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Aiming at the present situation that Surakarta game has a narrow audience and poor user interface and user experience, this paper designs and implements a Surakarta game system based on iOS platform. A chessboard data structure based on iOS platform is designed, which is suitable for different screen sizes of mobile phones. The animation representation of game process, especially the eating process, is realized. This paper studies and attempts to use GCD-based multi-threaded search to solve the problem of limited mobile phone resource performance. Experiments show that the Surakarta game system based on iOS platform can play man-machine game on portable devices and has good performance.

The hybrid power source needs to achieve the excellent power distribution control to enhance the vehicle performance, the optimization algorithm can automatically seek the optimal target according to vehicle requirements to achieve the best power distribution of hybrid power source. Power consumption is one of the core indicators for evaluating power distribution control of hybrid power source, as well as the current fluctuation of battery is an important factor that affects its power consumption and cycle life. Taking the fully-active hybrid power source configuration as the application object, a differential evolution algorithm with fast convergence speed and strong global search ability to achieve real-time power distribution control with multiple optimization goals is introduced by fully considering two important parameters of power consumption and battery current fluctuation, the power consumption model for the hybrid power source is established, the functional relationship between the power consumption of hybrid power source, current change of battery and its output current is given. In this algorithm, the minimum power consumption of the hybrid power source and the minimum change rate of the battery output current are selected as the optimization goals, the weight coefficients of the two optimization goals are assigned to seek the influence relationship between the two optimization goals. The empirical results from a simulation verify effectiveness and reliability of the designed scheme. The research results provide a reference for controlling the power distribution and optimizing the hybrid power source of electric vehicle. 混合电源需实现卓越的功率分配控制以提升车辆性能, 而优化算法可根据车辆需求自动地寻求既定目标的最优解, 以实现混合电源的最佳功率分配。功耗是评价功率分配控制的核心指标, 蓄电池的电流变化率是影响其功耗和寿命的重要因素。以全主动配置的混合电源拓扑结构为应用对象, 引入一种新颖的具有收敛速度快且全局搜索能力强的差分进化算法以实现多优化目标的实时功率分配控制；充分考虑功耗和蓄电池的电流变化率2个重要参数, 建立了混合电源的功耗模型, 给出了混合电源的功耗、蓄电池输出电流与蓄电池电流变化率之间的函数关系；以混合电源的功耗最小以及蓄电池输出电流变化率最小为优化目标, 赋予2个优化目标权重系数, 以寻求2个优化目标之间的影响关系。仿真实例结果验证了所设计方案的有效性和可靠性。研究结果为电动汽车混合电源功率分配控制及优化提供参考。

The hybrid power source needs to achieve the excellent power distribution control to enhance the vehicle performance, the optimization algorithm can automatically seek the optimal target according to vehicle requirements to achieve the best power distribution of hybrid power source. Power consumption is one of the core indicators for evaluating power distribution control of hybrid power source, as well as the current fluctuation of battery is an important factor that affects its power consumption and cycle life. Taking the fully-active hybrid power source configuration as the application object, a differential evolution algorithm with fast convergence speed and strong global search ability to achieve real-time power distribution control with multiple optimization goals is introduced by fully considering two important parameters of power consumption and battery current fluctuation, the power consumption model for the hybrid power source is established, the functional relationship between the power consumption

The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO 2 has been accepted as one of the most promising resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO 2 -based memristors. Here, the conductive filament system in the amorphous HfO 2 -based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf 6 O and its crystalline surroundings (monoclinic or tetragonal HfO x ). The phase of the HfO x shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO 2 is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO 2 -based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications. Understanding the mechanism of the formation and rupture of conductive filaments in HfO 2 -based memristors is essential to solve the problem of scalability of the devices. Here, Zhang et al. visualize this process by tracking atomic-scale evolution of conductive filaments during resistive switching cycles.

Backgrounds Malnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation‐based indicators of OS for patients with lung cancer. Materials and methods There were 16 nutrition or systemic inflammation‐based indicators included in this study. The cut‐off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan–Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time‐dependent receiver operating characteristic curves (time‐ROC) and C‐index were calculated to assess the predictive ability of the different indicators. Results There were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet‐to‐lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13–1.49]. The results of the time‐AUC and C‐index analyses indicated that the ALI (C‐index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub‐groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C‐index, 0.609 for men and 0.613 for women) or smoking status (C‐index, 0.629 for non‐smoker and 0.601 for smoker) and in patients aged <65 years (C‐index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non‐small cell lung cancer patients (C‐index, 0.639) or patients aged ≥65 years (C‐index, 0.610), and the glucose‐to‐lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C‐index, 0.601). Conclusions The prognostic ability of the ALI is superior to the other inflammation/nutrition‐based indicators for all patients with lung cancer.

Polyvinyl alcohol (PVA) fibers are commonly added to fiber-reinforced geopolymer composites (FRGC) to enhance their properties; however, systematic research on the dynamic mechanical properties of polyvinyl alcohol fiber-reinforced geopolymer composites (PVA-FRGC) is still required. In this study, an orthogonal experimental design was adopted to investigate the effects of the fly ash/slag ratio, fiber length, and fiber volume content on the dynamic mechanical properties (dynamic compressive strength, fragmentation degree, and energy absorption capacity) of PVA-FRGC. A split Hopkinson pressure bar (SHPB) was used to test the dynamic mechanical properties of the material. The results indicate that the fly ash/slag ratio, fiber length, and fiber volume content all exert significant effects on the dynamic compressive strength and energy absorption capacity of PVA-FRGC. The addition of PVA fibers significantly improves the dynamic compressive strength of PVA-FRGC, which reaches 157.52 MPa, 183.26 MPa, and 210.68 MPa under three different strain rates ranging from 75.4 s−1 to 179.6 s−1, respectively. Although the energy absorption capacity of PVA-FRGC is not significantly improved, the integrity of the specimens after fragmentation is remarkably enhanced. Specifically, under the three load levels, the average particle sizes of PVA-FRGC are 241.43%, 245.04%, and 127.80% higher than those of plain geopolymers, respectively. Considering the comprehensive dynamic mechanical properties, a fly ash/slag ratio of 5:5, a fiber length of 9 mm, and fiber volume content of 2.0% can be regarded as the local optimal mix proportion.

Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC.

Background Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods This prospective study analyzed data from 776 breast cancer patients enrolled in the “Investigation on Nutritional Status and its Clinical Outcome of Common Cancers” (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality ( p  < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

OSA is defined as the repeated occurrence of apnea or hypopnea during sleep caused by upper airway collapse. Obstructive sleep apnea (OSA) is characterized by a high apnea–hypopnea index (AHI) and persistent daytime sleepiness. Body roundness index (BRI), calculated using waist circumference and height, is a measure of obesity. BRI demonstrates a stronger correlation with body fat compared to BMI. However, no studies have thus far reported on the association between BRI and OSA. The data for this study were sourced from the National Health and Nutrition Examination Survey (NHANES) database (2005–2008 and 2015–2018). BRI was computed as 364.2–365.5 * (1 − [WC(m)/2π] 2 /[0.5 * height(m)] 2 ) ½ . Statistical methods for data analysis included multivariable logistic regression, trend tests, restricted cubic spline (RCS) plots, subgroup analysis, and interaction tests, with a significance level of p  < 0.05. This cross-sectional study investigated the relationship between BRI and OSA in 8106 American adults. After adjusting for all considered covariates, BRI was found to be positively associated with the risk of OSA, with each 1-unit increase in BRI raising the risk of OSA by 167% (95% CI [2.42, 2.95], p  < 0.001). The positive association between BRI and OSA was consistent across all subgroups ( p  < 0.001). The restricted cubic spline (RCS) plot further confirmed the positive correlation between BRI and OSA prevalence ( p value < 0.0001, p nonlinear < 0.0001). The results of this study demonstrate a positive correlation between BRI and OSA, suggesting that BRI could be utilized as a predictive factor for OSA. BRI could assist clinicians in the diagnosis of OSA in patients.

MicroRNAs (miRNAs) widely participate in plant growth and development. The miR396 family, one of the most conserved miRNA families, remains poorly understood in sorghum. To reveal the evolution and expression pattern of Sbi-miR396 gene family in sorghum, bioinformatics analysis and target gene prediction were performed on the sequences of the Sbi-miR396 gene family members. The results showed that five Sbi-miR396 members, located on chromosomes 4, 6, and 10, were identified at the whole-genome level. The secondary structure analysis showed that the precursor sequences of all five Sbi-miR396 potentially form a stable secondary stem–loop structure, and the mature miRNA sequences were generated on the 5′ arm of the precursors. Sequence analysis identified the mature sequences of the five sbi-miR396 genes were high identity, with differences only at the 1st, 9th and 21st bases at the 5’ end. Phylogenetic analysis revealed that Sbi-miR396a , Sbi-miR396b , and Sbi-miR396c were clustered into Group I, and Sbi-miR396d and Sbi-miR396e were clustered into Group II, and all five sbi-miR396 genes were closely related to those of maize and foxtail millet. Expression analysis of different tissue found that Sbi-miR396d / e and Sbi-miR396a / b / c were preferentially and barely expressed, respectively, in leaves, flowers, and panicles. Target gene prediction indicates that the growth-regulating factor family members ( SbiGRF1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 10 ) were target genes of Sbi-miR396d / e . Thus, Sbi-miR396d / e may affect the growth and development of sorghum by targeting SbiGRF s. In addition, expression analysis of different tissues and developmental stages found that all Sbi-miR396 target genes, SbiGRF s, were barely expressed in leaves, root and shoot, but were predominantly expressed in inflorescence and seed development stage, especially SbiGRF1 / 5 / 8 . Therefore, inhibition the expression of sbi-miR396d /e may increase the expression of SbiGRF1 / 5 / 8 , thereby affecting floral organ and seed development in sorghum. These findings provide the basis for studying the expression of the Sbi-mir396 family members and the function of their target genes.