Springs regarded as hydraulic features and interpreted in the context of basin-scale groundwater flow

Springs are sources of freshwater supply. Furthermore, they can also deliver valuable insight into the hydrogeologic processes of a mountainous region, a natural conservation area or a remote study site with no wells. In order to assess the appearance, peculiarities, quality, stability, longevity and resilience of springs and related ecosystems, they need to be regarded in the context of basin-scale groundwater flow systems. The application of spring data evaluation on a basin scale was demonstrated via the carbonate system of Transdanubian Mts., Hungary. The readily measurable physical parameters of springs, the elevation of spring orifice, temperature and volumetric discharge rate provided reasonable classification and characterisation of springs and the related groundwater flow systems. Applying these parameters seemed prospective in a basin-scale understanding of flow systems in data-scarce regions, as monitoring discharge rate and water temperature are cost-effective, requiring no specific tools and analysing procedures. The combined cluster and discriminant analysis (CCDA) can handle uneven data distribution, unequal length and spacing of time series, data gaps, and consider the time-dependent variability of parameters. The optimal number of groups can be determined based on frequently sampled springs (or other entities). The less monitored springs (or other entities) can be classified using a similarity-based approach and linear discriminant analysis (LDA). Diagnosing the relation of springs to groundwater flow systems can advance sustainable water resources management, considering the ecological water needs maintaining various ecosystem services, therefore enhancing the resilience of springs and groundwater-dependent ecosystems

Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)

In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives. Furthermore, we investigate a novel estimator of the noise level, which improves many existing model selection procedures (including the steepest drop to low levels), particularly for challenging frequent change point scenarios with low signal-to-noise ratios.Comment: To appear in the Journal of the Korean Statistical Societ

Seeded Binary Segmentation: A general methodology for fast and optimal change point detection

In recent years, there has been an increasing demand on efficient algorithms for large scale change point detection problems. To this end, we propose seeded binary segmentation, an approach relying on a deterministic construction of background intervals, called seeded intervals, in which single change points are searched. The final selection of change points based on the candidates from seeded intervals can be done in various ways, adapted to the problem at hand. Thus, seeded binary segmentation is easy to adapt to a wide range of change point detection problems, let that be univariate, multivariate or even high-dimensional. We consider the univariate Gaussian change in mean setup in detail. For this specific case we show that seeded binary segmentation leads to a near-linear time approach (i.e. linear up to a logarithmic factor) independent of the underlying number of change points. Furthermore, using appropriate selection methods, the methodology is shown to be asymptotically minimax optimal. While computationally more efficient, the finite sample estimation performance remains competitive compared to state of the art procedures. Moreover, we illustrate the methodology for high-dimensional settings with an inverse covariance change point detection problem where our proposal leads to massive computational gains while still exhibiting good statistical performance

Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries

As a classical and ever reviving topic, change point detection is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidate split points on the grid for finding the best one requires $O(T)$ evaluations of the gain function for an interval with $T$ observations. If each evaluation is computationally demanding (e.g. in high-dimensional models), this can become infeasible. Instead, we propose optimistic search strategies with $O(\log T)$ evaluations exploiting specific structure of the gain function. Towards solid understanding of our strategies, we investigate in detail the classical univariate Gaussian change in mean setup. For some of our proposals we prove asymptotic minimax optimality for single and multiple change point scenarios. Our search strategies generalize far beyond the theoretically analyzed univariate setup. We illustrate, as an example, massive computational speedup in change point detection for high-dimensional Gaussian graphical models. More generally, we demonstrate empirically that our optimistic search methods lead to competitive estimation performance while heavily reducing run-time.Comment: extended Table 1; added Model II and Lemma 5.3; added further minor explanation

Experiences with fecal microbiota transplantation in Clostridium difficile infections via upper gastrointestinal tract

Dramatic changes in the epidemiology of Clostridium difficile infections have been reported from the western world in the past decade. The proportion of severe cases is significantly elevating and clinicians now have to contend with the problem of additional and more frequent episodes of recurrences including an upward trend in the mortality rate. This situation led us to investigate the possibility of the fecal microbiota transplantation (FMT). An amount of 100 ml of fecal microbiota solution was instilled into a nasojejunal (NJ) tube in 16 cases and into a nasogastric (NG) tube in 44 cases. In all of the cases, where the solution was instilled via nasojejunal tubes, the symptoms resolved within 24 h. We did not note any recurrences in this group. When the material was flushed in through nasogastric tubes, the symptoms resolved in 39 (88.64%) cases within 24 h. In this group, we have experienced a recurrent episode of C. difficile infection in five (11.36%) cases. Three of them were cured with a second transplantation. We have found that in our practice the upper gastrointestinal tract methods had the primary cure rate of 91.67%, whereas the secondary cure rate is 96.67%. When we compared the NJ and NG methods, we have found that the differences in the outcomes are not significant statistically (p = 0.3113 using Fisher’s exact probability test). In conclusion, FMT proved to be very effective, particularly in recurrent infections and in cases where conventional treatment had failed

Faecal microbiota transplantation for Clostridium difficile infection using a lyophilized inoculum from non-related donors: A case series involving 19 patients

Faecal microbiota transplantation (FMT) has been reported to be effective in treating relapsing of refractory Clostridium difficile infections, although some practical barriers are limiting its widespread use. In this study, our objective was to evaluate the rate of resolution of diarrhea following administration of lyophilized and resolved FMT via a nasogastric (NG) tube. We recruited 19 patients suffered from laboratory-confirmed C. difficile infection. Each of them was treated by lyophilized and resolved inoculum through a NG tube. One participant succumbed following the procedure due to unrelated diseases. Out of 18 cases, 15 patients reportedly experienced a resolution of the symptoms. One patient was treated with another course of antibiotics, and two of the non-responders were successfully retreated with another course of FMT utilizing a lyophilized inoculum. Notably, no significant adverse activities were observed. In accordance to our clinical experiences, a patient will likely benefit from FMT treatment including lyophilized inoculum

Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?

The Fourth Industrial Revolution poses significant challenges to manufacturing companies from the technological, organizational and management points of view. This paper aims to explore how top executives interpret the concept of Industry 4.0, the driving forces for introducing new technologies and the main barriers to Industry 4.0. The authors applied a qualitative case study design involving 26 semi-structured interviews with leading members of firms, including chief digital officers and chief executive officers. Company websites and annual reports were also examined to increase the reliability and validity of the results. The authors found that management desire to increase control and enable real-time performance measurement is a significant driving force behind Industry 4.0, alongside production factors. Organizational resistance at both employee and middle management levels can significantly hinder the introduction of Industry 4.0 technologies, though these technologies can also transform management functions. Multinational enterprises have higher driving forces and lower barriers to industry 4.0 than small and medium-sized companies, but these smaller companies have good opportunities, too

Change point detection algorithms and methodology for large-scale data

The work presented in this thesis aims to extract signals from complex large-scale data. The contributions are at the intersection of developing novel statistical methodology, computationally efficient algorithms and corresponding open source software. The main topic is offline change point detection, where the goal is to find abrupt changes in the distribution of the data based on sequences of ordered observations. In the last 10−15 years, there has been a revival of this classical problem in statistics due to numerous applications, for instance in genomics. Recently emerging high-dimensional and large-scale problems call for novel approaches in order to tackle, amongst others, severe computational issues, missing values and (non)parametric assumptions. The first and third chapter describe two novel change point detection algorithms (Seeded Binary Segmentation and Optimistic Search Strategies) that are easy to adapt to various setups, be it univariate, multivariate or even high-dimensional. In simulations it is demonstrated that they lead to massive computational gains compared to existing techniques in large-scale problems while having competitive estimation performance. These observations also match the proven asymptotic guarantees. The second chapter is mainly a discussion and comparison of Seeded Binary Segmentation with a competing estimation approach called Wild Binary Segmentation 2. Additionally, an error variance estimator is introduced that shows promising performance in frequent change point scenarios in univariate problems. The fourth chapter considers a specific instance of computationally challenging change point detection: for high-dimensional Gaussian graphical models methodology and software are proposed, relying on the fast algorithms from the third chapter. The methodology is also capable of dealing with missing values arising in some applications. The fifth chapter is connected to the fourth chapter, as it considers further aspects of Gaussian graphical models. For a setting without change points, the herein proposed Graphical Elastic Net and target matrices generalize algorithms and software of the popular Graphical Lasso for estimating high-dimensional precision matrices

Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)

In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives. Furthermore, we investigate a novel estimator of the noise level, which improves many existing model selection procedures (including the steepest drop to low levels), particularly for challenging frequent change point scenarios with low signal-to-noise ratios.ISSN:1226-319