Search

Towards eliminating stimulus artifacts, alternating polarity stimuli have been widely adopted in eliciting the auditory brainstem response. However, considering the difference in the physiologic basis of the positive and negative polarity stimuli on the auditory system, it is unclear whether alternating polarity stimuli would adversely affect the auditory brainstem response characteristics. This research proposes a new polarity method for stimulus artifacts elimination, Sum polarity, that separately utilized the rarefaction and condensation stimuli and then summed the two evoked responses. We compared the waveform morphology and latencies of the auditory brainstem responses evoked by familiar stimuli (including click, tone-burst, and chirp) with different polarity methods in normal-hearing subjects to investigate the new method’s effectiveness. The experimental results showed that alternating polarity of the click and chirp had little effect on the auditory brainstem response. In contrast, alternating polarity affected the waveform morphology and latencies of the auditory brainstem responses to the low-frequency tone-burst, with the effect decreasing as the stimulus frequency increased. These results demonstrated the performance of any polarity method is related to the characteristics of the stimulus signal itself, and no polarity method is optimal for all types of stimuli. Based on the analysis of experimental results, a fixed polarity and alternating polarity were recommended for the click and chirp auditory brainstem responses, respectively. Furthermore, considering the apparent latency differences between the responses to opposite polarity stimuli, the Sum polarity was suggested for the tone-burst auditory brainstem responses. Moreover, this work verified the feasibility of the Sum polarity, which offers another choice for eliminating stimulus artifacts in an evoked potential acquisition.

The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters. ABR extraction method that can automatically adjust parameters to adapt different background noises was needed.An adaptive Kalman filtering (AKF) based ABR signal extraction method was proposed, in which two recursive rules were introduced to constantly update the parameters according to the real-time noise properties. It was used for ABR extraction from recordings in noises with different orders of larger magnitude.The AKF method demonstrated the best performance in obtaining reliable ABR waveform morphologies in the presence of large EMG noises compared with traditional methods of TDA or KF. It could extract satisfactory ABR signal with fewer trials of acoustic stimulus repetition, even from noise 10000 times larger than ABR signal. The AKF results also showed smaller absolute errors and higher correlation coefficients with the target ABR signal when different types (gum chewing, mouth opening and milk drinking) or levels of noises were introduced.The proposed AKF method is a great candidate to increase the robustness of current ABR measurements.It could provide reduced testing time and relaxed recording conditions for ABR and other evoked potentials extraction.

Introduction: Auditory brainstem response (ABR) is one of the commonly used methods in clinical settings to evaluate the hearing sensitivity and auditory function. The current ABR measurement usually adopts click sound as the stimuli. However, there may be partial ABR amplitude attenuation due to the delay characteristics of the cochlear traveling wave along the basilar membrane. To solve that problem, a swept-tone method was proposed, in which the show-up time of different frequency components was adjusted to compensate the delay characteristics of the cochlear basilar membrane; therefore, different ABR subcomponents of different frequencies were synchronized. Methods: The normal hearing group, moderate sensorineural hearing loss group, and severe sensorineural hearing loss group underwent click ABR and swept-tone ABR with different stimulus intensities. The latencies and amplitudes of waves I, III, and V in 2 detections were recorded. Results: It was found that the latency of each of the recorded I, III, and V waves detected by swept-tone ABR was shorter than that by click ABR in both the control group and experimental groups. In addition, the amplitude of each of the recorded I, III, and V waves, except V waves under 60 dB nHL in the moderate sensorineural hearing loss group, detected by swept-tone ABR was larger than that by click ABR. The results also showed that the swept-tone ABR could measure the visible V waves at lower stimulus levels in the severe sensorineural hearing loss group. Conclusion: Swept-tone improves the ABR waveforms and helps to obtain more accurate threshold to some extent. Therefore, the proposed swept-tone ABR may provide a new solution for better morphology of ABR waveform, which can help to make more accurate diagnosis about the hearing functionality in the clinic.

Community search has been wildly studied to retrieve relevant cluster from attributed graph for a given set of sample nodes. However, existing well-studied methods aim at searching a community containing sample nodes, and fail to capture communities without sample nodes but are similar with user preference deduced from the given sample nodes. To this end, we propose a community search method that is capable of finding multi-communities with user's preference using few given sample nodes and simultaneously identify outliers in attributed network. The method is termed as Integrating user Preference into Multi-community and Outliers Search (IPMOS), which collaborates user's preference into the process of searching to find interesting clusters of the entire network. Particularly, the strategy of truncated random walk is first used to expand few sample nodes. And then, the average partition similarity is defined based on exploring strategy to infer attribute subspace as user's latent interest. Finally, multiple communities and outliers in the whole network are detected via fractional-core and structural constraints. Extensive experiments on several synthetic networks and real-world networks with different scales and subjects demonstrate the effectiveness and efficiency of our approach.

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL. Specifically, for each original image, consider its 10 JPEG compressed versions with their quality factor (QF) values from {100,90,80,70,60,50,40,30,20,10}. Under the assumption that the ground truth label of the original image is known at the time of selecting an input, but unknown to the underlying DNN, we present a selector called Highest Rank Selector (HRS). It is shown that HRS is optimal in the sense of achieving the highest Top k accuracy on any set of images for any k among all possible selectors. When the underlying DNN is Inception V3 or ResNet-50 V2, HRS improves, on average, the Top 1 classification accuracy and Top 5 classification accuracy on the whole ImageNet validation dataset by 5.6% and 1.9%, respectively, while reducing the input size in bits dramatically—the compression ratio (CR) between the size of the original images and the size of the selected input images by HRS is 8 for the whole ImageNet validation dataset. When the ground truth label of the original image is unknown at the time of selection, we further propose a new convolutional neural network (CNN) topology which is based on the underlying DNN and takes the original image and its 10 JPEG compressed versions as 11 parallel inputs. It is demonstrated that the proposed new CNN topology, even when partially trained, can consistently improve the Top 1 accuracy of Inception V3 and ResNet-50 V2 by approximately 0.4% and the Top 5 accuracy of Inception V3 and ResNet-50 V2 by 0.32% and 0.2%, respectively. Other selectors without the knowledge of the ground truth label of the original image are also presented. They maintain the Top 1 accuracy, the Top 5 accuracy, or the Top 1 and Top 5 accuracy of the underlying DNN, while achieving CRs of 8.8, 3.3, and 3.1, respectively.

Since entrepreneurship was conceptualised as a panacea for achieving inclusive growth in the “base of the pyramid” (BoP) regions, various ways have been explored to leverage this powerful tool, such as helping potential entrepreneurs build the resource base and capabilities. However, given the severe resources constraints in the BoP regions, such a goal is difficult to achieve. Besides, due to the high demands on personal competence, only a few people can benefit from this method, which fails to solve the problem of social exclusion in the BoP regions. Therefore, we aim to find a better way to leverage entrepreneurship to tackle the problem of the BoP regions by calling for more attention to the inclusiveness of entrepreneurship. Based on data of inclusive entrepreneurs in Zhejiang, China, we construct a three-stage model for inclusive entrepreneurship. We also apply Transaction Cost Economics to look for determinants that foster inclusive entrepreneurship and validate our main assertion that decreasing transaction cost significantly helps to increase the inclusiveness of entrepreneurship, and different phases of inclusive entrepreneurship (i.e., opportunity inclusiveness, participation inclusiveness, and sharing inclusiveness) are influenced by different sets of determinants of transaction costs.

Excess chemical fertilizer application results in substantial N2O emission. Manure application into agricultural soil can reduce chemical fertilizer input while maintaining crop yield. However, little is known about the potential effects of reduction in chemical fertilizer with green manure or organic manure on yield-scaled N2O emissions in orchards. A two-year experiment was carried out in a citrus orchard, including (i) chemical fertilizer (urea), 0%, 70%, 85% and 100% of N supplied, respectively (CK, C70, C85, CF), (ii) organic manure (rapeseed cake) incorporation with 0%, 70%, 85% and 100% of N supplied from chemical fertilizer (OM, OMC70, OMC85, OMCF), (iii) green manure (smooth vetch) incorporation with 0%, 70%, 85% and 100% of N supplied from chemical fertilizer (GM, GMC70, GMC85, GMCF) treatments. The soil physical-chemical properties, N2O emissions and yields were measured during 2018–2019. N2O emissions were 1.57 and 1.80 kg N ha−1 yr−1 under CF treatment in 2018 and 2019, respectively, and decreased by 25.4%−31.6% under C70, 32.5%−55.4% under OMC70 and OMC85 treatments and by 16.1–32.2% under GMC70 and GMC85 treatments. The 70% N rate of chemical fertilizer was the optimal N rate for maximizing yield in no manure, organic manure and green manure treatments, respectively. The C70, OMC70 and GMC70 treatments both significantly decreased yield-scaled N2O emission with the lowest emission in OMC70 treatment relative to CF treatment. N2O fluxes in OMC70 and OMC85 treatments were mainly controlled by the DOC/NO3- ratio, while soil NH4+ was the dominant factor controlling N2O fluxes in GMC70 and GMC85 treatments, suggesting that more NH4+ provided from green manure than from organic manure contributed to higher N2O emission. We conclude that applying organic manure incorporation with 30% reductions of chemical fertilizer is a fertilizer management strategy worth pursuing for sustainable productivity and environmental protection in citrus orchards.

Brain computer interface (BCI) based on non-invasive electroencephalography (EEG) signals has become a promising and alternative method for electromyography (EMG) signal in the rehabilitation process of amputees with high level of amputation. This is because their residual muscles cannot provide sufficient myoelectric signals to accurately recognize limb movement intentions. One of the major challenges of BCI based EEG methods is the inevitable artifacts contained in multi-channels EEG recordings that would affect accurate characterization and decoding of limb movement intents from brain signals. Previous studies have applied different methods based on regression, blind source separation (BSS) and filtering techniques, though with limited efficacy in fully isolating artifacts from the entire EEG signals. Also, some of the existing methods are targeted towards the removal of one or two particular types of artifacts. In this study, a linear algorithm based on generalized eigenvalue decomposition and multi-channel Wiener filter (GEVD-MWF) was proposed to simultaneously remove different type of artifacts inherent in EEG signals, recorded from four transhumeral amputee subjects. Experimental results showed that the proposed method outperformed the commonly used approach which achieved an improved average classification accuracy of 22.03% across all the classes of motor imagery (MI) tasks and subjects. For real-time applications, a modified channel selection algorithm-based on sequential forward floating selection (mSFFS) was adopted to remove redundant channels and an average accuracy of 96.87% was achieved with 10 electrode channels located at the pre-motor cortex, primary motor cortex and somatosensory cortex of the brain. The overall performance of the proposed method suggested that multiclass MI tasks can be reliably and accurately characterized using artifact-free EEG signals from a few number of channels. Thus, the proposed methods can be a potential control method for rehabilitation devices for individuals with high level amputation.

Objective. The auditory brainstem response (ABR) audiometry is a means of assessing the functional status of the auditory neural pathway in the clinic. The conventional click ABR test lacks good neural synchrony and it mainly evaluates high-frequency hearing while the common tone-burst ABR test only detects hearing loss of a certain frequency at a time. Additionally, the existing chirp stimuli are designed based on average data of cochlear characteristics, ignoring individual differences amongst subjects. Approach. Therefore, this study designed a new stimulus approach based on a sweep-tone concept with a time variant and spectrum characteristics that could be customized based on an individual’s cochlear characteristics. To validate the efficiency of the proposed method, we compared its performance with the click and tone-bursts using ABR recordings from 11 normal-hearing adults. Main results. Experimental results showed that the proposed sweep-tone ABR achieved a higher amplitude compared with those elicited by the click and tone-bursts. When the stimulus level or rate was varied, the sweep-tone ABR consistently elicited a larger response than the corresponding click ABR. Moreover, the sweep-tone ABR appeared earlier than the click ABR under the same conditions. Specifically, the mean wave V peak-to-peak amplitude of the sweep-tone ABR was 1.3 times that of the click ABR at 70 dB nHL (normal hearing level) and a rate of 20 s−1, in which the former saved 40% of test time. Significance. In summary, the proposed sweep-tone approach is found to be more efficient than the traditional click and tone-burst in eliciting ABR.

This paper proposes an improved neural network model based on CNN for evaluating the aesthetic quality of images. Firstly, we create three input sources from different angles of the target image and establish a three-channel parallel network model. Secondly, mlpconv layer is used to replace the traditional linear convolution layer to obtain more nonlinear abstract features in the network model based on CNN; Then, the combination of the global average pooling and full connection layers are used to replace the full connection layer in traditional CNN, and the three-channel features are merged. Finally, the EMD function is used as the loss function in the softmax layer. The output is probability density mass function from 1 to 10, and the mean and variance are used as objective qualitative score of picture quality. Experiments show that the proposed algorithm is feasible and effective, which solves the problem that the traditional method only obtains the binary classification of aesthetic. And this method gives the objective quantization score of the image. At the same time, the algorithm can get the evaluation value which is consistent with the actual situation in the real-time aerial experiment.

Classical community search methods aim to detect local communities containing a set of sample nodes provided by users, which have been wildly studied in recent years. Existing efforts on community search have mainly detected communities where the sample nodes are located. Nevertheless, they may fail to capture communities without sample nodes but are similar with user’s preference deduced from the given sample nodes. We argue that community search should take user’s preference into account during searching process, steering the algorithm to capture more interesting parts of the entire attributed graph. In this work, we propose a community search model that is capable of finding multiple target communities with few given sample nodes and simultaneously identify outliers in attributed network. The model is termed as Searching Target Communities with Outliers (STCO), which collaborates user’s preference into the process of searching to find interesting clusters of the entire network. Particularly, we specify two STCO methods, named STCOE and STCOT, based on two strategies of exploring sample node candidates, respectively. The average partition similarity is defined on the expanded candidate node set to infer the attribute subspace as user’s latent interest. And then, multiple communities and outliers in the whole network are detected via fractional-core and structural constraints. We demonstrate the effectiveness and efficiency of our model on several synthetic and real-world attributed networks with different scales and subjects.

This study aims to explore the role of brine salinity in achieving sustainable operation of membrane distillation (MD), particularly in hypersaline applications where highly concentrated or saturated solutions are treated. Given the state-of-the-art MD modeling work mainly focused on the mass and heat transfer phenomena for dilute systems, our simulation work predicts the trends of permeation flux in direct contact MD (DCMD) with elevated feed concentrations up to saturation, by a newly-developed exponential decay function. Also, a semi-empirical equation of the solute transport coefficient Sherwood number (Sh) is derived as Sh = (α1ωF + α2) Reβ Sc0.33, which for the first time incorporates the influence of feed concentration into the concentration polarization calculation in MD. Numerical analysis on the supersaturation ratio, concentration factor and concentration polarization effect showed that low to modest membrane permeability, reasonably high feed temperature and modest hydrodynamics (500

Earthworms especially contribute to decomposition by fragmenting, incorporating, and mixing residues into the soil. However, residues are already mechanically fragmented and incorporated into the soil by combine harvesters in most arable cropping systems of China, which may diminish the effects of earthworms. How earthworms affect the decomposition of residues with different quality, apart from fragmentation and incorporation, is still not clear. Thus, the aim of the present 60-day soil microcosm incubation experiment was to assess how earthworms affect the decomposition of residues of different quality, aside from fragmentation and incorporation. Four residues with different quality (high quality: rapeseed cake; medium quality: corn leaf; low quality: rice straw and corn stalk) were fragmented to 1-mm pieces using a laboratory blender and then thoroughly mixed with soil. The decomposition rates of different residues with or without earthworms (Metaphire guillelmi) were determined by measuring the CO2 emission. We hypothesized that earthworms accelerate high-quality residue decomposition more than low-quality residues. Results showed that earthworms increased cumulative CO2 emission for each residue type separately. Carbon derived from the residues was calculated and the decomposition kinetics was fitted with the first-order exponential model. During the initial 20 days of the incubation, the decomposition rate constant (k) of low-quality residues was significantly decreased by earthworm addition and significantly increased during the later period (20–60 days) of the incubation. However, no significant difference was detected between earthworm presence and absence treatments on the k value with high- and medium-quality residue incorporation. In general, earthworm addition increased inorganic nitrogen contents in most residue treatments during the entire incubation period, except for the rapeseed cake-treated soil. Our results indicated that earthworms have no effect on the decomposition of high-quality residues, but have a positive effect on the later stages of low-quality residue decomposition.

Prosthetic control technology utilizing motion intentions decoded from surface electromyography (sEMG) signals is becoming more and more popular. Most of the traditional sEMG wet electrodes require the skin to be prepared by conductive gel, which could lead to skin allergy and patient discomfort. In this study, we proposed a new method of using nano-gold flexible sensors to measure muscle contraction in terms of changes in sensor impedance. The nano-gold flexible sensors were used to classify nine gestures in two training modes: the sequential training mode, in which the same motion was repeated six times, and the random training mode, in which the order of the motions was randomized. The results showed that the average gesture recognition rates of using nano-gold flexible sensors were above 90% for all the subjects participated in the experiments. There was no significant difference between the two training modes (94.54% for the sequential training mode and 94.16% for the random training mode), with a p-value of 0.7340. The study suggested that the nano-gold flexible sensors could be used as an alternative of the wet electrode for reliable gesture recognition.

Auditory brainstem response (ABR) audiometry is a widely used tool for detecting hearing loss. The ABR is generated within the first 10–15 ms in the brainstem as responses to external sound stimulations following the stimulus onset. However, due to the small amplitude of the ABR signal, a large number of repeated averaging trials are usually needed to obtain meaningful ABR waveform, leading to long time consumption in the clinical practices. The subjects need to be sedated in special cases to keep them still for the long recording time. In this study, the method of Kalman filter was used to adaptively separate noises from ABR signals so that less trials were necessary to improve the efficiency. The performance of the Kalman filter method was compared with the traditional averaging methods under different conditions. The results showed that Kalman filter could obtain reliable ABR waveforms by wisely filtering out noises with a reduced number of repeated trials as less as 500 times, and the morphology converged to a smooth and clean curve as the number of trials increased. The study showed that the Kalman filter might provide a useful tool to improve the efficiency and signal quality of the ABR measurements.

Electrocardiography (ECG) and electroencephalograph (EEG) are two physiological signals closely related with human health, and their measurements can help to detect various diseases in the clinic. Long-term physiological signal monitoring is usually needed to catch any possible diseases which may be missed in conventional short-term monitoring. Currently, wet electrodes are widely used in the clinic to obtain physiological signals. However, wet electrodes require adhesives or conductive gels to improve the quality of recorded signals, and may introduce skin irritation. Based on the Capacitive coupling principle, this study proposed a flexible non-contact electrode that can collect physiological electrical signals without direct contacts with the skin. Moreover, it can attach firmly than common conductive electrodes because of its flexibility. The results showed that the proposed non-contact electrode could reliably obtain high quality physiological signals, and the performance could be comparable to the conventional wet electrodes. Alpha waves could be clearly detected when the subjects closed their eyes, with the non-contact electrodes placed over the hair. This study might provide a reliable solution for long-term monitoring of physiological signals for those whose disorders could not be diagnosed over a short period of time.

Auditory brainstem response (ABR) is an objective method via which hearing loss could be detected. ABR induced by click, a broadband signal, is generally considered as the gold standard. However, due to the inherent delay of the cochlear traveling wave, click cannot excite the entire cochlear basement membrane at the same time, leading to the attenuation of the induced ABR waveform. In order to resolve this limitation, a sweep-tone-based stimulus that reconstructs the arrival time of different frequency components with respect to the delay characteristics of cochlear basement membrane was designed and used to induce ABR in this study. Subsequently, we compared the performance of the proposed sweep-tone-induced ABR method and the commonly adopted click induced ABR at different test levels and different stimulus rates. And the obtained results showed that the waveform morphology of sweep-tone-induced ABR was significantly better than that of click induced ABR across different test levels and stimulus rates. Moreover, compared to the click induced ABR at different sweeps, we found that the proposed sweep-tone-induced ABR effectively induced the ABR waveform at a relatively faster rate. Hence, the proposed sweep-tone-induced ABR approach provides a new method to improve the sensitivity of ABR detection in hearing loss.