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The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-year transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion. This study indicates that approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in China’s power system by 2050. The electricity supply costs would increase by 19.9% or 9.6 CNY¢/kWh.

The grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm proposed in 2017 mimics the biological behavior of grasshopper swarms seeking food sources in nature for solving optimization problems. Nonetheless, some shortcomings exist in the origin GOA, and GOA global search ability is more or less insufficient and precision also needs to be further improved. Although there are many different GOA variants in the literature, the problem of inefficient and rough precision has still emerged in GOA variants. Aiming at these deficiencies, this paper develops an improved version of GOA with Levy Flight mechanism called LFGOA to alleviate the shortcomings of the origin GOA. The LFGOA algorithm achieved a more suitable balance between exploitation and exploration during searching for the most promising region. The performance of LFGOA is tested using 23 mathematical benchmark functions in comparison with the eight well-known meta-heuristic algorithms and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of LFGOA. According to obtained results, it is possible to say that the LFGOA algorithm can be a potential alternative in the solution of meta-heuristic optimization problems as it has high exploration and exploitation capabilities.

Human-land coordination represents urbanization and is a key component of urban modernization. In this study, the theory of system dynamics was introduced, in which a \"production-living-ecological\" complex system was used based on the human-land coordination concept. Moreover, the characteristics of system dynamics of causal cycle, dynamic and sustainable development, man-land synergy, integrity and openness, and self-organization and adaptability were analyzed by dividing it into three subsystems: urban production, urban living, and urban ecological subsystems. Here, causal feedback and system structure flow diagrams were designed using causal loop diagrams and system structure flow diagrams to evaluate the causal relationships between variables and quantitatively analyzing their interactions between variables and predicting the future development of variables. Changsha City, China was selected as the case study area, where we constructed system dynamics practice equation model was then constructed to determine the interaction between the subsystems. Our findings indicate that by the year 2035 in the future, the overall trend of factors influencing the function of the subsystems such as population, GDP and built-up area are positively correlated with an increasing trend, and there are interactions between. Furthermore, these factors interact with each other, and a mutual correlation was found among the production-living-ecological functions system, Therefore, this study provides a novel perspective and exploratory practice for the study of the synergistic coupling of ecological, production, and living functions of cities and evaluating high-quality development of cities. Thus, the coupling and coordination of urban production, living and ecological functions reflects the coupling and coordination of the \"people-land\" relationship, which is the key to high-quality urban development.

Inverted perovskite solar cells still suffer from significant non-radiative recombination losses at the perovskite surface and across the perovskite/C 60 interface, limiting the future development of perovskite-based single- and multi-junction photovoltaics. Therefore, more effective inter- or transport layers are urgently required. To tackle these recombination losses, we introduce ortho-carborane as an interlayer material that has a spherical molecular structure and a three-dimensional aromaticity. Based on a variety of experimental techniques, we show that ortho-carborane decorated with phenylamino groups effectively passivates the perovskite surface and essentially eliminates the non-radiative recombination loss across the perovskite/C 60 interface with high thermal stability. We further demonstrate the potential of carborane as an electron transport material, facilitating electron extraction while blocking holes from the interface. The resulting inverted perovskite solar cells deliver a power conversion efficiency of over 23% with a low non-radiative voltage loss of 110 mV, and retain >97% of the initial efficiency after 400 h of maximum power point tracking. Overall, the designed carborane based interlayer simultaneously enables passivation, electron-transport and hole-blocking and paves the way toward more efficient and stable perovskite solar cells. Effective transport layers are essential to suppress non-radiative recombination losses. Here, the authors introduce phenylamino-functionalized ortho-carborane as an interfacial layer, and realise inverted perovskite solar cells with efficiency of over 23% and operational stability of T97 = 400 h.

Time series forecasting has attracted wide attention in recent decades. However, some time series are imbalanced and show different patterns between special and normal periods, leading to the prediction accuracy degradation of special periods. In this paper, we aim to develop a unified model to alleviate the imbalance and thus improving the prediction accuracy for special periods. This task is challenging because of two reasons: (1) the temporal dependency of series, and (2) the tradeoff between mining similar patterns and distinguishing different distributions between different periods. To tackle these issues, we propose a self-attention-based time-varying prediction model with a two-stage training strategy. First, we use an encoder-\"decoder module with the multi-head self-attention mechanism to extract common patterns of time series. Then, we propose a time-varying optimization module to optimize the results of special periods and eliminate the imbalance. Moreover, we propose reverse distance attention in place of traditional dot attention to highlight the importance of similar historical values to forecast results. Finally, extensive experiments show that our model performs better than other baselines in terms of mean absolute error and mean absolute percentage error.

Traditional Chinese Medicine (TCM) prescriptions are complex, multi-botanical drug systems in which dosage critically influences therapeutic efficacy. While network pharmacology is widely used to analyze TCM mechanisms, existing methods ignore the dosage of botanical drugs, a key limitation that may skew predictions. This study investigates how integrating dosage data alters network analysis outputs, addressing a fundamental gap in understanding TCM's dosage-dependent effects. Our analysis compared dosage-weighted and traditional non-dosage network approaches across 94 traditional Chinese medicine (TCM) prescriptions. We developed four custom indicators to quantify differences throughout the network pipeline: Dedis (input distance difference), DeSD (input standard deviation difference), DeDT (drug target prediction difference), and DePy (pathway prediction difference). The interrelationships among these indicators were examined to indicate when dosage adjustments influence predictions. A detailed case study further demonstrated the impact of dosage modifications on predictive outcomes. Among the indicators with inputs difference, Dedis, but not DeSD, exhibited a statistically significant relationship with output predictions, with target differences (DeDT) ranging from 0% to 68.9% and pathway differences (DePy) ranging from 0% to 74.6%. The interrelationships between these indicators were visualized using a clock model representation. The case study further demonstrated the impact of dosage on network outputs, revealing dosage refined both the predicted drug targets for individual botanical drugs and the subsequent pathway analysis results. Our study demonstrated that dosage significantly influences the outcomes of network analysis, with Dedis serving as a reliable indicator of whether such changes would occur. Specifically, changes resulting from dosage-dependent refinement of both drug target prediction and pathway analysis were observed.

According to emerging studies, the excessive activation of microglia and the subsequent release of pro-inflammatory cytokines play important roles in the pathogenesis and progression of Parkinson’s disease (PD). However, the exact mechanisms governing chronic neuroinflammation remain elusive. Findings demonstrate an elevated level of NLRP3 inflammasome in activated microglia in the substantia nigra of PD patients. Activated NLRP3 inflammasome aggravates the pathology and accelerates the progression of neurodegenerative diseases. Abnormal protein aggregation of α-synuclein (α-syn), a pathologically relevant protein of PD, were reported to activate the NLRP3 inflammasome of microglia through interaction with toll-like receptors (TLRs). This eventually releases pro-inflammatory cytokines through the translocation of nuclear factor kappa-B (NF-κB) and causes an impairment of mitochondria, thus damaging the dopaminergic neurons. Currently, therapeutic drugs for PD are primarily aimed at providing relief from its clinical symptoms, and there are no well-established strategies to halt or reverse this disease. In this review, we aimed to update existing knowledge on the role of the α-syn/TLRs/NF-κB/NLRP3 inflammasome axis and microglial activation in PD. In addition, this review summarizes recent progress on the α-syn/TLRs/NF-κB/NLRP3 inflammasome axis of microglia as a potential target for PD treatment by inhibiting microglial activation.

Understanding the interactions between the human sphere and the natural sphere in key places and regions of the world is crucial for promoting sustainability science and achieving sustainable development. As one of the emerging global city-regions in China and the Global South, the Yangtze River Delta (YRD) plays an increasingly nonnegligible role in the globalized economy and telecoupling social-ecological systems (SESs). Considering the well-known importance and representativeness, the YRD has been regarded as an appropriate experimental site of integrated research on geographical and sustainability science at the subnational scale. This paper tries to establish theoretical and practical linkages between regional integration and sustainable development at the subnational scale based on the sustainable development goals (SDGs), the Chinese contexts, and a literature review of relevant researches. We argue that future research should pay more attention to the interdisciplinary, transregional, and multi-scale attributes of issues related to regional integrated and sustainable development in the YRD. The following research agendas, such as linking SDGs to regional integrative development, analyzing the sustainability of regional SESs, assessing the integrated region at the subnational scale, investigating the YRD at different geographical scales, exploring applicable governance structures and institutions, as well as applying multi-source data and interdisciplinary methodologies, call for more scholarly attention. We hope that this paper could be an initial motion to expand and enrich relevant research.

The large-scale centralized development of wind and PV power resources is the key to China’s dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh.

As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.