Miracle-dz/UniFAR-FTUs huggingface dataset:
Leading for public value in multi-agency collaboration

This paper examines the relationship between leadership and public value in a multi-agency service, requiring the delicate navigation of tensions when there are diverse and competing interests among public service collaborators. The paper adopts an actor-focused perspective arguing for the need to develop theory about leadership in collaborative settings which includes understanding political astuteness in leadership, as this can have an impact on whether or not public value is created. The setting is a multi-agency service hub and the empirical research is based on interviews and document analysis. The paper makes two contributions: first, it analyses the pluralistic leadership processes exercised in the pursuit of public value; secondly, it identifies how political astuteness is a key capability in leading diverse interest in cross-organisational collaborations.
Systematic review of cost-effectiveness analysis of behavior change communication apps: Assessment of key methods

Objective Evidence backing the effectiveness of mobile health technology is growing, and behavior change communication applications (apps) are fast becoming a useful platform for behavioral health programs. However, data to support the cost-effectiveness of these interventions are limited. Suggestions for overcoming the low output of economic data include addressing the methodological challenges for conducting cost-effectiveness analysis of behavior change app programs. This study is a systematic review of cost-effectiveness analyses of behavior change communication apps and a documentation of the reported challenges for investigating their cost-effectiveness. Materials and methods Four academic databases: Medline (Ovid), CINAHL, EMBASE and Google Scholar, were searched. Eligibility criteria included original articles that use a cost-effectiveness evaluation method, published between 2008 and 2018, and in the English language. Results Out of the 60 potentially eligible studies, 6 used cost-effectiveness analysis method and met the inclusion criteria. Conclusion The evidence to support the cost-effectiveness of behavior change communication apps is insufficient, with all studies reporting significant study challenges for estimating program costs and outcomes. The main challenges included limited or lack of cost data, inappropriate cost measures, difficulty with identifying and quantifying app effectiveness, representing app effects as Quality-adjusted Life Years, and aggregating cost and effects into a single quantitative measure like Incremental Cost Effectiveness Ratio. These challenges highlight the need for comprehensive economic evaluation methods that balance app data quality issues with practical concerns. This would likely improve the usefulness of cost-effectiveness data for decisions on adoption, implementation, scalability, sustainability, and the benefits of broader healthcare investments.
Finite‐time extended state observer‐based exact tracking control of an unmanned surface vehicle

In the presence of complex unknowns consisting of system dynamics and environmental disturbances, it is rather meaningful to exactly track an unmanned surface vehicle (USV) to the desired trajectory in practical scenarios including routing inspection, marine survey, and guard patrol, etc. In this paper, the exact trajectory tracking problem is solved by establishing a finite‐time extended state observer‐ (FESO) based exact tracking control (FESO‐ETC) scheme. By virtue of nonsmooth analysis, the FESO is firstly devised by only requiring continuous differentiability and is incorporated into the nonsingular fast terminal sliding mode control framework, and thereby further enhancing disturbance rejection and tracking accuracy. Moreover, global finite‐time stability of the entire FESO‐ETC closed‐loop system is derived from rigorously theoretical analysis, and thereby contributing to a model‐free finite‐time control paradigm. Simulation studies and comparisons demonstrate that the proposed FESO‐ETC approach can achieve exact trajectory tracking in the presence of complex unknowns.
PV String-Level Isolated DC–DC Power Optimizer with Wide Voltage Range

This paper proposes a photovoltaic (PV) string-level isolated DC–DC power optimizer with wide voltage range. A hybrid control scheme in which pulse frequency modulation (PFM) control and pulse width modulation (PWM) control are combined with a variable switching frequency is employed to regulate the wide PV voltage range. By adjusting the switching frequency in the above region during the PWM control process, the circulating current period can be eliminated and the turn-on period of the bidirectional switch of the dual-bridge LLC (DBLLC) resonant converter is reduced compared to that with a conventional PWM control scheme with a fixed switching frequency, resulting in better switching and conduction loss. Soft start-up control under a no-load condition is proposed to charge the DC-link electrolytic capacitor from 0 V. A laboratory prototype of a 6.25 kW DBLLC resonant converter with a transformer, including integrated resonant inductance, is built and tested in order to verify the performance and theoretical claims.
Named Entity Recognition Model of Chinese Clinical Electronic Medical Record Based on XLNet-BiLSTM

The recognition of named entities in Chinese clinical electronic medical records is one of the basic tasks to realize smart medical care. Aiming at the insufficient text semantic representation of the traditional word vector model and the inability of the recurrent neural network (RNN) model to solve the problems of long-term dependence, a Chinese clinical electronic medical record named entity recognition model XLNet-BiLSTM-MHA-CRF based on XLNet is proposed. Use the XLNet pre-training language model as the embedding layer to vectorize the medical record text to solve the problem of ambiguity; use the bidirectional long and short-term memory network (BiLSTM) gate control unit to obtain the forward and backward semantic feature information of the sentence; Then input the feature sequence to the multi-head attention layer (multi-head attention, MHA), use MHA to obtain information represented by different subspaces of the feature sequence, enhance the relevance of context semantics and eliminate noise; finally, input the conditional random field CRF to identify the global maximum 优 sequence. The experimental results show that the XLNet-BiLSTM-Attention-CRF model has achieved good results on the CCKS-2017 named entity recognition data set.
Large Inverse Tunnel Magnetoresistance in Magnetic Tunnel Junctions with an 
Fe3O4
 Electrode

Spinel-type magnetite Fe 3 O 4 is predicted to be a half-metal material with negative spin polarization. However, magnetic tunnel junctions (MTJs) using an Fe 3 O 4 electrode exhibit a small tunnel magnetore-sistance (TMR) eﬀect, the sign of which has not been established experimentally. The development of Fe 3 O 4 as an excellent TMR material requires a better understanding of the characteristics of the interface and the phase transition of Fe 3 O 4 called the Verwey transition. We fabricate MTJs using epitaxial Fe 3 O 4 - based stacks on MgO ( 001 ) substrates and ﬁnd a large inverse TMR ratio of − 55.8% at 80 K, which corresponds to 126% by the optimistic deﬁnition of the TMR ratio. The temperature dependence of the TMR ratio is signiﬁcantly aﬀected by the Verwey transition. Moreover, we investigate the dependence of TMR on oxygen partial pressure during Fe 3 O 4 deposition. It is found that the magneto-transport properties of the MTJs show diﬀerent behaviors depending on the oxygen partial pressure because the Verwey transition is sensitive to the oxygen concentration. Furthermore, the electronic and magnetic properties at the interfacial regions are investigated by x-ray magnetic spectroscopy and ﬁrst-principles calculation. These ﬁndings greatly support the use of Fe 3 O 4 in spintronic devices and should lead to further developments in oxide spintronics.
RP-HPLC Method Development and Validation of Tapentadol Hydrochloride in Bulk and Pharmaceutical Formulations

Objective: Tapentadol Hydrochloride was approved (November 2008) by the United States Food and Drug Administration for the relief of moderate to severe pain. It is an opioid analgesic, acts by dual mechanism as opioid receptor agonist and norepinephrine reuptake inhibitor. The present research work was aimed to develop an accurate, precise, and rapid RP-HPLC method and subsequently validates the method according to the International Conference on Harmonization (ICH) guidelines for the determination of Tapentadol Hydrochloride. Methods: Tapentadol Hydrochloride was analyzed by using High-Performance Liquid Chromatography. Better separation of the drug was achieved by using a Symmetry C18 column (150x4.6mm, 3.5μm) with the mobile phase consisted of a mixture of Orthophosphoric acid (0.1% of Orthophosphoric acid in HPLC water) and acetonitrile in the ratio of 30:70 v/v at a flow rate of 1 ml/min, and the detection was at the wavelength of 219nm using a PDA detector. Results: The retention time of Tapentadol Hydrochloride was found to be 3.747 ± 0.127 min. The method was found to be linear in the range of 10-200 ug/ml with a correlation coefficient (r2) of 0.9991. The LOD and LOQ of the method were calculated to be 0.1 and 1μg/ml respectively. The method precision and system precision was estimated and the results were calculated as % RSD values, which were found to be within the limits. Recovery of Tapentadol Hydrochloride was found to be 100.1%, which confirms the efficiency of the method. Conclusion: The developed RP-HPLC method was validated using standard ICH guidelines. The developed method can be used for the analysis of both tapentadol hydrochloride bulk and formulations.
Green Synthesis of A Novel MXene–CS Composite Applied in Treatment of Cr(VI) Contaminated Aqueous Solution

The considerable amount of Cr(VI) pollutants in the aqueous environment is a significant environmental concern that cannot be ignored. A series of novel Mxene–CS inorganic–organic composite nanomaterials synthesized by using the solution reaction method was applied to treat the Cr(VI) contaminated water. The Mxene–CS composites were characterized through SEM (scanning electron microscope), XRD (X–ray diffraction), XPS (X–ray photoelectron spectroscopy), and FTIR (Fourier transform infrared). The XRD patterns (observed at 2θ of 18.1°, 35.8°, 41.5°, and 60.1°) and the FT–IR spectra (-NH2 group for 1635 and 1517 cm−1, and -OH group for 3482 cm−1) illustrated that CS was successfully loaded on the Mxene. The effects of solution pH, the dosage of Mxene–CS, and duration time on the adsorption of Cr(VI) by synthesized Mxene–CS were investigated. The removal efficiency of Cr(VI) was increased from 12.9% to 40.5% with Mxene–CS dosage ranging from 0.02 to 0.12 g/L. The adsorption process could be well fitted by the pseudo–second–order kinetics model, indicating chemisorption occurred. The Langmuir isotherm model could be better to describe the process with a maximum adsorption capacity of 43.1 mg/g. The prepared novel Mxene–CS composite was considered as an alternative for adsorption of heavy metals from wastewater.
Factors affecting the home range of Dinaric-Pindos brown bears

Studying how animals interact with their environment is fundamental to informing conservation and management efforts, especially when examining large, wide-ranging carnivores in human-dominated landscapes. We hypothesized that the home ranges of bears are configured to exploit supplemental food (corn) and avoid people. In 2004–2016, we tracked 10 brown bears from the Dinaric-Pindos population using GPS telemetry, then used Brownian bridge movement models to estimate their home ranges. We related seasonal home range size to circadian period and density of supplemental feeding sites using generalized linear mixed-effect models. We also used ecological-niche factor analysis to study habitat composition within home range core areas in study areas characterized by different levels of human encroachment. We found that home range size was inversely related to density of supplemental feeding sites, and bears had larger home ranges at night (x̄ = 103.3 ± 72.8 km2) than during the day (x̄ = 62.3 ± 16.6 km2). Our results also revealed that bears living in more human-influenced areas concentrated their use far from human settlements and agricultural lands but stayed close to supplemental feeding sites. Our data suggest that bears alter their space-use patterns at the home range level in response to anthropogenic land use and food availability.
Bimodal behavior of microlasers investigated with a two-channel photon-number-resolving transition-edge sensor system

We explore the photon-number distribution of bimodal quantum-dot micropillar lasers with a two-channel transition-edge sensor (TES) detection system. The two channels of the photon-number-resolving TES system simultaneously detect light emission of two orthogonal components of the micropillar’s fundamental emission mode. The applied experimental scheme provides unprecedented access to the joint photon-number distribution and enables a profound insight into the dynamics and photon statistics of the gain-coupled mode components. In particular, the two-channel TES measurements reveal an optical bistability of the correlated laser modes leading to temporal hopping between emission associated with Poissonian and thermal-like emission statistics. The experimental data and theoretical modeling based on Monte Carlo simulations are in good agreement and reveal the anticorrelated behavior of the mode hopping, which results in intensity ﬂuctuations and superthermal values of the autocorrelation function. Our investigations clearly demonstrate the great beneﬁt of using photon-number-resolving detectors in nanophotonics to explore the rich physics of multimode micro- and nanolasers.
An Evolutionary and Ecological Community Model for Distribution of Phenotypes and Abundances among Competing Species

Here, we propose a theory for the structure of communities of competing species. We include ecologically realistic assumptions, such as density dependence and stochastic fluctuations in the environment, and analyze how evolution caused by r- and K-selection will affect the packing of species in the phenotypic space as well as the species abundance distribution. Species-specific traits have the same matrix G of additive genetic variances and covariances, and evolution of mean traits is affected by fluctuations in population size of all species. In general, the model produces a shape of the distributions of log abundances that is skewed to the left, which is typical of most natural communities. Mean phenotypes of the species in the community are distributed approximately uniformly on the surface of a multidimensional sphere. However, environmental stochasticity generates selection that deviates species slightly from this surface; nonetheless, phenotypic distribution will be different from a random packing of species. This model of community evolution provides a theoretical framework that predicts a relationship between the structure of the phenotypic space and the form of species abundance distributions that can be compared against time series of variation in community structure.
Reconstructing dietary ecology of extinct strepsirrhines (Primates, Mammalia) with new approaches for characterizing and analyzing tooth shape

Abstract. The morphological and ecological diversity of lemurs and lorisiformes once rivaled that of the rest of the primate order. Here, we assemble a dataset of 3D models representing the second mandibular molars of a wide range of extant and fossil strepsirrhines encompassing this diversity. We use these models to distill quantitative descriptors of tooth form and then analyze these data using new analytical methods. We employ a recently developed dental topography metric (ariaDNE), which is less sensitive to details of random error in 3D model quality than previously used metrics (e.g., DNE); Bayesian multinomial modeling with metrics designed to measure overfitting risk; and a tooth segmentation algorithm that allows the shapes of disaggregated tooth surface features to be quantified using dental topography metrics. This approach is successful at reclassifying extant strepsirrhine primates to known dietary ecology and indicates that the averaging of morphological information across the tooth surface does not interfere with the ability of dental topography metrics to predict dietary adaptation. When the most informative combination of dental topography metrics is applied to extinct species, many subfossil lemurs and the most basal fossil strepsirrhines are predicted to have been primarily frugivorous or gummivorous. This supports an ecological contraction among the extant lemurs and the importance of frugivory in the origins of crown Strepsirrhini, potentially to avoid competition with more insectivorous and folivorous members of Paleogene Afro-Arabian primate faunas.
A Torque Demand Model Predictive Control Approach for Driving Energy Optimization of Battery Electric Vehicle

In this paper, based on model predictive control algorithm, a torque demand control approach is proposed to optimize driving energy consumption of battery electric vehicle, which consists of demand control approach and model predictive controller. The demand control approach is developed to compute the driving mode and the desired vehicle speed. For the design of control law, a novel driving dynamics model of battery electric vehicle is formulated into a set of differential equations by vehicle speed, the front and rear wheel speed. A model predictive control law is designed to compute the optimized torque of electric motor. The torque demand model predictive control algorithm is downloaded into vehicle control unit, which is equipped on the battery electric vehicle for experimental validation. The New European Driving Cycle is utilized to test the control law on the real road. The experimental results indicate that the proposed model predictive controller has a preferable performance in reducing energy consumption, which can improve 1.81% over the original control strategy in the urban road cycle and 1.67% in the city highway condition. It can be considered that the torque demand model predictive control approach is a good candidate for driving energy optimization of battery electric vehicle.
Two-Dimensional Modeling of the NAPL Dissolution in Porous Media: Heterogeneities Effects on the Large Scale Permeabilities and Mass Exchange Coefficient

In this paper, we are interested by the dissolution of NAPL (Non-Aqueous Phase Liquid) contaminants in heterogeneous soils or aquifers. The volume averaging technique is applied to 2D systems with Darcy-scale heterogeneities. A large-scale model is derived from a Darcy-scale dissolution model in the case of small and large Damkholer numbers, i.e., for smooth or sharp dissolution fronts. The resulting models in both cases have the mathematical structure of a non-equilibrium dissolution model. It is shown how to calculate the resulting mass exchange and relative permeability terms from the Darcy-scale heterogeneities and other fluid properties. One of the important finding is that the obtained values have a very different behavior compared to the Darcy-scale usual correlations. The large scale correlations are also very different between the two limit cases. The resulting large-scale models are compared favorably to Darcy-scale direct simulations.
Fabrication of a Porous Three-Dimensional Scaffold with Interconnected Flow Channels: Co-Cultured Liver Cells and In Vitro Hemocompatibility Assessment

The development of large-scale human liver scaffolds equipped with interconnected flow channels in three-dimensional space offers a promising strategy for the advancement of liver tissue engineering. Tissue-engineered scaffold must be blood-compatible to address the demand for clinical transplantable liver tissue. Here, we demonstrate the construction of 3-D macro scaffold with interconnected flow channels using the selective laser sintering (SLS) fabrication method. The accuracy of the printed flow channels was ensured by the incorporation of polyglycolic acid (PGA) microparticles as porogens over the conventional method of NaCl salt leaching. The fabricated scaffold was populated with Hep G2, followed by endothelization with endothelial cells (ECs) grown under perfusion of culture medium for up to 10 days. The EC covered scaffold was perfused with platelet-rich plasma for the assessment of hemocompatibility to examine its antiplatelet adhesion properties. Both Hep G2-covered scaffolds exhibited a markedly different albumin production, glucose metabolism and lactate production when compared to EC-Hep G2-covered scaffold. Most importantly, EC-Hep G2-covered scaffold retained the antiplatelet adhesion property associated with the perfusion of platelet-rich plasma through the construct. These results show the potential of fabricating a 3-D scaffold with interconnected flow channels, enabling the perfusion of whole blood and circumventing the limitation of blood compatibility for engineering transplantable liver tissue.
Low-rank constrained weighted discriminative regression for multi-view feature learning

In recent years, multi-view learning has attracted much attention in the fields of data mining, knowledge discovery and machine learning, and been widely used in classification, clustering and information retrieval, and so forth. A new supervised feature learning method for multi-view data, called low-rank constrained weighted discriminative regression (LWDR), is proposed. Different from previous methods handling each view separately, LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective. Based on least squares regression model, the high-dimensional multi-view data is mapped into a common subspace, in which different views have different weights in projection. The weights are adaptively updated to estimate the roles of all views. To improve the intra-class similarity of learned features, a low-rank constraint is designed and imposed on the multi-view features of each class, which improves the feature discrimination. An iterative optimization algorithm is designed to solve the LWDR model efficiently. Experiments on four popular datasets, including Handwritten, Caltech101, PIE and AwA, demonstrate the effectiveness of the proposed method.
Identifying heterogeneous diffusion states in the cytoplasm by a hidden Markov model

Diffusion of nanoparticles in the cytoplasm of live cells has frequently been reported to exhibit an anomalous and even heterogeneous character, i.e. particles seem to switch gears during their journey. Here we show by means of a hidden Markov model that individual trajectories of quantum dots in the cytoplasm of living cultured cells feature a dichotomous switching between two distinct mobility states with an overall subdiffusive mode of motion of the fractional Brownian motion (FBM) type. Using the extracted features of experimental trajectories as input for simulations of different variants of a two-state FBM model, we show that the trajectory-intrinsic and the ensemble-wise heterogeneity in the experimental data is mostly due to variations in the (local) transport coefficients, with only minor contributions due to locally varying anomaly exponents. Altogether, our approach shows that diffusion heterogeneities can be faithfully extracted and quantified from fairly short trajectories obtained by single-particle tracking in highly complex media.