Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.
Linking species‐level network metrics to flower traits and plant fitness

Theoretical models indicate that the structure of plant–pollinator networks has important implications for the reproduction and survival of species. However, despite the growing information on the mechanisms underlying such a structure, it is still difficult to predict the functional consequences of species’ structural positions in these networks. From the plant perspective, species position and roles in pollination networks might be related to traits describing flower attractiveness, availability and dependence on pollinators. In turn, both network metrics and plant traits might influence plant species fitness. During two field seasons, we collected data from the 23 most abundant plant species in a rich coastal community, to evaluate the association between population and floral traits (floral abundance at population level and flowers per individual, flower shape and size, flowering length, nectar volume, pollinator dependence), species‐level network metrics (linkage level, specialization –d′–, weighted closeness centrality, network roles related to modularity) and plant fitness (seeds/flower, seed weight). Flowering length, flower size, flower abundance and pollinator dependence were positively related to greater generalization, as measured by various indices. More abundant species and those with larger flowers showed higher linkage levels (i.e. higher number of pollinator species), whereas longer flowering periods were negatively related to d′ and positively related to closeness centrality and important roles in the network. Likewise, plants species more dependent on pollinators occupied more central positions in the network. Furthermore, centrality in the networks was significantly associated with plant fitness. Specifically, central species in the network produced more and heavier seeds than the others. However, other plant traits, such as flower size and pollinator dependence had additional direct effects on seed production. Synthesis. Our study highlights how population and floral traits define the positions and roles of species structuring the pollination communities. Moreover, the relationships between network metrics and plant reproduction indicate, for the first time, the functional implications of these structural positions at the interspecific level of community assembly.
Surface Heat Flux Induced by Mesoscale Eddies Cools the Kuroshio‐Oyashio Extension Region

Sea surface temperature (SST) is a key player in the air‐sea interaction, influencing storm tracks, atmospheric circulation, and climate modes. Although prevailing theories attribute variations of large‐scale SST to atmosphere forcing and ocean internal dynamics, we find that sea surface heat flux anomalies induced by mesoscale eddies exert significant influences on the upper‐ocean heat budget in the Kuroshio‐Oyashio extension region. Despite making nearly no contribution to the net heat exchange at the air‐sea interface, the eddy‐induced heat flux anomalies weaken the thermal stratification in the upper ocean and result in pronounced sea surface cooling. The underlying dynamics is the efficient dissipation of eddy potential energy by eddy‐induced heat flux anomalies. This makes the conversion of eddy potential energy to eddy kinetic energy significantly reduced, corresponding to a weaker eddy‐induced restratification flux. The finding complements the existing theories on large‐scale SST dynamics and has important implications for understanding extratropical climate variability.
Space-Time Tomographic Reconstruction of Deforming Objects

Space-Time Tomographic Reconstruction of Deforming Objects Guangming Zang X-ray computed tomography (CT) is a popular imaging technique used for reconstructing volumetric properties for a large range of objects. Compared to traditional optical means, CT is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with. In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. The second application is about space-time tomography: Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging (e.g., 4D soot imaging of a combustion process, a mixing fluid process, a fuel injection process, and view synthesis for visible light tomography), which further demonstrates the flexibility of our optimization frameworks.
S3-Leitlinie „Diagnostik und Therapie des Morbus Crohn”

# Mitgetragen durch die – Deutsche Gesellschaft f r Allgemeinund Viszeralchirurgie – Deutsche Gesellschaft f r Chirurgie – Deutsche Gesellschaft f r Ern hrungsmedizin – Deutsche Gesellschaft f r Innere Medizin – Deutsches Kollegium f r Psychosomatische Medizin – Deutsche Gesellschaft f r Koloproktologie – Gesellschaft f r P diatrische Gastroenterologie und Ern hrung – Deutsche Gesellschaft f r Pathologie. Unter Mitarbeit der Deutschen Morbus Crohn/Colitis ulcerosa Vereinigung – DCCV e.V. Die Leitlinie wurde erarbeitet mit Unterst tzung des Leitlinien-Entwicklungsportals www.leitlinienentwicklung. de, einem gemeinsamen Forschungsprojekt der Charit – Universit tsmedizin und der Telematikplattform f r Medizinische Forschungsnetze (TMF) e. V.
Collisionless Magnetic Reconnection in an Asymmetric Oxygen Density Configuration

Combined with the magnetic field, the distribution of charged particles in the inflow region is expected to control the rate of magnetic reconnection. This paper investigates how the reconnection process is altered by a cold, asymmetrically distributed, oxygen population, which is initially located away from the current layer in the inflow regions. A particle‐in‐cell simulation is used to gain further insight into the dynamics of the system. The time evolution of the reconnection process proceeds rapidly compared to the cyclotron period of O + . Therefore, the oxygen remains, to a good approximation, demagnetized. Thus, Alfvén scaling is not an adequate description of the reconnection rate. A scaling relation for the reconnection rate for an asymmetrically distributed, demagnetized species has been developed. Additionally, we find that an asymmetric density configuration leads to a distinct motion of the reconnection site and generates an asymmetry of the diffusion region and the Hall electric field.
A systematic approach to model-based engineering of cyber-physical Systems of Systems

This thesis describes and evaluates methods for the model-based engineering of Systems of Systems (SoSs) where constituents comprise both computational and physical elements typical of Cyber-Physical Systems (CPSs). Such Cyber-Physical Systems of Systems (CPSoSs) use sensors and actuators to link the digital and physical worlds, and are composed of operationally and managerially independent constituent systems that interact to deliver an emerging service on which reliance is placed. The engineering of CPSoSs requires a combination of techniques associated with both CPS engineering and SoS engineering. Model-based SoS engineering techniques address organisation and integration of diverse systems through the use of disciplined architectural frameworks and contractual modelling approaches. Advances in model-based CPS engineering address the additional challenges of integrating semantically heterogeneous models of discrete and continuous phenomena. This thesis combines these approaches to develop a coherent framework for the model-based engineering of CPSoSs. The proposed approach utilises architectural frameworks to aid in the development of rich abstract models of CPSoSs. This is accompanied by the specification of an automated transformation process to generate heterogeneous co-models based on the architectural description. Verification of the proposed engineering approach is undertaken by its application to a case study describing the control of trains over a section of rail network, in which the (cyber) behaviour of control infrastructure must be considered in conjunction with the (physical) dynamics of train movements. Using the proposed methods, the development of this CPSoS uses architectural descriptions to generate an executable model to enable the analysis of safety and efficiency implications of the implemented control logic. The utility of the approach is evaluated by consideration of the impact of the proposed techniques on advancing the suitability and maturity of baseline technologies for the engineering of CPSoS. It is concluded that the proposed architectural framework provides effective guidance for the production of rich architectural descriptions of CPSoSs, and that the conversion between architectural and executable models is viable for implementation in a suitable open tools framework.
DNN-based Speech Synthesis considering Dialogue-Act Information and its Evaluation with Respect to Illocutionary Act Naturalness

This study aimed at improving synthesized speech generated by a text-to-speech (TTS) system used for a spoken dialogue sys-tem in regard to how naturally the synthesized speech conveys the system’s intention to the hearer. We call the measure of nat-uralness in this case “illocutionary act naturalness”. To achieve our aim, we utilized dialogue-act (DA) information as an auxiliary feature for a deep neural network (DNN)-based speech synthesis system. First, we constructed a speech database with DA tags. Second, we used the database to build the speech synthesis system. Third, we evaluated the method by comparing its performance with a DNN making use of conventional linguistic features and hidden Markov models (HMMs) supplemented with DAs. We conducted a listening test designed to evaluate illocutionary act naturalness. The results show that the proposed method improves the illocutionary act naturalness compared with the conventional method. We also found that the illo-cutionary act naturalness score depended on certain features of the test sentence as well as the DA and speech synthesis method. The results shows that a test set designed by considering these features will improve the reproducibility of the illocutionary act naturalness evaluation.
A control allocation technique to recover from driver-induced oscillations

A CONTROL ALLOCATION TECHNIQUE TO RECOVER FROM DRIVER-INDUCED OSCILLATIONS Ayesha Sarwar M.S. in Mechanical Engineering Advisor: Yildiray Yildiz December 2019 The focus of this thesis is the study of driver induced oscillations. When the rear tires of a car are force saturated due to aggressive driver behaviour, high velocity on a slippery road, sudden steering action or heavy braking, rear end of the car tends to lose traction on the road and starts skidding. At the same time, the driver, having the direct steering control over the front two tires, feels a time delay at the response of the rear tires to the steering actions. The delay between the driver’s action and the vehicle’s response may eventually instigate the swinging of the rear end of the vehicle, which is generally referred to as “fishtailing”. In the literature, few studies exist regarding fishtailing motion and in those studies, the theoretical background of this motion is not studied in detail. In this thesis, fishtailing motion dynamics are investigated in detail by employing a non linear vehicle model, and this motion is recreated for a specific vehicle configuration. Then, a control allocation technique is presented to recover from this driverinduced fishtailing motion. The proposed control allocation method helps the vehicle recover from these undesired oscillations by minimizing the phase shift between the commanded and realized forces and moments. The simulation results demonstrate that using the proposed method, it is possible to make the vehicle recover from driver induced oscillations even at high velocities where conventional approaches fail. For the cases of actuator failure, for example loss of tire inflation pressure, an adaptive version of the control allocation is also proposed that can stabilize the vehicle even when the actuator effectiveness is reduced significantly.
Non-Contact Fault Diagnosis of Bearings in Machine Learning Environment

Timely detection of faults in bearings can save time, efforts and maintenance costs of rotating equipments. To avoid the physical connection of vibration pickup to the machine tool, a non-contact type vibration pickup has been designed and developed in this study to acquire the vibration data for bearing health monitoring under load and speed variation. Fault diagnosis has been accomplished using a Hilbert transform for denoising the signal. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the number of input features and finding the most optimal feature set. Finally, these selected features have been passed to Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for identifying and further classifying the various bearing defects. A comparative analysis of the effectiveness of SVM and ANN has been carried out. The results reveal that the vibration signatures obtained from developed non-contact sensor (NCS) compare well with the accelerometer data obtained under the same conditions. Classification accuracy achieved by the developed NCS with other sensors reported in the literature compares very well. The proposed strategy can be used for automatic recognition of machine faults which will help in providing early warnings to avoid unwanted and unplanned system shutdowns due to failure of the bearings.
Latitudinal clines in the timing and temperature‐sensitivity of photoperiodic reproductive diapause inDrosophila montana

Reproductive diapause is a primary mechanism used by arthropods to synchronize their life cycle with seasonal changes in temperate regions. Our study species, Drosophila montana, represents the northern insect species where flies enter reproductive diapause under short day conditions and where the precise timing of diapause is crucial for both survival and offspring production. We have studied clinal variation in the critical day length for female diapause induction (CDL) and their overall susceptibility to enter diapause (diapause incidence), as well as the temperature sensitivity of these traits. The study was performed using multiple strains from four latitudinal clines of the species – short clines in Finland and Alaska and long clines in the Rocky Mountains and the western coast of North America – and from one population in Kamchatka, Russia. CDL showed strong latitudinal clines on both continents, decreasing by one hour per five degrees decline in latitude, on average. CDL also decreased in all populations along with an increase in fly rearing temperature postponing the diapause to later calendar time, the effects of temperature being stronger in southern than in northern population. Female diapause incidence was close to 100% under short day/low temperature conditions in all populations, but decreased below 50% even under short days in 19°C in the southern North American western coast populations and in 22°C in most populations. Comparing a diversity of climatic data for the studied populations showed that while CDL is under a tight photoperiodic regulation linked with latitude, its length depends also on climatic factors determining the growing season length. Overall, the study deepens our understanding of how spatial and environmental parameters affect the seasonal timing of an important biological event, reproductive diapause and helps to estimate the evolutionary potential of insect populations to survive in changing climatic conditions.
Analysis and Implementation of a Phase-Shift Pulse-Width Modulation Converter with Auxiliary Winding Turns

A phase-shift pulse-width modulation converter is studied and investigated for railway vehicle or solar cell power converter applications with wide voltage operation. For railway vehicle applications, input voltage range of dc converters is requested to have 30–40% voltage variation of the nominal input voltage. The nominal input voltages of dc converters on railway vehicles applications may be 37.5 V, 48 V, 72 V, 96 V and 110 V. Therefore, a new dc converter with wide input voltage operation from 25 to 150 V is presented to withstand different nominal input voltage levels such as 37.5–110 V on railway power units. To realize wide input voltage operation, an auxiliary switch and auxiliary transformer windings are used on output side of conventional full-bridge converter to have different voltage gains under different input voltage values. Phase-shift pulse-width modulation is adopted in the developed dc converter to accomplish soft switching operation on power switches. To confirm and validate the practicability of the presented converter, experiments based on a 300 W prototype were provided in this paper.
Accelerating Monte Carlo modeling of structured-light based diffuse optical imaging via “photon sharing”

The increasing use of spatially-modulated imaging and single-pixel detection techniques demand computationally efficient methods for light transport modeling. Herein, we report an easy-to-implement yet significantly more efficient Monte Carlo (MC) method for simultaneously simulating spatially modulated illumination and detection patterns accurately in 3-D complex domains. We have implemented this accelerated algorithm, named “photon sharing”, in our open-source MC simulators, reporting 13.6× and 5.5× speedups in mesh- and voxel-based MC benchmarks respectively. In addition, the proposed algorithm is readily used for accelerating the solution of inverse problems in spatially-modulated imaging systems by building Jacobians of all illumination-detection pattern pairs concurrently, resulting in a 12.4-fold speed improvement.
Antioxidant-Loaded Mucoadhesive Nanoparticles for Eye Drug Delivery: A New Strategy to Reduce Oxidative Stress

There are several approaches to treat ocular diseases, which can be invasive or non-invasive. Within the non-invasive, new pharmaceutical strategies based on nanotechnology and mucoadhesive polymers are emerging methodologies, which aim to reach an efficient treatment of eye diseases. The aim of this work was the development of novel chitosan/hyaluronic acid nanoparticle systems with mucoadhesive properties, intended to encapsulate antioxidant molecules (e.g., crocin) aiming to reduce eye oxidative stress and, consequently, ocular disease. An ultraviolet (UV) absorber molecule, actinoquinol, was also added to the nanoparticles, to further decrease oxidative stress. The developed nanoparticles were characterized and the results showed a mean particle size lower than 400 nm, polydispersity index of 0.220 ± 0.034, positive zeta potential, and high yield. The nanoparticles were also characterized in terms of pH, osmolality, and viscosity. Mucoadhesion studies involving the determination of zeta potential, viscosity, and tackiness, showed a strong interaction between the nanoparticles and mucin. In vitro release studies using synthetic membranes in Franz diffusion cells were conducted to unravel the drug release kinetic profile. Ex vitro studies using pig eye scleras in Franz diffusion cells were performed to evaluate the permeation of the nanoparticles. Furthermore, in vitro assays using the ARPE-19 (adult retinal pigment epithelium) cell line showed that the nanoparticles can efficiently decrease oxidative stress and showed low cytotoxicity. Thus, the developed chitosan/hyaluronic acid nanoparticles are a promising system for the delivery of antioxidants to the eye, by increasing their residence time and controlling their delivery.
Noninvasive and Improved Torque and Efficiency Calculation Toward Current Advance Angle Determination for Maximum Efficiency Control of PMSM

This article proposes improved mathematical models for torque and system efficiency used toward obtaining accurate current advance angle for maximizing the efficiency of an interior permanent magnet (IPM) synchronous motor. First, improved torque and efficiency calculation procedure that consider the effects of parameter variations, such as inductance, stator resistance and PM flux linkage simultaneously, and motor and inverter losses, have been developed from a combination of analytical models and practical experiments. Subsequently, an offline search procedure has been utilized to determine the optimal current angle using the improved dq-axis-based models. The novelty of the efficiency model is that the method uses preliminary noninvasive experimental tests to consider the saturation and temperature effects simultaneously and successfully determine the relationship between stator and rotor temperatures by using only controller command voltages and currents. Experimental investigations are performed on a laboratory IPM for validating the developed control method through interpolation of improved look-up tables with the derived current angle values for varying speed, torque, and temperature conditions. The effectiveness of the proposed method in improving efficiency is also verified and compared with maximum efficiency and maximum torque per ampere methods using experimental sweep tests.