Dynamic patterns of flow in the workplace: characterizing within-individual variability using a complexity science approach

As a result of the growing interest in studying employee well-being as a complex process that portrays high levels of within-individual variability and evolves over time, this present study considers the experience of flow in the workplace from a nonlinear dynamical systems approach. Our goal is to offer new ways to move the study of employee well-being beyond linear approaches. With nonlinear dynamical systems theory as the backdrop, we conducted a longitudinal study using the experience sampling method and qualitative semi-structured interviews for data collection; 6981 registers of data were collected from a sample of 60 employees. The obtained time series were analyzed using various techniques derived from the nonlinear dynamical systems theory (i.e., recurrence analysis and surrogate data) and multiple correspondence analyses. The results revealed the following: 1) flow in the workplace presents a high degree of within-individual variability; this variability is characterized as chaotic for most of the cases (75%); 2) high levels of flow are associated with chaos; and 3) different dimensions of the flow experience (e.g., merging of action and awareness) as well as individual (e.g., age) and job characteristics (e.g., job tenure) are associated with the emergence of different dynamic patterns (chaotic, linear and random).

The single principle of compositional data analysis, continuing fallacies, confusionsand misunderstandings and some suggested remedies

In any discipline, where uncertainty and variability are present, it is important to haveprinciples which are accepted as inviolate and which should therefore drive statisticalmodelling, statistical analysis of data and any inferences from such an analysis.Despite the fact that two such principles have existed over the last two decades andfrom these a sensible, meaningful methodology has been developed for the statisticalanalysis of compositional data, the application of inappropriate and/or meaninglessmethods persists in many areas of application. This paper identifies at least tencommon fallacies and confusions in compositional data analysis with illustrativeexamples and provides readers with necessary, and hopefully sufficient, arguments topersuade the culprits why and how they should amend their ways

Analysis of the tensile modulus of polypropylene composites reinforced with stone groundwood fibers

One of the most relevant properties of composite materials to be considered is stiffness. Fiberglass has been used traditionally as a fibrous reinforcing element when stiff materials are required. However, natural fibers are been exploited as replacements for synthetic fibers to satisfy environmental concerns. Among the different natural fibers, wood fibers show the combination of relatively high aspect ratio, good specific stiffness and strength, low density, low cost, and less variability than other natural fibers of such those from annual crops. In this work, composites from polypropylene and stone groundwood fibers from softwood were prepared and mechanically characterized under tensile loads. The Young’s moduli of the ensuing composites were analyzed and their micromechanics aspects evaluated. The reinforcing effect of stone groundwood fibers was compared to that of conventional reinforcement such fiberglass. The Halpin-Tsai model with the modification proposed by Tsai-Pagano accounted fairly for the behavior of PP composites reinforced with stone groundwood fibers. It was also demonstrated that the aspect ratio of the reinforcement plays a role in the Young’s modulus of injection molded specimens

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

CHEMICAL COMPOSITION VARIABILITY IN THE Uncaria tomentosa (cat’s claw) WILD POPULATION

Uncaria tomentosa (cat's claw) is a vine widely distributed throughout the South-American rainforest. Many studies investigating the chemical composition of cat's claw have focused on the pentacyclic (POA) and tetracyclic oxindole alkaloids (TOA), quinovic acid glycosides (QAG), and polyphenols (PPH). Nevertheless, it is still uncertain how environmental factors affect chemical groups. The aim of this work was to better understand the influence of environmental factors (geographic origin, altitude, and season) on cat's claw chemical composition. Stem bark, branches and leaf samples were extracted and analyzed by HPLC-PDA. The data obtained were explored by multivariate analysis (HCA and PCA). Higher amounts of oxindole alkaloids and PPH were found in leaves, followed by stem bark and branches. No clear relationship was verified among geographic origin or altitude and chemical composition, which remained unchanged regardless of season (dry or rainy). However, three oxindole alkaloid chemotypes were clearly recognized: chemotype I (POA with cis D/E ring junction); chemotype II (POA with trans D/E ring junction); and chemotype III (TOA). Thus, environmental factors appear to have only a minor influence on the chemical heterogeneity of the cat's claw wild population. Nevertheless, the occurrence of different chemotypes based on alkaloid profiles seems to be clear.

Molecular and pathogenic variability of Drechslera isolates from oats

Severe epidemics of leaf blotch and black leaf spot of oat (Avena sativa) caused by Drechslera avenae and Drechslera sp., respectively, are frequently observed in the State of Paraná, Brazil. Although some morphological differences between the isolates causing two different symptoms were noticed, the genetic relationship between them was not clear. Twenty-four isolates of D. avenae and Drechslera sp, collected between 1996-98, were assessed for the genetic variability by molecular and pathogenic analyses. The amplification products using primer pair ITS4/ITS5 showed a fragment length of approximately 600 bp for all the isolates except for one black spot isolate, where the fragment length was approximately 550 bp. Restriction enzymes Hinf I and Taq I, that cut in the ITS region, produced similar restriction patterns for all the isolates, whereas four others produced variable restriction patterns. RAPD analysis also showed distinctive patterns for some isolates. No clear difference between the black spot and the leaf blotch isolates was observed either by the molecular or by the pathogenicity analysis. Nonetheless, the rDNA analysis suggests that Drechslera probably comprises at least three distinct taxa. The results indicate that the difference observed between the isolates originating from two types of symptoms is due to intra-specific variants of D. avenae.

Sequence analysis of the capsid protein gene of an isolate of Apple stem grooving virus, and its survey in Southern Brazil

Apple stem grooving virus (ASGV) is one of the most important viruses infecting fruit trees. This study aimed at the molecular characterization of ASGV infecting apple (Malus domestica) plants in Santa Catarina (SC). RNA extracted from plants infected with isolate UV01 was used as a template for RT-PCR using specific primers. An amplified DNA fragment of 755 bp was sequenced. The coat protein gene of ASGV isolate UV01 contains 714 nucleotides, coding for a protein of 237 amino acids with a predicted Mr of approximately 27 kDa. The nucleotide and the deduced amino acid sequences of the coat protein gene showed identities of 90.9% and 97.9%, respectively, with a Japanese isolate of ASGV. Very high amino acid homologies (98.7%) were also found with Citrus tatter leaf capillovirus (CTLV), a very close relative of ASGV. These results indicate low coat protein gene variability among Capillovirus isolates from distinct regions. In a restricted survey, mother stocks in orchards and plants introduced into the country for large scale fruit production were indexed and shown to be infected by ASGV (20%), usually in a complex with other (latent) apple viruses (80%).

Multilevel converter modulation: implementation and analysis

Multilevel converters provide an attractive solution to bring the benefits of speed-controlled rotational movement to high-power applications. Therefore, multilevel inverters have attracted wide interest in both the academic community and in the industry for the past two decades. In this doctoral thesis, modulation methods suitable especially for series connected H-bridge multilevel inverters are discussed. A concept of duty cycle modulation is presented and its modification is proposed. These methods are compared with other well-known modulation schemes, such as space-vector pulse width modulation and carrier-based modulation schemes. The advantage of the modified duty-cycle modulation is its algorithmic simplicity. A similar mathematical formulation for the original duty cycle modulation is proposed. The modified duty cycle modulation is shown to produce well-formed phase-to-neutral voltages that have lower total harmonic distortion than the space-vector pulse width modulation and the duty cycle modulation. The space-vector-based solution and the duty cycle modulation, on the other hand, result in a better-quality line-to-line voltage and current waveform. The voltage of the DC links in the modules of the series-connected H-bridge inverter are shown to fluctuate while they are under load. The fluctuation causes inaccuracies in the voltage production, which may result in a failure of the flux estimator in the controller. An extension for upper-level modulation schemes, which changes the switching instants of the inverter so that the output voltage meets the reference voltage accurately regardless of the DC link voltages, is proposed. The method is shown to reduce the error to a very low level when a sufficient switching frequency is used. An appropriate way to organize the switching instants of the multilevel inverter is to make only one-level steps at a time. This causes restrictions on the dynamical features of the modulation schemes. The produced voltage vector cannot be rotated several tens of degrees in a single switching period without violating the above-mentioned one-level-step rule. The dynamical capabilities of multilevel inverters are analyzed in this doctoral thesis, and it is shown that the multilevel inverters are capable of operating even in dynamically demanding metal industry applications. In addition to the discussion on modulation schemes, an overvoltage in multilevel converter drives caused by cable reflection is addressed. The voltage reflection phenomenon in drives with long feeder cables causes premature insulation deterioration and also affects the commonmode voltage, which is one of the main reasons for bearing currents. Bearing currents, on the other hand, cause fluting in the bearings, which results in premature bearing failure. The reflection phenomenon is traditionally prevented by filtering, but in this thesis, a modulationbased filterless method to mitigate the overvoltage in multilevel drives is proposed. Moreover, the mitigation method can be implemented as an extension for upper-level modulation schemes. The method exploits the oscillations caused by two consecutive voltage edges so that the sum of the oscillations results in a mitigated peak of the overvoltage. The applicability of the method is verified by simulations together with experiments with a full-scale prototype.

Analysis and Control of Excitation, Field Weakening and Stability in Direct Torque Controlled Electrically Excited Synchronous Motor Drives

Direct torque control (DTC) is a new control method for rotating field electrical machines. DTC controls directly the motor stator flux linkage with the stator voltage, and no stator current controllers are used. With the DTC method very good torque dynamics can be achieved. Until now, DTC has been applied to asynchronous motor drives. The purpose of this work is to analyse the applicability of DTC to electrically excited synchronous motor drives. Compared with asynchronous motor drives, electrically excited synchronous motor drives require an additional control for the rotor field current. The field current control is called excitation control in this study. The dependence of the static and dynamic performance of DTC synchronous motor drives on the excitation control has been analysed and a straightforward excitation control method has been developed and tested. In the field weakening range the stator flux linkage modulus must be reduced in order to keep the electro motive force of the synchronous motor smaller than the stator voltage and in order to maintain a sufficient voltage reserve. The dynamic performance of the DTC synchronous motor drive depends on the stator flux linkage modulus. Another important factor for the dynamic performance in the field weakening range is the excitation control. The field weakening analysis considers both dependencies. A modified excitation control method, which maximises the dynamic performance in the field weakening range, has been developed. In synchronous motor drives the load angle must be kept in a stabile working area in order to avoid loss of synchronism. The traditional vector control methods allow to adjust the load angle of the synchronous motor directly by the stator current control. In the DTC synchronous motor drive the load angle is not a directly controllable variable, but it is formed freely according to the motor’s electromagnetic state and load. The load angle can be limited indirectly by limiting the torque reference. This method is however parameter sensitive and requires a safety margin between the theoretical torque maximum and the actual torque limit. The DTC modulation principle allows however a direct load angle adjustment without any current control. In this work a direct load angle control method has been developed. The method keeps the drive stabile and allows the maximal utilisation of the drive without a safety margin in the torque limitation.

Sensibility analysis of the pearl model for pesticide leaching in the State of Mato Grosso do Sul, Brazil

For an accurate use of pesticide leaching models it is necessary to assess the sensitivity of input parameters. The aim of this work was to carry out sensitivity analysis of the pesticide leaching model PEARL for contrasting soil types of Dourados river watershed in the state of Mato Grosso do Sul, Brazil. Sensitivity analysis was done by carrying out many simulations with different input parameters and calculating their influence on the output values. The approach used was called one-at-a-time sensitivity analysis, which consists in varying independently input parameters one at a time and keeping all others constant with the standard scenario. Sensitivity analysis was automated using SESAN tool that was linked to the PEARL model. Results have shown that only soil characteristics influenced the simulated water flux resulting in none variation of this variable for scenarios with different pesticides and same soil. All input parameters that showed the greatest sensitivity with regard to leached pesticide are related to soil and pesticide properties. Sensitivity of all input parameters was scenario dependent, confirming the need of using more than one standard scenario for sensitivity analysis of pesticide leaching models.

GPS receivers for georeferencing of spatial variability of soil attributes

The characterization of the spatial variability of soil attributes is essential to support agricultural practices in a sustainable manner. The use of geostatistics to characterize spatial variability of these attributes, such as soil resistance to penetration (RP) and gravimetric soil moisture (GM) is now usual practice in precision agriculture. The result of geostatistical analysis is dependent on the sample density and other factors according to the georeferencing methodology used. Thus, this study aimed to compare two methods of georeferencing to characterize the spatial variability of RP and GM as well as the spatial correlation of these variables. Sampling grid of 60 points spaced 20 m was used. For RP measurements, an electronic penetrometer was used and to determine the GM, a Dutch auger (0.0-0.1 m depth) was used. The samples were georeferenced using a GPS navigation receiver, Simple Point Positioning (SPP) with navigation GPS receiver, and Semi-Kinematic Relative Positioning (SKRP) with an L1 geodetic GPS receiver. The results indicated that the georeferencing conducted by PPS did not affect the characterization of spatial variability of RP or GM, neither the spatial structure relationship of these attributes.

Spatial variability of air dry bulb temperature and black globe humidity index in a broiler house during the heating phase

The air dry-bulb temperature (t db),as well as the black globe humidity index (BGHI), exert great influence on the development of broiler chickens during their heating phase. Therefore, the aim of this study was to analyze the structure and the magnitude of the t db and BGHI spatial variability, using geostatistics tools such as semivariogram analysis and also producing kriging maps. The experiment was conducted in the west mesoregion of the states of Minas Gerais in 2010, in a commercial broiler house with heating system consisting of two furnaces that heat the air indirectly, in the firsts 14 days of the birds' life. The data were registered at intervals of five minutes in the period from 8 a.m. to 10 a.m. The variables were evaluated by variograms fitted by residual maximum likelihood (REML) testing the Spherical and Exponential models. Kriging maps were generated based on the best model used to fit the variogram. It was possible to characterize the variability of the t db and BGHI, which allowed observing the spatial dependence by using geostatistics techniques. In addition, the use of geostatistics and distribution maps made possible to identify problems in the heating system in regions inside the broiler house that may harm the development of chicks.

Influence of the sample area in the variability of losses in the mechanical harvesting of soybeans

The mechanical harvesting is an important stage in the production process of soybeans and, in this process; the loss of a significant number of grains is common. Despite the existence of mechanisms to monitor these losses, it is still essential to use sampling methods to quantify them. Assuming that the size of the sample area affects the reliability and variability between samples in quantifying losses, this paper aimed to analyze the variability and feasibility of using different sizes of sample area (1, 2 and 3 m²) in quantifying losses in the mechanical harvesting of soybeans. Were sampled 36 sites and the cutting losses, losses by other mechanisms of the combine and total losses were evaluated, as well as the water content in seeds, straw distribution and crop productivity. Data were subjected to statistical analysis (descriptive statistics and analysis of variance) and Statistical Control Process (SCP). The coefficients of variation were similar for the three frames available. Combine losses showed stable behavior, whereas cutting losses and total losses showed unstable behavior. The frame size did not affect the quantification and variability of losses in the mechanical harvesting of soybeans, thus a frame of 1 m² can be used for determining losses.

Spatial variability of mechanical resistance to penetration evaluated with mobile unit on haplic vertisol cultivated with mango

Since the advent of mechanized farming and intensive use of agricultural machinery and implements on the properties, the soil began to receive greater load of machinery traffic, which can cause increased soil compaction. The aim of this study was to evaluate the spatial variability of soil mechanical resistance to penetration (RP) in the layers of 0.00-0.10, 0.10-0.20, 0.20-0.30 and 0.30-0.40m, using geostatistics in an area cultivated with mango in Haplic Vertisol of the northeastern semi-arid, with mobile unit equipped with electronic penetrometer. The RP data was collected in 56 points from an area of 3 ha, and random soil samples were collected to determine the soil moisture and texture. For RP data analysis we used descriptive statistics and geostatistics. The soil mechanical resistance to penetration presented increased variability, with adjustment of the spherical and exponential semivariograms in the layers. We found that 42% of the area in the layer of 0.10-0.20m showed RP values above 2.70 MPa. Maximum values of RP were found in the layer of 0.19-0.27m, predominantly in 56% of the area.

Spatial variability of apparent electrical conductivity and soil properties in a coffee production field

Precision agriculture based on the physical and chemical properties of soil requires dense sampling to determine the spatial variability of these properties. This dense sampling is often expensive and time-consuming. One technique used to reduce sample numbers involves defining management zones based on information collected in the field. Some researchers have demonstrated the importance of soil electrical variables in defining management zones. The objective of this study was to evaluate the relationship between the spatial variability of the apparent electrical conductivity and the soil properties in the coffee production of mountain regions. Spatial variability maps were generated using a geostatistical method. Based on the spatial variability results, a correlation analysis, using bivariate Moran's index, was done to evaluate the relationship between the apparent electrical conductivity and soil properties. The maps of potassium (K) and remaining phosphorus (P-rem) were the closest to the spatial variability pattern of the apparent electrical conductivity.