MFDNet: Towards Real-time Image Denoising On Mobile Devices

Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deployments on a mobile device. Some recent efforts in designing lightweight denoising networks focus on reducing either FLOPs (floating-point operations) or the number of parameters. However, these metrics are not directly correlated with the on-device latency. By performing extensive analysis and experiments, we identify the network architectures that can fully utilize powerful neural processing units (NPUs) and thus enjoy both low latency and excellent denoising performance. To this end, we propose a mobile-friendly denoising network, namely MFDNet. The experiments show that MFDNet achieves state-of-the-art performance on real-world denoising benchmarks SIDD and DND under real-time latency on mobile devices. The code and pre-trained models will be released.Comment: Under review at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Hydroxytyrosol Improves Obesity and Insulin Resistance by Modulating Gut Microbiota in High-Fat Diet-Induced Obese Mice

Obesity is a common chronic metabolic disease that is harmful to human health and predisposes the affected individuals to a cluster of pathologies. Insulin resistance (IR) is one of the most frequent complications of obesity. Hydroxytyrosol (HT) may reduce obesity and IR in high-fat diet (HFD)-fed mice; however, the mechanism underlying is still unknown. Systemic low-grade inflammation and intestinal dysfunction are thought to be associated with obesity and IR. In this study, we found that HFD feeding for 8 weeks altered the intestinal microbiota, injured intestinal barrier function, increased endotoxin release into the blood, enhanced the expression of inflammatory factors (TNF-α, IL-1β, IL-6) and lipid accumulation in liver, caused obesity, and aggravated IR via the JNK/IRS (Ser 307) pathway in HFD mice. We also found that HT gavage could reverse those effects and the beneficial effects of HT were transferable through fecal microbiota transplantation. Our data indicate that HT can improve obesity and IR by altering the composition of the intestinal microbiota and improving integrity of the intestinal wall. We propose that HT replenishment may be used as a dietary intervention strategy to prevent obesity and IR

Online extrinsic parameters calibration of on-board stereo cameras based on certifiable optimization

The extrinsic parameters of on-board stereo cameras can be slightly altered due to temperature fluctuations, vibrations, and accidental impacts during automobile driving, leading to significant performance loss in dense stereo matching. In this paper, we propose an online calibration method based on certifiable optimization to address this issue. Initially, sparse feature points are collected based on plane distribution and disparity. A robust optimization model is then developed to minimize the epipolar error, utilizing iterative local optimization to eliminate outliers and determine the 5DOF extrinsic parameters. Subsequently, a relaxation problem is constructed using inliers, and global optimization is performed to certify that the locally optimal results are indeed globally optimal. Comparative experimental results demonstrate that the proposed method offers high accuracy and reliability. Additionally, the quality of the disparity map generated by our calibration method is comparable to that achieved through offline calibration

Industrial Internet of Learning (IIoL): IIoT based Pervasive Knowledge Network for LPWAN – concept, framework and case studies

Industrial Internet of Things (IIoT) is performed based on the multiple sourced data collection, communication, management and analysis from the industrial environment. The data can be generated at every point in the manufacturing production process by real-time monitoring, connection and interaction in the industrial field through various data sensing devices, which creates a big data environment for the industry. To collect, transfer, store and analyse such a big data efficiently and economically, several challenges have imposed to the conventional big data solution, such as high unreliable latency, massive energy consumption, and inadequate security. In order to address these issues, edge computing, as an emerging technique, has been researched and developed in different industries. This paper aims to propose a novel framework for the intelligent IIoT, named Industrial Internet of Learning (IIoL). It is built using an industrial wireless communication network called Low-power wide-area network (LPWAN). By applying edge computing technologies in the LPWAN, the high-intensity computing load is distributed to edge sides, which integrates the computing resource of edge devices to lighten the computational complexity in the central. It cannot only reduce the energy consumption of processing and storing big data but also low the risk of cyber-attacks. Additionally, in the proposed framework, the information and knowledge are discovered and generated from different parts of the system, including smart sensors, smart gateways and cloud. Under this framework, a pervasive knowledge network can be established to improve all the devices in the system. Finally, the proposed concept and framework were validated by two real industrial cases, which were the health prognosis and management of a water plant and asset monitoring and management of an automobile factory

Multi-layer reconstruction of skull base after endoscopic transnasal surgery for invasive pituitary adenomas

Objective. To explore the efficacy of multi-layer skull base reconstruction after endoscopic transnasal surgery for invasive pituitary adenomas (IPAs). Clinical rationale for the study. Skull base reconstruction for IPAs. Material and methods. This retrospective analysis involved 160 patients with IPAs who underwent operations from October 2018 to October 2020. All patients were diagnosed with IPAs by pituitary enhanced magnetic resonance imaging, and all tumours were confirmed to be Knosp grades 3a, 3b, or 4. The experimental group and the control group comprised 80 patients in each, and we used different methods to reconstruct the skull base in each group. The comparison indicators included cerebrospinal fluid leakage, sellar floor bone flap (or middle turbinate) shifting, delayed healing of the skull base reconstructed tissue, nasal discomfort, and epistaxis. We used the chi-square test, and p < 0.05 was considered statistically significant. Results. In the experimental group, cerebrospinal fluid leakage occurred intraoperatively in 73 patients, two of whom had cerebrospinal fluid leakage postoperatively. Brain CT 12 months postoperatively showed no sellar floor bone flap (or middle turbinate) shifting. Endoscopic transnasal checks performed seven days after surgery showed that the skull base reconstructed tissue had healed in 74 patients and had failed to heal in six. However, endoscopic transnasal checks showed that all six of these patients’ pedicled nasoseptal flaps had healed well by 14 days after surgery. Other sequelae comprised nasal discomfort in four patients, and epistaxis in four. In the control group, cerebrospinal fluid leakage occurred intraoperatively in 71 patients, 14 of whom had cerebrospinal fluid leakage postoperatively. Brain CT 12 months postoperatively showed floor bone flap (or middle turbinate) shifting in 12 patients. Endoscopic transnasal checks performed seven days after surgery showed that the skull base reconstructed tissue had healed in 65 patients. In 12 patients, pedicled nasoseptal flaps had healed well by 14 days after surgery, while the remaining three patients required reoperation. Other sequelae comprised nasal discomfort in five patients, and epistaxis in six. Conclusions. This new method of multi-layer skull base reconstruction could play an important role in endoscopic transnasal IPA surgery

Chk1 Inhibition Ameliorates Alzheimer's Disease Pathogenesis and Cognitive Dysfunction Through CIP2A/PP2A Signaling

Alzheimer's disease (AD) is the most common neurodegenerative disease with limited therapeutic strategies. Cell cycle checkpoint protein kinase 1 (Chk1) is a Ser/Thr protein kinase which is activated in response to DNA damage, the latter which is an early event in AD. However, whether DNA damage-induced Chk1 activation participates in the development of AD and Chk1 inhibition ameliorates AD-like pathogenesis remain unclarified. Here, we demonstrate that Chk1 activity and the levels of protein phosphatase 2A (PP2A) inhibitory protein CIP2A are elevated in AD human brains, APP/PS1 transgenic mice, and primary neurons with A beta treatment. Chk1 overexpression induces CIP2A upregulation, PP2A inhibition, tau and APP hyperphosphorylation, synaptic impairments, and cognitive memory deficit in mice. Moreover, Chk1 inhibitor (GDC0575) effectively increases PP2A activity, decreases tau phosphorylation, and inhibits A beta overproduction in AD cell models. GDC0575 also reverses AD-like cognitive deficits and prevents neuron loss and synaptic impairments in APP/PS1 mice. In conclusion, our study uncovers a mechanism by which DNA damage-induced Chk1 activation promotes CIP2A-mediated tau and APP hyperphosphorylation and cognitive dysfunction in Alzheimer's disease and highlights the therapeutic potential of Chk1 inhibitors in AD

Evolution towards dispatchable PV using forecasting, storage, and curtailment: A review

The 2050 net-zero emission goal has pushed the global transition of power systems from fuel-powered to renewable-powered. Solar photovoltaic (PV) power is anticipated to contribute significantly to renewable generation. However, the intermittent nature of solar power hinders the growth of PV capacity during this global transition. Integrating PV into power systems usually requires abundant support resources. Typical facilities include dispatchable fuel-based generators and energy storage systems. However, newer PV systems should not assume sufficient support from fuel-based generators to facilitate the net-zero transition. Although the option to use energy storage, especially batteries, to replace fuel-based generators exists, scaling the capacity can have affordability issues. Despite the challenges, PV penetration is growing and needs to grow further. Overcoming the challenges means eliminating intermittency using minimum storage and negligible fuel. The solution is effectively converting PV to a dispatchable source. The research about forecasting and controlling PV power has centered on reducing the impact of PV power intermittency. However, the need for developing dispatchability out of PV power has yet to be sufficiently addressed. This paper is conducted to identify the research directions needed to facilitate dispatchable PV and, thus, global high PV penetration. To describe the dispatchability of PV power, uncertainty, variability, and flexibility are chosen as descriptors. As the PV power gains flexibility, uncertainty and variability reduce for a PV system. Eventually, PV power can become flexible enough to be dispatchable. Moreover, the support services needed by PV power can be undertaken mainly by itself, thus enabling high penetration. From the literature, PV forecasting, energy storage, and inverter-controlled curtailment are identified to be cornerstones of dispatchable PV power. Power system dispatch algorithms have used PV forecasts to compensate for uncertainty efficiently. Storage, especially batteries, and PV inverters, have been used to control PV power output against undesirable variation. In this review paper, the practice of utilizing PV in power systems is uniquely divided into four categories according to the descriptors. Unlike the convention that curtailment should be avoided, this review emphasizes the practicality of overbuilding PV capacity and curtailing PV power. It ultimately will be possible to effectively eliminate uncertainty and variability as the three cornerstone technologies evolve. By presenting the road map for reducing the impact of PV power intermittency, this paper elaborates on the motivation for researching dispatchable PV. From the past and low penetration to the contemporary situation, PV power evolved from being unconstrained to forecasted and constrained. Based on the literature about forecasting, energy storage, and curtailment, this paper concludes that dispatchable PV power will be needed and is achievable

CO₂ Emission Allocation for Urban Public Buildings Considering Efficiency and Equity: An Application at the Provincial Level in China

China is currently recognized as the leading global energy consumer and CO2 emitter. A significant amount of carbon emissions can be attributed to urban public buildings. Establishing an equitable and efficient carbon emission allocation mechanism is a crucial step to meeting the ambitious targets in China’s 2030 carbon peak plan. In this study, we estimate the total amount of CO2 emissions from urban public buildings by 2030 and propose a preliminary scheme of carbon quota assignment for each province. By means of applying the zero-sum gains data envelopment analysis (ZSG-DEA) model, the carbon emission quotas allocation of urban public buildings in China’s 30 provinces is proposed, and the corresponding pressure to reduce provincial carbon emissions is analyzed. The results indicate that Qinghai has the lowest carbon emission rate (0.01%) for urban public buildings, while Guangdong has the highest (9.06%). Among the provinces, Jiangsu, Jiangxi, and Tianjin face the least pressure in reducing carbon emissions from urban public buildings. On the other hand, Hebei, Beijing, and Anhui are under great pressure to decrease carbon emissions. Notably, Hebei is predicted to have the highest emission reduction requirement of 95.66 million tons. In terms of pressures on carbon emissions reduction for urban public buildings, Jiangsu, Jiangxi, and Tianjin exhibit the least pressure. Hebei, Beijing, and Anhui are facing intense pressure to decrease carbon emissions. These findings offer policymakers valuable insights into developing a fair and efficient carbon allowance allocation strategy, while also contributing to China’s efforts to mitigate carbon emissions and combat climate change

T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor

Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme

Bringing Geospatial Data Closer to Mobile Users: A Caching Approach Based on Vector Tiles for Wireless Multihop Scenarios

Mobile applications based on geospatial data are nowadays extensively used to support people’s daily activities. Despite the potential overlap among nearby users’ geospatial data demands, it has not been feasible to share geospatial data with peer wireless devices directly. To address this issue, we designed a scheme based on vector tiles to organize spatial data and proposed a system named GeoTile for geospatial data caching and sharing. In GeoTile, a tile request from the mobile client relies on multihop communication over intermediate nodes to reach the server. Since GeoTile enables all network nodes to cache and process geospatial data tiles, requests may be handled before they actually reach the server. We implement the GeoTile prototype system and conduct comprehensive real-world experiments to evaluate the performance. The result shows that the GeoTile system can serve vector tiles for users conveniently and friendly. In addition, the caching mechanism based on vector tiles can substantially reduce the response time and network throughput under the wireless multihop scenarios