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Abstract In non‐orthogonal multiple access (NOMA) systems, it may not be advisable to send the superimposed signal of all users to individual users with retransmission requirements during the retransmission phase. This paper investigates a novel cognitive radio network based on hybrid NOMA/orthogonal multiple access (CR‐H‐NOMA/OMA) in underlay spectrum sharing, whereby a hybrid NOMA/OMA cooperative mechanism is proposed for the secondary network by considering that the access modes of secondary users (SUs) and relays can be switched between NOMA and OMA. In order to compensate the loss of throughput caused by successive interference cancellation (SIC) during the cooperative phase, one or two relays with the best channel quality between themselves and the destination users are selected according to the states of the initial signal decoded by SUs, using truncated automatic repeat request (T‐ARQ) to retransmit only the desired signal of SU instead of the entire superimposed signal. Exact closed‐form expressions for outage probabilities of SUs and system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the performance of the proposed cooperative scheme. Numerical and simulation results demonstrate the superiority of the proposed hybrid NOMA/OMA cooperative scheme when compared with conventional pure NOMA and pure OMA based on time division multiple access (TDMA) in terms of secondary throughput. Also, the outage and throughput performance depend critically on the choices of key paraments such as power allocation factor and target rate.

The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.

Iterative signal clipping (ISC) and symbol decomposing with serial transmission (SDST) techniques decompose optical orthogonal frequency division multiplexing (O-OFDM) symbol into the fixed number of multiple small-amplitude symbols.However,at low O-OFDM symbol variances,the decomposition symbols contain a large amount of all-zero symbols without load information,which results in bit error rate degradation,optical power penalty and information rate reduction.Aim at this problem,an adaptive O-OFDM symbol decomposing with serial transmission (ASDST) system was proposed,the symbol decomposition was adaptively determined according to the O-OFDM symbol amplitude.Symbol decomposition was terminated until O-OFDM symbol was completely decomposed or the maximum symbol decomposition times were attained,thereby eliminated all-zero symbols and reduced the average symbol decomposition times.The performance of ASDST system was verified by theoretical analysis and Monte Carlo simulation.

In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC) system. It takes advantage of the long short-term memory (LSTM) network in the recurrent neural network (RNN) to alleviate the interdependence problem of adjacent symbols. Moreover, an LSTM attention decoder is constructed by employing the attention mechanism, which can alleviate the shortcomings in conventional LSTM. The simulation results show that the bit error rate (BER) performance of the proposed LSTM attention neural network is 1 dB better than that of the back propagation (BP) neural network and outperforms by 2.5 dB when compared with the maximum likelihood sequence estimation (MLSE) detection method.

Significant energy transition would be needed in China under the carbon neutrality target. Since it would have important impacts on water resource through increased usage, understanding the water demand under specific climate change targets could enable better policy-making for the energy transition. In order to quantitatively analyze the impacts, this study proposed a methodology that combines CO2 emissions, energy transition, and water demand. An energy-water integrated assessment model was built, to simulate the future energy and CO2 emissions pathways up to 2050 in China. The water demand of energy systems under both policy scenario (PS) and two mitigation scenarios (2C and 1.5C) are calculated. The key influencing factors of water demand are analysed in two low-water-demand mitigation scenarios (2C-LW and 1.5C-LW). The results showed that China’s future energy system and CO2 emissions pathways change significantly under mitigation scenarios. The timing of the CO2 emissions peak advances from around 2030 under the PS scenario to between 2020 and 2025 in the mitigation scenarios. Near-zero emissions are achieved by 2050 under the 1.5C scenario. However, with no further water-saving measures, water consumption in energy sector would continue to increase under both the policy scenario and mitigation scenarios. This pressure is compounded by certain mitigation technologies, such as inland nuclear power, biomass energy, and CCS technologies. As such, the potential for water conservation in energy system under climate mitigation targets is studied. The results showed that water-saving measures can significantly reduce long-term water demand in the energy system.

In recent years, many studies on the effects of salinity on fish growth performance have been performed. A series of research results confirmed that appropriate salinity could guarantee healthy fish growth, while under extremely high or low salinity, the fish growth would be inhibited. Therefore, salinity is one of the critical factors ensuring aquaculture success. However, most previous studies only focused on fish growth and survival, without systematically analyzing the physiological and biochemical indicators or performing regression analysis of optimal salinity, especially for the hybrid grouper (Epinephelus fuscoguttatus♀×E. lanceolatus♂). A 28-day experiment was conducted to evaluate the effects of different salinities on growth performance, body composition, digestive enzyme activities, antioxidant indices, and serum biochemical indices of the juvenile hybrid grouper [initial mean weight of (29.99±2.60) g]. This study used six salinity levels of 10, 15, 20, 25, 30, and 35, named S1 (control), S2, S3, S4, S5, and S6, respectively. During the experiment, the water temperature was kept at 25–28 ℃, DO≥6 mg/L, pH 8–9, and the contents of ammonia and nitrite nitrogen were both lower than 0.1 mg/L. Firstly, the results showed that different salinity levels had different impacts on the juvenile hybrid grouper growth performance. The weight growth rate (WGR) and special growth rate (SGR) first increased and then decreased with the rise in salinity and peaked in the S4 group, which were significantly higher than those of the S1 and S6 groups (P < 0.05). The feed efficiency (FE) showed a similar tendency to WGR and SGR (P < 0.05). Moreover, the survival rate had no significant difference among all groups (P > 0.05). The quadratic regression model analysis based on WGR and FE indicated that the optimal salinities were 22.18 and 23.16. Secondly, the whole fish crude lipid content in the S4 group (10.86%) was significantly higher than in the S1 (9.30%), S2 (9.62%), S3 (9.79%), and S6 (9.22%) groups (P < 0.05). The muscle crude lipid content in the S3 (2.84%) and S4 (2.95%) groups were significantly higher than that of the S1 (2.23%), S2 (2.34%), S5 (2.44%), and S6 (2.19%) groups (P < 0.05). The whole fish crude protein content peaked in the S3 group (65.24%), which was significantly higher than that of the S5 group (63.74%) (P < 0.05), with no significant differences observed among other groups (P > 0.05). The muscle crude protein content and the whole fish and muscle moisture and ash had no significant differences among all groups (P > 0.05). Thirdly, juvenile hybrid grouper´s intestinal lipase and trypsin activities in the S3 and S4 groups were significantly higher than in other groups (P < 0.05). In addition, the intestinal amylase activity was kept at 0.19–0.21 U/(mg prot) among all groups, with no significant differences (P > 0.05). Fourthly, salinity changes greatly impacted the antioxidant capacity of the juvenile hybrid grouper liver. The activities of the liver superoxide dismutase (SOD), total antioxidant capacity (T-AOC), and catalase (CAT) were significantly higher in the S1 and S6 groups than in other groups (P < 0.05). The liver malondialdehyde (MDA) content in the S3 and S4 groups were significantly lower than that of other groups (P < 0.05). Fifthly, lower activity levels of the aspartate aminotransferase (AST), cereal third transaminase (ALT), and alkaline phosphatase (AKP) in serum were found in the S3 and S4 groups, which were significantly lower than those of the S1 and S6 groups (P < 0.05). Moreover, the ALT activity in the S6 group was significantly lower than in the S1 group (P < 0.05). The lower activity level of lactate dehydrogenase (LDH) in serum was also found in the S3 and S4 groups, which were significantly lower than in other groups (P < 0.05). In summary, these results indicated that optimal salinity (20–25 mmol/L) could improve the juvenile hybrid grouper growth, while increasing the activities of digestive enzymes, antioxidant properties, and nonspecific immunity capacity, protecting the fish health. At the same time, the quadratic regression model analysis based on WGR and FE indicated that the optimal salinity for juvenile hybrid grouper is between 22.18 and 23.16.

To evaluate the effectiveness of current national policies in achieving global temperature targets is important but a systematic multi-model evaluation is still lacking. Here the authors identified a reduction of 3.5 GtCO2 eq of current national policies relative to a baseline scenario without climate policies by 2030 due to the increasing low carbon share of final energy and the improving final energy intensity.

Under the Paris Agreement, targets implemented for 2100 specify temperature increases well below 2 °C, with an ambitious target of 1.5 °C. China signed this agreement and will support these global targets. The question remains whether they are possible, especially considering the slow progress in recent decades, despite the fact that the Kyoto Protocol implemented these targets in 2010. The Intergovernmental Panel on Climate Change (IPCC) required modeling research teams to analyze possible pathways, policy options, and cost benefit analyses for GHG mitigation. China’s CO2 emissions from the energy and cement industries already accounted for almost 29% of global emissions in 2017, and this trend is expected to continue increasing. The role of China in global GHG mitigation is therefore crucial. This study presents a scenario analysis for China’s power generation against the background of the global 2 °C and 1.5 °C targets. We discuss the possibility of a lower CO2 emission power generation scenario in China in order to evaluate the national emission pathway towards these targets. Our findings suggest that China can accomplish rapid transition in the power generation sector, reaching its emission peak before 2025. This would make the global 2 °C target possible because energy system development is a key factor. Furthermore, the recent progress of key power generation technologies, potential for further investment in the power generation sector, and recent policy implementation all significantly contribute to China following a low carbon emission development pathway. Keywords: GHG mitigation, Emission scenarios, Global target, China, Modeling

In the Paris Agreement, there are targets set up for 2100 to be well below 2 °C. A more ambitious target of 1.5 °C also appears in the agreement. In order to address whether this target is achievable or not, studies about the 1.5 °C target's emission pathway are essential. Recently a few studies have presented modelling results of the global emission pathway for the 1.5 °C target. This paper presents an analysis for China under the global 1.5 °C pathway and budget, by looking at key options to go beyond the 2 °C target pathway. Similar to the global emission pathway, China's CO2 emissions have to be reduced quickly and reach zero emission between 2050 and 2060. China's energy system needs to make a rapid transition to much greater reduction in fossil fuel use from now on. End-use sectors need to increase electricity use significantly. Power generation will achieve negative emission before 2050. CCS will be widely used, and biomass energy with CCS (BECCS) must be adopted on a large scale by 2040. This is doable in China, but very near-term changes in policy are needed to make such a pathway happen.

Glioma, one of the most common brain tumors, remains a challenge worldwide. Due to the specific biological barriers such as blood–brain barrier (BBB), cancer stem cells (CSCs), tumor associated macrophages (TAMs), and vasculogenic mimicry channels (VMs), a novel versatile targeting delivery for anti-glioma is in urgent need. Here, we designed a hyaluronic acid (HA) ion-pairing nanoparticle. Then, these nanoparticles were encapsulated in liposomes, termed as DOX-HA-LPs, which showed near-spherical morphology with an average size of 155.8 nm and uniform distribution (PDI = 0.155). HA was proven to specifically bind to CD44 receptor, which is over-expressed on the surface of tumor cells, other associated cells (such as CSCs and TAMs) and VMs. We systematically investigated anti-glioma efficacy and mechanisms in vivo and in vitro. The strong anti-glioma efficacy could attribute to the accumulation in glioma site and the regulation of tumor microenvironment with depletion of TAMs, inhibition of VMs, and elimination of CSCs.

The data files contain the assumptions and results for the construction of cumulative availability curves for coal, oil and gas for the five Shared Socioeconomic Pathways. The files include the maximum availability (also known as cumulative extraction cost curves) and the assumptions that are applied to construct the SSPs. The data is differentiated into twenty regions. The resulting cumulative availability curves are plotted and the aggregate data as well as cumulative availability curves are compared across SSPs. The methodology, the data sources and the assumptions are documented in a related article (N. Bauer, J. Hilaire, R.J. Brecha, J. Edmonds, K. Jiang, E. Kriegler, H.-H. Rogner, F. Sferra, 2016) [1] under DOI: http://dx.doi.org/10.1016/j.energy.2016.05.088.

Emission reduction from the coal-dominated power sector is vital for achieving China’s carbon mitigation targets. Although the coal expansion has been slowed down due to the cancellation of and delay in new construction, coal-based power was responsible for over one third of China’s energy-related CO _2 emissions by 2018. Moreover, with a technical lifetime of over 30 years, current investment in coal-based power could hinder CO _2 mitigation until 2050. Therefore, it is important to examine whether the current coal-based power planning aligns with the long-term climate targets. This paper introduces China’s Nationally Determined Contribution (NDC) goals and an ambitious carbon budget along with global pathways well-below 2 degrees that are divided into five integrated assessment models, which are two national and three global models. We compare the models’ results with bottom-up data on current capacity additions and expansion plans to examine if the NDC targets are in line with 2-degree pathways. The key findings are: 1. NDC goals alone are unlikely to lead to significant reductions in coal-based power generation. On the contrary, more plants may be built before 2030; 2. this would require an average of 187–261 TWh of annual coal-based power capacity reduction between 2030 and 2050 to achieve a 2 °C compatible trajectory, which would lead to the stranding of large-scale coal-based power plants; 3. if the reduction in coal power can be brought forward to 2020, the average annual coal-based power reduction required would be 104–155 TWh from 2020 to 2050 and the emissions could peak earlier; 4. early regulations in coal-based power would require accelerated promotion of alternatives between 2020 and 2030, with nuclear, wind and solar power expected to be the most promising alternatives. By presenting the stranding risk and viability of alternatives, we suggest that both the government and enterprises should remain cautious about making new investment in coal-based power sector.