In the traditional power system demand response, customers respond to electricity price or incentive and change their original power consumption pattern accordingly to gain additional benefits. With ...the development of multi-energy systems (MES) in which electricity, heat, natural gas and other forms of energy are coupled with each other, all types of energy customers are able to participate in demand response, leading to the concept of integrated demand response (IDR). In IDR, energy consumers can response not only by reducing energy consumption or opting for off-peak energy consumption but also by changing the type of the consumed energy. Taking the traditional demand response in power system as a starting point, the studies of the fundamental theory, framework design and potential estimation of demand response in power system are reviewed, and the practical cases and software development of demand response are introduced. Finally, the current theoretical research and application of IDR are assessed.
With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled ...samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.
Self-assembly of rigid building blocks with explicit shape and symmetry is substantially influenced by the geometric factors and remains largely unexplored. We report the selective assembly behaviors ...of a class of precisely defined, nanosized giant tetrahedra constructed by placing different polyhedral oligomeric silsesquioxane (POSS) molecular nanoparticles at the vertices of a rigid tetrahedral framework. Designed symmetry breaking of these giant tetrahedra introduces precise positional interactions and results in diverse selectively assembled, highly ordered supramolecular lattices including a Frank-Kasper A15 phase, which resembles the essential structural features of certain metal alloys but at a larger length scale. These results demonstrate the power of persistent molecular geometry with balanced enthalpy and entropy in creating thermodynamically stable supramolecular lattices with properties distinct from those of other self-assembling soft materials.
Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. ...Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.
The non-uniformity of infrared detectors' readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. ...Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm's effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm's superiority and efficacy.
This paper presents a cognitive psychology experiment to explore the differences between 2D and virtual reality (VR) film editing techniques. We recruited sixteen volunteers to view a range of ...different display modes and edit types of experimental material. An electroencephalogram (EEG) was recorded simultaneously while the participants watched. Subjective results showed that the VR mode reflects higher load scores, particularly in the effort dimension. Different editing types have no effect on subjective immersion scores. The VR mode elicited stronger EEG energy, with differences concentrated in the occipital, parietal, and central regions. On the basis of this, visual evoked potential (VEP) analyses were conducted, and the results indicated that VR mode triggered greater spatial attention, while editing in 2D mode induced stronger semantic updating and active understanding. Furthermore, we found that while the effect of different edit types in both display modes is similar, cross-axis editing triggered greater cognitive violations than continuity editing, which could serve as scientific theoretical support for the development of future VR film editing techniques.
In 6G era, service forms in which computing power acts as the core will be ubiquitous in the network. At the same time, the collaboration among edge computing, cloud computing and network is needed ...to support edge computing service with strong demand for computing power, so as to realize the optimization of resource utilization. Based on this, the article discusses the research background, key techniques and main application scenarios of computing power network. Through the demonstration, it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing, storage and network, and adapt to the integration needs of computing power and network invarious scenarios, such as user oriented, government enterprise oriented, computing power open and so on.
Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus ...only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance⁻energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.
Background
While targeted systemic inflammatory modulators show promise in preventing chronic kidney disease (CKD) progression, the causal link between specific inflammatory factors and CKD remains ...uncertain.
Methods
Using a genome-wide association study of 41 serum cytokines from 8,293 Finnish individuals, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. In addition, we genetically predicted causal associations between inflammatory factors and 5 phenotypes, including CKD, estimated glomerular filtration rate (eGFR), dialysis, rapid progression of CKD, and rapid decline in eGFR. Inverse variance weighting (IVW) served as the primary MR method, while MR-Egger, weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO) were utilized for sensitivity analysis. Cochrane’s Q test for heterogeneity. Leave-one-out method ensured stability of MR results, and Bonferroni correction assessed causal relationship strength.
Results
Seventeen cytokines were associated with diverse renal outcomes. Among them, after Bonferroni correction test, higher tumor necrosis factor alpha levels were associated with a rapid decrease in eGFR (OR = 1.064, 95% CI 1.028 – 1.103,
P
= 0.001), higher interleukin-4 levels were associated with an increase in eGFR (β = 0.003, 95% CI 0.001 – 0.005,
P
= 0.002), and higher growth regulated oncogene alpha (GROα) levels were associated with an increased risk of CKD (OR=1.035, 95% CI 1.012 - 1.058,
P
= 0.003). In contrast, genetic susceptibility to CKD was associated with an increase in GROa, and a decrease in eGFR may lead to an increase in stem cell factor. We did not find the presence of horizontal pleiotropy during the analysis.
Conclusion
We discovered causally related inflammatory factors that contribute to the initiation and progression of CKD at the genetic prediction level.
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