Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme and fatigue load reducing control for wind turbines

The cost of energy for power generated using wind turbines is largely driven by the cost of the wind turbines itself. As many turbine components are dimensioned by the mechanical loads, they need to be able to withstand, reducing those loads through the use of smart control algorithms directly impacts turbine costs. Model Predictive Control (MPC) is an advanced control technique that originated in the process industries but is now used for a large variety of applications. This thesis exemplarily demonstrates the benefits of MPC for wind turbine control and shows how MPC can be employed to reduce both extreme and fatigue loading on wind turbines. An analytical, nine-state, non-linear model of a wind turbine is developed and its parameters identified via closed-loop system identification. This model is used to design an Extended Kalman Filter for state and wind speed estimation and a Model Predictive Controller. The Model Predictive Controller is continuously linearized, i.e., the plant model is linearized and the optimization problem is formulated as a quadratic program (QP) and solved numerically at each time step. The stability of the linear MPC is ensured through the use of terminal cost and constraint terms in the problem formulation. The controller contains both control and state constraints. Using the example of the rotor overspeed constraint, it is exemplarily demonstrated how state constraints can be robustified against uncertainties in future wind speeds. The developed Model Predictive Controller includes the trajectory of future wind speeds explicitly in the problem formulation. This allows integration of preview signals, for example measured using a turbine mounted Light Detection and Ranging (LiDAR) device, in the controller without increasing complexity and retaining all MPC properties such as optimality and constraint handling. The performance of the MPC is evaluated in extensive simulations using the aero-elastic simulation tool FAST and the NREL 5 MW reference turbine. Results show that a preview MPC controller significantly outperforms both non-preview MPCs as well as a classically designed baseline controller and significantly reduces extreme and fatigue loads. If no preview information is available, the differences between MPC and the baseline controller are more gradual. However, it is also shown that, due to the use of control and state constraints, the MPC provides benefits especially in nonnormal operating conditions such as extreme gusts or turbine fault situations where it reduces loads or helps to keep the turbine online. It is concluded that MPC is a natural fit for integrating preview signals into the turbine control system. Even if no preview information is available, it offers significant benefits especially in special scenarios. Future research on MPC for wind turbine applications should therefore also consider the case where no LiDAR/ preview is available.
Microclimate and Plant Transpiration of Tomato (Solanum lycopersicum L.) in a Sunken Solar Greenhouse in North China

The solar greenhouse is a common protected structure for crop production when ambient temperatures are low. In the North China Plain (NCP) winter temperatures are very low and an improved solar greenhouse with a lowered soil surface (0.5–1.5 m deep), referred to as a sunken solar greenhouse (SSG), is used. A four-season experiment was conducted in a commercial SSG with tomato crops to characterize internal microclimate, sap flow (SF) and crop coefficients. Results show that temperature inside the SSG could be more than 20 °C higher than outside in winter, which favors tomato growth and resulted in acceptable yields. Daily total SF was related to solar radiation, vapor pressure deficit (VPD) and temperature, in that order, both in winter and summer. The decoupling coefficient (which is the ratio of radiative to aerodynamic influences on evapotranspiration) in daytime was 0.76 in winter and 0.84 in summer, indicating strong decoupling (i.e., predominance of radiative influences) of the internal environment where wind speed was low. Basal crop coefficients at the mid stage of crop growth averaged 1.15–1.43 in winter and 0.91–0.92 in spring and summer. Thus, in the SSG, for similar climatic conditions sap flow in winter was higher than that in summer, which should be considered in irrigation scheduling.
Effects of a grazing permit market on pastoralist behavior and overgrazing in Kenya

The success of market-based mechanisms in reducing conflicts and internalizing externalities depends on their ability to clarify property rights amongst heterogenous resource users. We investigate the effectiveness of novel markets in achieving their goals using the case study of grazing markets in Laikipia County, Kenya. In this system, sheep- and goat (shoat)- and cattle-rearing pastoralists negotiate land access for cattle with neighboring cattle ranchers. Using data on pastoralists’ livestock and contracting preferences and a model of pastoral herd management, we show that contracting for cattle grazing access on private property alters relative input shadow prices for grazing resources in communal pastoral lands, ultimately resulting in relieved cattle grazing pressure. However, the permitting process is less attractive to pastoralists who prefer rearing shoats instead of cattle. These shoat-rearing pastoralists instead fill some of the vacated space with shoats instead of purchasing permits themselves. This leakage offsets some of the conservation benefits arising from the contracting program and results in a greater share of shoats in the communal herd mix. Approximately 0.59 cows’ worth of free space persists on the commons per permit sold, indicating reduced grazing pressure, but this represents a small proportion (3.8%) of the total livestock in the system. The narrow introduction of the cattle-focused permit market and lack of strong management institutions on the commons dampen the permitting program’s conservation benefits, necessitating further interventions. Alleviating these factors and dramatically scaling up the program has the potential to turn the permitting system into a successful conservation tool.
Transport models and intelligent transportation system to support urban evacuation planning process

This study focuses on risk management and specifically the exposure component. The exposure may be reduced by means of evacuation. This measure allows people and goods to be rescued from the area where the calamitous effects are announced. In the past years, many models have been proposed to simulate and design transport system under evacuation conditions. It must be underlined that the models applied to ordinary conditions cannot be directly applied to evacuation conditions due to very strong differences in demand, supply and interaction. Other models have been developed for emergency conditions. Nevertheless they consider only specific aspects and do not provide a comprehensive framework. This study presents the main quantitative results of a long-lasting research project for which a unifying approach for the simulation and design of a transportation system under evacuation conditions is adopted. In this project, an intelligent transportation system (ITS) is implemented to monitor real evacuation tests in a town where an emergency event has been simulated and experimented. In this study, a general methodology is reported with the description of the main models and procedures. Models are calibrated and validated by means of observed data obtained from ITS.
Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching

This paper proposes a fast method to robustly perform Visual Graph SLAM in underwater environments. Since Graph SLAM is not resilient to wrong loop detections, the key of our proposal is the Visual Loop Detector, which operates in two steps. First, a lightweight Siamese Neural Network performs a fast check to discard non loop closing image pairs. Second, a RANSAC based algorithm exhaustively analyzes the remaining image pairs and filters out those that do not close a loop. The accepted image pairs are then introduced as new graph constraints that will be used during the graph optimization. By executing RANSAC only on a previously filtered set of images, the gain in speed is considerable. The experimental results, which evaluate each component separately as well as the whole Visual Graph SLAM system, show the validity of our proposal both in terms of quality of the detected loops, error of the resulting trajectory and execution time.
Alinhando estratégia e competências em contexto organizacional: Um estudo em uma indústria química Aligning Strategy and Competences in Organizational Context: A Study in a Chemical Industry

This article aims to contribute to the debate about de challenges and advantages of applying the strategic alignment in the organizations. Theoretical and empirical references show that the strategic alignment is in the seminal stage in the organizations, especially when the possible positive results of this alignment are considered. With the objective of observing this relation empirically, a case study was developed in a chemistry industry. This enterprise has a strategic plan formalized and a model of competence implemented. The results show that the researched organization has a well-developed managerial structure, but not aligned when considered the relation between strategy and competences.
Cost-Effectiveness of Molecularly Guided Treatment in Diffuse Large B-Cell Lymphoma (DLBCL) in Patients under 60

Simple Summary Diffuse large B cell Lymphoma (DLBCL) can be categorized into cell of origin (COO) subtypes. Genetic tests are used to detect which subtype of DLBCL a patient has. One subtype of DLBCL, activated B-cell like (ABC), is associated with a comparatively poorer prognosis. New evidence suggests that patients under 60 with ABC-DLBCL may benefit from ibrutinib added to their standard care treatment regimen. Genetic testing and treatments like ibrutinib can be costly to publicly funded healthcare systems. Here, we report a cost-effectiveness analysis of the addition of Ibrutinib to treat patients under the age of 60 with ABC-DLBCL. Our analysis found that the use of genetic testing to diagnose ABC-DLBCL and providing ibrutinib along with standard care treatment has a low to moderate probability of being cost-effective, depending on decision maker willingness to pay and the cost of the genetic test. Abstract Background: Classifying diffuse large B-cell lymphoma (DLBCL) into cell-of-origin (COO) subtypes could allow for personalized cancer control. Evidence suggests that subtype-guided treatment may be beneficial in the activated B-cell (ABC) subtype of DLBCL, among patients under the age of 60. Methods: We estimated the cost-effectiveness of age- and subtype-specific treatment guided by gene expression profiling (GEP). A probabilistic Markov model examined costs and quality-adjusted life-years gained (QALY) accrued to patients under GEP-classified COO treatment over a 10-year time horizon. The model was calibrated to evaluate the adoption of ibrutinib as a first line treatment among patients under 60 years with ABC subtype DLBCL. The primary data source for efficacy was derived from published estimates of the PHOENIX trial. These inputs were supplemented with patient-level, real-world data from BC Cancer, which provides comprehensive cancer services to the population of British Columbia. Results: We found the cost-effectiveness of GEP-guided treatment vs. standard care was $77,806 per QALY (24.3% probability of cost-effectiveness at a willingness-to-pay (WTP) of $50,000/QALY; 53.7% probability at a WTP of $100,000/QALY) for first-line treatment. Cost-effectiveness was dependent on assumptions around decision-makers’ WTP and the cost of the assay. Conclusions: We encourage further clinical trials to reduce uncertainty around the implementation of GEP-classified COO personalized treatment in this patient population.
Lubrication characteristics of a textured porous sliding bearing

This study investigates the lubrication characteristics of a textured bearing sliding on a porous layer. To properly implement the porous layer effect in the bearing, the Brinkman-extended-Darcy model is employed, which results in a modified Reynolds equation. This equation is then evaluated to examine the effects of the porous layer’s material and geometric properties on the lubrication characteristics through the use of (semi-)analytic and numerical approaches. The cavitation effect is reflected in the numerical treatment of the model equation, and an adaptive finite difference scheme is employed to efficiently model the bearing system with finite dimples that may be far smaller than the overall bearing dimensions. Along with the variation trends of the bearing performance with respect to the material and geometric properties of the porous layer, the pattern effects of textured surfaces on the bearing performance indices (load capacity and equivalent friction coefficient) are also thoroughly investigated. Here, the pattern comprised a set of regularly spaced identical dimples either in semi-spherical or semi-ellipsoidal shape, and the effects of size and orientation of the dimples are comprehensively explored to separately identify their influence on the bearing performance indices.
Micro-Influencer Recommendation by Multi-Perspective Account Representation Learning

Influencer marketing is emerging as a new marketing method, changing the marketing strategies of brands profoundly. In order to help brands find suitable micro-influencers as marketing partners, the micro-influencer recommendation is regarded as an indispensable part of influencer marketing. However, previous works only focus on modeling the individual image of brands/micro-influencers, which is insufficient to represent the characteristics of brands/micro-influencers over the marketing scenarios. In this case, we propose a micro-influencer ranking joint learning framework which models brands/micro-influencers from the perspective of individual image, target audiences, and cooperation preferences. Specifically, to model accounts’ individual image, we extract topics information and images semantic information from historical content information, and fuse them to learn the account content representation. We introduce target audiences as a new kind of marketing role in the micro-influencer recommendation, in which audiences information of brand/micro-influencer is leveraged to learn the multi-modal account audiences representation. Afterward, we build the attribute co-occurrence graph network to mine cooperation preferences from social media interaction information. Based on account attributes, the cooperation preferences between brands and micro-influencers are refined to attributes’ co-occurrence information. The attribute node embeddings learned in the attribute co-occurrence graph network are further utilized to construct the account attribute representation. Finally, the global ranking function is designed to generate ranking scores for all brand-micro-influencer pairs from the three perspectives jointly. The extensive experiments on a publicly available dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.
Matrix Converter for Reducing Harmonics in Micro- Turbine Generation System

This paper evaluates the performance of a micro- turbine generation system which is interfaced to a utility system through a matrix converter. The micro-turbine generation unit is composed of a high-speed gas turbine and a permanent-magnet synchronous generator. The performance of the system, based on time-domain simulation in the MATLAB package, shows that the matrix converter is a viable alternative to the conventional converter system as the interface unit of a micro-turbine generation system. In this paper a new switching strategy and a control approach for the matrix converter is introduced. The proposed switching strategy provides symmetrical input/output waveforms.
Problems with Applying the Ozawa–Avrami Crystallization Model to Non-Isothermal Crosslinking Polymerization

Ozawa has modified the Avrami model to treat non-isothermal crystallization kinetics. The resulting Ozawa–Avrami model yields the Avrami index (n) and heating/cooling function (χ(T)). There has been a number of recent applications of the Ozawa–Avrami model to non-isothermal crosslinking polymerization (curing) kinetics that have determined n and have used χ(T) in place of the rate constant (k(T)) in the Arrhenius equation to evaluate the activation energy (E) and the preexponential factor (A). We analyze this approach mathematically as well as by using simulated and experimental data, highlighting the following problems. First, the approach is limited to the processes that obey the Avrami model. In cases of autocatalytic or decelerating kinetics, commonly encountered in crosslinking polymerizations, n reveals a systematic dependence on temperature. Second, χ(T) has a more complex temperature dependence than k(T) and thus cannot produce exact values of E and A via the Arrhenius equation. The respective deviations can reach tens or even hundreds of percent but are diminished dramatically using the heating/cooling function in the form [χ(T)]1/n. Third, without this transformation, the Arrhenius plots may demonstrate breakpoints that leads to questionable interpretations. Overall, the application of the Ozawa–Avrami model to crosslinking polymerizations appears too problematic to be justified, especially considering the existence of well-known alternative kinetic techniques that are flexible, accurate, and computationally simple.
A spectral method of modularity for community detection in bipartite networks

Community detection in bipartite networks is a popular topic. Two widely used methods to capture community structures in bipartite networks are the method of modularity and the method of graph partitioning. Our analytical results show that the modularity maximization problem can be reformulated as a spectral problem after relaxing the discreteness constraints. This means that the method of modularity and the method of graph partitioning are essentially equivalent. As an application, a spectral algorithm of modularity is devised for identifying community structures in bipartite networks. Experimental results on synthetic networks and real-world networks indicate that our algorithm performs better than those algorithms of modularity local maximization, such as BRIM (bipartite recursively induced moduls) and bLP (bipartite label propagation). Therefore, our results shed light on the methods of community detection in bipartite networks.
In Situ Peroxidase Labeling Followed by Mass-Spectrometry Reveals TIA1 Interactome

Simple Summary T-cell intracellular antigen 1 (TIA1) is a DNA/RNA-binding protein best known for its different roles in RNA metabolism. Currently, little is known about the interacting protein partners of TIA1 in control and stress conditions that could shed light on its multiple context-specific molecular functions. Proximity labeling is a technique in which a labeling enzyme, here APEX2, that is fused to the protein of interest, in this case TIA1, marks the protein’s interacting network in living cells, allowing for subsequent ex vivo analysis using protein identification methods such as mass spectrometry. Hereby, combining these two techniques, it was revealed that the TIA1 interactome has very distinct protein partners in control and unstressed cells and that these partners are involved in not only previously identified processes such as splicing, nucleocytoplasmic transport, and different levels of protein translation control, but also in novel ones such as DNA double-strand break repair and mitochondrial metabolism. Overall, these findings provide a more precise definition of TIA1’s function in cells and pave the way to dissect its role in each of these processes. Abstract TIA1 is a broadly expressed DNA/RNA binding protein that regulates multiple aspects of RNA metabolism. It is best known for its role in stress granule assembly during the cellular stress response. Three RNA recognition motifs mediate TIA1 functions along with a prion-like domain that supports multivalent protein-protein interactions that are yet poorly characterized. Here, by fusing the enhanced ascorbate peroxidase 2 (APEX2) biotin-labeling enzyme to TIA1 combined with mass spectrometry, the proteins in the immediate vicinity of TIA1 were defined in situ. Eighty-six and 203 protein partners, mostly associated with ribonucleoprotein complexes, were identified in unstressed control and acute stress conditions, respectively. Remarkably, the repertoire of TIA1 protein partners was highly dissimilar between the two cellular states. Under unstressed control conditions, the biological processes associated with the TIA1 interactome were enriched for cytosolic ontologies related to mRNA metabolism, such as translation initiation, nucleocytoplasmic transport, and RNA catabolism, while the protein identities were primarily represented by RNA binding proteins, ribosomal subunits, and eicosanoid regulators. Under acute stress, TIA1-labeled partners displayed a broader subcellular distribution that included the chromosomes and mitochondria. The enriched biological processes included splicing, translation, and protein synthesis regulation, while the molecular function of the proteins was enriched for RNA binding activity, ribosomal subunits, DNA double-strand break repair, and amide metabolism. Altogether, these data highlight the TIA1 spatial environment with its different partners in diverse cellular states and pave the way to dissect TIA1 role in these processes.
SIMULATOR SICKNESS ON THE INCREASE

The usefulness of innovations in simulation technology may be compromised by a poorly understood phenomenon, vit, simulator sickness. Simulator sickness refers to motion sickness-like symptoms that occur in alrcrew during and following training. This paper will: 1) describe and summarize the implications of simulator ddmess, and 2) discuss a blocybernetic approach to the control of the problem.
Accelerated Airborne Virus Spread Simulation: Coupling Agent-based Modeling with GPU-accelerated Computational Fluid Dynamics

The Coronavirus Disease 2019 (COVID-19) has shown us the necessity to understand its transmission mechanisms in detail in order to establish practice in controlling such infectious diseases. An important instrument in doing so are mathematical models. However, they do not account for the spatiotemporal heterogeneity introduced by the movement and interaction of individuals with their surroundings. Computational fluid dynamics (CFD) simulations can be used to analyze transmission on micro- and mesostructure level, however become infeasible in larger scale scenarios. Agent-based modeling (ABM) on the other hand is missing means to simulate airborne virus transmission on a micro- and mesostructure level. Therefore, we present a system that combines CFD simulations with the dynamics given by trajectories from an ABM to form a basis for producing deeper insights. The proposed system is still work in progress;thus we focus on the system architecture and show preliminary results.