Solar cells based on the poly(N-vinylcarbazole):porphyrin:tris(8-hydroxyquinolinato) aluminium blend system

Organic solar cells based on poly（N-vinylcarbazole） （PVK）： porphyrin： tris （8-hydroxyquinolinato） aluminium （Alq3） blend p-n junction systems have been fabricated in this work. The roles of the different components in the blend system and of the amount of porphyrin have been investigated. The 5, 10, 15, 20-tetraphenylporphyrin （TPP） and 5, 10, 15, 20-tetra（o-chloro）phenylporphyrinato-copper （CuTC1PP） are used in the solar cells. The results show that TPP is better than CuTC1PP in enhancing the performance of PVK：Alq3 solar cells. When the weight ratio of PVK：TPP：Alq3 is 1：1.5：1, the best performance of solar cell is obtained. The open circuit voltage （Voc） is 0.87 V, and the short circuit current （Jsc） is 17.5 μA·cm-2. In the ternary bulk hereojunction system, the device may be regarded as a cascade of three devices of PVK：TPP, TPP：Alq3 and PVK：Alq3. PVK, TPP and Alq3 can improve the hole mobility, light absorption intensity and electron mobility of the ternary bulk hereojunction system, respectively.

Study of a ternary blend system for bulk heterojunction thin film solar cells

In this research, we report a bulk heterojunction（BHJ） solar cell consisting of a ternary blend system. Poly（3-hexylthiophene） P3 HT is used as a donor and [6,6]-phenyl C61-butyric acid methylester（PCBM） plays the role of acceptor whereas vanadyl 2,9,16,23-tetraphenoxy-29 H, 31H-phthalocyanine（VOPc Ph O） is selected as an ambipolar transport material. The materials are selected and assembled in such a fashion that the generated charge carriers could efficiently be transported rightwards within the blend. The organic BHJ solar cells consist of ITO/PEDOT：PSS/ternary BHJ blend/Al structure. The power conversion efficiencies of the ITO/ PEDOT：PSS/P3HT：PCBM/Al and ITO/PEDOT：PSS/P3HT：PCBM：VOPcPhO/Al solar cells are found to be 2.3% and 3.4%, respectively.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Secure e-Prescription Management System:Mitigating Blended Threat in IoBE

New information and communication technologies(ICT)are being applied in various industries to upgrade the value of the major service items.Moreover,data collection,storage,processing,and security applications have led to the creation of an interrelated ICT environment in which one industry can directly influence the other.This is called the“internet of blended environ-ments”(IoBE),as it is an interrelated data environment based on internet-of-things collection activities.In this environment,security incidents may increase as size and interconnectivity of attackable operations grow.Consequently,pre-emptive responses to combined security threats are needed to securely utilize IoBE across industries.For example,the medical industry has more stringent information protection measures than other industries.Consequently,it has become a major target of attackers,as more clinician–patient interactions occur over the internet owing to COVID-19.Therefore,this study aims to acquire security for IoBE while focusing on the medical industry.Among the various types of medical ICT services,this study analyzes dataflow and potential security threats from the e-prescription lifecycle perspective,which is highly utilized,strongly data-centric,and has numerous security issues.Based on our analysis,we propose a secure authentication and data-sharing scheme.

Blended Regenerative Anti-Lock Braking System and Electronic Wedge Brake Coordinate Control Ensuring Maximal Energy Recovery and Stability of All-Wheel-Motor-Drive Electric Vehicles

ABS is an active safety system which showed a valuable contribution to vehicle safety and stability since it was first introduced. Recently, EVs with in-wheel-motors have drawn increasing attention owing to their greatest advantages. Wheels torques are precisely and swiftly controlled thanks to electric motors and their advanced driving techniques. In this paper, a regenerative-ABS control RABS is proposed for all-in-wheel-motors-drive EVs. The RABS is realized as a pure electronic braking system called brake-by-wire. A coordination strategy is suggested to control RABS compromising three layers. First, wheels slip control takes place, and braking torque is calculated in the higher layer. In the coordinate interlayer, torque is allocated between actuators ensuring maximal energy recovery and vehicle stability. While in the lower layer, actuator control is performed. The RABS effectiveness is validated on a 3-DOF EVSimulink model through two straight-line braking manoeuvres with low and high initial speeds of 50 km/h and 150 km/h, respectively. Both regular and emergency braking manoeuvres are considered with ABS enabled and disabled for comparison. Simulation results showed the high performance of the proposed RABS control in terms of vehicle stability, brake response, stopping distance, and battery re-charging.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

基于Prophet算法和Blending集成学习的实时负荷中期预测

目前的中期负荷预测一般未考虑负荷实时状态,而负荷数据的非线性、季节性、随机性、时序性特征将影响实时负荷的中期预测。构建一个实时负荷中期预测的框架,采用Prophet算法提取负荷数据的季节性部分,采用Blending集成学习对负荷数据的非季节部分进行滚动预测,将季节性部分和非季节性部分合成中期负荷实时数据。爱尔兰电力系统的算例结果验证了模型的有效性和稳定性。

Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms

Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and reliable.Based on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was established.The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression.Then,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination.Finally,the design of a target fuel was fully validated by experiments.This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.

Umami taste components in chicken-spices blends and potential effect of aroma on umami taste intensity

In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.

A review:Health benefits and physicochemical characteristics of blended vegetable oils

Oil blending is the method of choice used worldwide to improve oxidative stability and nutritional value.There is no such edible oil/fat that meets all the recommendations from the health point of view.The fatty acid composition of vegetable oils decides the fate of the oil.Pure single oil is unable to provide a balanced amount of fatty acids(FAs)required/recommended on a daily intake basis.Blending oils/fats is an appropriate procedure of physically mixing multiple oils in suitable proportions which may provide functional lipids with improved antioxidant potential and desirable physical and chemical properties.This review piled up the accessible data on the blending of diverse oils/fats in the combination of binary,ternary,quaternary,or other types of oils into a single blended oil.Blending can be found very convincing towards appropriate FA profile,enhancement in physicochemical characteristics,and augmented stability for the period of storage or when used as cooking/frying processes which could ultimately serve as an effectual dietary intervention towards the health protectiveness.

Influence of Blended Course Teaching Reform on Student Satisfaction

With the rapid development of information technology in contemporary times,the blended teaching mode that blends online and offline courses has become an international trend in higher education.Taking blended tourism management courses at Chongqing Three Gorges University as an example,we explored the impact of such teaching reform on student satisfaction based on the SERVPERF model.Empirical analysis of 179 valid questionnaires revealed that five elements of the reform,namely,reliability,assurance,valuableness,responsiveness,and empathy,have a significant positive impact on students’learning satisfaction.Specifically,in the context of blended courses,factors such as a stable and reliable teaching environment,comprehensively guaranteed educational conditions,teaching content that highly aligns with students’demands and value expectations,prompt responses to students’needs and feedback,and empathetic consideration of students’perspectives are critical for enhancing student satisfaction.Based on these conclusions,we propose several strategies and methods for improving the effectiveness of blended teaching in the hope of propelling its continuous improvement and optimization,thus further elevating the quality of higher education.

Investigating Transport Properties of Environmentally Friendly Azeotropic Binary Blends Based on Evaporation in Auto-Cascade Refrigeration

The exploration of performance and prediction of environmentally friendly refrigerant physical properties represents a critical endeavor.Equilibriummolecular dynamics simulationswere employed to investigate the density and transport properties of propane and ethane at ultra-low temperatures under evaporative pressure conditions.The results of the density simulation of the evaporation conditions of the blends proved the validity of the simulation method.Under identical temperature and pressure conditions,increasing the proportion of R170 in the refrigerant blends leads to a density decrease while the temperature range in which the gas-liquid phase transition occurs is lower.The analysis of simulated results pertaining to viscosity,thermal conductivity,and self-diffusion coefficient reveals heightened deviation levels within the phase transition temperature zone.This increase in deviation attributed to intensified molecular activity.In terms of uncovering the physical mechanism of gas-liquid phase transition,the work illustrates the macroscopic phenomenon of the intensified existing disorder during phase transitions at the molecular level.Molecular dynamics simulations analyzing the thermophysical properties of refrigerant blends from a microscopic point of view can deepen the comprehension of the thermal optimization of refrigeration processes.

Research on Blended Teaching Mode:A Case Study of Floristry

In order to better carry out research on education and teaching,the author consulted relevant literature on blended teaching mode from 2011-2021 through CNKI,Web of Science and other websites,summarized and analyzed the research status of blended teaching mode,in order to lay a good foundation for studying blended teaching mode in course teaching.

An Analysis on the Blended Teaching Mode of College English

This paper explores blended teaching in college English from five aspects:the connotation,theoretical basis,design principles,advantages,and challenges of blended teaching and blended teaching models.The purpose is to attract more foreign language practitioners and learners to pay attention to this model and put it into practice.

The Effectiveness of the Havriliak-Negami Model in Predicting the Master Curves of the Asphalt Blends with SBS Triblock Copolymer and Organic-Montmorillonite at Different Temperatures and Frequencies

The dynamic viscoelastic properties of asphalt AC-20 and its composites with Organic-Montmorillonite clay (OMMt) and SBS were modeled using the empirical Havriliak-Negami (HN) model, based on linear viscoelastic theory (LVE). The HN parameters, α, β, G0, G∞and τHN were determined by solving the HN equation across various temperatures and frequencies. The HN model successfully predicted the rheological behavior of the asphalt and its blends within the temperature range of 25˚C - 40˚C. However, deviations occurred between 40˚C - 75˚C, where the glass transition temperature Tg of the asphalt components and the SBS polymer are located, rendering the HN model ineffective for predicting the dynamic viscoelastic properties of composites containing OMMt under these conditions. Yet, the prediction error of the HN model dropped to 2.28% - 2.81% for asphalt and its mixtures at 100˚C, a temperature exceeding the Tg values of both polymer and asphalt, where the mixtures exhibited a liquid-like behavior. The exponent α and the relaxation time increased with temperature across all systems. Incorporating OMMt clay into the asphalt blends significantly enhanced the relaxation dynamics of the resulting composites.

基于Blending集成学习的多源信息液压系统多类故障诊断研究

针对传统故障诊断方法准确性不高、耗时长问题,研究通过多个EfficientNet模型对传感器数据进行预训练,并使用XGBoost作为元学习器,提出了一种基于Blending集成学习的多源信息液压系统多类故障诊断方法。实验结果表明,各个子分类器在训练次数达到300次后趋于收敛,准确率均达到95%左右。该方法具有较高的准确性和鲁棒性,为液压系统故障诊断提供了一种有效的解决方案。

Predicting the Stitch Density of Finished Fabrics Using Weft Blended Grey Knit Fabrics

Stitch density is one of the critical quality parameters of knit fabrics. This parameter is closely related to other physical quality parameters like fabric weight, fabric tightness factor, fiber types, blend ratio, yarn diameter and linear density, and fabric structure. Selecting stitch density (wales per inch, course per inch) is essential to getting the appropriate fabric weight and desired quality. Usually, no rules or assumptions exist to get the desired stitch density in the finished fabric stage. Fifteen types of blended knit fabrics were prepared to conduct the study. The varying percentages of cotton, polyester, and elastane are incorporated in the blends. Regression analysis and regression ANOVA tests were done to predict the stitch density of finished fabrics. A suitable regression equation is established to get the desired results. The study also found that the stitch density value in the finished stage fabric decreases by approximately 15% compared to the stitch density in the grey fabric stage. This study will help the fabric manufacturers get the finished fabric stitch density in advance by utilizing the grey fabric stitch density data set. The author expects this research to benefit the knitting and dyeing industry, new researchers, and advanced researchers.

Research Status,Practical Dilemmas,and Optimization Strategies of Blended Teaching in Universities

Blended teaching has emerged as a prominent subject in the recent reform and innovation of higher education.It has become imperative and guiding for colleges and universities to embrace a mixed teaching approach that aligns with the evolving needs of education and teaching in the new era.This paper aims to provide a comprehensive overview of the research status surrounding blended teaching,encompassing fundamental issues,teaching design,practical guidance,teaching effectiveness,and evaluation.By critically examining the current challenges associated with blended teaching,this study proposes optimization strategies including enhancing student participation and interaction,promoting deep learning,improving teachers’preparedness,teaching technologies,and curriculum design capabilities,strengthening top-level design,and perfecting evaluation and incentive mechanisms.These strategies provide new directions for the reform of blended teaching.

Formulation of Motor Oil from Blends of Rubber Latex Cup Bottom Oil (RLBO) and Used Frying Oil (UFO)

Most motor oils are made from mineral oils derived from petroleum, the reserves of which are limited and exhaustible. The aim of this study is to produce and characterize motor oil formulations based on mixtures of rubber latex cup bottom oil (RLCBO) and used frying oil (UFO). The results show that these formulations have a density between 0.91 and 0.92. These densities evolve linearly with the proportion of cup bottom oil and temperature. Similarly, the kinematic viscosity of the blends follows an exponential relationship with temperature. By plotting the logarithm of these kinematic viscosities against the inverse of the temperature, we were able to determine the activation energy of the various blends and deduce that the formulations behave Newtonian.

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment:Learning Experience as a Mediator

Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.