The validation and further development of a multidimensional cognitive load scale for virtual environments

Measuring cognitive load is important in virtual learning environments (VLE). Thus, valid and reliable measures of cognitive load are important to support instructional design in VLE. Through three studies, we investigated the validity and reliability of Leppink's Cognitive Load Scale (CLS) and developed the extraneous cognitive load (EL) dimension into three sub-scales relevant for VLE: EL instructions, EL interaction, and EL environment. We investigated the validity of the measures using the Partial Credit Model (PCM), Confirmatory Factor Analysis (CFA), and correlations with retention tests. Study 1 (n = 73) investigated the adapted version of the CLS. Study 2 describes the development and validation of the Multidimensional Cognitive Load Scale for Virtual Environments (MCLSVE), with 140 students in higher education. Study 3 tested the generalizability of the results with 121 higher education students in a more complicated VLE. The results provide initial evidence for the validity and reliability of the MCLSVE.     

Military crisis responses to COVID‐19

Countries across the globe have mobilized their armed forces in response to the COVID‐19 pandemic. Current contributions of armed forces have resulted from an urgent need for additional personnel and resources, and were facilitated by a framing of the crisis in terms of war. These deployments were in the interest of armed forces and enabled them to improve their operational readiness, boost their societal standing and support societies with their expertise. Even though armed forces may provide crucial aid in times of need, it is important that civilian crisis organizations are not undermined, civilian control and civil rights are guaranteed, and the effects on other military operations are considered and discussed. This Forum contribution offers some reflections and recommendations.     

Operational scheduling of responsive prosumer farms for day‐ahead peak shaving by agricultural demand response aggregators

This paper proposes a novel structure to optimize the operational strategies of responsive farms for day‐ahead peak shaving. To achieve the aim, the modern irrigation system of farms, including groundwater, surface water, and booster water pumps, are modeled mathematically. To develop the demand response (DR) potentials of the farms, electrical storage systems, and self‐generation facilities, including thermal distributed generations and on‐farm solar sites, are addressed. In order to facilitate the integration of the agricultural DR programs into the electricity market, a mathematical formulation for agricultural demand response aggregator (ADRA) is suggested. The ADRA participates in the day‐ahead electricity market on behalf of the responsive farms. To overcome the price uncertainty of the electricity market, a robust optimization approach is addressed. This approach determines the robust decisions of farms in the worst‐case realizations of the uncertain electricity price. Regarding on‐farm solar sites located in rural areas, a data‐driven approach is used to estimate the solar power generation of a significant number of sites without needing to install costly communication and measurement infrastructures. Finally, the proposed approach is implemented on agricultural lands in the northeast of Iran. The numerical results show that the suggested approach provides day‐ahead peak shaving for the power systems meeting the crop's water requirements.     

Microwave vacuum drying and puffing of the meat tissue - process analysis

Tenderization based on papain and microwave vacuum drying and puffing (MVDP) as well as adequate processing conditions enabled expansion of slices of the raw meat tissue. The main aim of the study was to use the response surface method and find the conditions of puffing that could maximize the expansion of meat during MVDP. The bound was set on the expansion: the most expanded puffings should be of good quality, including a desirable porous structure. The best combination of values of the MVDP process conditions (e.g., the initial moisture content in meat slices, microwave energy, and the vacuum absolute pressure) was found during experiments using the central composite design. The volume of slices puffed under these conditions was 1.60–1.78 times greater than the volume of slices before MVDP, and the sensory quality index remained acceptable. The research showed that pretreatment reduced the hardness of sample by over 25% and increased its crispiness, which was confirmed in cryo-scanning electron microscopy images.     

Small scale experiments and Fe model validation of structural response during hydrogen vented deflagrations

University of Pisa (UNIPI) conducted a series of vented deflagration tests at B. Guerrini Laboratory. The tests were part of the experimental campaign performed by UNIPI for the European HySEA project (Hydrogen Safety for Energy Applications). Experiments included homogeneous hydrogen-air mixture contained in an about 1 m³ enclosure, called SSE (Small Scale Enclosure). The mixture concentration was variable between 10% and 18% vol. During the deflagrations, structural response was investigated by measuring the displacement of a test plate. The collected data were used to validate the FE model developed by IMPETUS Afea. In this paper experimental facility, displacement measurement system and FE model are briefly described, then comparison between experimental data and simulation results is discussed.     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

Generation-expansion planning with linearized primary frequency response constraints

As renewable energy resources increasingly penetrate the electric grid, the inertia capability of power systems has become a developmental bottleneck. Nevertheless, the importance of primary frequency response (PFR) when making generation-expansion plans has been largely ignored. In this paper, we propose an optimal generation-expansion planning framework for wind and thermal power plants that takes PFR into account. The model is based on the frequency equivalent model. It includes investment, startup/shutdown, and typical operating costs for both thermal and renewable generators. The linearization constraints of PFR are derived theoretically. Case studies based on the modified IEEE 39-bus system demonstrate the efficiency and effectiveness of the proposed method. Compared with methods that ignore PFR, the method proposed in this paper can effectively reduce the cost of the entire planning and operation cycle, improving the accommodation rate of renewable energy.     

考虑流固耦合的管道机器人冲击环焊缝过程动力学建模与分析

以管道机器人（Pipeline inspection gauge，PIG）为载体的内检测技术是保障油气管道安全运输的重要手段。针对管内高压流体作用下，管道机器人在冲击管内环焊缝过程中产生的动力学行为突变问题。建立了管道周向受限空间中基于Kelvin弹簧阻尼的管道机器人密封盘等效动力学模型，结合管道机器人本体建立了多体系管道机器人动力学模型；详细推导了管道机器人轴向振动微分方程，以及管内流体的流动方程；并使用Matlab/Simulink与Adams进行流固耦合仿真，作为重要的工艺参数之一，研究了管道机器人速度改变时，其在冲击环焊缝过程中的动力学响应情况。结果表明：所建立的密封盘及管道机器人动力学模型能够很好地表征密封盘在管道轴向、径向以及周向的力学特性；运行速度越快，管道机器人通过环焊缝引起的轴向振动越剧烈，冲击振动越明显；而垂向和俯仰振动现象随运动速度增大而显著减弱。     

Comparison of modeling methods for wind power prediction: a critical study

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.     

β-环糊精协同乙烯基咪唑离子液体脱除溶剂油中的萘

采用1-乙烯基-3-正丁基溴代咪唑离子液体（[VBIM]Br）为脱除剂，在β-环糊精协同作用下萃取溶剂油中的萘。本实验考察了不同单因素条件在[VBIM]Br离子液体与β-环糊精共同作用下对溶剂油中脱萘率的影响，同时通过响应面法优化脱除工艺条件。实验结果表明：[VBIM]Br离子液体与β-环糊精共同作用下对萘的萃取率比单独利用[VBIM]Br离子液体提高了15%以上，萃取率达到90%以上。离子液体简单回收后，重复使用5次过后，脱除率仍在80%以上。实验通过红外分析和紫外光谱分析了[VBIM]Br离子液体协同β-环糊精脱萘的机理，与[VBIM]Br离子液体协同作用下，β-环糊精对萘存在包合作用，实现了对溶剂油中萘的脱除。