Yes / Cells encapsulation is a micro-technology widely applied in cell and tissue research, tissue transplantation, and regenerative medicine. In this paper, we proposed a growth of microtissue model for the human keratinocytes (HaCaT) cell line and an oral squamous cell carcinoma (OSCC) cell line (ORL-48) based on a simple aerosol microencapsulation technique. At an extrusion rate of 20 μL/min and air flow rate of 0.3 L/min programmed in the aerosol system, HaCaT and ORL-48 cells in alginate microcapsules were encapsulated in microcapsules with a diameter ranging from 200 to 300 μm. Both cell lines were successfully grown into microtissues in the microcapsules of alginate within 16 days of culture. The microtissues were characterized by using a live/dead cell viability assay, field emission-scanning electron microscopy (FE-SEM), fluorescence staining, and cell re-plating experiments. The microtissues of both cell types were viable after being extracted from the alginate membrane using alginate lyase. However, the microtissues of HaCaT and ORL-48 demonstrated differences in both nucleus size and morphology. The microtissues with re-associated cells in spheroids are potentially useful as a cell model for pharmacological studies. / Malaysia Ministry of Education (Fundamental Research Grant Scheme, FRGS Vot. 1482 and IGSP Vot. 679).

Accurate simulations of the global radiative impact of anthropogenic emissions must employ a tropospheric chemistry model that predicts realistic distributions of aerosols of all types. The need for a such a comprehensive yet computationally efficient tropospheric chemistry model is addressed in this research via systematic development of the various sub-models/mechanisms representing the gas-, aerosol-, and cloud-phase chemistries. The gas-phase model encompasses three tropospheric chemical regimes - background and urban, continental rural, and remote marine. The background and urban gas-phase mechanism is based on the paradigm of the Carbon Bond approach, modified for global-scale applications. The rural gas-phase chemistry includes highly condensed isoprene and a-pinene reactions. The isoprene photooxidation scheme is adapted for the present model from an available mechanism in the literature, while an a-pinene photooxidation mechanism, capable of predicting secondary organic aerosol formation, is developed for the first time from the available kinetic and product formation data. The remote marine gas- phase chemistry includes a highly condensed dimethylsulfide (DMS) photooxidation mechanism, based on a comprehensive scheme available in the literature. The proposed DMS mechanism can successfully explain the observed latitudinal variation in the ratios of methanesulfonic acid to non-sea-salt sulfate concentrations. A highly efficient dynamic aerosol growth model is developed for condensing inorganic gases. Algorithms are presented for calculating equilibrium surface concentrations over dry and wet multicomponent aerosols containing sulfate, nitrate, chloride, ammonium, and sodium. This alternative model is capable of predictions as accurate for completely dissolved aerosols, and more accurate for completely dry aerosols than some of the similar models available in the literature. For cloud processes, gas to liquid mass-transfer limitations to aqueous-phase reactions within cloud droplets are examined for all absorbing species by using the two-film model coupled with a comprehensive gas and aqueous-phase reaction mechanisms. Results indicate appreciable limitations only for the OH, HO₂, and NO₃ radicals. Subsequently, an accurate highly condensed aqueous-phase mechanism is derived for global-scale applications. / Ph. D.

Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique that has been widely used to derive aerosol extinction coefficient profiles and trace gas concentrations. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval. This dissertation serves as the author's exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem and the ML models are trained on synthetic datasets simulated by radiative transfer models.newline By starting with a feasibility study, it is first shown that a ML model with appropriate architecture (CNN+LSTM) is capable of extracting aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan. Then more realistic atmosphere states were used for generating the training set. Due to the high time cost of radiative transfer simulations, a data augmentation strategy was put forward to increase the number of samples in the training set. A physics-guided machine learning (PGML) algorithm was designed to retrieve aerosol information and trace gas concentrations simultaneously. The model is named as PGML model because: (1) its prediction is based on the physical laws it has learnt from the radiative transfer simulations and (2) introduction of the physical constraints and the pseudo-inverse layer. The PGML model was tested on both a synthetic test set and real MAX-DOAS measurements from Pandora instruments. Evaluation on the synthetic dataset suggests that with similar data distribution, the PGML model is capable of retrieving aerosol extinction coefficient profile, trace gas concentration profile and the box-AMFs with good accuracy. Validation on real data was done via comparisons with inversion results given by other algorithms. Generally, moderate linear correlation were found between the inversion results. Limitation of current version of the PGML model and factors might lead to the discrepancies between inversion results given by the PGML model and other algorithms were discussed. / Doctor of Philosophy / Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique for deriving aerosol and trace gas information in the lower atmosphere. A MAX-DOAS instrument is a ground-based system consists of a scanning telescope, a stepping motor and a spectrometer. It collects scattered solar photons at multiple elevation angles. And from spectrum analysis and inversion algorithms, aerosol properties such as aerosol extinction coefficient profile (a vertical profile describing how much the solar radiation is weakened by the atmosphere), single scattering albedo (the ratio of scattered light to incoming light) and trace gas concentrations can be retrieved. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval. This dissertation serves as the author's exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem. In supervised learning, a training set is used to teach the ML model to yield the desired output. And the ML models are trained on synthetic datasets simulated by radiative transfer models for two reasons: (1) There is no reliable dataset combining real MAX-DOAS measurements and observations of aerosol properties (macrophysical properties and aerosol extinction coefficient profiles) and trace gas concentrations. (2) Most of the existing algorithms somewhat rely on empirical knowledge (e.g.: a priori information (optimal estimation methods), introduction of parameters for representing the state vector (parameterized retrieval algorithms)). However, the method purely relies on the rules it has learned from the training set. By using simulated data, it is expected that the ML model to capture the radiative transfer theory and give predictions based on the physical laws.newline By starting with a feasibility study, it is first shown that by applying a machine learning model with appropriate architecture (combination of convolutional layers and long short-term memory layer), it is possible to extract aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan. And this architecture is capable of retrieving elevated layers of aerosol extinction coefficient profiles. Then more realistic atmosphere states were used for generating the training set and designed a physics-guided machine learning (PGML) model to retrieve aerosol information and trace gas concentrations simultaneously. The model is named as PGML model because: (1) its prediction is based on the physical laws it has learnt from the radiative transfer simulations and (2) introduction of the physical constraints and the pseudo-inverse layer. Due to the high time cost of running radiative transfer simulations, a data augmentation strategy was put forward to increase the number of samples in the training set. The PGML model was tested on both a synthetic test set and real MAX-DOAS measurements from Pandora instruments. Evaluation on the synthetic dataset suggests that with similar data distribution, the PGML model is capable of retrieving aerosol extinction coefficient profile, trace gas concentration profile and the box-AMFs with good accuracy. Validation on real data was done via comparisons with inversion results given by other algorithms. Generally, moderate linear correlation were found between the inversion results. Limitation of current version of the PGML model and factors might lead to the discrepancies between inversion results given by the PGML model and other algorithms were discussed.

Silver nanoparticles (nanosilver) are gaining significant attention from the academic and regulatory communities, not only because of their antimicrobial effects and subsequent product applications, but also because of their potential health and environmental impacts. Although some human health effects of silver nanoparticles have been reported, realistic exposure levels from the use of consumer products are still largely unknown. The objective of this work was to characterize the release of silver and silver- containing particles during the normal use of silver nanotechnology consumer products. Specific objectives were to review the environmental and human health risks of airborne, engineered nanoparticles, to characterize aerosol emissions from nanosilver spray products, and to characterize nanosilver that may be released from childrenʼs consumer products under conditions of normal use. We identified possible routes of aerosolization of nanosilver from the production, use, and disposal of consumer products and estimated that about 14% of silver nanotechnology products that have been inventoried could potentially release silver particles into the air during use. The spray products investigated emitted 0.24 – 56 ng of silver in aerosols per spray action, and the plurality of aerosols were 1 – 2.5 μm in diameter, easily inhaled, for two products. Both the products' liquid characteristics and the bottles' spraying mechanisms played roles in determining the aerosol size distributions, but the size of silver-containing aerosols was largely independent of the liquid phase size distributions. We compiled an inventory of 82 children's consumer products that claim to contain nanosilver, of which 13 products were examined for presence of silver and tested for release of silver into liquid media and air, and onto skin. All products contained some form of silver, but silver-containing particles were observed in only four products, with sizes ranging from nanoscale up to 10 μm in size. Silver leached preferably into synthetic biological media with higher chloride concentrations, such as sweat and urine. We determined that levels of silver to which children would be exposed during normal use of these products are likely to be low, and bioavailable silver is expected to be in ionic rather than particulate form. / Ph. D.

The objective of this study was to purify IgG from commercially available hyperimmune Rhodococcus equi plasma and to assess the delivery of IgG as an aerosol to the equine lung. IgG was purified from plasma, and the IgG concentration of both the plasma and the purified IgG was determined by ELISA. The purified IgG was aerosolized using a vibrating mesh nebulizer and aerosol characterization was performed using cascade impaction. The purified IgG was nebulized to six healthy adult horses in order to assess the efficacy of pulmonary delivery and safety of administration. Bronchoalveolar fluid was retrieved endoscopically using a low volume technique prior to aerosolization (time 0) and at 0.5, 4 and 24 hours post aerosolization. The BAL fluid IgG concentration was determined and cytologic analysis was performed. The IgG concentrations of the plasma and purified IgG were 2,175 mg/dL and 1,145 mg/dL, respectively. The MMAD of the purified IgG aerosol was 4.7 microns. The mean BAL fluid IgG concentration increased 61% from 19.33 µg/dL at time 0 to 31.5 µg/dL at 0.5 hours, but this increase was not significant (P=0.603). No significant change was observed in inflammatory cell numbers over time or at any time point during the study. This study demonstrated that IgG antibodies were purified at a concentration acceptable for nebulization, and that the nebulization unit generated aerosol particles from the IgG solution of appropriate size for pulmonary delivery. Nebulization of purified IgG to adult horses was well tolerated and caused no local or systemic adverse effects. / Master of Science

Aerosol Jet® Printing (AJP) is a direct-write based additive manufacturing process that is capable of printing electronics with fine features and various materials. It eliminates the complex masking process in traditional semiconductor manufacturing, thus enables flexible electronics design and reduces manufacturing cost. However, the quality control of AJP processes is still a challenging problem, primarily due to the lack of understanding of the potential root causes of the quality issues. There is a complex interaction among process setting variables, in situ feature variables, and quality variables in AJP processes. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the in situ feature variables, and the effect of the in situ feature variables on quality variables in a two-level hierarchical way. By identifying significant in situ feature variables as responses for the process setting variables, as well as predictors for product quality in a joint estimation problem, the proposed models have a hierarchical variable relationship to enable in situ process control for variation reduction and defect mitigation. A real case study is investigated to demonstrate the advantages of the proposed method. / Master of Science

Las secuencias de accidente con rotura de tubos en el generador de vapor (secuencias SGTR) están consideradas como contribuyentes del riesgo en reactores de agua a presión. Su relevancia radica en la potencial liberación de aerosoles radioactivos al medio ambiente en caso de accidente severo. Sin embargo, dichas partículas podrían quedar retenidas parcial o totalmente sobre las superficies del generador de vapor, incluso en condiciones extremas de ausencia de agua en el generador de vapor. La carencia de conocimiento en cuanto a la capacidad de retención de término fuente de este componente ha eludido su consideración en los estudios probabilistas de seguridad y en las guías de gestión de accidente severo. Esta tesis es una contribución a la comprensión y cuantificación de los procesos naturales de mitigación que tienen lugar dentro del generador de vapor como consecuencia de los accidentes SGTR. La principal actividad llevada a cabo ha sido el desarrollo de un modelo teórico que calcula la capacidad de retención de aerosoles en la etapa de rotura de un generador de vapor seco. El modelo, llamado ARI3SG, está basado en una aproximación de filtro y tiene una naturaleza semi-empírica. En él se tienen en cuenta tanto la dinámica de aerosoles como la hidrodinámica de aerosoles que tiene lugar dentro del generador de vapor en este tipo de escenarios. Para esto último, se han llevado a cabo una serie de simulaciones con el código tridimensional FLUENT 6.2, que han sido validadas con datos experimentales. El comportamiento del modelo ha sido evaluado en profundidad: primero, a través de un proceso de verificación con el que se ha visto que es robusto. Segundo, a través de un proceso de validación frente a los datos experimentales disponibles. Tercero, a través del estudio del efecto de las incertidumbres del escenario y del modelo sobre los resultados. La comparación frente a los datos experimentales ha sido satisfactoria y muestra la viabilidad del uso de formulaciones como la de ARI3SG en códigos de sistema. / López Del Prá, C. (2012). A THEORETICAL INVESTIGATION OF AEROSOL RETENTION WITHIN THE SECONDARY SIDE OF A STEAM GENERATOR UNDER A SGTR SEVERE ACCIDENT SEQUENCE IN A PWR NUCLEAR POWER PLANT [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15183

Traffic congestion negatively affects traveler mobility and air quality. Stop and go vehicular movements associated with traffic jams typically result in higher fuel consumption levels compared to cruising at a constant speed. The first objective in the dissertation is to investigate the spatial relationship between air quality and traffic flow patterns. We developed and applied a recursive Bayesian estimation algorithm to estimate the source location (associated with traffic jam) of an airborne contaminant (aerosol) in a simulation environment. This algorithm was compared to the gradient descent algorithm and an extended Kalman filter algorithm. Results suggest that Bayesian estimation is less sensitive to the choice of the initial state and to the plume dispersion model. Consequently, Bayesian estimation was implemented to identify the location (correlated with traffic flows) of the aerosol (soot) that can be attributed to traffic in the vicinity of the Old Dominion University campus, using data collected from a remote sensing system. Results show that the source location of soot pollution is located at congested intersections, which demonstrate that air quality is correlated with traffic flows and congestion caused by signalized intersections. Sustainable mobility can help reduce traffic congestion and vehicle emissions, and thus, optimizing the performance of available infrastructure via advanced traffic signal controllers has become increasingly appealing. The second objective in the dissertation is to develop a novel de-centralized traffic signal controller, achieved using a Nash bargaining game-theoretic framework, that operates a flexible phasing sequence and free cycle length to adapt to dynamic changes in traffic demand levels. The developed controller was implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was compared to the operation of an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. Testing was initially conducted on an isolated intersection, showing a 77% reduction in queue length, a 17% reduction in vehicle emission levels, and a 64% reduction in total delay. In addition, the developed controller was tested on an arterial network producing statistically significant reductions in total delay ranging between 36% and 67% and vehicle emissions reductions ranging between 6% and 13%. Analysis of variance, Tukey, and pairwise comparison tests were conducted to establish the significance of the proposed controller. Moreover, the controller was tested on a network of 38 intersections producing significant reduction in the travel time by 23.6%, a reduction in the queue length by 37.6%, and a reduction in CO2 emissions by 10.4%. Finally, the controller was tested on the Los Angeles downtown network composed of 457 signalized intersections, producing a 35% reduction in travel time, a 54.7% reduction in queue length, and a 10% reduction in the CO2 emissions. The results demonstrate that the proposed decentralized controller produces major improvements over other state-of-the-art centralized and de-centralized controllers. The proposed controller is capable of alleviating congestion as well as reducing emissions and enhancing air quality. / PHD / Traffic congestion affects traveler mobility and also has an impact on air quality, and consequently, on public health. Stop-and-go driving, which is typically associated with traffic jams, results in increased fuel consumption when compared to cruising at a constant speed. This in turn contributes to the amount of vehicle emissions that create air pollution, which contributes to global warming. Consequently, studying the spatial relationships between air quality and traffic flow patterns is directly related to enhancing air quality, as improving these patterns can reduce traffic congestion. The first objective in this dissertation is to investigate the spatial relationship between air quality and traffic flow patterns. We developed and applied a recursive Bayesian estimation algorithm to estimate the source location of an airborne contaminant (aerosol) in a simulation environment. This algorithm was compared to the gradient descent algorithm and the extended Kalman filter. Results suggest that Bayesian estimation is less sensitive to the choice of the initial state and to the plume dispersion model when compared to the other two approaches. Consequently, an experimental investigation using Bayesian estimation was conducted to identify the location (correlated with traffic flows) of the aerosol (soot) that can be attributed to traffic in the vicinity of the Old Dominion University campus, using data collected from a remote sensing system (a compact light detection and ranging [LiDAR] system). The results show that the location of soot pollution in the study area is located at congested intersections, which demonstrates that air quality is correlated with traffic flows and congestion caused by signalized intersections. Sustainable mobility could enhance air quality and alleviate congestion. Accordingly, optimizing the utilization of the available infrastructure using advanced traffic signal controllers has become necessary to mitigate traffic congestion in a world with growing pressure on financial and physical resources. The second objective in the dissertation is to develop a novel de-centralized traffic signal controller that is achieved using a Nash bargaining game-theoretic framework. This framework has a flexible phasing sequence and free cycle length, and thus can adapt to dynamic changes in traffic demand. The controller was implemented and evaluated using the INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was tested and compared to state-of-the-art isolated and coordinated traffic signal controllers. The proposed controller was tested on an isolated intersection, producing a reduction in the queue length ranging from 58% to 77%, and a reduction in vehicle emission levels ranging from 6% to 17%. In the case of the arterial testing, the controller was compared to an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. On the arterial network, the proposed controller produced reductions in the total delay ranging from 36% to 67%, and a reduction in vehicle emissions ranging from 6% to 13%. Statistical tests show that the proposed controller produces major improvements over other state-of-the-art centralized and de-centralized controllers. In the domain of large scale networks, simulations were conducted on the town of Blacksburg, Virginia composed of 38 signalized intersections. The results show significant reductions on the intersection approaches with travel time savings of 23.6%, a reduction in the average queue length of 37.6%, a reduction in the average number of vehicle stops of 23.6%, a reduction in CO₂ emissions of 10.4%, a reduction in the fuel consumption of 9.8%, and a reduction in NO<sub>X<\sub> emissions of 5.4%. In addition, the proposed controller was tested on downtown Los Angles, California, including the most congested downtown area, which has 457 signalized intersections, and compared to the performance of a decentralized phase split and cycle length controller. The results show significant reductions on the intersections links in the average travel time of 35.1%, a reduction in the average queue length of 54.7%, a reduction in the average number of stops of 44%, a reduction in CO₂ emissions of 10%, a reduction in the fuel consumption of 10%, and a reduction in NO<sub>X<\sub> emissions of 11.7%. Furthermore, simulations were conducted at lower traffic flow levels and showed significant reductions on the network performance producing reductions in vehicle average total delay of 36.7%, a reduction in the stopped delay by 90.2%, and a reduction in the average number of stops by 35%, over a decentralized phase split and cycle length controller. The results demonstrate that the proposed decentralized controller reduces traffic congestion, fuel consumption and vehicle emission levels, and produces major improvements over other state-of-the-art centralized and de-centralized controllers.

This thesis describes laboratory measurements of the complex refractive index of volcanic ash particles. These measurements are needed to model the radiative impact of volcanic ash, vital for accurate satellite remote sensing. Three experimental methods have been developed, and the results for the complex refractive index and optical properties of a wide range of volcanic ash samples are presented. Measurements were made of the spectral transmission of radiation through suspended volcanic ash particles inside an aerosol cell, using a Fourier transform spectrometer at infrared wavelengths and two diffraction grating spectrometers covering ultraviolet, visible, and near-infrared wavelengths. In addition to the optical measurements, a suite of sampling and sizing instruments were connected downstream of the aerosol cell to measure the particle size distribution. The method was calibrated using two quartz samples. Mass extinction coefficients for nine volcanic ash samples, at 0.3-14 μm, are presented and show considerable variation. These variations are linked to the composition of the samples, measured using X-ray fluorescence (XRF) analysis. The complex refractive index, at 0.3-14 μm, of the two quartz samples and two samples of volcanic ash from the 2010 Eyjafjallajökull eruption were retrieved from the extinction measurements. The forward model used Mie theory and a classical damped harmonic oscillator (CDHO) model to represent the complex refractive index of the samples in terms of a finite set of band parameters, as well as the real refractive index of the sample in the small wavelength limit. Previous studies have shown that there is a redundancy in the retrievals between the band strength parameters and the real refractive index in the small wavelength limit, which can lead to spurious values for the retrieved complex refractive index. This problem was overcome by using an independent measurement of the real refractive index at a visible wavelength, to constrain the model parameter of the real refractive index in the short wavelength limit. Independent measurements of the complex refractive index at visible wavelengths are also important because the extinction produced at these wavelengths is highly sensitive to the particle size distribution, and any uncertainty in the measured size distribution will contribute to significant systematic error in the refractive index retrieved from extinction. The retrieved spectral complex refractive index of Eyjafjallajökull ash was applied using the ORAC retrieval scheme to measurements of the 2010 Eyjafjallajökull eruptionmade by theMODIS instrument aboard NASA's Terra satellite. Significant difference were found in the retrieved plume parameters of optical path, effective radius, and plume altitude, compared to assuming a literature measurement for the refractive index of pumice. For three discrete visible wavelengths (450, 546.7, and 650 nm) an optical microscope was used to make measurements of the complex refractive index of the volcanic ash samples. The long-established Becke line method was used to measure the real refractive index of the samples. For the imaginary refractive index, a new and novelmethod was developed involving measurements of the attenuation of light in individual particles. A strong linear correlation was found between the SiO₂ content of the samples and both their real and imaginary refractive indices at the visible wavelengths investigated. Furthermore, from the XRF compositional analysis of the samples values were calculated for the ratio of non-bridging oxygen atoms per tetrahedral cation (NBO/T), and it was found that NBO/T was an even stronger predictor of real refractive index at visible wavelengths. The optical microscope measurements could only be applied to particles with a radius larger than 10 μm. A new refractometer method was investigated for retrieving the real refractive index of submicron particles from colloidal reflectance measurements close to the critical angle in an internal reflection configuration. A coherent scattering model (CSM) was used to model the coherent reflection from a half-space of monodisperse or polydisperse particles, and a simple extension of the model is presented to properly account for the modified size distribution at the interface in an internal reflection set-up. A rigorous sensitivity analysis was performed to determine how experimental uncertainties propagate into uncertainty associated with the retrieved real refractive index, and the uncertainty due to non-spherical effects was estimated using T-matrix methods. Experimental reflectance data at a wavelength of 635 nm were obtained for spherical monodisperse polystyrene calibration particles, a polydisperse sand sample, and a polydisperse volcanic ash sample. The retrieved values for the real refractive index agreed, within propagated uncertainties, with values measured using other techniques. The method is shown to be a viable technique for measuring the real refractive index of small quantities of submicron particles, and can also retrieve the concentration and size of particles.

In this diploma thesis there was studied a composition of a solid fraction of the atmospheric aerosol of the daily measurements from Pilsen by X-ray powder diffraction by the method of the parallel beam. The phase analysis was accomplished by comparing of the diffraction dates with a database of compounds usually occurring in the solid fraction of the atmospheric aerosol in this area. This database was created from works published earlier. In these samples there were also identified asbestos fibers of actinolite by the scanning electron microscope with EDS module. In this thesis there was also described a transport of several anthropogenic compounds of the atmospheric aerosol in dependence on a meteorological situation. Powered by TCPDF (www.tcpdf.org)