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Recently, the ever-increasing demands including higher rate and connection stability have bottlenecked the user experience of the traditional cellular networks. To this end, the Cell-Free (CF) network, which is characterized as a user-centric architecture, is viewed as a promising paradigm to enhance the user experience. Aimed at this point, this paper investigates the joint optimization problem including user matching, sub-channel allocation and power controlling for the mmWave CF network to maximize the revenue of the operators. Firstly, the revenue maximization oriented joint optimization problem of the mmWave CF network is formulated as mixed integer non-convex and non-linear programming, which is NP-hard problem and is intractable to search an optimal solution in within polynomial time. Secondly, the original problem is decomposed into three sub-problems, i.e., user association sub-problem, the sub-channel allocation sub-problem and the power controlling sub-problem under the consideration of the matching quotas, rate demand and transmission power, etc. Thirdly, a many-to-many matching based user association algorithm and an alternating iterative joint resource management algorithm, which is composed of the harmony search based sub-channel allocation sub-algorithm and the interior-point method based power controlling sub-algorithm, are proposed to obtain a sub-optimal solution, and the computational complexity of the proposed algorithms are also analyzed. Finally, the performance superiorities of the proposed algorithms are demonstrated through extensive simulations, and it is demonstrated that the proposed algorithms can outperform the proposed algorithm outperforms the PUAA algorithm by 5.73% and the SRAA algorithm by 11.25% in terms of operator revenue. [ABSTRACT FROM AUTHOR]

Dysregulation of oxidative stress serves as a pivotal predisposing or exacerbating factor in the intricate development of numerous pathological processes and diseases. In recent years, substantial evidence has illuminated the crucial role of reactive oxygen species (ROS) in many fundamental cellular functions, including proliferation, inflammation, apoptosis, and gene expression. Notably, producing free radicals within ROS profoundly impacts a wide range of biomolecules, such as proteins and DNA, instigating cellular damage and impairing vital cellular functions. Consequently, oxidative stress emerges as a closely intertwined factor across diverse disease spectra. Remarkably, the pathogenesis of several eye diseases, including age-related macular degeneration, glaucoma, and diabetic retinopathy, manifests an intrinsic association with oxidative stress. In this comprehensive review, we briefly summarize the recent progress in elucidating the intricate role of oxidative stress in the development of ophthalmic diseases, shedding light on potential therapeutic avenues and future research directions. [ABSTRACT FROM AUTHOR]

Traffic prediction is an important link in building a smart city, accurate prediction of future traffic conditions can provide a reference for people to travel and facilitate the work of relevant departments, and is vital for urban applications. Nevertheless, due to the cyclical nature of traffic conditions and uncertain factors, as well as the spatial heterogeneity in the traffic network, it is difficult to model the dynamic temporal and spatial correlation, so traffic prediction is facing severe challenges. Existing methods usually use a dynamic graph to model and analyze traffic conditions at a certain moment, lacking consideration of spatial heterogeneity and trend changes in traffic flow, so prediction results are often unsatisfactory. To address the above challenges, we propose a Multi-scale Spatial-temporal Fusion Graph Network for Traffic Prediction framework (MFSTGN). Specifically, we design a spatial-temporal graph convolution network module (STGCN) that dynamically models spatial-temporal correlations while preserving the inherent structure of the traffic network, and describes the trend variation of traffic flows through a kind of trend graph convolution, while modeling the spatial heterogeneity of the traffic network using spatial-temporal embedding. STGCN allows MFSTGN to enjoy a perceptual field that facilitates long-term prediction. In addition, we have developed a Gated Attention mechanism that adaptively fuses cyclical dependence and trend dependence, enabling MFSTGN to enjoy multiple scale information. Experimental results on four datasets for two types of traffic prediction tasks show that MFSTGN outperforms the state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

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Internet of Remote Things (IoRT) is widely used in both military and civilian applications. However, due to the unique characteristics, which is characterized as long-distance and high-latency, the design of the routing protocol is a great challenge of IoRT. In this paper, a QoS-oriented and traffic-aware multi-path routing protocol (QoTa-MRP) is proposed for IoRT. QoTa-MRP is composed of two parts. The first part is the link traffic-aware based muti-paths source route discovery mechanism, which is used to establish multi-paths with lower link disjoint degree. The second part is path similarity and traffic priority based multi-path selection mechanism, which is used to determine the transmission mode of the traffic flows to enhance the reliability or effectiveness of the transmission. Simultaneously, the protocol is theoretically analyzed in terms of the successful transmission rate of routing transmission data packet. Finally, the dynamic source routing, which is a very representative protocol and is usually used as the baseline comparison protocol, is revisited for the performance verification of the QoTa-MRP in IoRT. It is shown in the simulation results that there are significant superiorities of QoTa-MRP than that of DSR in terms of network throughput, packet loss rate and routing packet header overhead in IoRT. [ABSTRACT FROM AUTHOR]

From now until 2030, China will be in a sprint to achieve reductions of 40–45% in carbon emission intensity by 2020 and 60–65% by 2030 compared to 2005; rigid requirements have thus been imposed for controlling carbon emission intensity. In this study, a spatial Durbin model that integrates a spatial lag model and a spatial error model is used to measure the degree of influence held by the energy consumption structure and other factors over carbon emission intensity and the spatial spillover effect. The results show that there is a spatial demonstration effect on the reduction in interregional carbon emission intensity in China. While the carbon emission intensity in the adjacent region decreases by 1%, the carbon emission intensity in this region will decrease by 0.05%, indicating that China's regional low-carbon development model is also applicable to neighboring provinces and plays a large role in driving and demonstrating a low-carbon economy. Every additional 1% improvement toward optimizing the energy consumption structure enables the carbon emission intensity of the region to decrease by 0.21%; further, there is a positive spatial spillover effect driving carbon emission intensity decreases in neighboring areas of 0.25%. Industrial structure, energy intensity, energy price, and level of openness are the main factors influencing regional carbon emission intensity. According to the "14th Five-Year Plan," there is an urgent need to optimize the energy consumption structure in the medium and long term and give full play to its ability to contribute to declines in carbon emission intensity. [ABSTRACT FROM AUTHOR]

The millimeter wave self-backhaul network (mW-SBN) is one of the key solutions in 5G small cell backhaul. However, a lot of new challenges will be faced when the routing protocol of mW-SBN is designed, due to the dynamic traffic requirement and the directional transmission. To solve the problem well, the remaining bandwidth is described in the paper firstly, which plays a key role in the process of the path discovery. Secondly, a remaining bandwidth based multi-path routing (RBMR) protocol is proposed for the mW-SBN, which is mainly composed of the interaction of the remaining bandwidth information between adjacent nodes, the source route discovery that meets the data backhaul bandwidth requirements and the effective maintenance of the routing table. Thirdly, the upper limit of the number of multi-path is analyzed indirectly. Finally, the proposed protocol is simulated and compared. The simulation results show that RBMR protocol has greater gain than the three variations, i.e. remaining bandwidth based single-path routing (RBSR), non-remaining bandwidth based multi-path routing (NBMR) and non-remaining bandwidth based single-path routing (NBSR), in terms of network average throughput, routing overhead and packet loss rate. [ABSTRACT FROM AUTHOR]

Location-based social networks (LBSNs) have become a popular platform for people to communicate with each other. The recommendation problem has attracted considerable attention in both academia and industry as increasingly more users share their experiences and feelings using LBSNs. Machine learning has been widely used in many recommendation systems for recommending new friends or places of interest (POIs) to users in LBSNs. However, the majority of the existing recommendation systems were single function and only used small-scale datasets to provide recommendation services. In the era of big data, recommendation systems should have the ability to fully utilize limited computing resources for mining potential relationships from large-scale LBSN data. In this paper, a novel generic recommendation system is proposed by utilizing a distributed extreme learning machine called GR-DELM, which considers both friend recommendation and POI recommendation in large-scale datasets. For POI recommendation, three features are extracted: (1) geography-influenced feature, (2) popularity-influenced feature, and (3) social-influenced feature. For friend recommendation, two features are extracted: (1) neighborhood-based feature and (2) path-based feature. These features further improve the efficiency and accuracy of large-scale recommendation. Finally, a series of experiments demonstrate that the GR-DELM system outperforms the existing recommendation systems. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning, artificial intelligence (AI) has drawn much more attention. Under this circumstances, abstract strategy games, such as chess, checkers and Go, have been a fascinating problem of AI research. Most of the existing state-of-the-art Go programs used deep neural network technology, like convolutional neural networks (CNNs). However, CNNs require multiple iterations to optimize weights and spend a lot of training time. Therefore, in this paper, in order to solve the above shortcomings, we propose a new learning algorithm ECNN, which integrates CNNs with extreme learning machine (ELM). We remove pooling layers of CNNs and insert ELM layers between convolutional layers. The newly added ELM layers will be updated in back-propagation process, and they accelerate the convergence of weights in CNNs. Therefore, our ECNN can reduce the training time of CNNs. Further, we propose ECNN-Go algorithm, which applies ECNN to Go game. Because of the advantage of ECNN, ECNN-Go algorithm has the fast learning speed to make move prediction in Go game. Finally, the experimental results show the efficiency and accuracy of ECNN algorithm and demonstrate the strength of ECNN-Go. [ABSTRACT FROM AUTHOR]

A series of global actions have been made to address climate change. As a recent developed climate policy, Intended Nationally Determined Contributions (INDC) have renewed attention to the importance of exploring temperature rise levels lower than 2 °C, in particular a long-term limit of 1.5 °C, compared to the preindustrial level. Nonetheless, achieving the 2 °C target under the current INDCs depends on dynamic socioeconomic development pathways. Therefore, this study conducts an integrated assessment of INDCs by taking into account different Shared Socioeconomic Pathways (SSPs). To that end, the CEEP-BIT research community develops the China’s Climate Change Integrated Assessment Model (C3IAM) to assess the climate change under SSPs in the context of with and without INDCs. Three SSPs, including “a green growth strategy” (SSP1), “a more middle-of-the-road development pattern” (SSP2) and “further fragmentation between regions” (SSP3) form the focus of this study. Results show that after considering INDCs, mitigation costs become very low and they have no evident positive changes in three SSPs. In 2100, a temperature rise would occur in SSP1-3, which is 3.20, 3.48 and 3.59 °C, respectively. There are long-term difficulties to keep warming well below 2 °C and pursue efforts toward 1.5 °C target even under INDCs. A drastic reduction in greenhouse gas emissions is needed in order to mitigate potentially catastrophic climate change impacts. This work contributes on realizing the hard link between the earth and socioeconomic systems, as well as extending the economic models by coupling the global CGE model with the economic optimum growth model. In C3IAM, China’s energy consumption and emissions pattern are investigated and refined. This study can provide policy makers and the public a better understanding about pathways through which different scenarios could unfold toward 2100, highlights the real mitigation and adaption challenges faced by climate change and can lead to formulating effective policies. [ABSTRACT FROM AUTHOR]

The low-lying electric dipole strengths in proton-rich nuclei F and Ne, which can be produced at HIRFL-CSR in Lanzhou, are investigated. In the framework of the covariant density functional theory the self-consistent relativistic Hartree Bogoliubov model and the relativistic quasiparticle random phase approximation with the NL3 parameter set and Gogny pairing interaction are adopted in the calculations. A pronounced dipole peak appears below 10 MeV in Ne, but does not occur in F. The properties of this low-lying E1 excitation in Ne are studied, which may correspond to a proton pygmy resonance with different characteristics from those of giant dipole resonance. [ABSTRACT FROM AUTHOR]

The relativistic microscopic optical potential in the asymmetric nuclear matter is studied in the framework of the Dirac Brueckner-Hartree-Fock method. A new decomposition of the Dirac structure of the nuclear self-energy in nuclear matter is adopted. The self-energy of a nucleon with E>0 in nuclear matter is calculated with the G matrix in the Hartree-Fock approach. The optical potential of a nucleon in the nuclear medium is identified with the nucleon self-energy. The energy and asymmetric parameter dependence of the relativistic optical potentials for proton and neutron are discussed. The resulting Schroedinger equivalent potentials have reasonable behaviors of the energy dependence. The asymmetric parameter dependence of relativistic optical potentials and Schroedinger potentials are emphasized. [ABSTRACT FROM AUTHOR]

We have studied the ground state properties of N=8 and N=9 isotones in the framework of the nonlinear relativistic mean-field (RMF) theory using force parameters NL-SH. Calculations show that the RMF theory can describe experimental data of binding energies and radii for these nuclei. The RMF theory can also reproduce the level inversion of N=9 isotones well if the ρ tensor coupling is included. One-neutron halos in 15C and 14B are predicted. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study that compares CTOM, a microscopic optical model potential (OMP), which is an optical model co-created by the China Nuclear Data Center & Tuebingen University, to CH89, which is a typical phenomenological OMP. The respective OMPs were tested by applying them to the modelling of nucleon elastic scattering and (d, p) transfer reactions involving 14C, 36S, and 58Ni targets at both low and relatively high energies. The results demonstrated that although both potentials successfully accounted for the angular distributions of both the elastic scattering and transfer cross sections, the absolute values of the transfer cross sections calculated using CTOM were approximately 25% larger than those calculated using CH89. This increased transfer cross sections allowed CTOM to produce single particle strength reduction factors for the three reactions that were consistent with those extracted from (e, e′ p) reactions as well as with more recent (p, 2p) and (p, pn) reactions. Notch tests suggested that nucleon elastic scattering and transfer reactions are sensitive to different regions of the OMP; accordingly, phenomenological OMPs, which are constrained only by elastic scattering cross sections, may not be sufficient for nucleon transfer reactions. Therefore, we suggest that microscopic OMPs, which reflect more theoretical considerations, should be preferred over phenomenological ones in calculations of direct nuclear reactions. [ABSTRACT FROM AUTHOR]