Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights into the effect of high temperature on the shear behavior of the calcium silicate hydrate by reactive molecular dynamics simulations

When subjected to fires, cementitious composites can be seriously damaged. However, the mechanical behavior of nanoscale calcium silicate hydrate (C–S–H) grains, which are the main binder of cementitious composites, exposed to elevated temperatures under shear deformations remain poorly investigated. In this paper, considering different calcium/silicate (C/S) molar ratios (i.e., C/S = 1.10, 1.33, and 1.64), the shear behavior of the C–S–H grain after exposure to different high-temperature levels (i.e., 300 K, 500 K, 700 K, 900 K, and 1000 K) is studied by conducting series of reactive molecular dynamics simulations. Results reveal that the C–S–H grain exhibits good plasticity under shear deformation. Furthermore, the shear modulus of the C–S–H grain is between 14 GPa and 17 GPa and exhibits a decrease with the calcium/silicon (C/S) molar ratio at ambient temperature. While the shear strength is around 1.0 GPa and reaches the lowest value at C/S = 1.64. Interestingly, we report that heating can lead to the increase of the shear modulus and shear strength due to the evaporation of the interlayer water which generally acts as the lubricant. Additionally, we show that heating has no clear influence on the shear strain corresponding to the onset of yielding when the C/S ratio is high but can indeed improve the shear strain corresponding to the shear strength. Furthermore, the yielding area of the C–S–H grain under the shear deformation can be enlarged. Finally, factors affecting the strength degradation of cementitious composites after being heated to different temperature levels are further discussed based on the simulation results.
Modeling and Control of a Tailsitter with a Ducted Fan

Modeling and Control of a Tailsitter with a Ducted Fan Matthew Elliott Argyle Department of Electrical and Computer Engineering, BYU Doctor of Philosophy There are two traditional aircraft categories: fixed-wing which have a long endurance and a high cruise airspeed and rotorcraft which can take-off and land vertically. The tailsitter is a type of aircraft that has the strengths of both platforms, with no additional mechanical complexity, because it takes off and lands vertically on its tail and can transition the entire aircraft horizontally into high-speed flight. In this dissertation, we develop the entire control system for a tailsitter with a ducted fan. The standard method to compute the quaternion-based attitude error does not generate ideal trajectories for a hovering tailsitter for some situations. In addition, the only approach in the literature to mitigate this breaks down for large attitude errors. We develop an alternative quaternion-based error method which generates better trajectories than the standard approach and can handle large errors. We also derive a hybrid backstepping controller with almost global asymptotic stability based on this error method. Many common altitude and airspeed control schemes for a fixed-wing airplane assume that the altitude and airspeed dynamics are decoupled which leads to errors. The Total Energy Control System (TECS) is an approach that controls the altitude and airspeed by manipulating the total energy rate and energy distribution rate, of the aircraft, in a manner which accounts for the dynamic coupling. In this dissertation, a nonlinear controller, which can handle inaccurate thrust and drag models, based on the TECS principles is derived. Simulation results show that the nonlinear controller has better performance than the standard PI TECS control schemes. Most constant altitude transitions are accomplished by generating an optimal trajectory, and potentially actuator inputs, based on a high fidelity model of the aircraft. While there are several approaches to mitigate the effects of modeling errors, these do not fully remove the accurate model requirement. In this dissertation, we develop two different approaches that can achieve near constant altitude transitions for some types of aircraft. The first method, based on multiple LQR controllers, requires a high fidelity model of the aircraft. However, the second method, based on the energy along the body axes, requires almost no aerodynamic information.
Reliability Modeling and Assessment for a Cyber-Physical System With a Complex Boundary Behavior

This article investigates the reliability of a special cyber-physical system (CPS) with an unreliable service and a complex boundary behavior. A flat semi-dormant multicontroller (FSDMC) model is constructed on a special CPS named arbitrated networked control system (ANCS) with dual channels. In this study, the dual-channel ANCS is considered as a Markov repairable system, which integrates the binary state of physical device failure and the multistate of information flow. The FSDMC is modeled as an N/(d,c)-M/M/c/K/SMWV queuing system with an unreliable service. A dual rate matrix method is proposed to solve the stationary distribution of the queuing system and obtain the closed-form matrix solution of the distribution. Based on the queuing model, an optimization model is established to minimize the proposed cost performance rate function. A particle swarm optimization algorithm is used to solve the optimization model and obtain the optimal values of the system parameters under stable conditions. The closed-form expression of the instantaneous availability of the FSDMC on the physical failure rate and repair rate of the controller is yielded iteratively. The linear relationship between system instantaneous failure rate and task instantaneous failure rate is expressed. The sensitivity of task failure rate to system parameters is analyzed. Several reliability metrics are used to evaluate system reliability and task reliability. Experiments are conducted in real application scenarios to compare the task reliability using redundancy technology and real parallel applications. Experiments show that the proposed reliability model can more effectively guarantee system reliability goals compared with its counterparts.
Reliability of an airport noise prediction model in the long distance from the airport

This paper discusses an improvement in the reliability of an airport noise prediction model, currently used in Japan, in the long distance from the airport. The validity of the model has been always checked since its initial development in 1978, but attention was basically confined to the confirmation of the model accuracy in regions rather close to the airport, e.g., in order to check the validity of noise zones specified for measures based on the Aircraft Noise Prevention Law. Recently, however, during the process of environmental impact assessment for the construction of fourth runway at Tokyo International Airport, it was said that there is a large discrepancy between predictions and measurements of noise levels in regions twenty to thirty kilometers distant from the airport, especially below landing paths. This paper describes a result of investigation to check and improve the reliability of noise-distance data and performance data of the noise prediction model, based on field measurement of noise and flight path with flight-deck data at three airports.
Analyses of the connection mode of anti-resonance using 4 PT in the neutral non-effectively grounded system

This paper analyzes the principle of ferromagnetic resonance in the neutral non-effectively grounded power system,and points out the mechanism of several common ways of harmonic elimination,then focuses on the analysis of the principle of connecting the 4 PT to prevent the ferromagnetic resonance over-voltage.The validity of the 4 PT's connection in harmonic elimination is proved.Through the analysis of the secondary connection way of common 4 PT harmonic elimination,the paper shows their sensitivity to the abnormal situation such as single-phase grounded and PT break.It points out the advantages and disadvantages and then gives some notes to each connection mode,providing evidence for rectifications and optimizations in the future.As for the 4 PT connection,the improved connection method with high impedance of 4 PT is recommended.
The Validity of Prenatal Assessments of Mothers’ Emotional, Cognitive, and Physiological Reactions to Infant Cry

SYNOPSIS Objective. The current study examines the stability, convergent validity, and predictive validity of assessing pregnant women’s emotional, cognitive, and physiological responses to infant cry. Design. In an ethnically diverse sample of 259 first-time mothers and their infants, during the prenatal period physiological arousal (skin conductance) and regulation (respiratory sinus arrythmia) were recorded as mothers were exposed to four 1-min videos of crying infants. After each clip, mothers completed questionnaires and were interviewed about their cognitive (attributions, ability to detect distress, efficacy) and emotional responses (empathy, negative emotions). When infants were 6 months old, mothers’ physiological arousal and regulation were assessed while interacting with their own infants during distress-eliciting tasks, then mothers were interviewed about their emotional and cognitive responses using a video-recall method. A subset of mothers (n = 103) was re-administered the prenatal interview using the standard cry videos. Maternal sensitivity was observed during distress-eliciting tasks when infants were 6 months, 1 year, and 2 years old. Results. Mothers’ prenatal responses to cry videos were moderately stable until 6 months postpartum, converged with postnatal measures from own infant stimuli, and illustrated modest predictive validity to maternal sensitivity during the first 2 years that was comparable to predictive validity from mothers’ postpartum responses to their own infants. Conclusions. How mothers respond to cry stimuli during the prenatal period is reflective of later responses toward their own infants, and as such prenatal cry stimuli are a useful tool for parenting researchers.
Prototyping an IoT transparency toolkit to support communication, governance and policy in the smart city

Abstract This paper describes the design of a prototype transparency tool for communities, public and private organisations, and other stakeholders to use when developing, deploying and interrogating public space Internet of Things implementations as part of wider smart city initiatives. These deployments need to be accompanied by effective public communications and policy, and consideration of factors such as data privacy, so as not to negatively impact citizens. The intention of the tool is to guide transparency of these deployments in order to support trusted IoT ecosystems. The tool, which was developed as both a physical card deck and a digital implementation, was developed through a combination of participatory work and other research to understand the information requirements of citizens and other stakeholders. We suggest that further development of such tools can effectively support trustworthy IoT in smart cities.
Evaluating long‐term One‐Way Atmosphere‐Hydrology simulations and the impacts of Two‐Way coupling in four mountain watersheds

Joint hydrologic‐atmospheric model frameworks offer novel insights into the terrestrial hydrologic cycle and the potential for improved predictive capabilities for stream discharge and other hydrologic fluxes. In this study, we examine both one‐ and two‐way coupled integrations of the Weather Research and Forecasting (WRF v3.8.1) atmospheric and WRF‐Hydro (v5.0) hydrologic models for four 1000–2000 km2 snow‐dominated mountain watersheds (1500–2100 m mean elevation) in Idaho's Rocky Mountains. In watersheds where anthropogenic withdrawals are minimal (3 of 4 watersheds), we simulate stream discharge with high confidence (KGE > 0.63) for a 20 year period in the uncoupled scenarios, and find that WRF winter precipitation accumulations have less than 15% average error for all but two of the fourteen comparison NRCS Snotel sites. However, annual streamflow biases are highly correlated (r2 > 0.8 in some cases) with the annual errors in WRF cold‐season precipitation, suggesting that process representation of winter orographic precipitation limits hydrologic predictability. In the second part of the study, we evaluate the potential for ‘two‐way’ model coupling to influence hydrologic predictability by examining a 2 month case‐study period with active spring season convective precipitation. We quantify the impacts of resolving hillslope‐scale soil water redistribution on the ABL, and find that while resolving overland and saturated subsurface soil moisture flow influences soil moisture distributions and surface energy fluxes, the impact on precipitation is non‐systematic, as precipitation is generally atmospherically controlled during the study period. Consequently, future efforts should focus on improving winter orographic process representation, as streamflow is highly sensitive to errors in these processes.
Comparing the Incomparable? Performance Measures for National Libraries

The article describes the process of developing performance measures for national libraries, which culminated in the publication in April 2009 of the International Organization for Standardization Technical Report ISO 28118 Information and documentation – performance indicators for national libraries. An ISO working group began by defining the general mission and main tasks of national libraries. Most existing performance indicators, devised mainly for academic and public libraries, compared a library's input and output to the size of its primary user group. However, national libraries do not have such a defined user group – a fact that had to be reflected in the chosen indicators, which cover: building the national collection; making services accessible; offering reference services; building potentials for development; preserving the collection; and managing efficiently.
First-order differential filtering spectrum division method and information fusion multi-scale network for fault diagnosis of bearings under different loads

In recent years, data-driven intelligent diagnosis methods have been widely applied in the field of bearing fault diagnosis. However, these methods involve some expert experience and knowledge, and cannot accurately mine bearing fault characteristics under different loads. To solve this problem, this paper proposes a first-order differential filtering spectrum division (FDFSD) method and an information fusion multi-scale network (IFMSNet) to realize bearing fault diagnosis under different working conditions. First of all, the proposed spectrum division method based on the first-order differential filtering, the first-order differential processing of time domain signals, and the introduction of triangular filter, reclassify the spectrum features, highlight feature information, can accurately extract bearing fault features. Secondly, a new multi-scale network model of information fusion is constructed in this paper. Convolution kernels of different sizes are used to extract fault features of bearings of different scales, and information fusion is carried out to identify bearing working conditions and realize intelligent diagnosis of bearings under different loads. Finally, in order to verify the effectiveness and accuracy of the proposed method, it is verified on a variety of bearing experimental data sets. The results show that the average prediction accuracy of the proposed method is 99.11% and 97.74%, respectively. Compared with the proposed three single-scale network, K-nearest neighbor, Naive Bayes, support vector machine and random forest methods, the proposed method has more advantages in bearing intelligent diagnosis under different loads.
Binary classification of welding defect based on deep learning

The current study investigated the effects of data augmentation, convolutional layers depth, and learning rate on inspection accuracy of a deep learning model which detects welding defects. The experimental results suggested that simultaneous use of these two methods improved the model's performance more than the sum of using each of the two methods alone. As the number of convolutional layers increases above 10, the network cannot extract defect features effectively, and the model's accuracy will decrease. Learning rate could significantly influence convergence rate and accuracy, and using an initial learning rate of 1e-4 and decaying it at epoch 250 could reduce the loss function and increase model accuracy.
Skills-focused lab instruction improves critical thinking skills and experimentation views for all students

Instructional labs are fundamental to an undergraduate physics curriculum, but their possible learning goals are vast with limited evidence to support any particular goal. In this study, we evaluate the efficacy of labs with different goals and structures on students ’ critical thinking skills and views about experimentation, using an extensive database of survey responses from over 20 000 students at over 100 institutions. Here, we show that labs focused on developing experimentation skills improve students ’ critical thinking skills and experimentation views compared to labs focused on reinforcing lecture concepts. We further demonstrate the positive impacts of skills-based labs over concepts-based labs on these outcomes across students ’ gender and race or ethnicity. Our analysis also shows that activities to support students ’ decision making and communication explain over one-half and one-third of the effect of skills-based labs on students ’ critical thinking skills and experimentation views, respectively, while modeling activities have only a small effect on performance. ’ each
Steady-State Availability Evaluation for Heterogeneous Edge Computing-Enabled WSNs with Malware Infections

To evaluate the steady-state availability of heterogeneous edge computing-enabled wireless sensor networks (HECWSNs) with malware infections, we ﬁ rst propose a Stackelberg attack-defence game to predict the optimal strategies of malware and intrusion detection systems (IDSs) deployed in heterogeneous sensor nodes (HSNs). Next, we present a new malware infection model — heterogeneous susceptible-threatened-active-recovered-dead (HSTARD) based on epidemic theory. Then, considering the heterogeneity of sink sensor nodes and common sensor nodes and the malware attack correlation, we derive the state transition probability matrix of an HSN based on a semi-Markov process (SMP), as well as the steady-state availability of an HSN. Furthermore, based on a data ﬂ ow analysis of HSNs, we deduce the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology. Finally, numerical analyses illustrate the in ﬂ uence of the IDS parameters on the optimal infection probability of malware and reveal the e ﬀ ect of multiple factors on the steady-state availability of HSNs, including the initial infection rate, the infection change rate, and the malware attack correlation. In addition, we present data analyses of the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology, which provide a theoretical basis for the design, deployment, and maintenance of high-availability HECWSNs.
Deep Reinforcement Learning Based Adaptive Controller of DC Electric Drive for Reduced Torque and Current Ripples

Artificial Intelligence and Machine Learning-based intelligent control algorithms are replacing traditional control algorithms due to their adopting and self-learning capabilities. The DC-DC Buck converter fed DC motor has a wide variety of applications from household appliances to industry level. Conventionally, the proportional-integral controller is widely used to optimally control the speed of DC-DC Buck converter fed DC Motor but the performance deteriorates for parameters changes which lead to increase ripples in torque and current. The demanding task is to control the electric drives for mitigated ripple content in speed operation. This work proposes a new adaptive controller based on deep reinforcement learning (DRL) to mitigate those problems. The simulation results are illustrating that the Deep Deterministic Policy Gradient (DDPG) algorithm performs superior to the PI controller under parameter changes and also proving that the DDPG making the system stable where the PI fails to stabilize it for a wider range of parameter changes.
Bioactive Compounds and Their Derivatives: An Insight into Prospective Phytotherapeutic Approach against Alzheimer's Disease

Alzheimer's disease (AD) is a common neurodegenerative brain disorder that causes cellular response alterations, such as impaired cholinergic mechanism, amyloid-beta (Aβ) AD aggregation, neuroinflammation, and several other pathways. AD is still the most prevalent form of dementia and affects many individuals across the globe. The exact cause of the disorder is obscure. There are yet no effective medications for halting, preventing, or curing AD's progress. Plenty of natural products are isolated from several sources and analyzed in preclinical and clinical settings for neuroprotective effects in preventing and treating AD. In addition, natural products and their derivatives have been promising in treating and preventing AD. Natural bioactive compounds play an active modulatory role in the pathological molecular mechanisms of AD development. This review focuses on natural products from plant sources and their derivatives that have demonstrated neuroprotective activities and maybe promising to treat and prevent AD. In addition, this article summarizes the literature pertaining to natural products as agents in the treatment of AD. Rapid metabolism, nonspecific targeting, low solubility, lack of BBB permeability, and limited bioavailability are shortcomings of most bioactive molecules in treating AD. We can use nanotechnology and nanocarriers based on different types of approaches.
The zero‐inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology

For frequency counts, the situation of extra zeros often arises in biomedical applications. This is demonstrated with count data from a dental epidemiological study in Belo Horizonte (the Belo Horizonte caries prevention study) which evaluated various programmes for reducing caries. Extra zeros, however, violate the variance–mean relationship of the Poisson error structure. This extra‐Poisson variation can easily be explained by a special mixture model, the zero‐inflated Poisson (ZIP) model. On the basis of the ZIP model, a graphical device is presented which not only summarizes the mixing distribution but also provides visual information about the overall mean. This device can be exploited to evaluate and compare various groups. Ways are discussed to include covariates and to develop an extension of the conventional Poisson regression. Finally, a method to evaluate intervention effects on the basis of the ZIP regression model is described and applied to the data of the Belo Horizonte caries prevention study.