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Using undercooling technology combined with melt purification of glass and recirculation overheating, different undercoolings were successfully obtained for Cu60Ni38Co2 alloy samples. Applying high speed photography technique, the physical process of undercooled solidification was observed and relationship between undercooling and solidification rate was analyzed. Experimental results indicate the critical undercoolings exist in the structure evolution process. Electron backscatter diffraction (EBSD) analysis was applied to characterize the Cu60Ni38Co2 alloy sample with a maximum undercooling of 255K, revealing grain orientation and the corresponding orientation difference distribution. In addition, transmission electron microscopy (TEM) showed high density dislocation networks. Combining EBSD and TEM data analysis, an important conclusion was drawn: recrystallization is the main factor for grain refinement at high undercooling. This finding is of great significance for a deeper understanding of the structure evolution in the physical process of undercooled alloys.

In an integrated electricity-gas system (IEGS), load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system. The static voltage stability region (SVSR) method is a tool for analyzing the overall static voltage stability in a power system. However, in an IEGS, the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses. Thus, the SVSR method cannot be directly applied to an IEGS. In this paper, the concept of the SVSR is extended to the IEGS-static stability region (SSR) while considering voltage and gas pressure. First, criteria for static gas pressure stability in a natural gas system are proposed, based on the static voltage stability criteria in a power system. Then, the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow (MEF) equations and static voltage and gas pressure stability constraints in the active power injection space of natural gas-fired generator units (NGUs). To determine the IEGS-SSR, a continuation MEF (CMEF) method is employed to trace the boundary point in one specific NGU scheduling direction. A multidimensional hyperplane sampling method is also proposed to sample the NGU scheduling directions evenly. The obtained boundary points are further used to form the IEGS-SSR in three-dimensional (3D) space via a Delaunay triangulation hypersurface fitting method. Finally, the numerical results of typical case studies are presented to demonstrate that the proposed method can effectively form the IEGS-SSR, providing a tool for IEGS online monitoring and dispatching.

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

Breast cancer has been reported to transcend lung cancer as the most commonly diagnosed cancer in women all over the world. Adipocytes, serving as energy storage and endocrine cells, are the major stromal cells in the breast. Cancer-associated adipocytes (CAAs) are adjacent and dedifferentiated adipocytes located at the invasive front of human breast tumors. Adipocytes can transform into CAA phenotype with morphological and biological changes under the remodeling of breast cancer cells. CAAs play an essential role in breast cancer progression, including remodeling the tumor microenvironment (TME), regulating immunity, and interacting with breast cancer cells. CAAs possess peculiar secretomes and are accordingly capable to promote proliferation, invasiveness, angiogenesis, metastasis, immune escape, and drug resistance of breast cancer cells. There is a complex and coordinated crosstalk among CAAs, immune cells, and breast cancer cells. CAAs can release a variety of cytokines, including IL-6, IL-8, IL-1β, CCL5, CCL2, VEGF, G-CSF, IGF-1, and IGFBP, thereby promoting immune cell recruitment and macrophage polarization, and ultimately stimulating malignant behaviors in breast cancer cells. Here, we aim to provide a comprehensive description of CAA-derived cytokines, including their impact on cancer cell behaviors, immune regulation, breast cancer diagnosis, and treatment. A deeper understanding of CAA performance and interactions with specific TME cell populations will provide better strategies for cancer treatment and breast reconstruction after mastectomy.

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

With the gradual development of photomedicine, biocompatible optical fiber (BOF) has become more and more important for delivering light energy into deep-tissue in recent years. By this, it can realize light induced medical treatments, such as optogenetics, photothermal cancer treatment, photodynamic therapy, physiological information sensing transmission, etc. In this review, we discuss (i) the characteristics of BOF, (ii) the fabricating methods of BOF, (iii) the application fields of BOF, and (iv) the recent progress and perspectives on the future of BOF. We envision that this review will provide readers with some meaningful guidance on the preparation of BOFs and inspire them to explore more potential applications.

A rapid rise in the popularity of e-scooters has brought forth an increasing number of e-scooter-related conflicts, crashes, and injuries to pedestrians in many cities. There is a pressing need to understand the factors influencing the severity of injury to pedestrians involved in e-scooter collisions. This paper investigates the dynamics of e-scooter-pedestrian collisions and presents a new method for relating the probability of severe head injury to collision speed in e-scooter-pedestrian collisions. A total of 160 computer simulations representing different collision scenarios have been analyzed. Our results have shown that e-scooter speed is the main determinant of the severity of pedestrian head injury. E-scooter speed ranging from 10 to 15 km/h is found to be critical for pedestrian safety as the probability of severe head injury rises rapidly within this speed range. Moreover, an e-scooter-pedestrian collision is more likely to cause severe head injury to the pedestrian than a bicycle-pedestrian collision within the same speed range. It has also been found that the weight of the e-scooter and the direction of impact do not have a strong influence on the collision metrics, especially on the probability of severe head injury. The study has also investigated the post-collision fall mechanism for different pedestrian profiles and the influence of different impact angles. Finally, some recommendations have been proposed, including a speed limit of not more than 11 km/h for e-scooterist on shared paths where the likelihood of pedestrian and e-scooter conflicts is higher. The recommendations could help authorities develop legislation for safe micro-mobility.

Under the global zero-carbon campaign (United Nations Climate Change, 2021), more stochastic renewable generations are being integrated into the system. This trend significantly expands the state space and increases the computational burden for the model-driven reliability assessment. Data-driven approaches are developed to improve efficiency based on artificial intelligence. However, the requirement of large-scale samples limits it in applications. To address that, this paper adopts a self-supervised stage to avoid the high cost of labeling, while ensuring the efficiency and accuracy of reliability assessment. The training process of this method is split into two stages. In the first stage, feature reconstruction and unsupervised learning are used to provide the initial network parameters. Thereafter, the second learning stage can be trained in a task-agnostic way with fewer labels. The results of case study demonstrate the effectiveness of the proposed approach.

This paper presents a new efficient reliability assessment method for multiple energy systems (MES) based on shadow price (SP). The coupling and interaction of multiple energy flows are quantitatively described by the energy hub (EB) model. The shadow price theory can accelerate the calculations of optimal load shedding in the reliability assessment for MES. Large numbers of system states are generated by the fluctuations of renewable energy generation. These numerous system states can be evaluated by the shadow price theory instead of cumbersome optimization computations. The case study shows that the proposed SP approach can greatly improve the efficiency of MES.

With the continuous expansion of renewable energy in the power grid, a massive number of operating states have been added to the power system. The heavy computational task brings new challenges to the efficiency of reliability assessment. In this regard, a stacked-denoising-auto-encoders-based (SDAE) model is proposed to replace the time-consuming optimal power flow (OPF) algorithm in the reliability assessment of power system operation. The system state and the optimal load curtailment are used as training samples, and the corruption process of SDAE is improved to make it more suitable for power systems. Moreover, unsupervised pretraining and supervised fine-tuning are used to get the optimal coding parameters which can establish the nonlinear mapping relationship between the system states and the minimum load curtailment. Case studies are performed on the RTS-79 system considering renewable energy. Results indicate that the proposed method can greatly improve the efficiency of reliability assessment for power system operation.

The optimal load shedding calculations of an enormous number of contingency states are the most challenging impediments for power system reliability assessment, especially in a large-scale system with multiple types of loads. To deal with that, this paper proposes a fast method that accelerates the calculation of optimal load shedding based on the shadow price. This method calculates the optimal load shedding of systems by shadow-price-based linear functions, rather than the time-consuming OPF algorithms. The hundred percentage criterion is used to look for the matching shadow price of states. Case studies are performed on the RTS-79 system. The results show that the proposed methodology can significantly simplify the solving process and reduce the calculation time.

Mobile energy storage (MES) is a typical flexible resource, which can be used to provide an emergency power supply for the distribution system. However, it is inevitable to consider the complicated coupling relations of mobile energy storage, transportation network, and power grid, which can cause issues of complex modeling and low efficiency. To address that, this paper proposes a mobile energy storage dispatch model to minimize the load curtailment. The framework of rolling optimization is established to update the optimal strategy with the real-time factors, including grid, transportation, and MES. To increase solving efficiency, the model is extended to a mixed integer second-order cone programming model. Finally, the simulation results demonstrate the effectiveness of the proposed method in various scenarios.

The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.

Utilizing energy storage systems have been considered as a feasible pathway to achieve carbon neutrality. However, the common battery type for energy storage systems is the cheap lithium iron phosphate battery, which has low output efficiency and is almost impossible to charge in cold areas. Lithium titanate battery has high output efficiency and charge efficiency in cold areas. Meanwhile, the price of a lithium titanate battery is three times that of a lithium iron phosphate battery with the same capacity. To achieves the complementary advantages of lithium iron phosphate battery and lithium titanate battery, this paper proposes the dual battery framework of energy storage systems. Based on this, the operation model of energy storage systems is formulated. Besides, five cases are designed to demonstrate the effectiveness of the proposed method and the results show that the dual battery framework has high output efficiency with a cheaper cost than lithium titanate battery.

Traditional Chinese medicine (TCM) has accumulated a wealth of ecological wisdom and is regarded as an outstanding cultural and medical resource in China. However, in the context of serious environmental pollution, the potential harm caused by TCM materials (TCMMs) due to toxic heavy metals has seriously affected the sustainable development of TCM. Cadmium (Cd) is an internationally recognized heavy metal contaminant. In this paper, 270 reports on Cd in TCMMs were screened from 1969 publications covering 243 species in 81 families. According to the source of the TCMMs, the data were divided into the following categories: aboveground part, bark, flower and fruit, herb, leaf and rhizome. The temporal dynamics of the Cd content and its correlations with the habitats and categories (parts) of TCMMs were also studied. The results show that 22.05 % of the investigated TCMM samples exceeded the relevant domestic standards (Cd≤0.3 mg/kg), among which the maximum Cd content was 17.75 mg/kg. Myrtaceae and Syzygium aromaticum were the family and species with the highest mean Cd content, respectively. Regarding the source of TCMMs of great concern, the mean Cd content of TCMMs decreased in the order of herb > aboveground part > flower and fruit > leaf > rhizome > bark. In addition, in terms of the spatiotemporal distribution, the spatial distribution of the mean Cd content of TCMMs was significantly higher on the Qinghai-Tibet Plateau, followed by Southwest China. When comparing different times, more serious Cd pollution of TCMMs existed after 2000, and the highest mean Cd content was observed in 2000–2004. In summary, soil acidity must be decreased and the cultivation conditions of Cd-hyperaccumulators such as Ligusticum chuanxiong and Lonicera japonica must be modified, in conjunction with a scientific health risk assessment, to ensure the sustainable development of TCMMs.

Cutaneous melanoma is an aggressive cancer type derived from melanocytes and its incidence has rapidly increased worldwide. Despite the vast improvement in therapy, melanoma is still confronted with high invasion, metastasis, and recurrence rate. Recent studies have confirmed that the exosomes are naturally occurring membranous extracellular vesicles with nano-sized lipid bilayers, performing as information messagers within cellular reciprocal action. Exosomes are unquestionably endowed with multifaceted roles in various diseases, including melanoma. Notably, tumor-derived exosomes play a pivotal role in conditioning the tumor microenvironment to promote the growth, metastasis, immune escape, and even drug-resistance of melanoma by transferring carcinogenic nucleic acids and proteins. Clinically, the dynamic expressions of exosomal components and loadings in melanoma patients with different tumor stages confer the clinical application of melanoma exosomes as diagnostic biomarkers. Hence, this review highlights the recent complicated roles and mechanisms of melanoma exosomes, as well as their potential as diagnostic and therapeutic targets in melanoma. The in-depth insights into the properties and behaviors of melanoma exosomes are of great potential to yield attractive therapeutic methods for melanoma.

Background: Doxorubicin, an anthracycline chemotherapeutic agent, is widely used in the treatment of many cancers. However, doxorubicin posts a great risk of adverse cardiovascular events, which are thought to be caused by oxidative stress. We recently reported that the ubiquitin E3 ligase TRIM21 interacts and ubiquitylates p62 and negatively regulates the p62-Keap1-Nrf2 antioxidant pathway. Therefore, we sought to determine the role TRIM21 in cardiotoxicity induced by oxidative damage. Methods: Using TRIM21 knockout mice, we examined the effects of TRIM21 on cardiotoxicity induced by two oxidative damage models: the doxorubicin treatment model and the Left Anterior Descending (LAD) model. We also explored the underlying mechanism by RNA-sequencing of the heart tissues, and by treating the mouse embryonic fibroblasts (MEFs), immortalized rat cardiomyocyte line H9c2, and immortalized human cardiomyocyte line AC16 with doxorubicin. Findings: TRIM21 knockout mice are protected from heart failure and fatality in both the doxorubicin and LAD models. Hearts of doxorubicin-treated wild-type mice exhibit deformed mitochondria and elevated level of lipid peroxidation reminiscent of ferroptosis, which is alleviated in TRIM21 knockout hearts. Mechanistically, TRIM21-deficient heart tissues and cultured MEFs and H9c2 cells display enhanced p62 sequestration of Keap1 and are protected from doxorubicin-induced ferroptosis. Reconstitution of wild-type but not the E3 ligase-dead and the p62 binding-deficient TRIM21 mutants impedes the protection from doxorubicin-induced cell death. Interpretation: Our study demonstrates that TRIM21 ablation protects doxorubicin-induced cardiotoxicity and illustrates a new function of TRIM21 in ferroptosis, and suggests TRIM21 as a therapeutic target for reducing chemotherapy-related cardiotoxicity.

Groundwater is an important water resource in arid and semi-arid regions. The impact of human activities on groundwater is increasing. After 60 years running, the groundwater quality and its formation mechanism are imperative questions needed to be answered in Jiaokou Irrigation District, Guanzhong Basin, China. In this study, the quality of groundwater in Jiaokou Irrigation District was assessed by a new integrated-weight water quality index (IWQI), and the groundwater chemistry is studied through integrated statistical, geostatistical and hydrogeochemical approaches. The patterns for the average anion and cation concentrations were HCO3− > SO42− > Cl− > NO3− > CO32− > NO2−, and Na+ > Mg2+ > Ca2+ > K+ > NH4+, respectively. Statistics showed that the major water chemistry types were HCO3-Na, SO4·Cl-Na, and Cl·SO4-Na. A new integrated-weight water quality index (IWQI) was proposed based on the entropy-weighted method and CRITIC method and showed excellent performance for explaining and evaluating the groundwater quality. The IWQI results show 65.33% of groundwater, mainly distributed in the central and western parts of this study area, was unsuitable for drinking. Furthermore, SO42－, HCO3－, Cl－, NO3－, and Na+ had more important effects on groundwater quality. The weathering process affecting groundwater quality in the study area is carbonate dissolution, followed by silicate weathering and evaporite dissolution, whereas the major geochemical processes include the dissolution and precipitation of calcite, as well as the dissolution of dolomite and gypsum (anhydrite). Cation exchange also plays an important role in the hydrogeochemical evolution of groundwater with a long residence time. Anthropogenic activities affecting groundwater quality included long-term irrigation infiltration and excessive use of fertilizers. The findings and the results of the study can improve understanding of the processes driving groundwater chemistry in Jiaokou Irrigation District, and can be used for reference to other similar regions in the world.

Change in future climate will either expand, contract or shift the climatic niche of many species and this could lead to shifting of their geographical ranges. Prediction of suitable cultivation regions through modeling has evolved as a useful tool for the assessment of habitat suitability and resource conservation to protect medicinal plants. We modelled current and future distribution of C. tinctorius based on three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) for the year 2050s and 2070s using a maximum entropy model (MaxEnt) and geographical information system (GIS) in China. For modeling procedure 99 occurrence records and 11 Worldclim environmental factors was used in this paper. The results show that the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate model performance. All of the AUCs were greater than 0.970, thereby placing these models in the “Excellent” category. The jackknife test also showed that maximum temperature in March (tmax3), maximum temperature in February (tmax2), maximum temperature in November (tmax11), precipitation in July (prec7) and precipitation in August (prec8) were the main variables. From the area of current distribution, of the total 6276666.77 km2 (65.36%) area, 27.28% area (2619895.88 km2) was highly suitable for safflower in China, and the highly suitable area (with suitability between 66 and 100) was mainly concentrated in Sichuan (2.74%), Yunnan (2.09%), Shaanxi (1.82%), Hubei (1.76%), Guizhou (1.66%), Henan (1.66%) and Hunan (1.64%). Xinjiang was the largest suitable area, which accounts for 9.70%. It was worth noting, realized niche was little planting in Sichuan province due to market and economic price factors, social factors and other factors. Compared to the current distribution, the total area of the poorly suitable regions and moderately suitable regions for safflower under the three RCPs (RCP2.6, RCP4.5 and RCP8.5) would increase in 2050s and 2070s. However, the total area of the highly suitable regions under the three RCPs (RCP2.6, RCP4.5 and RCP8.5) in 2050s and two RCPs (RCP2.6 and RCP4.5) in 2070s would decrease. This research would provide theoretical suggestions for the protection, cultivation management, and sustainable utilization of C. tinctorius resources to face the challenge of global climate change. Keywords: C. tinctorius, MaxEnt model, Climate change, ArcGIS, Suitable habitat

Schizophrenia, influenced by genetic and environmental factors, may involve epigenetic alterations, notably histone modifications, in its pathogenesis. This review summarizes various histone modifications including acetylation, methylation, phosphorylation, ubiquitination, serotonylation, lactylation, palmitoylation, and dopaminylation, and their implications in schizophrenia. Current research predominantly focuses on histone acetylation and methylation, though other modifications also play significant roles. These modifications are crucial in regulating transcription through chromatin remodeling, which is vital for understanding schizophrenia's development. For instance, histone acetylation enhances transcriptional efficiency by loosening chromatin, while increased histone methyltransferase activity on H3K9 and altered histone phosphorylation, which reduces DNA affinity and destabilizes chromatin structure, are significant markers of schizophrenia.