustrated that the proposed method can help to identify essential proteins more accurately.

Constructing dynamic protein interaction networks (DPIN) is a common way to improve identification accuracy of essential proteins. The existing methods usually aggregate DPIN into a single-layer network where all nodes are sorted by their importance. This treatment makes the dynamic information about proteins in multiple layers lost in the single layer, and thus affects the identification accuracy of essential proteins. This paper proposes a node ranking method based on multiple layers for DPIN to address the problem. First, we calculate the centrality values of all nodes for each time-specific layer, then work out the centrality score of each node by dividing the total of its centrality values across all layers by its layer activity, and finally sort the importance of all nodes by their centrality scores. Different from the methods based on single layer, our method makes full use of centrality values of each protein in time-specific layers, and thus can more effectively utilize the dynamic information of proteins. To evaluate the effectiveness of the node ranking method based on multiple layers, we apply ten network-based centrality methods on multiple layers and compare the results with those on a single layer. Then the predictive performance of the ten centrality methods are validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure and accuracy. The experimental results for the identification of essential proteins show that the node ranking method based on multiple layers is superior to those based on a single layer and can help to identify essential proteins more accurate.

As the main forest firefighting equipment, portable wind extinguisher plays a major role in forest and grassland firefighting. The same as other hand-held vibration machines, portable wind extinguisher inevitably causes hand-transmitted vibration which is an important cause of discomfort for operators, decreased work efficiency, and even health hazards. In this paper, a biomechanical response model of hand-transmitted vibration is established based on the operating posture of the wind fire extinguisher. A standard vibration test platform was set up for the test, and the model parameters were identified through the standard vibration test data. The vibration response predicted by the model is compared with the measured response of portable wind extinguisher to verify the feasibility of the model prediction. The vibration response of the standard vibration experiment platform was used to compare the influence of hand force on the hand-transmitted vibration response. The results show that the goodness of fit between the vibration transmissibility response predicted by the arm model and the response measured by the standard vibration test platform is between 75% and 92%, and the goodness of fit between the vibration transmissibility response predicted by the arm model and the response measured by the portable wind fire machine is within the range of 77%. The research in this article can provides theory and data basis for improvement of vibration reduction protection strategy and device of portable pneumatic extinguisher.

As the population and the distribution of Crested Ibis (Nipponia nippon) become larger, it is necessary to propose a highly efficient census method to estimate the population size of the Crested Ibis. Passive acoustic monitoring (PAM) has a very good prospect for the Crested Ibis monitoring. To realize the automatic census of the Crested Ibis with PAM, the automatic individual identification method based on the vocalization is the key technology. A novel individual identification model was proposed in this paper, which built the autoencoder based on LSTM to obtain the meaningful latent representation from the raw recording directly, further, embedded self-attention and putted forward a combined training mode to achieve distinctive latent representation. With this model, nine Crested Ibis individuals were identified accurately, the highest accuracy is 0.971, and the average accuracy reaches 0.958. As for other three species, Little owl (Athene noctua), Chiffchaff (Phylloscopus collybita) and Tree pipit (Anthus trivialis), the better performances were achieved than the existing method, which means the proposed model can provide an alternative method for the individual identification of other bird species.

To improve the thermoelectric conversion efficiency of solar thermoelectric power, a concentration solar thermoelectric generator (CTEG) unit based on concentrating and storing energy is designed. A Fresnel lens is used to concentrate thermal energy, and a phase change material (PCM) is used to store thermal energy to increase the temperature difference between the hot and cold ends of thermoelectric (TE) sheets. The heat stored in the PCM container will help to generate continuous solar energy at night and improve the thermal power conversion efficiency of the TEGs. The energy conversion equilibrium equation is established for the CTEG unit. By numerical calculation, we conclude that the absorption rate of the coating surface is reduced by 0.1 and the maximum thermoelectric conversion efficiency is reduced by approximately 0.4%. By selecting different heights of the heat exchanger, the unit supplies 1.025 v, 0.4204 v and 0.299 v open circuit voltages on average for approximately 30, 44 and 53 minutes, respectively. Therefore, the height of the heat exchanger affects the rate of heat energy absorption and release. The reversible operation of the CTEG-PCM unit is conducive to the day and night operation of solar power generation and is more suitable to solving the self-supplying power problem for wireless temperature sensors in forests.

The stable exchange of energy and the effective conduction of heat are particularly critical to ensuring the stable operation of thermoelectric components in entire thermoelectric conversion processes. Poor heat exchange and conduction will cause thermal energy in a thermoelectric system to accumulate, leading to the consequence of burning the energy conversion modules. To solve these problems, a thermoelectric energy conversion system equipped with a water-cooling thermal energy adjustment structure settled on the surface of the heat source was proposed. The working temperature of the heat source, the output voltage, current, and power of the thermoelectric system with and without the structure were obtained and compared. Through comparative analysis, benefiting from the above structure, the temperature fluctuation of the heat sink surface of the heat source was found to drop from 10.36 K to 1.18 K, and the maximum output of the system increased from 255 to 290 mV through the process of input voltage from 1 to 6 V. Furthermore, in the process of gradually increasing the load from 0 to 180 ohms, the system achieves an increased output of 53.8 mV, 1.81 mA and 52.1 mV, 1.83 mA when the input voltage was 4 and 5 V, respectively. In conclusion, the application and design of the above structure obviously promotes the stability and output capacity during thermoelectric energy conversion. The combination of the water-cooling thermal energy adjustment structure with pulse-width modulation (PWM) or maximum power point tracking (MPPT) theory is worth further study.

RGB-D cameras, which can be attached to any mobile device and work under different operation platforms (e.g., iOS, Android, and Windows), have great potential for indoor 3D modeling and navigation due to their low cost and small size. The main problems of RGB-D cameras for such applications are their range limitations and deteriorated depth accuracy. For example, for a 7-m range, the distance error of structure sensor (one type of RGB-D camera) reaches nearly 0.5 m. We propose a new calibration procedure for RGB-D sensors to improve the depth accuracy. First, the baseline between RGB and IR cameras is calibrated using the direct linear transform method. The distortions of the RGB and IR cameras and the IR projector are then calibrated using the newly proposed two-lens distortion model. Finally, the remaining depth systematic errors are calibrated using an empirical model. Compared to existing calibration methods, the new calibration method considers distortions from both the IR camera and projector and significantly improves the accuracy of far-range depth measurements. The experimental results show that the proposed calibration method can precisely calibrate the full range of the RGB-D sensor, up to 7 m, with an overall depth accuracy of 1.9%, compared to the 5.5% accuracy of the manufacturer’s depth estimation. To demonstrate the significance of calibration in indoor mapping, the 3D point cloud of a room (4.5 m x 3.5 m) is generated using the RGB-D SLAM system. The accuracy of the 3D model with the proposed calibration method is approximately 1.5 cm, compared to 7.0 cm using the manufacturer’s calibration parameters.

The operational trajectory of a skilled operator holds significant guiding implications for the motion control of unmanned excavators. This study collects and analyzes the operational trajectories of experienced operators under specific working conditions, acquires standardized data, and proposes fluctuation judgment data in terms of joint motion speed and angle values at specific stages. Utilizing standardized data, a motion control trajectory is generated and combined with the Lagrange function and MPC algorithm to achieve decoupling of the four composite motions of excavators. Simulation verification and experimental comparison are conducted using the MPC algorithm. By adopting MPC motion control without altering the structure, hydraulic system, or electrical system of the experimental object, operating efficiency reaches 108.1% compared to that of skilled operators while significantly reducing fluctuations in operation trajectory. The average reduction in relative standard deviation and maximum fluctuation value, by 29.3% and 3.14deg/s respectively, signifies enhancements in velocity stability and position stability of over 36.2% and 12.85deg respectively, thereby presenting a novel approach for controlling work devices on unmanned excavators.

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of gene interactions. Applying machine learning methods to microarray gene expression profiles for disease classification is a popular method to predict disease state or outcome. Traditional computational methods that detect genes differentially expressed between cancer and normal samples are ineffective in independent cohorts of patients. However, current methods consider pathways as simple gene sets and include pathway topological information but ignore significant individual genes and interactions between genes, which are essential to infer a more robust pathway activity. In this study, we proposed a novel approach to describe the activity of a pathway that incorporates both the differential expression degree of genes between the case and control and the interaction strength between genes. We applied the method to the classifications of seven cancers. Within-dataset experiments and cross-dataset experiments demonstrated that our novel method achieved robust and superior performance when compared to the five existing methods.

Bipolar disorder (BPD) and schizophrenia (SCZ) are two severe worldwide psychiatric disorders. Identifying genetic components contributing to both disorders will provide meaningful insights into their pathogenesis and widely-existed misdiagnosis. In this study, we employ allele-skewed DNA modification (ASM-SNP) data to investigate the two psychiatric disorders via state-of-the-art manifold learning, data-driven feature selection, and novel pathway analysis. We propose a novel manifold learning analysis for ASM-SNP data of bipolar disorder and schizophrenia based on a data-driven feature selection algorithm: nonnegative singular value approximation (NSVA). Our results indicate that t-distributed stochastic neighbor embedding (t-SNE) outperforms its peers in distinguishing psychiatric disorder samples from normal ones in both visualization and phenotype classification. It achieves the best phenotype diagnosis results with the average AUC 0.95 by using only about 20% top-ranked SNPs. Furthermore, our results from manifold learning along with support vector machine analysis suggest that the possible non-separability of SCZ and BPD in genetics. We also validate that SCZ and BPD both share the same or similar genetic variations from pathway analysis. This study indicates the inevitable misdiagnosis issue between BPD and SCZ from a machine learning and systems biology approach. The result sheds light on the existing psychiatry research to reexamine the existing behavior-based classification for BPD and SCZ. To the best of our knowledge, this study is the first comprehensive investigation of BPD and SCZ in bioinformatics.

In clinical medicine, the contrast-enhanced ultrasound (CEUS) has been a commonly used imaging modality for diagnosis of breast tumor. However, most researchers in computer vision field only focus on B-mode ultrasound image which does not get good results. To improve the accuracy of classification, first, we propose a novel method, i.e., a Temporal Sequence Dual-Branch Network (TSDBN) which, for the first time, can use B-mode ultrasound data and CEUS data simultaneously. Second, we designed a new Gram matrix to model the temporal sequence, and then proposed a Temporal Sequence Regression Mechanism (TSRM), which is a novel method to extract the enhancement features from CEUS video based on the matrix. For B-mode ultrasound branch, we use the traditional ResNeXt network for feature extraction. While CEUS branch uses ResNeXt + R(2 + 1) D network as the backbone network. We propose a TSRM to learning temporal sequence relationship among frames, and design a Shuffle Temporal Sequence Mechanism (STSM) to shuffle temporal sequences, the purpose of which is to further enhance temporal information among frames. Experimental results show that the proposed TSRM could use temporal information effectively and the accuracy of TSDBN is higher than that of state-of-art approaches in breast cancer classification by nearly 4%.

Ischemic stroke subtyping was not only highly valuable for effective intervention and treatment, but also important to the prognosis of ischemic stroke. The manual adjudication of disease classification was time-consuming, error-prone, and limits scaling to large datasets. In this study, an integrated machine learning approach was used to classify the subtype of ischemic stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importances of features were ranked by the Shapiro-Wilk algorithm and Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features important to ischemic stroke subtyping. Furthermore, the importances of selected features were determined by Extra-Trees-Classifier. Finally, the selected features were used by Extra-Trees-Classifier and a simple deep learning model to classify the ischemic stroke subtype on IST dataset. It was suggested that the described method could classify ischemic stroke subtype accurately. And the result showed that the machine learning approaches outperformed human professionals.

The emergence of drug resistance is one of the main reasons for the failure of disease treatment. More and more studies have shown that miRNAs are associated with the resistance or sensitivity of certain therapeutic drugs by regulating target genes. However, only a few associations have been reported between miRNAs and drug resistance or sensitivity. In this study, we first constructed a heterogeneous network by integrating the miRNA similarity network, drug similarity network and miRNA-drug effect associations network. Subsequently, we predicted the potential miRNA-drug effect associations by the Bi-Random walk (BiRW) algorithm. The cross-validation and De novo validation methods were applied to verify the prediction performance. Results show that the proposed method can effectively predict potential miRNA-drug effect associations. This study will enhance our understanding of the important roles of miRNAs in drug therapeutic effects and provide a valuable resource for designing effective therapeutic strategies.

Tremor, which is one of the most common movement disorders, is a repetitive movement that is caused by periodic muscle contraction and relaxation. To suppress tremors of upper-limb tremor patients, such as essential tremor (ET) patients, many kinds of devices have been developed. On the other hand, when mechanical vibration stimulation is applied to a human muscle, sustained muscle contraction, which is referred to as the tonic vibration reflex (TVR), is induced in the stimulated muscle. In this study, a novel tremor suppression method that utilizes the periodic TVR to induce muscle contraction/relaxation to generate the counterphase motion of the ET is proposed and applied to the forearm pronation-supination ET. In the proposed method, periodic vibration stimulation is applied to generate the periodic TVR in the pronator teres muscle and/or supinator muscle. First, the results confirmed that the TVR can be induced by applying mechanical vibration stimulation to the pronator teres muscle and supinator muscle since the forearm pronation-supination tremor is one of the key features of the ET. Furthermore, the findings also confirmed that the TVR intensity that is induced in these muscles can be adjusted by changing the vibration stimulation frequency. Second, the results show that the counterphase motion of the ET (i.e., periodic pronation-supination motion) can be generated by applying the proposed method. The effectiveness of the proposed method for tremor suppression is evaluated by comparing the generated motion with the ET motion.

Sparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra- and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective 〈cell, task〉 pairs by using reinforcement learning. To effectively capture the intra- and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms.