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Energy storage is one of the key means for improving the flexibility, economy and security of power system. It is also important in promoting new energy consumption and the energy Internet. Therefore, energy storage is expected to support distributed power and the micro-grid, promote open sharing and flexible trading of energy production and consumption, and realize multi-functional coordination. In recent years, with the rapid development of the battery energy storage industry, its technology has shown the characteristics and trends for large-scale integration and distributed applications with multi-objective collaboration. As a grid-level application, energy management systems (EMS) of a battery energy storage system (BESS) were deployed in real time at utility control centers as an important component of power grid management. Based on the analysis of the development status of a BESS, this paper introduced application scenarios, such as reduction of power output fluctuations, agreement to the output plan at the renewable energy generation side, power grid frequency adjustment, power flow optimization at the power transmission side, and a distributed and mobile energy storage system at the power distribution side. The studies and application status of a BESS in recent years were reviewed. The energy management, operation control methods, and application scenes of large-scale BESSs were also examined in the study.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by synovial inflammation, pannus formation, and bone and cartilage damage. It has a high disability rate. The hypoxic microenvironment of RA joints can cause reactive oxygen species (ROS) accumulation and mitochondrial damage, which not only affect the metabolic processes of immune cells and pathological changes in fibroblastic synovial cells but also upregulate the expression of several inflammatory pathways, ultimately promoting inflammation. Additionally, ROS and mitochondrial damage are involved in angiogenesis and bone destruction, thereby accelerating RA progression. In this review, we highlighted the effects of ROS accumulation and mitochondrial damage on inflammatory response, angiogenesis, bone and cartilage damage in RA. Additionally, we summarized therapies that target ROS or mitochondria to relieve RA symptoms and discuss the gaps in research and existing controversies, hoping to provide new ideas for research in this area and insights for targeted drug development in RA.

The aramid honeycomb composite material plays an important role in industry. Defects of this material seriously influence its performance. However, conventional detecting tools such as X-ray or computer tomography (CT) imaging, ultrasonic testing, and visual inspection are not able to meet the requirements of fast, safe, and high resolution at the same time. In this study, we numerically use rapid terahertz time−domain spectroscopy (THz-TDS) to identify defects in the aramid paper composite structure effectively. Simulation results demonstrate that THz-TDS technology enables the non-destructive reflection imaging of layered defects in glass fiber covering and glue layers as supporting components within the composite structure, with a spatial resolution of 0.5 mm and a depth range exceeding 10 mm. During the study, the finite difference time domain (FDTD) simulation with a real pulse waveform is achieved, and the defect position can be recognized by the anomaly in the reflection profile when compared with the waveform reflected by non-defect samples. At the same time, it is found that the defect identification ability is obviously affected by the incident position. The numerical research illustrates that the detectable defect is as thick as 0.1 mm and has a diameter of 1 mm. The results will offer valuable guides to the real application of THz-TDS systems in the detection of a similar structure.

The prevalence of cardiovascular diseases continues to be a challenge for global health, necessitating innovative solutions. The potential of high-density lipoprotein (HDL) mimetic nanotherapeutics in the context of cardiovascular disease and the intricate mechanisms underlying the interactions between monocyte-derived cells and HDL mimetic showing their impact on inflammation, cellular lipid metabolism, and the progression of atherosclerotic plaque. Preclinical studies have demonstrated that HDL mimetic nanotherapeutics can regulate monocyte recruitment and macrophage polarization towards an anti-inflammatory phenotype, suggesting their potential to impede the progression of atherosclerosis. The challenges and opportunities associated with the clinical application of HDL mimetic nanotherapeutics, emphasize the need for additional research to gain a better understanding of the precise molecular pathways and long-term effects of these nanotherapeutics on monocytes and macrophages to maximize their therapeutic efficacy. Furthermore, the use of nanotechnology in the treatment of cardiovascular diseases highlights the potential of nanoparticles for targeted treatments. Moreover, the concept of theranostics combines therapy and diagnosis to create a selective platform for the conversion of traditional therapeutic medications into specialized and customized treatments. The multifaceted contributions of HDL to cardiovascular and metabolic health via highlight its potential to improve plaque stability and avert atherosclerosis-related problems. There is a need for further research to maximize the therapeutic efficacy of HDL mimetic nanotherapeutics and to develop targeted treatment approaches to prevent atherosclerosis. This review provides a comprehensive overview of the potential of nanotherapeutics in the treatment of cardiovascular diseases, emphasizing the need for innovative solutions to address the challenges posed by cardiovascular diseases.

Background Although many studies have analyzed health insurance worldwide, most focus on whole populations rather than specific vulnerable groups. There is a lack of studies that compare different schemes. This paper evaluates the impact of different types of social health insurance and other associated factors on healthcare utilization and costs among middle-aged and elderly Chinese adults. Methods Data were obtained from a nationally representative middle-aged and elderly household survey, the China Health and Retirement Longitudinal Study, which was conducted in 2015. Middle-aged and elderly are defined as people who are ≥45 years. Descriptive statistics were used to show the prevalence of each variable. Both logistic and multiple linear regression models were used to evaluate the association between healthcare utilization/healthcare costs and health insurance in addition to other related factors. Results Although the rapid expansion of social health insurance coverage has significantly improved the healthcare utilization among middle-aged and elderly adults, the difference between three schemes is large. Urban Employee Medical Insurance (UEMI) has had a greater effect in improving healthcare utilization than New Cooperative Medical Insurance (NCMI) or Urban Resident Medical Insurance (URMI). Unification of health insurance programs and optimization of health resource allocations should be a practical way to alleviate healthcare utilization inequality across schemes. People having social health insurance spend more on total and out-of-pocket (OOP) healthcare costs than people not covered by social health insurance, suggesting that enrollment in social health insurance induces significant increases in both total and OOP healthcare expenses. UEMI for the urban employed has relatively higher funding criteria and reimbursement rate, which makes the greatest extent to induce increase in healthcare costs. Some demographic or socioeconomic factors significantly affect healthcare utilization and costs among middle-aged and elderly adults. Conclusion Our study demonstrates the differences in healthcare utilization and costs between those with and without social health insurance and between those with different health insurance schemes. Policy efforts should further focus on adjusting social health insurance and optimizing healthcare resource allocation in order to enhance effective utilization of healthcare services and control cost increases among middle-aged and elderly adults.

Existing experience-based methods cannot effectively assist commercial building operators in allocating workforce resources according to contracts and balance multiple workforce management objectives under resource constraints, leading to misaligned allocation strategies. To address this issue, this study develops a workforce resource allocation optimization model based on BERT and the NSGA-II. First, a natural language processing (NLP) model is trained to extract operational tasks from contracts and match required workforce types, thereby establishing the framework for workforce allocation schemes. Second, a mathematical optimization model for workforce allocation strategies is constructed with the objectives of minimizing workforce wage costs (B1), maximizing average service levels (B2), and maximizing average digital technology acceptance (B3). An algorithm based on NSGA-II is then designed to solve the model and obtain the optimal Pareto solution set of allocation schemes. Third, the CRITIC–VIKOR method evaluates the Pareto set and determines the final recommended schemes. A case study was conducted on a university campus in Shandong, China, to validate the model’s effectiveness. The results show that the NLP model successfully identified 14 operational tasks and 13 required workforce types from the contract. Compared with the operator’s expected values (B1 = 46,0000 CNY, B2 = 65 points, B3 = 50 points), the optimal allocation scheme calculated using NSGA-II and the CRITIC–VIKOR method reduces B1 by 10.79%, increases B2 by 18.02%, and improves the B3 by 16.79%. This study formulates the workforce allocation problem in the operation stage as a mathematical optimization model and, for the first time, incorporates the workforce’s digital technology acceptance as an optimization objective, thereby filling a theoretical gap in workforce management for commercial building operations. The proposed model provides operators with a semi-automated decision-support tool to enhance workforce management, thereby promoting the sustainable operation of commercial buildings.

Due to inappropriate operational strategies, the operational outcomes of education PPP projects often fail to meet expected goals, posing challenges to the sustainable operation of these projects. Through an operational performance evaluation, deviations between operational outcomes and intended goals and the underlying causes of these deviations can be identified, thereby supporting the adjustment of operational strategies. Therefore, this study proposed a performance evaluation model based on the Limited Cloud Model for education PPP projects. Firstly, this study refined the evaluation dimensions of the Balanced Scorecard based on the stakeholder needs and the Asset-Classified Operation characteristics of education PPPs, and an indicators system for the operational stage was developed. Secondly, a performance evaluation model was constructed using the COWA-Critic-Game Theory weighting method and the Finite Cloud Model. Finally, the performance evaluation model was applied to a newly operated university PPP project in Yantai, China, to conduct a case study. The evaluation results demonstrated the practicality and superiority of the proposed model in addressing the complex performance management challenges of education PPPs. This model assisted Special Purpose Vehicles (SPVs) in identifying issues within operational strategies and making necessary adjustments.

Due to the security weaknesses of chaos-based pseudorandom number generators, in this paper, a new pseudorandom number generator (PRNG) based on mixing three-dimensional variables of a cat chaotic map is proposed. A uniformly distributed chaotic sequence by a logistic map is used in the mixing step. Both statistical tests and a security analysis indicate that our PRNG has good randomness and is more complex than any one-dimensional variable of a cat map. Furthermore, a new image encryption algorithm based on the chaotic PRNG is provided to protect the content of artwork images. The core of the algorithm is to use the sequence generated by the pseudorandom number generator to achieve the process of disruption and diffusion of the image pixels, so as to achieve the effect of obfuscation and encryption of the image content. Several security tests demonstrate that this image encryption algorithm has a high security level.

CD24 is a glycosylphosphatidylinositol-anchored protein that is expressed in a wide range of tissues and cell types. It is involved in a variety of physiological and pathological processes, including cell adhesion, migration, differentiation, and apoptosis. Additionally, CD24 has been studied extensively in the context of cancer, where it has been found to play a role in tumor growth, invasion, and metastasis. In recent years, there has been growing interest in CD24 as a potential therapeutic target for cancer treatment. This review summarizes the current knowledge of CD24, including its structure, function, and its role in cancer. Finally, we provide insights into potential clinical application of CD24 and discuss possible approaches for the development of targeted cancer therapies.

With the continuous advancement of electromagnetic countermeasures, new types of interference signals (e.g., multi-polarization suppression interference) pose a significant threat to conventional Global Navigation Satellite System (GNSS) services, even when the receiver employs a right-handed circularly polarized (RHCP) anti-jamming array. This paper proposes a receiving signal model for orthogonal circularly polarized (OCP) interference signals based on conventional arrays, following an analysis of the non-ideal characteristics of actual arrays. Furthermore, the mechanism by which OCP interference signals affect anti-jamming performance is examined. Power inversion (PI) and linear constrained minimum variance (LCMV) techniques, applied to both uniform linear arrays and central circular arrays, are utilized to verify the impact of these interference signals. Simulation and physical testing demonstrate that OCP interference significantly affects the interference subspace of the conventional RHCP array, potentially leading to a reduction in the anti-jamming performance of the receiver. To effectively suppress multi-polarization interference, anti-jamming GNSS receivers must either ensure the consistency of cross-polarization among the elements of the array or adopt polarization-sensitive arrays.