Antecedentes da intenção de uso de comentários de viagem on-line na escolha de um meio de hospedagem

The Internet is present in each step of a trip planning. The constant technological advances has made major changes in the tourism industry. This is noticeable by the growing number of people who share their travel experiences on the Internet. This study has aimed to analyze the factors that influence the use of the Online Travel Reviews (OTR) in choosing an accommodation. It was done an investigation into the comments available on the internet about information on touristic products and services, specifically about accommodations. The research proposed to understand the influencing factors of OTR, in the Brazilian context, through the Technology Acceptance Model, Motivational Theory, Similarity, and Trustworthiness. The methodology used was a descriptive-exploratory study, with a quantitative approach, and bibliographic research. The study used a Structural Equation Modeling technique called Partial Least Squares (PLS), to test and evaluate the proposed research model. Data collection was performed with 308 guests hosted in five hotels in Ponta Negra (Natal/RN), who have used the OTRs in choosing an accommodation. The research tested fifteen hypotheses, where nine were confirmed, and six were rejected. The results showed that guests have attitude and intention to use the OTRs to choose an accommodation.

Degradação fotocatalítica oxidativa do fenol utilizando carvão obtido da pirólise de diferentes biomassas

The modern industrial progress has been contaminating water with phenolic compounds. These are toxic and carcinogenic substances and it is essential to reduce its concentration in water to a tolerable one, determined by CONAMA, in order to protect the living organisms. In this context, this work focuses on the treatment and characterization of catalysts derived from the bio-coal, by-product of biomass pyrolysis (avelós and wood dust) as well as its evaluation in the phenol photocatalytic degradation reaction. Assays were carried out in a slurry bed reactor, which enables instantaneous measurements of temperature, pH and dissolved oxygen. The experiments were performed in the following operating conditions: temperature of 50 °C, oxygen flow equals to 410 mL min-1 , volume of reagent solution equals to 3.2 L, 400 W UV lamp, at 1 atm pressure, with a 2 hours run. The parameters evaluated were the pH (3.0, 6.9 and 10.7), initial concentration of commercial phenol (250, 500 and 1000 ppm), catalyst concentration (0, 1, 2, and 3 g L-1 ), nature of the catalyst (activated avelós carbon washed with dichloromethane, CAADCM, and CMADCM, activated dust wood carbon washed with dichloromethane). The results of XRF, XRD and BET confirmed the presence of iron and potassium in satisfactory amounts to the CAADCM catalyst and on a reduced amount to CMADCM catalyst, and also the surface area increase of the materials after a chemical and physical activation. The phenol degradation curves indicate that pH has a significant effect on the phenol conversion, showing better results for lowers pH. The optimum concentration of catalyst is observed equals to 1 g L-1 , and the increase of the initial phenol concentration exerts a negative influence in the reaction execution. It was also observed positive effect of the presence of iron and potassium in the catalyst structure: betters conversions were observed for tests conducted with the catalyst CAADCM compared to CMADCM catalyst under the same conditions. The higher conversion was achieved for the test carried out at acid pH (3.0) with an initial concentration of phenol at 250 ppm catalyst in the presence of CAADCM at 1 g L-1 . The liquid samples taken every 15 minutes were analyzed by liquid chromatography identifying and quantifying hydroquinone, p-benzoquinone, catechol and maleic acid. Finally, a reaction mechanism is proposed, cogitating the phenol is transformed into the homogeneous phase and the others react on the catalyst surface. Applying the model of Langmuir-Hinshelwood along with a mass balance it was obtained a system of differential equations that were solved using the Runge-Kutta 4th order method associated with a optimization routine called SWARM (particle swarm) aiming to minimize the least square objective function for obtaining the kinetic and adsorption parameters. Related to the kinetic rate constant, it was obtained a magnitude of 10-3 for the phenol degradation, 10-4 to 10-2 for forming the acids, 10-6 to 10-9 for the mineralization of quinones (hydroquinone, p-benzoquinone and catechol), 10-3 to 10-2 for the mineralization of acids.

O impacto dos fatores de escolha na lealdade dos estudantes internacionais mediado pela satisfação: o caso do Politécnico de Leiria

Num mundo cada vez mais globalizado como o de hoje, a internacionalização das Instituições de Ensino Superior tem assumido uma importância estratégica e crescente, não só pela necessidade de preparar adequadamente os seus estudantes para esta nova realidade, como igualmente pelo aumento da competição entre as mesmas. A presença de estudantes internacionais é, assim, uma mais-valia a todos os níveis para as Instituições, pelo que é fundamental não só procurar atrair os melhores, como igualmente garantir que fiquem satisfeitos com as opções tomadas e leais à Instituição escolhida. Foi com o objetivo de conhecer os fatores da satisfação e da lealdade dos estudantes internacionais que esta investigação foi desenvolvida. Assim, com base na informação recolhida através de um questionário numa amostra de 190 estudantes internacionais a estudar no Politécnico de Leiria, efetuou-se o estudo quantitativo, transversal e de natureza conclusiva descritiva, com a finalidade de investigar o impacto dos fatores de escolha e das fontes de informação na lealdade dos estudantes internacionais, mediado pela satisfação com a escolha efetuada. Os resultados da estimação de um modelo estrutural através do “partial least squares” (PLS) evidenciaram que os fatores com maior impacto na satisfação pela escolha efetuada foram: a perceção que têm de Portugal, o ambiente e localização do Politécnico de Leiria e as fontes de informação (em particular as fontes online). As motivações pessoais e a perceção da Instituição não revelaram capacidade para explicar a satisfação dos estudantes, muito embora os mesmos os reconheçam como fatores importantes na decisão de estudar no estrangeiro e no Politécnico de Leiria. Concluiu-se ainda que a imagem corporativa do Politécnico de Leiria influencia a recomendação de terceiros; as caraterísticas de Portugal influenciam a perceção que os estudantes têm do ambiente e localização da Instituição; por sua vez, Portugal e a região onde se situa o Politécnico de Leiria influenciam a sua imagem corporativa; a satisfação e a imagem corporativa do Politécnico de Leiria explicam a lealdade dos estudantes internacionais, medida em termos da intenção de a recomendar a familiares, amigos e conhecidos e de partilhar informações sobre a mesma nas redes sociais. Para completar o presente estudo, foi ainda efetuada uma análise comparativa dos estudantes em função da sua proveniência, tendo-se observado diferenças significativas entre eles. De notar que os estudantes latino-americanos declaram-se mais satisfeitos e com maior propensão para recomendar o Politécnico de Leiria aos seus conhecidos. Com base nos resultados obtidos é apresentado um conjunto de recomendações ao Politécnico de Leiria, úteis para implementar estratégias de marketing e de comunicação mais adequadas à captação e fidelização dos estudantes internacionais e, assim, contribuir para a sua internacionalização.

Radiotherapy Assessment Using Diffusion Weighted MRI

Purpose: There are two goals of this study. The first goal of this study is to investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment among a unique cohort of early stage breast cancer patients who received the single-dose preoperative radiotherapy. The second goal of this study is to investigate the clinical feasibility of using classic texture features as potential biomarkers which are supplementary to regional apparent diffusion coefficient in gynecological cancer radiotherapy response assessment.

Methods and Materials: For the breast cancer study, 15 patients with early stage breast cancer were enrolled in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE-MRI scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b = 500 mm2/s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T1-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (Ktrans) and kep were analyzed using the two-compartment Tofts pharmacokinetic model. For pharmacokinetic parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature’s change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction.

For the gynecological cancer study, 12 female patients with gynecologic cancer treated with fractionated external beam radiotherapy (EBRT) combined with high dose rate (HDR) intracavitary brachytherapy were studied. Each patient first received EBRT treatment followed by five fractions of HDR treatment. Before EBRT and before each fraction of brachytherapy, Diffusion Weighted MRI (DWI-MRI) and CT scans were acquired. DWI scans were acquired in sagittal plane utilizing a spin-echo echo-planar imaging sequence with weighting factors of b = 500 s/mm2 and b = 1000 s/mm2, one set of images of b = 0 s/mm2 were also acquired. ADC maps were calculated using linear least-square fitting method. Distributed diffusion coefficient (DDC) maps and stretching parameter α were calculated. For ADC and DDC maps, 33 classic texture features were generated utilizing the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from high-risk clinical target volume (HR-CTV). Wilcoxon signed-rank statistics test was applied to determine the significance of each feature’s numerical value change after radiotherapy. Significance level was set to 0.05 with multi-comparison correction if applicable.

Results: For the breast cancer study, regarding ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of Ktrans and 33 features of kep changed significantly.

For the gynecological cancer study, regarding ADC maps, 28 out of 33 HR-CTV texture features showed significant changes after the EBRT treatment. 28 out of 33 HR-CTV texture features indicated significant changes after HDR treatments. The texture features that indicated significant changes after HDR treatments are the same as those after EBRT treatment. 28 out of 33 HR-CTV texture features showed significant changes after whole radiotherapy treatment process. The texture features that indicated significant changes for the whole treatment process are the same as those after HDR treatments.

Conclusion: Initial results indicate that certain classic texture features are sensitive to radiation-induced changes. Classic texture features with significant numerical changes can be used in monitoring radiotherapy effect. This might suggest that certain texture features might be used as biomarkers which are supplementary to ADC and DDC for assessment of radiotherapy response in breast cancer and gynecological cancer.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Near real time surface ocean velocity, Tyrrhenian Sea and Ligurian Sea in September 2016

The data set consists of maps of total velocity of the surface current in the North-Western Tyrrhenian Sea and Ligurian Sea averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations.

Near real time surface ocean velocity in the Bay of Biscay - Basque Country from Mai to September 2016

The data set consists of maps of total velocity of the surface current in the Southeastern Bay of Biscay averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by this HF Radar(4.65 MHz) are representative of the upper 2-3 meters of the ocean. The main objective of near real time processing is to produce the best product from available data at the time of processing. Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations.

Winter sea surface temperature reconstruction from planktic foraminiferal transfer functions in the northeastern Arabian Sea over the last two millennia

The Indian monsoon system is an important climate feature of the northern Indian Ocean. Small variations of the wind and precipitation patterns have fundamental influence on the societal, agricultural, and economic development of India and its neighboring countries. To understand current trends, sensitivity to forcing, or natural variation, records beyond the instrumental period are needed. However, high-resolution archives of past winter monsoon variability are scarce. One potential archive of such records are marine sediments deposited on the continental slope in the NE Arabian Sea, an area where present-day conditions are dominated by the winter monsoon. In this region, winter monsoon conditions lead to distinctive changes in surface water properties, affecting marine plankton communities that are deposited in the sediment. Using planktic foraminifera as a sensitive and well-preserved plankton group, we first characterize the response of their species distribution on environmental gradients from a dataset of surface sediment samples in the tropical and sub-tropical Indian Ocean. Transfer functions for quantitative paleoenvironmental reconstructions were applied to a decadal-scale record of assemblage counts from the Pakistan Margin spanning the last 2000?years. The reconstructed temperature record reveals an intensification of winter monsoon intensity near the year 100 CE. Prior to this transition, winter temperatures were >1.5°C warmer than today. Conditions similar to the present seem to have established after 450 CE, interrupted by a singular event near 950 CE with warmer temperatures and accordingly weak winter monsoon. Frequency analysis revealed significant 75-, 40-, and 37-year cycles, which are known from decadal- to centennial-scale resolution records of Indian summer monsoon variability and interpreted as solar irradiance forcing. Our first independent record of Indian winter monsoon activity confirms that winter and summer monsoons were modulated on the same frequency bands and thus indicates that both monsoon systems are likely controlled by the same driving force.

Multivariate Techniques in Biomechanical Analysis

This work outlines the theoretical advantages of multivariate methods in biomechanical data, validates the proposed methods and outlines new clinical findings relating to knee osteoarthritis that were made possible by this approach. New techniques were based on existing multivariate approaches, Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF) and validated using existing data sets. The new techniques developed, PCA-PLS-LDA (Principal Component Analysis – Partial Least Squares – Linear Discriminant Analysis), PCA-PLS-MLR (Principal Component Analysis – Partial Least Squares –Multiple Linear Regression) and Waveform Similarity (based on NMF) were developed to address the challenging characteristics of biomechanical data, variability and correlation. As a result, these new structure-seeking technique revealed new clinical findings. The first new clinical finding relates to the relationship between pain, radiographic severity and mechanics. Simultaneous analysis of pain and radiographic severity outcomes, a first in biomechanics, revealed that the knee adduction moment’s relationship to radiographic features is mediated by pain in subjects with moderate osteoarthritis. The second clinical finding was quantifying the importance of neuromuscular patterns in brace effectiveness for patients with knee osteoarthritis. I found that brace effectiveness was more related to the patient’s unbraced neuromuscular patterns than it was to mechanics, and that these neuromuscular patterns were more complicated than simply increased overall muscle activity, as previously thought.

Application of computer-aided tomography techniques to visualize kelp holdfast structure reveals the importance of habitat complexity for supporting marine biodiversity

Ecosystem engineers that increase habitat complexity are keystone species in marine systems, increasing shelter and niche availability, and therefore biodiversity. For example, kelp holdfasts form intricate structures and host the largest number of organisms in kelp ecosystems. However, methods that quantify 3D habitat complexity have only seldom been used in marine habitats, and never in kelp holdfast communities. This study investigated the role of kelp holdfasts (Laminaria hyperborea) in supporting benthic faunal biodiversity. Computer-aided tomography (CT-) scanning was used to quantify the three-dimensional geometrical complexity of holdfasts, including volume, surface area and surface fractal dimension (FD). Additionally, the number of haptera, number of haptera per unit of volume, and age of kelps were estimated. These measurements were compared to faunal biodiversity and community structure, using partial least-squares regression and multivariate ordination. Holdfast volume explained most of the variance observed in biodiversity indices, however all other complexity measures also strongly contributed to the variance observed. Multivariate ordinations further revealed that surface area and haptera per unit of volume accounted for the patterns observed in faunal community structure. Using 3D image analysis, this study makes a strong contribution to elucidate quantitative mechanisms underlying the observed relationship between biodiversity and habitat complexity. Furthermore, the potential of CT-scanning as an ecological tool is demonstrated, and a methodology for its use in future similar studies is established. Such spatially resolved imager analysis could help identify structurally complex areas as biodiversity hotspots, and may support the prioritization of areas for conservation.

Application of computer-aided tomography techniques to visualize kelp holdfast structure reveals the importance of habitat complexity for supporting marine biodiversity

Ecosystem engineers that increase habitat complexity are keystone species in marine systems, increasing shelter and niche availability, and therefore biodiversity. For example, kelp holdfasts form intricate structures and host the largest number of organisms in kelp ecosystems. However, methods that quantify 3D habitat complexity have only seldom been used in marine habitats, and never in kelp holdfast communities. This study investigated the role of kelp holdfasts (Laminaria hyperborea) in supporting benthic faunal biodiversity. Computer-aided tomography (CT-) scanning was used to quantify the three-dimensional geometrical complexity of holdfasts, including volume, surface area and surface fractal dimension (FD). Additionally, the number of haptera, number of haptera per unit of volume, and age of kelps were estimated. These measurements were compared to faunal biodiversity and community structure, using partial least-squares regression and multivariate ordination. Holdfast volume explained most of the variance observed in biodiversity indices, however all other complexity measures also strongly contributed to the variance observed. Multivariate ordinations further revealed that surface area and haptera per unit of volume accounted for the patterns observed in faunal community structure. Using 3D image analysis, this study makes a strong contribution to elucidate quantitative mechanisms underlying the observed relationship between biodiversity and habitat complexity. Furthermore, the potential of CT-scanning as an ecological tool is demonstrated, and a methodology for its use in future similar studies is established. Such spatially resolved imager analysis could help identify structurally complex areas as biodiversity hotspots, and may support the prioritization of areas for conservation.

Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

Model reduction for nonlinear aerodynamics and aeroelasticity using a Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of Computational Fluid Dynamics (CFD) solvers, for fast nonlinear aerodynamic and aeroelastic modeling. A nonlinear function is identified on selected interpolation points by
a discrete empirical interpolation method (DEIM). The flow field is then reconstructed using a least square approximation of the flow modes extracted
by proper orthogonal decomposition (POD). The aeroelastic reduce order
model (ROM) is completed by introducing a nonlinear mapping function
between displacements and the DEIM points. The proposed model is investigated to predict the aerodynamic forces due to forced motions using
a N ACA 0012 airfoil undergoing a prescribed pitching oscillation. To investigate aeroelastic problems at transonic conditions, a pitch/plunge airfoil
and a cropped delta wing aeroelastic models are built using linear structural models. The presence of shock-waves triggers the appearance of limit
cycle oscillations (LCO), which the model is able to predict. For all cases
tested, the new ROM shows the ability to replicate the nonlinear aerodynamic forces, structural displacements and reconstruct the complete flow
field with sufficient accuracy at a fraction of the cost of full order CFD
model.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.