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Recent advancements in multielectrode methods and spike-sorting algorithms enable the in vivo recording of the activities of many neurons at a high temporal resolution. These datasets offer new opportunities in the investigation of the biological neural code, including the direct testing of specific coding hypotheses, but they also reveal the limitations of present decoder algorithms. Classical methods rely on a manual feature extraction step, resulting in a feature vector, like the firing rates of an ensemble of neurons. In this paper, we present a recurrent neural-network-based decoder and evaluate its performance on experimental and artificial datasets. The experimental datasets were obtained by recording the auditory cortical responses of rats exposed to sound stimuli, while the artificial datasets represent preset encoding schemes. The task of the decoder was to classify the action potential timeseries according to the corresponding sound stimuli. It is illustrated that, depending on the coding scheme, the performance of the recurrent-network-based decoder can exceed the performance of the classical methods. We also show how randomized copies of the training datasets can be used to reveal the role of candidate spike-train features. We conclude that artificial neural network decoders can be a useful alternative to classical population vector-based techniques in studies of the biological neural code.

The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

In this paper, a set of machine learning (ML) tools is applied to estimate the water saturation of shallow unconsolidated sediments at the Bátaapáti site in Hungary. Water saturation is directly calculated from the first factor extracted from a set of direct push logs by factor analysis. The dataset observed by engineering geophysical sounding tools as special variants of direct-push probes contains data from a total of 12 shallow penetration holes. Both one- and two-dimensional applications of the suggested method are presented. To improve the performance of factor analysis, particle swarm optimization (PSO) is applied to give a globally optimized estimate for the factor scores. Furthermore, by a hyperparameter estimation approach, some control parameters of the utilized PSO algorithm are automatically estimated by simulated annealing (SA) to ensure the convergence of the procedure. The result of the suggested ML-based log analysis method is compared and verified by an independent inversion estimate. The study shows that the PSO-based factor analysis aided by hyperparameter estimation provides reliable in situ estimates of water saturation, which may improve the solution of environmental end engineering problems in shallow unconsolidated heterogeneous formations.

In this paper, a new inversion method is proposed to process laboratory-measured induced polarization (IP) data. In the new procedure, the concept of the series expansion-based inversion is combined with a more general definition of the objective function. The time constant spectrum of the IP effect is assumed a line spectrum approximated by a series of Dirac’s delta function resulting in a square-integrable forward problem formula. This gives the applicability of the generalized objective function. The expansion coefficients as unknowns represent the model parameters of the inversion procedure. We use the new inversion procedure on an apparent polarizability dataset measured on a rock sample originated from the Recsk ore complex, northeast Hungary. The inversion results was compared to those of three additional laboratory datasets, which were measured on samples rich in ore minerals collected from the same area. The results are compared to those given by the traditional series expansion-based least squares method. It is shown that the newly proposed method gives more accurate and stable parameter estimation.

The members of the genus Carabus are among the most intensively studied beetle taxa, but many aspects of their autecology are still unexplored. We aimed to study the relationship between measured abiotic parameters and the spatial and temporal distribution of signature carabid species. Carabus assemblages were sampled by pitfalls at six sites belonging to two nearby locations, both forest habitats: in valley and in hill-top position. The sites showed variation in microclimatic and soil characteristics, to which the seven species caught showed specific spatial associations. Carabus scheidleri and C. coriaceus were ubiquitists, occurring at all sites. The habitat specialist C. violaceus germari indicated valley sites with high humidity, lower temperature, limy soil and higher pH, whereas the other specialists, C. nemoralis and C. convexus were strongly associated with the dry, warm, more acidic hill habitat. Remaining species were associated with specific sites and environmental features. The species also exhibited specific phenological patterns corresponding with their habitat preference. Hill habitat species exhibited peak activity density during the hottest summer period, whereas most other species had an activity depression during that period. The results suggest that although Carabus activity density patterns are species specific, they are largely affected by temperature both spatially and temporally.

The cold and thermal karst water resources of the Bükk Mountains play a significant role in the hydrogeological setting of northeastern Hungary. Although there is much geological and hydrogeological information available, the regional hydrogeology of the Bükk Mountains had not been described in a standalone study involving all this information. The drinking water supply of the city of Miskolc and some other neighboring cities is based on the cold karst water resources of the Bükk Mountains. The thermal water resources have been getting more important, in terms of balneology and geothermal energy production, in this region over the last decade. An evaluation of the measurements of a 27-year-old groundwater quantity and quality monitoring system was performed. As a result, several types of new field hydrogeology, meteorology and spring discharge measurements were added to the monitoring scheme to provide information for studying the tendencies connected to extreme weather conditions. This will help to achieve sustainable utilization of the karst water resources. The main conclusions drawn in this study underline the importance of the strict measures associated with the water management of the Bükk Mountains because of the limited natural recharge and the continuously increasing demands on the cold and thermal water resources. A success story is found in Miskolc, which demonstrates the sustainable utilization of the deep thermal karst water; Miskolc hosts the largest geothermal heating plant in Central Europe with 60 MWt capacity.

Despite the great benefits of plastics in different aspects of life and due to the increase in plastic production and use, plastic wastes are becoming a major environmental concern. It is well known that inappropriate use and disposal lead to the accumulation of plastic litter in different aquatic environments. Microbial biofilm is able to develop on the surface of plastics (plastisphere) in aquatic environments over time. The aim of this study was to describe the bacterial communities associated with plastics in freshwater. Thus, in our first test, a total of six self-designed plastic colonizers were submerged under the surface of the water in Vácszentlászló lake, located in central Hungary, for a period of 3 months. Two plastic colonizers were cultivated monthly. Associated microbial communities were then analyzed as follows: (a) bacterial communities were studied by amplicon sequencing and (b) culturable bacteria were isolated from plastic surfaces and identified by 16S rRNA gene sequencing. Coinciding with these analyses of plastic colonizing communities, surface water samples from the lake were also taken, and in a second test, other materials (eg. wood, glass) associated bacterial communities were also investigated with the same methods. Amplicon sequencing showed notable differences between the plastic and other materials colonizing, and lake waterborne microbial community composition. Using the LB agar, no novel species were found; however, several known pathogenic species were identified. The self-designed plastic colonizer was successfully used during the winter over a 3-month period, suggesting that it could be an appropriate method of choice to study microplastic-associated microbes for longer periods and in variable environmental conditions.

The present studies were aimed at developing the Hungarian version of the Short Dark Triad questionnaire (SD3-HU). The internal structure of the translated questionnaire was examined with confirmatory factor analysis and exploratory structural equation modeling. Then the construct and concurrent validity of the Hungarian version was tested. The obtained results were based on a total of seven independent samples (NTOTAL = 2161). While the internal structure of the SD3-HU showed inconsistencies with that of the original SD3, it proved consistent with adaptations developed in other languages. The SD3-HU showed adequate construct and concurrent validity. In line with the conceptual framework of, and previous empirical findings on the Dark Triad, each dark trait showed the expected associations with broad personality dimensions, sensation seeking, character strengths, work motivation, and counterproductive work behaviors. Furthermore, self-ratings on the SD3-HU were consistent with peer ratings. In sum, the SD3-HU is a reliable and valid measure of the dark traits.

In Hungary, the economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV) led to the launching of a national PRRSV Eradication Program. An important element of the program was investigating the spread of PRRSV among swine herds and the possible ways of introduction by sequencing of the open reading frame 5 (ORF5) gene. However, the classical phylogenetic tree presentation cannot explain several genetic relationships clearly, while more precise visualization can be represented by network tree diagram. In this paper, we describe a practical and easy-to-follow enriched minimum spanning similarity network application for improved representation of phylogenetic relations among viral strains. This method eliminated the necessity of applying a predefined, arbitrary cut-off or computationally extensive algorithms. The network-based visualization allowed processing and visualizing large amount of data equally for the laboratory, private and official veterinarians, and helped identify the potential connections between different viral sequences that support data-driven decisions in the eradication program. By applying network analysis, previously unknown epidemiological connections between infected herds were identified, and virus spreading was analyzed within short period of time. In our study, we successfully built and applied network analysis tools in the course of the Hungarian PRRSV Eradication Program.

An evolutionary approach is applied to solve the nonlinear well logging inverse problem. In the framework of the proposed interval inversion method, nuclear, sonic, and laterolog resistivity data measured at an arbitrary depth interval are jointly inverted, where the depth variation of porosity, water saturation, and shale volume is expanded into series using Legendre polynomials as basis functions. In the interval inversion procedure, the series expansion coefficients are estimated by using an adaptive float-encoded genetic algorithm. Since the solution of the inverse problem using traditional linear optimization tools highly depends on the selection of the initial model, a heuristic search is necessary to reduce the initial model dependence of the interval inversion procedure. The genetic inversion strategy used in interval inversion seeks the global extreme of the objective function and provides an estimate of the vertical distribution of petrophysical parameters, even starting the inversion procedure from extremely high distances from the optimum. For a faster computational process, after a couple of thousand generations, the genetic algorithm is replaced by some linear optimization steps. The added advantage of using the Marquardt algorithm is the possibility to characterize the accuracy of the series expansion coefficients and derived petrophysical properties. A Hungarian oil field example demonstrates the feasibility and stability of the improved interval inversion method. As a significance, the genetic inversion method does not require prior knowledge or strong restrictions on the values of petrophysical properties and gives highly reliable estimation results practically independent of the initial model and core information.

Academic self-handicapping is a very popular subject of research in educational psychology, in which a growing number of related findings are reported. However, apart from a recent meta-analysis (Schwinger et al. in J Educ Psychol 106(3):744–761, 2014), the last comprehensive review of the subject was published 17 years ago (Urdan and Midgley in Educ Psychol Rev 13(2):115–138, 2001). This paper integrates relevant findings accumulated since then, which are discussed in a coherent interpretive framework. The first part of the paper discusses the definition and theoretical grounds of academic self-handicapping, then its manifestations in an academic context are enumerated. The second part of the study focuses on the self-protective function of stereotypes based on self-handicapping as well as on the impressions self-handicapping students may make on others at school. A summary of the contributions of goal orientation theory is provided in relation to self-handicapping, and the effects of various goal orientations and goal structures on academic performance are analysed. Based on an analysis of interactions between self-handicapping and academic performance, the authors argue for the importance of prevention and suggest possible ways to prevent self-handicapping. In conclusion, measurement issues of academic self-handicapping and the methodological paradox inherent in questionnaire measures are discussed.

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.

Factor analysis of well logging data can be effectively applied to calculate shale volume in hydrocarbon formations. A global optimization approach is developed to improve the result of traditional factor analysis by reducing the misfit between the observed well logs and theoretical data calculated by the factor model. Formation shaliness is directly calculated from the factor scores by a nonlinear regression relation, which is consistent in the studied area in Alaska, USA. The added advantage of the implementation of the Simulated Annealing method is the estimation of the theoretical values of nuclear, sonic, electrical as well as caliper well-logging data. The results of globally optimized factor analysis are compared and verified by independent estimates of self-potential log-based deterministic modeling. The suggested method is tested in two different shaly-sand formations in the North Aleutian Basin of Alaska and the comparative study shows that the assumed nonlinear connection between the factor scores and shale volume is applicable with the same regression constants in different burial depths. The study shows that factor analysis solved by the random search technique provides an independent in situ estimate to shale content along arbitrary depth intervals of a borehole, which may improve the geological model of the hydrocarbon structure in the investigated area.

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Joreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.

Environmental change can be viewed as the combined result of long-term processes and singular events. While long-term trends appear to be readily available for observation (in the form of temporal comparisons or space-for-time substitution), it is more difficult to gain information on singular events in the past, although these can be equally significant in shaping ecosystems. We examined the past 700 years in the history of a lowland wetland landscape in the Czech Republic with the help of palaeoecological, ecological, landscape archaeological, and archival data. Macrofossil and pollen data were compared to known drainage works in the area and historical climatological data. Trends and events in habitat conditions were assessed using species indicator values. Results showed that ecological succession was the general process in the study area, detected as a trend towards eutrophication, desiccation, and vegetation closure. Short-term events influenced development at the sites mainly from the second half of the nineteenth century. This is consistent with drainage history, although bias related to sample frequency cannot be excluded. On the whole, long-term trends and discrete events were complementary on different scales. We conclude that humans facilitated and accelerated background processes, which can be most likely associated with the succession of open wetlands towards terrestrial ecosystems.

This paper addresses the course, extent, and impacts of a windstorm that occurred on 20–21 December 1740, in the Czech Lands. The analysis is based on documentary data included in chronicles, “books of memory”, memoirs, damage reports, urbaria, and cadastral records, as well as secondary sources. The windstorm started with a thunderstorm in the afternoon of 20 December, continued during the night, and was followed by a flood. It also appeared in documentary data from Bavaria, Thuringia, Saxony, Silesia, Slovakia, and Hungary. The event may be related to a cyclone north-west of the Czech territory moving to the east with an intense western flow over central Europe. The storm did great material damage to houses, farm buildings, churches, and forests and is recorded in various documentary sources for 85 places in the Czech Lands. The windstorm had a significant influence on the development of local plantation forestry (discussed in greater detail). Judging by territorial extent and damage done, this windstorm, compared to other similar events, has been classified as “the windstorm of the eighteenth century” in the Czech Lands. This contribution demonstrates the potential of documentary evidence for the elucidation of heavy windstorms in the pre-instrumental period in Europe.

A multivariate statistical method is presented for providing hydrogeological information on groundwater formations. Factor analysis is applied to borehole logs in Hungary and the USA to estimate the vertical distribution of hydraulic conductivity of rocks intersected by the borehole. Earlier studies showed a strong correlation between a statistical variable extracted by factor analysis and shale volume in primary porosity rocks. Hydraulic conductivity as a related quantity can be derived directly by factor analysis. In the first step, electric and nuclear logs are transformed into factor logs, which are then correlated to hydraulic properties of aquifers. It is shown that a factor explaining the major part of variance of the measured variables is inversely proportional to hydraulic conductivity. By revealing the regression relation between the above quantities, an estimate for hydraulic conductivity can be given along the entire length of the borehole. Synthetic modeling experiments and field cases demonstrate the feasibility of the method, which can be applied both in primary and secondary porosity aquifers. The results of factor analysis show consistence with those of the Kozeny-Carman method and hydraulic aquifer tests. The application of the statistical analysis of well logs together with independent ground geophysical and hydrogeological methods serves a more efficient exploration of groundwater resources.

The Kozeny–Carman equation has achieved widespread use as a standard model for estimating hydraulic conductivity of aquifers. An empirically modified form applicable in shallow formations called Csokas’ formula is discussed, which is based on the relation between the effective grain-size and formation factor of freshwater-bearing unconsolidated sediments. The method gives a continuous estimate of hydraulic conductivity along a borehole by using electric and nuclear logging measurements without the need of grain-size data. In the first step, synthetic well-logging data sets of different noise levels are generated from an exactly known petrophysical model to test the noise sensitivity of the Csokas’ method and to assess the degree of correlation between the results of Csokas’ and Kozeny–Carman model. In the next step, borehole logs acquired from Hungarian sites are processed to make a comparison between the Csokas’ formula and the Kozeny–Carman equation including grain-size data measured on rock samples. The hydraulic conductivity logs derived separately from the Csokas’ and Kozeny–Carman formulae show reliable interpretation results, which are also validated by the Hazen’s formula and statistical factor analysis. The fundamental goal of Professor Csokas’ research was to derive some useful hydraulic parameters solely from well-logging observations. This idea may be of importance today since the input parameters can be determined more accurately by advanced measurement techniques. Hence, the Csokas’ formula may inspire the hydrogeophysicists to make further developments for a more efficient exploration of groundwater resources.

Water-soluble, nonionic cellulose-based fibers were prepared from aqueous hydroxypropyl cellulose gels of 5–13-μm diameter by using a high-speed rotary spinning technique. A combination of texture analysis and viscosity measurement was applied to determine the optimum concentration of hydroxypropyl cellulose gels for fiber formation. The examined concentration range of hydroxypropyl cellulose gels was 38–52 % w/w. The textural properties including the adhesiveness of gels of different concentrations were determined based on the load-distance and load-time curves, while the obtained fiber formation was visually observed with an optical microscope. The texture analysis method enabled the determination of the optimum gel concentration from the point of fiber formation. An unequivocal correlation was determined between the adhesiveness of gels and their fiber-forming ability. The adhesiveness has a local minimum where the productivity of the fiber formation process and the micromorphology of the emitted fibers are optimal. Statistical analysis of the distribution of fiber diameters confirmed that in case of the optimum concentration, the distribution approaches normality. Mechanical properties of the prepared fibers were also evaluated using texture analysis, which indicated that the fibers made of gels of the suggested optimum concentration had the most desirable elastic behavior. An optimum concentration range of hydroxypropyl cellulose exists that enables fiber formation with the required characteristics from the point of further pharmaceutical formulation processing.

The calculation of groundwater reserves in shaly sand aquifers requires a reliable estimation of effective porosity and permeability; the amount of shaliness as a related quantity can be determined from well log analysis. The conventionally used linear model, connecting the natural gamma-ray index to shale content, often gives only a rough estimate of shale volume. A non-linear model is suggested, which is derived from the factor analysis of well-logging data. An earlier study of hydrocarbon wells revealed an empirical relationship between the factor scores and shale volume, independent of the well site. Borehole logs from three groundwater wells drilled in the northeastern Great Hungarian Plain are analyzed to derive depth logs of factor variables, which are then correlated with shale volumes given from the method of Larionov. Shale volume logs derived by the statistical procedure are in close agreement with those derived from Larionov’s formula, which confirms the validity of the non-linear approximation. The statistical results are in good accordance with laboratory measurements made on core samples. Whereas conventional methods normally use a single well log as input, factor analysis processes all available logs to provide groundwater exploration with reliable estimations of shale volume.

The currently available medical treatment options of adrenocortical cancer (ACC) are limited. In our previous meta-analysis of adrenocortical tumor genomics data, ACC was associated with reduced retinoic acid production and retinoid X receptor-mediated signaling. Our objective has been to study the potential antitumoral effects of 9-cis retinoic acid (9-cisRA) on the ACC cell line NCI-H295R and in a xenograft model. Cell proliferation, hormone secretion, and gene expression have been studied in the NCI-H295R cell line. A complex bioinformatics approach involving pathway and network analysis has been performed. Selected genes have been validated by real-time qRT-PCR. Athymic nude mice xenografted with NCI-H295R have been used in a pilot in vivo xenograft model. 9-cisRA significantly decreased cell viability and steroid hormone secretion in a concentration- and time-dependent manner in the NCI-H295R cell line. Four major molecular pathways have been identified by the analysis of gene expression data. Ten genes have been successfully validated involved in: (1) steroid hormone secretion (HSD3B1, HSD3B2), (2) retinoic acid signaling (ABCA1, ABCG1, HMGCR), (3) cell-cycle damage (GADD45A, CCNE2, UHRF1), and the (4) immune response (MAP2K6, IL1R2). 9-cisRA appears to directly regulate the cell cycle by network analysis. 9-cisRA also reduced tumor growth in the in vivo xenograft model. In conclusion, 9-cisRA might represent a promising new candidate in the treatment of hormone-secreting adrenal tumors and adrenocortical cancer.

Geophysical surveying methods are of great importance in environmental exploration. Inversion-based data processing methods are applied for the determination of geometrical and physical parameters of the target model. The use of this geoelectric inversion method is advantageous in environmental research where highly reliable information with large spatial resolution is required. The 2D combined geoelectric inversion (CGI) method performs more accurate parameter estimation than conventional 1D single inversion methods by efficiently decreasing the number of unknowns of the inverse problem (single means that data sets of individual vertical electric sounding stations are inverted separately). The quality improvement in parameter space is demonstrated by comparing the traditional 1D inversion procedure with a 2D series expansion-based inversion technique. The CGI method was further developed by weighting individual direct current geoelectric data sets automatically in order to improve inversion results. The new algorithm was named combined geoelectric weighted inversion, which extracts the solution by a special weighted least squares technique. It is shown that the new inversion methodology is applicable to resolve near-surface structures such as rapidly varying layer boundaries, laterally inhomogeneous formations and pinch-outs.

A multivariate statistical procedure is developed for the estimation of shale volume in clastic sedimentary formations. The method offers an alternative for extracting the shale content from borehole geophysical measurements. Factor analysis of various well-logging data types generates a new well log that correlates with the shale content of shaly-sandy rocks. The mathematical relationship between shale volume and factor scores is represented by a nonlinear equation, which seems to be applicable for data sets originating from different sedimentary basins. A comparative study is made between three different data sets originating from Hungary and the United States of America in order to check the validity of the proposed empirical formula. Shale volumes predicted from factor analysis are confirmed by estimates from independent deterministic and inverse modeling. Petrophysical information derived by factor analysis of logs recorded in deep wells can be used for a more accurate and reliable estimation of effective porosity and absolute permeability of reservoir rocks, for decreasing the estimation error of inversion estimates and for reducing the ambiguity in the solution of the well-logging inverse problem.

A joint inversion method for the evaluation of well-logging data is presented, which is applicable to determine textural parameters, i.e., cementation exponent, saturation exponent and tortuosity factor, simultaneously with conventional petrophysical properties. The inversion techniques used today perform local interpretation. Since the number of unknowns is slightly lower than that of the data estimated locally to one depth-point, a set of marginally overdetermined inverse problems has to be solved. For preserving the overdetermination, textural parameters must be kept constant for longer depth intervals (i.e., 200–300 m), despite the fact that they seem to be varying faster with depth according to field experiences. An inversion method was developed, which inverts data of a greater depth interval jointly in a highly overdetermined inversion procedure and gives a better resolution (10 m or less) estimate for the textural parameters. In the paper, a set of inversion tests on synthetic data as well as a field example prove the feasibility of the method.

In the paper factor analysis is applied to well-logging data in order to extract petrophysical information about sedimentary structures. Statistical processing of well logs used in hydrocarbon exploration results in a factor log, which correlates with shale volume of the formations. The so-called factor index is defined analogously with natural gamma ray index for describing a linear relationship between one special factor and shale content. Then a general formula valid for a longer depth interval is introduced to express a nonlinear relationship between the above quantities. The method can be considered as an independent source of shale volume estimation, which exploits information inherent in all types of well logs being sensitive to the presence of shale. For demonstration, two wellbore data sets originated from different areas of the Pannonian Basin of Central Europe are processed, after which the shale volume is computed and compared to estimations coming from independent inverse modeling.

1967 wurde in Ungarn in bestimmten Versuchsgebieten (mit mehr als 200 000 uber 14 Jahre alten Einwohnern, auf 37 Fursorgestellen verteilt) eine Studie uber die epidemiologische Bedeutung der rontgenologisch erfasbaren Residuen von Lungentuberkulose durchgefuhrt. Rontgenreihenuntersuchungen dieser Gebiete ergaben 28424 (14,2%) Falle von Kalklasionen (R1), 5357 (2,7%) von fibrosen Lasionen (R2) und 2853 (1,4%) von inaktiver Tuberkulose (R3). Der Anteil fibroser Lasionen steigt in beiden Geschlechtsgruppen mit dem Alter an, 75,6% der Personen war uber 50 Jahre alt. Bei den Gruppen R2 und R3 wurden intensive Durchuntersuchungen (Tomographie und mindestens 5 kulturelle Untersuchungen auf M. tuberculosis) vorgenommen. 1968 entstammten 67% der neuregistrierten aktiven Lungentuberkulosen aus diesen beiden Gruppen, die 4,1% der Erwachsenenbevolkerung ausmachen.