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This review comprehensively explores the critical role of calcium as an essential small-molecule biomessenger in skeletal muscle function. Calcium is vital for both regulating muscle excitation–contraction coupling and for the development, maintenance, and regeneration of muscle cells. The orchestrated release of calcium from the endoplasmic reticulum (ER) is mediated by receptors such as the ryanodine receptor (RYR) and inositol 1,4,5-trisphosphate receptor (IP3R), which is crucial for skeletal muscle contraction. The sarcoendoplasmic reticulum calcium ATPase (SERCA) pump plays a key role in recapturing calcium, enabling the muscle to return to a relaxed state. A pivotal aspect of calcium homeostasis involves the dynamic interaction between mitochondria and the ER. This interaction includes local calcium signaling facilitated by RYRs and a “quasi-synaptic” mechanism formed by the IP3R-Grp75-VDAC/MCU axis, allowing rapid calcium uptake by mitochondria with minimal interference at the cytoplasmic level. Disruption of calcium transport can lead to mitochondrial calcium overload, triggering the opening of the mitochondrial permeability transition pore and subsequent release of reactive oxygen species and cytochrome C, ultimately resulting in muscle damage and atrophy. This review explores the complex relationship between the ER and mitochondria and how these organelles regulate calcium levels in skeletal muscle, aiming to provide valuable perspectives for future research on the pathogenesis of muscle diseases and the development of prevention strategies.

Iron tailings ponds are engineered dam and dyke systems used to capture iron tailings. They are high-risk hazards with high potential energy. If the tailings dam broke, it would pose a serious threat to the surrounding ecological environment, residents’ lives, and property. Rainfall is one of the most important influencing factors causing the tailings dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk tailings ponds, 16 moderate-risk tailings ponds, and 4 high-risk tailings ponds in the study area. This method could be useful for selecting targeted tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk tailings ponds in rainy season.

Dielectric ceramics possess a unique competitive advantage in electronic systems due to their high-power density and excellent reliability. Na 1/2 Bi 1/2 TiO 3 -based ceramics, one type of extensively studied energy storage dielectric, however, often experience A-site element volatilization and Ti 4+ reduction during high-temperature sintering. These issues may result in increased energy loss, reduced polarization and low dielectric breakdown electric field, ultimately making it challenging to achieve both high energy storage density and efficiency. To address these issues, we introduce a synergistic optimization strategy that combine polarization engineering and grain alignment engineering. First principles calculations and experimental analyses show that the doping of Mn 2+ can suppress the reduction of Ti 4+ in Na 1/2 Bi 1/2 TiO 3 -based ceramics and enhance ion off-centering displacements, thereby reducing energy loss and improving polarization. In addition, we prepared multilayer ceramic capacitors with grains oriented along the direction using the template grain growth method. This approach effectively reduces electric-field-induced strain by 37% and markedly enhances breakdown electric field by 42% when compared with nontextured counterpart. As a result of this comprehensive strategy, <111 >-textured Na 1/2 Bi 1/2 TiO 3 -based multilayer ceramic capacitors achieve an ultra-high energy density of 15.7 J·cm −3 and an excellent efficiency beyond 95% at 850 kV·cm −1 , exhibiting a superior overall energy storage performance. Grain alignment and polarization engineering were simultaneously utilized to enhance the energy storage performance of Na 1/2 Bi 1/2 TiO 3 -based multilayer ceramic capacitors, leading to an energy density of 15.7 J·cm −3 and energy efficiency over 95%.

Lonicera caerulea L. (blue honeysuckle) is rich in antioxidant polyphenols, and rapid and accurate determination of its polyphenol content is of great significance for functional food quality control. This study proposed a hybrid variable selection strategy designed for high-dimensional small-sample scenarios and developed a quantitative prediction model for polyphenol content based on mid-infrared (MIR) spectroscopy. A total of 191 Lonicera caerulea samples were collected from Northeast China, and 7468-dimensional spectral data were acquired using a Fourier transform infrared spectrometer. Polyphenol reference values were determined by the Folin–Ciocalteu method. Samples were divided into calibration (n = 152) and prediction (n = 39) sets using the SPXY algorithm. Among the 10 preprocessing methods evaluated, MSC combined with Savitzky–Golay first derivative achieved the best performance and was therefore used for subsequent modeling. The proposed hybrid variable selection method (VIP1.0∩RFR30%) intersected PLS variable importance in projection (VIP ≥ 1.0) with the top 30% important variables from random forest regression, selecting 984 key wavelengths and achieving 86.8% dimensionality reduction. A three-stage hyperparameter tuning strategy was implemented across four models (PLS, RFR, SVR, and XGBoost) to validate feature stability and control overfitting. The optimized XGBoost model achieved excellent performance on the independent test set (R2 = 0.92, RMSE = 0.098, RPD = 3.47). Compared with the classical CARS method (R2 = 0.78, RPD = 2.14), R2 improved by 16.3% and RPD improved by 55.2%. The results demonstrate that the proposed hybrid variable selection strategy can effectively address the challenges of high-dimensional MIR spectral data in small-sample modeling, providing a reliable tool for rapid and non-destructive quantitative analysis of polyphenols in Lonicera caerulea.

This study proposes an Attention Mechanism–Multi-Feature Fusion Physics-Informed Neural Network (AM-MFF-PINN) for accurate and physically consistent estimation of the State of Health (SOH) of lithium-ion batteries in practical battery management systems (BMSs). The model integrates multi-domain features, including time-domain, frequency–domain, and wavelet–domain indicators, to capture both macroscopic degradation trends and microscopic dynamical behaviors under varying operating conditions. A dual-correlation feature selection strategy that combines the Pearson correlation coefficient and the maximal information coefficient (MIC) is adopted to automatically retain the most degradation-sensitive variables, while a dynamic loss balancing mechanism adaptively coordinates data-fitting and physics-based constraints to ensure robust convergence. Experimental results on the Xi’an Jiaotong University (XJTU) and Tongji University (TJU) datasets demonstrate that AM-MFF-PINN achieves superior performance, with a mean absolute error (MAE) of approximately 0.002, a root mean square error (RMSE) of about 0.004, and a coefficient of determination (R2) of 0.99 for the XJTU dataset, and an MAE of 0.005, an RMSE of 0.006, and an R2 of 0.97 for the TJU dataset. These results indicate that the proposed method can provide reliable SOH estimates across different chemistries, temperatures, and charging protocols, using only standard charging data that are readily available in on-board and stationary BMSs. Therefore, AM-MFF-PINN offers a generalizable and practically deployable evaluation methodology to support early fault warning, predictive maintenance, and life-cycle optimization of lithium-ion batteries in electric vehicles and energy storage systems.

Myeloid cells are vital components of the immune system and have pivotal functions in orchestrating immune responses. Understanding their functions within the tumor microenvironment and their interactions with tumor-infiltrating lymphocytes presents formidable challenges across diverse cancer types, particularly with regards to cancer immunotherapies. Here, we explore tumor-infiltrating myeloid cells (TIMs) by conducting a pan-cancer analysis using single-cell transcriptomics across eight distinct cancer types, encompassing a total of 192 tumor samples from 129 patients. By examining gene expression patterns and transcriptional activities of TIMs in different cancer types, we discern notable alterations in abundance of TIMs and kinetic behaviors prior to and following immunotherapy. We also identify specific cell-cell interaction targets in immunotherapy; unique and shared regulatory profiles critical for treatment response; and TIMs associated with survival outcomes. Overall, our study illuminates the heterogeneity of TIMs and improves our understanding of tissue-specific and cancer-specific myeloid subsets within the context of tumor immunotherapies. Myeloid cells show variability in different tumour types and treatment outcomes. Here the authors use a single cell sequencing approach to characterise the myeloid cell population before and after checkpoint therapy in eight distinct tumour types and identify cell populations and interactions associated with tumour immunotherapy.

In the digital economy, the relationship between customers and companies is a win-win cooperation, and value co-creation has become the mainstream business development concept. Against this background, customer citizenship behaviours have received increasing and widespread attention in marketing and consumer behaviour research. However, previous studies have not sufficiently considered the importance of trait emotions in predicting customer citizenship behaviours. By focusing on a specific emotional disposition with positive functions, dispositional awe, this study develops an integrative model based on the prototype model of awe and the elaborated model of awe’s prosocial effects. This model examines the impact of dispositional awe on customer citizenship behaviours and analyses the roles of construal level and social connectedness in it. Drawing on a sample of 701 questionnaires from Chinese adults and using structural equation modelling, this study finds that dispositional awe contributes positively to three types of customer citizenship behaviours: making recommendations, helping other customers, and providing feedback. In addition, dispositional awe can influence customer citizenship behaviours through the independent mediating effect of social connectedness as well as the serial mediating effect of construal level and social connectedness. These findings suggest that frequent experiences of awe help develop an individual’s internal abstract mindset and subjective sense of connection to external society, thereby motivating customer citizenship behaviours. This study provides valuable insights into whether and how dispositional awe can influence customer citizenship behaviours and offers operational strategies for marketing practice.

In this article, input-to-output stability (IOS) for stochastic complex networked control systems (SCNCS) is investigated. By applying Kirchhoff’s matrix tree theorem in graph theory, an appropriate Lyapunov function is established which is related to topological structure and the Lyapunov function of each node system of SCNCS. Combining Lyapunov method and stochastic analysis skills, some sufficient criteria are provided to ensure SCNCS to satisfy IOS. In order to further analyze and verify the validity of our theoretical results, the results are applied to a class of stochastic Lurie coupled control systems on networks (SLCCSN) and the numerical test is performed.

Relaxor ferroelectric ceramics have emerged as promising candidates for electrocaloric cooling systems due to their relatively higher heating and cooling capacities. However, simultaneously achieving high temperature changes (Δ T ) and a wide operating temperature range remains a significant challenge, limiting their practical applications. This work proposes a synergistic strategy that involves precise compositional tuning of the BaTiO 3 - x KNbO 3 system to customize the rhombohedral-to-cubic phase boundary around room temperature, coupled with engineering grain orientation of the ceramics. Based on this approach, a maximum Δ T of 3.9 K is achieved in c -texture BaTiO 3 -KNbO 3 ceramics, outperforming most environmentally friendly ceramics. Notably, the Δ T variation remains within ±10% across a temperature range of 30 °C to 80 °C, demonstrating a promising material for the design and application of electrocaloric cooling devices. This work provides new insights for the design of ceramics with optimized electrocaloric properties, offering significant potential for improving the efficiency and functionality of next-generation cooling technologies and devices. The authors achieve a high electrocaloric temperature change (Δ T  = 3.9 K) in c-textured BaTiO 3 -KNbO 3 ceramics via compositional tuning and grain orientation engineering. The Δ T variation remain within ±10% across 30–80 °C, demonstrating exceptional stability.