Miracle-dz/UniFAR-FTUs huggingface dataset:
XAS and XMCD Reveal a Cobalt(II) Imide Undergoes High-Pressure-Induced Spin Crossover

: The Spin Crossover (SCO) transition is investigated in two molecular Co 2+ bis-imide compounds, Co( dpzca ) 2 and Co( pypzca ) 2 (where H dpzca = N -(2-pyrazylcarbonyl)-2-pyrazinecarboxamide, and H pypzca = N-(2-pyrazylcarbonyl)-2- pyridinecarboxamide). They crystallize solvent-free with similar crystal structures, but have been reported to exhibit different temperature-dependent magnetic behaviors. Using temperature-and pressure-dependent element selective X-ray absorption spectroscopy (XAS and XMCD) measurements, it is revealed herein that although Co( pypzca ) 2 does not afford a temperature-induced SCO, it undergoes a reversible pressure-induced SCO transition that is more gradual and complete at a higher pressure (3.5 GPa) than for Co( dpzca ) 2 (0.5 GPa). Wave function-based calculations performed on isolated complexes confirm the LS state nature of Co( dpzca ) 2 at low temperatures, and the values of spin and orbital magnetic moments are determined. Calculations show the similarity of ground-state properties for HS Co( dpzca ) 2 and HS Co( pypzca ) 2 and the existence of a double-well HS-LS in the potential energy surface for both compounds. It is concluded that the observed, significant, differences in pressure-and temperature-induced SCO transitions of Co( dpzca ) 2 versus Co( pypzca ) 2 are probably due to different intermolecular interactions between Co( dpzca ) 2 and Co( pypzca ) 2 , which would hamper the temperature-induced SCO in Co( pypzca ) 2 .
Non-Contact Ultrasonic Guided Wave Inspection of Rails: Next Generation Approach

In the last ten years, over 1,000 train derailments that occurred in the US railroad system were caused by undetected rail internal defects that suddenly and dramatically emerged as track breakages. The total cost of those accidents is quantifiable in a few hundred millions of dollars, without considering the associated tragic losses of life and bodily injuries. While there already exist a few methods for the detection of rail internal flaws, several well-known limitations prevent each of them from detecting all of the critical flaws. As a proposal to address this issue, the object of this dissertation has been the development of a novel rail inspection system based on ultrasonic guided waves propagating through rails. Both the generation and the detection of these waves are achieved in a non-contact manner through the use of piezoelectric air-coupled transducers. One of the advantages of employing such non-contact method is the potential to perform tests as the train or inspector car travels at high speed along the railroad. Nevertheless, the main drawback of using air-coupled transducers on steel rail is represented by the significant energy losses occurring at the interface between air and steel due to the large acoustic impedance mismatch between these media. As a result, the signal to noise ratio available when analyzing the data is severely penalized. In an attempt to overcome this limitation, very effective electrical impedance networks have been designed. In parallel, a statistical analysis method based on multivariate outlier detection has been implemented to enhance the defect- sensitivity of the system. Numerical analyses of the ultrasonic wave propagation and interaction with different types of rail internal defects have been carried out using both a finite difference method, based on the Local Interaction Simulation Approach (LISA), and a commercial finite element method software. The results of these analyses were instrumental in understanding salient aspects of the guided wave propagation phenomenon in rails and throughout the ever ending decision-making process for the definition of the many system operating parameters involved. prototype based on these technologies has been built and tested both in-house at the UCSD Rail Defect Test Facility located at Camp Elliott, and in the field at the Transportation Technology Center located in Colorado, in October 2014. Receiver Operating Characteristics curves were used to characterize the performance of the defect detection based on the trade-off between defect detection rate and false alarm rate. In particular, the results of the field test were found to be quite satisfactory and perfectly in line with the predictions of the numerical analyses. Future work should be aimed at improving the prototype performance, particularly in terms of test speed, based on the lessons learnt from the October 2014 field tests and in the laboratory
The comparison of frenectomy using blade versus laser (Nd-YAG): A case report

Introduction: Abnormalities of frenulum can initiate periodontal disease and orthodontic problems. Objectives: This case report aims to performing and comparing the procedure of frenectomy using blade and LASER (Nd-YAG). Cases: First case, boy 14 years, with abnormalities of labial frenulum that extended to the palate. The second case, a woman 19 years old comes with a severe malocclusion and recession on her lower incisor due to tension of the frenulum. Case Management: Conventional frenectomy with blade for the first case, whereas in the second case we do frenectomy with LASER (NdYAG). Conclusion: Both of two techniques show each strength, same patient safety, similar outcomes and also patient comfort. Further studies should be recommended by adding greater amount of sample.
Development of V2G and G2V Power Profiles and Their Implications on Grid Under Varying Equilibrium of Aggregated Electric Vehicles

Abstract The objective of this paper is to examine the vehicle-to-grid (V2G) power capability of aggregated electric vehicles (EV) in the manner that they are being adopted by the consumers with their growing infiltration in the vehicles market. The proposed modeling of V2G and grid-to-vehicle (G2V) energy profiles blends the heterogeneous attributes namely, driven mileages, arrival and departure times, travel and parking durations, and speed dependent energy consumption of mobility trends. Three penetration percentages of 25 %, 50 % and 100 % resulting in varied compositions of battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) in the system, as determined by the consumers’ acceptance, have been considered to evaluate the grid capacity for V2G. Distinct charge-discharge powers have been selected as per charging standards to match contemporary vehicles and infrastructure requirements. Charging and discharging approaches have been devised to replicate non-linear characteristics of Li-ion battery. Effects of simultaneous conjunction of V2G and G2V power curves with daily conventional load profile are quantified drawn upon workplace-discharging home-charging scheme. Results demonstrated a marked drop in load and hence in market price during morning hours which is hurriedly overcompensated by the hike during evening hours with rising penetration level and charge-discharge power.
Task Switching for Supervisory Control of Multi-Robot Teams

Human multi-robot control is a complex process: an operator’s attention constantly shifts among various tasks to maintain the necessary awareness of a given situation, such as exploring environments and locating targets simultaneously. To minimize the laborious monitoring, if a robot can self-reflect its needs for interaction and alert an operator to any abnormalities, the operator’s attention could be appropriately directed to urgent events in a timely manner. This type of human-robot interaction can be viewed as a queuing system, in which an operator acts as a server, and robots’ requests for services are the jobs. This paper presents two experiments that investigated scheduling interventions. In the first experiment, participants performed search and rescue tasks while assisted by an alarmed system: Open-queue, in which all the alarms are displayed at once, or FIFO-queue (first-in-first-out approach), which shows only a single alarm at a time. The second study compared the FIFO and Open conditions from the first study with a shortest-job-first (SJF) queue system. The results show that operator attention can be effectively directed toward interaction with needed robots without degrading performance, which suggest the uses of the queueing disciplines could lead to superior performance while choosing between robot requests becomes more challenging. Additionally, to better understand operators’ intention and resulting behaviors, a Hidden Markov Model (HMM) was applied to investigate human control processes in interactions with multi-robot teams. The HMM results demonstrated fundamental differences among queuing mechanisms, which are difficult to observe through conventional approaches.
The Effects of Institutions on Emerging Market Firms’ International Assignment Location Decisions

We investigate international assignment (IA) location decisions of emerging market firms as determined by the institutional contexts of their home and host countries. Using an institutional perspective, assignment patterns of the entire firm population in Slovenia to either other emerging or developed host countries in Europe are analysed. The findings show that both institutional quality and distance influence expatriation flows in firms from a low quality institutional context. These firms expatriate more to markets with high quality institutions and choose host countries with higher rather than smaller institutional distance for their IAs. We refine institutional theory with respect to host and home country institutional determinants of expatriation decisions by taking into consideration the particular features of emerging markets and their firms e separately and compared to developed markets and their firms.
Electronic scattering in half-Heusler thermoelectrics from resistivity data

A key part of optimising thermoelectric materials is understanding the electronic scattering mechanism. For half-Heusler (HH) thermoelectrics, the dominant mechanisms are acoustic phonon scattering in pure systems and alloy scattering in highly alloyed systems. In this report, the significance of the residual resistivity ρ 0 is highlighted. Large ρ 0 values can lead to misidentification of the dominant scattering mechanism when only high-temperature ρ(T) data is available. A straightforward approach to analyse ρ(T) is proposed and applied to a range of HH systems. This reveals large levels of structural disorder in XIVNiSn, whilst XVFeSb has the strongest coupling with acoustic phonons. The electronic scattering mechanism depends sensitively on composition, with acoustic (ρ sim T 1.5), metallic (simT 1) and alloy (simT 0.5) scattering observed within the main HH families. With the aid of velocity of sound, band mass and carrier concentration data, the deformation potential can be obtained, enabling quantification of the interaction between phonons and carriers, from fits to resistivity data. This work provides a route for the analysis of experimental ρ(T) data that can be applied to a range of thermoelectric materials.
Abnormal interfacial bonding mechanisms of multi-material additive-manufactured tungsten–stainless steel sandwich structure

Tungsten (W) and stainless steel (SS) are well known for the high melting point and good corrosion resistance respectively. Bimetallic W–SS structures would offer potential applications in extreme environments. In this study, a SS→W→SS sandwich structure is fabricated via a special laser powder bed fusion (LPBF) method based on an ultrasonic-assisted powder deposition mechanism. Material characterization of the SS→W interface and W→SS interface was conducted, including microstructure, element distribution, phase distribution, and nano-hardness. A coupled modelling method, combining computational fluid dynamics modelling with discrete element method, simulated the melt pool dynamics and solidification at the material interfaces. The study shows that the interface bonding of SS→W (SS printed on W) is the combined effect of solid-state diffusion with different elemental diffusion rates and grain boundary diffusion. The keyhole mode of the melt pool at the W→SS (W printed on SS) interface makes the pre-printed SS layers repeatedly remelted, causing the liquid W to flow into the sub-surface of the pre-printed SS through the keyhole cavities realizing the bonding of the W→SS interface. The above interfacial bonding behaviours are significantly different from the previously reported bonding mechanism based on the melt pool convection during multiple material LPBF. The abnormal material interfacial bonding behaviours are reported for the first time.
Aerodynamic Development of a Refueling Pod for Tanker Aircraft

Computational fluid dynamics (CFD) was utilized for the aerodynamic design/development of an aerial refueling pod for installation on KC130J tanker aircraft. The computational work was conducted using the full aircraft geometry (including several flap settings) along with propeller and engine exhaust effects. Several refueling pods were designed/analyzed using CFD and later flight tested. Numerous CFD solutions were generated to develop aerodynamic loads on the pod/pylon over the desired flight envelope. Pressure data on the pod/pylon were obtained during two flight tests and were used to validate the CFD predicted loads. The excellent correlation between the predicted and measured pressures permitted the use of CFD alone to develop the aerodynamic loads for the structural analysis, eliminating the need for expensive testing. This computational design/analysis effort led to the development of a refueling pod design that was successfully flight tested over the entire flight envelope and is currently in production for all KC-130J tanker aircraft. INTRODUCTION Advances in computational power and numerical prediction capabilities have led to increased utilization of computational fluid dynamics (CFD) for aircraft and component design. One such recent application at Lockheed Martin was the aerodynamic design of a refueling pod for installation on the KC-130J aircraft used by the United States Marines Corps. This aircraft utilizes a hose and drogue type system for refueling high performance aircraft at high speed conditions as well as helicopters at low speed refueling conditions, as depicted in Figure 1. The large variety of operating conditions greatly increases the complexity of the aerodynamic design problem.
Vibration Fault Diagnosis of Centrifugal Pump Based on Empirical Mode Decomposition Complexity Feature and Least Square Support Vector Machine

Aiming at the non-stationary and non-linearity characteristics of the vibration signals of centrifugal pump,a new method based on complexity feature of empirical mode decomposition(EMD) and least square support vector machine (LS-SVM) was presented.First of all,EMD method was used to decompose the vibration signals into a finite number of stationary intrinsic mode functions(IMF),and then complexity features of each IMF was extracted as the fault characteristics vectors and served as input parameters of LS-SVM classifier to diagnosis fault.Radial basis function(RBF) function was used as kernel function;the differential evolution(DE) method was proposed to select hyper-parameter of LS-SVM.Application results show that the proposed method is very effective,which can better extract the nonlinear features of the fault and more exactly diagnosis fault,optimized LS-SVM model has strong capability of classification.
Mucuna pruriens, Crotalaria juncea, and chickpea (Cicer arietinum) have the potential for improving productivity of banana‐based systems in Eastern Democratic Republic of Congo

Intercropping banana and shorter annual crops in small holder systems is inevitable despite shade being a limitation. Current production is also limited to the wet seasons. Strategies to exploit the spaces under banana shade and drier seasons are crucial for optimal production of these systems. We determined the performance of two legume cover crops, Mucuna pruriens and Crotalaria juncea, and chickpea (Cicer arietinum), a grain legume, under different banana shade levels and over the wet and dry seasons in eastern Democratic Republic of Congo. Banana and legume monocrops served as controls. Shade reduced weed biomass and legume root nodulation, biomass, and grain yields. Chickpea root nodulation had a lower sensitivity to shade (3–9% reduction) compared with mucuna (30–60%) and crotalaria (31–71%). Legume biomass yield declines varied from 37–83%, 56–93%, and 80–98% for mucuna, crotalaria, and chickpea, respectively. Higher nodulation occurred in the rainy compared with the dry season. Biomass yield declines, albeit low occurred in the dry season for mucuna (15%) and crotalaria (30%). In contrast, chickpea biomass and grain yields increased by 394% and 4487%, respectively, in the dry season. A higher banana vegetative growth occurred in the intercropped plots. Land equivalent ratios of 1.15–1.34 under dense shading for mucuna and crotalaria and 1.10–1.62 for chickpea occurred irrespective of the seasons. These findings suggest that these cover crops and chickpea could be exploited to enhance biomass (for fodder, mulch, or manure) and grain yields under banana shade and over the drier seasons.
Open-closed loop iterative learning control of piezoelectric actuators

As open-closed loop iterative learning control scheme can up the convergence and reduce the tracking error, this paper uses the scheme to improve high frequency trajectory tracking accuracy of a piezoelectric actuator(PZT). First, a discrete-time open-loop P-type with closed-loop PI-type iterative learning law was proposed and its convergence condition was given. Then, a discrete-time open-closed loop iterative learning controller was designed for the PZT system. Finally, as to 50 Hz and 25Hz+50Hz triangular desired trajectories with single and compound frequencies, the tracking control experiment was performed. Experimental results indicate that the proposed iterative learning controller can achieve 10.6 nm and 12.5 nm maximum tracking errors for the 2 kinds of trajectories, which are 96.25% and 95.62% less than that of the PID controller. It concludes that the proposed control scheme can be easily implemented and can obtain the high accuracy of trajectory tracking without precise hysteresis and PZT system models. It meets both the requirements of single and compound frequency trajectory tracking for high tracking accuracy.
Reveal non-linear system coupling network with information theory

In a complex system, the strength and direction of feedback among different agents are difficult to quantified, especially due to the non-linearity. To quantitively estimate the coupling network of complex system, multiple statistical approaches have been widely adopted, including e.g., pairwise correlative analysis, Granger causality analysis. However, these analytic approaches often assume linear system and relative simple coupling pattern. In this work, I employed the concept of information entropy transfer and explore the non-linear interactions of near surface water and energy. This way could acutely find weak coupling effect of data and construct a chart of overview which can reflect the coupling impacts between variables directly. The goal of the analysis is to gain results about how energy circulate in the environment on the earth. Change in ways energetic transfer may imperil the survival of living matters on the path.I found that 1)From 1850 to 2005 the influences of existing variables other than the coupling relationship between precipitation(pr) and latent heat(hfls) are weak. 2)From the predictive model between 2006 and 2100,the effecting strength becomes relatively moderate and concordant.3)The time lag to surface temperature become affected by 3 variables(solar radiation,latent heat and sensitive heat) covering a whole year in the period between 2006 and 2100 in comparison with the other period with 2 variables(sensitive heat and latent heat) in the same month.
Extraction of Psychological Effects of COVID-19 Pandemic through Topic-Level Sentiment Dynamics

The rapid increase in COVID-19 cases has become the symbol of fear, anxiety, and panic among people around the globe. Mass media has played an active role in community education by addressing the health information of this pandemic. People interact by sharing their ideas and feelings through social media platforms. There is a considerable need to implement different measures and better perceive COVID-19 pertinent facts and information by demystifying public sentiments. In this study, the Quarantine Life dataset of thousand tweets is based on #Quarantine, #Quarantine Days, #Quarantine Life, #My Pandemic Plan, and #Quarantine and Chill from January to September 2020 has been collected from Twitter. The extracted data have been scrubbed through preprocessing techniques. The sentiments and topics extracted from tweets have been analyzed through the TEXT BLOB, VADER, and AFFIN approach. Results show that people were distressed and fearful due to the COVID-19 pandemic. However, most people enjoyed by playing games, watching movies, and reading books during the lockdown period. According to the present meta-analysis, physical activity interventions are beneficial for patients with dementia in terms of cognition. The proposed framework illustrates the insight impact of COVID-19 on human physiological and mainly focuses on the evaluation of sentiment dynamics at the topical level.
A probabilistic model for assistive robotics devices to support activities of daily living

A Probabilistic Model for Assistive Robotics Devices to Support Activities of Daily Living This thesis explores probabilistic techniques to model interactions between humans and robotic devices. The work is motivated by the rapid increase in the ageing population and the role that assistive robotic devices can play in maintaining independence and quality of life as assistants and/or companions for these communities. While there are substantial social and ethical implications in this pursuit, it is advocated that robotic systems are bound to acquire more sophisticated assistive capabilities if they are to op- erate in unstructured, dynamic, human-centred environments, responsive to the needs of their human operators. Such cognitive assistive systems postulate advances along the complete processing pipeline, from sensing, to anticipating user actions and environmen- tal changes, and to delivering natural supportive actuation. Within the boundaries of the human-robot interaction context, it can be expected that acute awareness of human intentions plays a key role in delivering practical assistive actions. This work is thereby focused on the human behaviours likely to result from merging sensed human-robot in- teractions and the learning gained from past experiences, proposing a framework that facilitates the path towards integrating tightly knit human-robot interaction models. Human behaviour is complex in nature and interactions with the environment and other objects occur in different and unpredictable ways. Moreover, observed sensory data is often incomplete and noisy. Inferring human intention is thus a challenging problem. This work defends the thesis that in many real-world scenarios these complex behaviours can be naturally simplified by decomposing them into smaller activities, so