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Objectives. Until now, limited knowledge remains regarding the association among childhood obesity, cognitive behavior, and brain networks. Utilizing a publicly available dataset, we aimed to investigate the relationships between childhood obesity and functional networks during the stop-signal task. Results. Given the huge conflict-monitoring and inhibitory control demands of the task, both enhanced network connectivity and properties were observed under the “No-go” compared to the “Go” condition for both obese and non-obese preadolescents. Obese preadolescents exhibited significantly increased frontal-parietal, frontal-occipital, and frontal-temporal linkages, as well as heightened network efficiency under both “Go” and “No-go” conditions compared to non-obese counterparts. Additionally, significant correlations were found between network connectivity and properties and preadolescents’ body mass index (BMI), with their combination predicting BMI scores successfully. Conclusions. These findings support that childhood obesity is not simply a deviant habit with restricted physical health consequences, but rather associated with the atypical development of frontal-based networks involved in inhibitory control and cognitive performance.

Frontotemporal dementia (FTD) is frequently misdiagnosed as Alzheimer’s disease (AD) due to similar clinical symptoms. In this study, we constructed frequency-based multilayer resting-state electroencephalogram (EEG) networks and extracted representative network features to improve the differentiation between AD and FTD. When compared with healthy controls (HC), AD showed primarily stronger delta-alpha cross-couplings and weaker theta-sigma cross-couplings. Notably, when comparing the AD and FTD groups, we found that the AD exhibited stronger delta-alpha and delta-beta connectivity than the FTD. Thereafter, by extracting the representative network features and then applying these features in the classification between AD and FTD, an accuracy of 81.1% was achieved. Finally, a multivariable linear regressive model was built, based on the differential topologies, and then adopted to predict the scores of the Mini-Mental State Examination (MMSE) scale. Accordingly, the predicted and actual measured scores were indeed significantly correlated with each other ( ${r}$ = 0.274, ${p}$ = 0.036). These findings consistently suggest that frequency-based multilayer resting-state networks can be utilized for classifying AD and FTD and have potential applications for clinical diagnosis.

Pneumothorax is a potentially life-threatening disease that requires urgent diagnosis and treatment. Clinically, a chest X-ray examination is the first choice for diagnosing pneumothorax. However, it is difficult to diagnose pneumothorax by only frontal chest X-ray imaging when the lesion area is only composed of a small amount of air. Therefore, we propose a pneumothorax diagnosis neural network based on feature fusion, where frontal and lateral X-ray information are fused. In this network, there are two inputs and three outputs. The two inputs are the frontal chest X-ray image and the lateral chest X-ray image. The three outputs are the classification results of the frontal chest X-ray image, the classification results of the lateral chest X-ray image, and the classification results integrating the characteristics of the fused frontal chest X-ray image and lateral chest X-ray image. Our algorithm considers the vanishing gradient problem in the pneumothorax recognition model and introduces the residual block to alleviate this problem. Because of the large number of channels in this model, we also utilize channel attention mechanisms to improve the model’s performance. Our comparative experiments show that neural network fusion of frontal and lateral chest image features can achieve higher accuracy than the single task model. Using only image-level annotation, our pneumothorax model can achieve high recognition accuracy.

The resolution related with the image quality of acoustic imaging using a microphone array is limited by the size and density of the array. However, non-synchronous measurements can exceed the constraints defined by measurements with a single fixed array. The non-synchronous measurements can achieve a larger and denser aperture array by moving the single array. The key problem of archiving the non-synchronous measurements of a microphone array is the matrix completion of a block diagonal spectral matrix. A comprehensive investigation of the block diagonal spectral matrix completion is still missing in the microphone array signal processing literature since it is usually considered as a difficult problem if without the integrated physical information of the sound field. In this paper, the mathematical relation between the max-norm and the nuclear-norm is pointed out, and the max-norm minimization of the spectral matrix is discussed, which is an effective model with integrated spatial representation of the sound field for the spectral matrix completion. First, the max-norm is utilized for the low complexity modeling and the corresponding Proximal-Point method for Max-norm based Spectral matrix completion (PPMSMC) is proposed; Second, the rank, nuclear-norm and max-norm have been investigated as complexity measures in the context of spectral matrix completion, and their performances are compared. The proposed low-rank methods have constructed a concrete theorem foundation for the non-synchronous measurements of the microphone array.

Beamforming is a powerful technique to achieve acoustic imaging in far-field. However, its spatial resolution is strongly blurred by the point spread function (PSF) of phased microphone array. Due to the limitation of array aperture and microphone density, the PSF is far from Dirac delta function, so that it is difficult to obtain a high-resolution beamforming image at low-frequencies (e.g.500-1500Hz). This paper proposes a Bayesian inference method based on Non-synchronous Array Measurements (Bi-NAM) so as to refine the PSF and break through the beamforming limitation for low-frequency source imaging. Firstly, by sequentially moving prototype array at different positions, the non-synchronous measurements can get a sizeable synthetic aperture and high density of microphones. The synthetic cross-spectrum matrix (CSM) can significantly improve the beamforming performance. To confine the approximation error of synthetic CSM and the uncertainty of forward model, as well as the noise interference, a Bayesian inference based on joint maximum a posterior (JMAP) is proposed to solve an ill-posed inverse problem. A Student-t prior is employed to enforce the sparse property of acoustic strength distribution. The background noise can be adaptively modeled by the Student-t distribution, which is related to some of the typical symmetric distributions. Then the hyper-parameters in JMAP inference are efficiently estimated by the Bayesian hierarchical framework. Through experimental data, the proposed Bi-NAM approach is confirmed to achieve high-resolution acoustic imaging at 1000Hz and 800Hz, respectively, even under the Laplace noise interference.

Educational buildings consume about 2% of the total energy in a country, which leads to energy cost concerns for building operators. To reduce the building energy cost, an effective way is to intelligently schedule heating, ventilation, and air conditioning (HVAC) systems, which account for above 40% of the total energy consumption in educational buildings. In this paper, we investigate an energy optimization problem for HVAC systems in university campus buildings. To be specific, we first formulate a total cost optimization problem that minimizes the sum of energy cost related to HVAC systems and thermal discomfort cost associated with occupants considering zone occupancy pattern and thermal preference of occupants in each zone. Due to the existence of uncertain parameters as well as spatially and temporally coupled constraints, it is very challenging to solve the formulated problem. To this end, we propose a distributed model predictive control algorithm based on the alternating direction method of multipliers, which has high scalability with an increasing number of zones and can protect user privacy. Extensive simulations based on the real-world traces show that the proposed distributed algorithm could effectively reduce the total cost and offer a flexible tradeoff between energy cost and thermal discomfort.

In this paper, we intend to reduce the operational cost of cloud data centers with the help of fog devices, which can avoid the revenue loss due to wide-area network propagation delay and save network bandwidth cost by serving nearby cloud users. Since fog devices may not be owned by a cloud service provider, they should be compensated for serving the requests of cloud users. When taking economical compensation into consideration, the optimal number of requests processed locally by each fog device should be decided. As a result, existing load balancing schemes developed for cloud data centers cannot be applied directly and it is very necessary to redesign a cost-ware load balancing algorithm for the fogcloud system. To achieve the above aim, we first formulate a fog-assisted operational cost minimization problem for the cloud service provider. Then, we design a parallel and distributed load balancing algorithm with low computational complexity based on proximal Jacobian alternating direction method of multipliers. Finally, extensive simulation results show the effectiveness of the proposed algorithm.

Cloud service providers are typically faced with three significant problems when running their cloud data centers, i.e., rising electricity bills, growing carbon footprints, and unexpected power outages. To mitigate these issues, running cloud data centers in smart microgrids (SMGs) is a good choice, since SMGs can enhance the energy efficiency, sustainability, and reliability of electrical services. Thus, in this paper, we investigate the problem of energy management for cloud data centers in SMGs. To be specific, we would minimize the time average expected energy cost (including electricity bill, battery depreciation cost, the total generation cost of conventional generators, and revenue loss due to the unfinished workloads) with the consideration of three practical factors, i.e., the ramping constraints of backup generators, the charging and discharging efficiency parameters of batteries, and two kinds of data center workloads. A stochastic programming is formulated by integrating the constraints associated with workload allocation, electricity buying/selling, battery management, backup generators, and power balancing. To solve the stochastic programming problem, an online algorithm is designed, and the algorithmic performance is analyzed. Simulation results show the advantages of the designed algorithm over other baselines.