Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecosystem management by local steward associations A case study from “Kristianstads Vattenrike”, the Swedish MA

Recent research indicates that involvement of a diversity of stakeholders in ecosystem management may be beneficial in many respects. Management plans that involve local resource users and stewards create stronger incentives for ecosystem management in the community. In addition, local ecological knowledge in steward associations can provide unique information about local conditions and ecosystem dynamics. In this study, which is part of the Swedish sub-global Millennium Ecosystem Assessment, we have carried out a social-ecological inventory of local steward associations in the lower parts of the Helge River basin, southern Sweden. We assess ecosystem services and existing management systems behind these services. We identify steward associations in the landscape, key individuals in these associations, their ecological knowledge and their management practices. We analyze the potential contribution of the associations to adaptive co-management in the area. Methods included interviews, participatory observations and various forms of written material. The assessment reveals a rich diversity of steward associations that manage a range of ecosystem components. It also reveals local ecological knowledge among the members of these steward associations considering species and their biology, ecological processes and functions, and how ecosystems are linked across scales. For example, farmers contribute to bird habitats and other wetland services by letting cattle graze by the river shore, forest owners apply management practices that support the ecological functions of the forest, hunting and fishing associations improve habitats for fish and game, and village associations maintain management practices that enhance biodiversity. Furthermore, regional and national organizations contribute by engaging people in the landscape in monitoring, management and conservation of ecosystems and their services. A municipality organization, the Ecomuseum Kristianstads Vattenrike (EKV), coordinates many of these efforts in a shared vision for the region. This coordinating team fosters an ecosystem approach to landscape management, builds on and generates local knowledge (by combining scientific and practical knowledge) and engagement and links efforts to higher scales. In this way the EKV manages to bridge epistemologies as well as scales. Designing ecosystem management based on social-ecological inventories has the potential of improving the management system and thereby strengthening the capacity of actors to sustain desired ecosystem services. Local steward associations could provide long-term monitoring of ecosystem changes, local ecological knowledge, extensive local networks, and facilitate links across scales. They can be crucial in adaptive co-management systems and a coordinating team like the EKV can facilitate such processes.
Conditional Activation GAN: Improved Auxiliary Classifier GAN

A conditional generative adversarial network (cGAN) is a generative adversarial network (GAN) that generates data with a desired condition from a latent vector. Among the different types of cGAN, the auxiliary classifier GAN (ACGAN) is the most frequently used. In this study, we describe the problems of an AC-GAN and propose replacing it with a conditional activation GAN (CAGAN) to reduce the number of hyperparameters and improve the training speed. The loss function of a CAGAN is defined as the sum of the loss of each GAN created for each condition. The proposed CAGAN is an integration of multiple GANs, where each GAN shares all hidden layers, and their integration can be considered as a single GAN. Therefore, the structure of the integrated GANs does not significantly increase the number of computations. Additionally, to prevent the conditions given in the discriminator of a cGAN from being ignored with batch normalization, we propose mixed batch training, in which every batch for the discriminator keeps the ratio of the real and generated data consistent.
Bayesian Computation in Recurrent Neural Circuits-a Neuro-Control Approach

Over the past years, Bayesian probability theory became a popular tool for modeling human sensorimotor control and behavior. It is commonly used in the creation of optimal controllers due to its robustness in the presence of uncertainty and noise which is also a pervasive property within human sensory measurements and dynamic control tasks like saccadic eye motion. The Hidden Markov Model is an explicit example for a dynamic Bayesian network. The idea of this work is to implement the latter as a state estimator of velocity, location, and movement direction of a moving object within a robotic controller in order to recreate human-like saccadic motion. The visual input is given by a neuromorphic dynamic-vision sensor (DVS). This is fed into the Bayesian network which is built as a recurrent neural model inspired by visual processing in the brain. The motion detection network proves to be robust against noise, detects multiple stimuli, and is able to react to motion perturbations. A winner-takes-all policy, with additional movement cost considerations, then generates output initiating a saccadic movement in order to center the moving stimulus in the foveal area of the visual field. Moreover, further model extensions are addressed which could enable the controller to produce smooth pursuit behavior a more complex eye movement for continuous tracking.
Results of Revision TKA

The actual results of revision knee arthroplasty are difficult to ascertain. Many registries, while providing incomparable information about primary arthroplasty do not report similar information for revisions. Additionally, surgeries only enter the record as failures if a subsequent surgical procedure has been performed. There is wide variation in registries and published studies as to what constitutes a “revision arthroplasty. ” Furthermore, there is no concensus as to classifications of modes of failure, limited reporting by diagnosis and wide variation in the problems being addressed by revision surgery. Finally, surgical technique is non-uniform even within studies. The first revision arthroplasty can yield superb and gratifying results. Most evidence indicates that clinical practice falls short of that mark, and that literature is still probably missing many poor results. Clinical research data needs to be standardized and comprehensive education in revision knee arthroplasty is needed.
A high-resolution sedimentary charcoal- and geochemistry-based reconstruction of late Holocene fire regimes in the páramo of Chirripό National Park, Costa Rica

Abstract Multiproxy analysis of two sediment cores recovered from lagos Morrenas 3C and Ditkebi, located in the páramo of Costa Rica's Chirripó National Park, was undertaken to develop multidecadal-scale reconstructions of late Holocene fire regimes for the region. Analysis of macroscopic charcoal and sediment geochemistry (C%, N%, δ13C, δ15N, and C/N ratios) documents periodic burning of the páramo in Chirripó National Park during the past ~1700 yr. The charcoal records provide evidence of high fire frequency between AD ~560 and 720 and between AD ~980 and 1230. Severe fire episodes are reflected by a rapid increase in the flux of carbon (C) and nitrogen (N) from the surrounding catchment because of the volatilization of páramo vegetation. Additionally, δ15N, which sharply increases following local fire events, captures postfire changes in nutrient loading and, likely, the decadal-scale rate of postfire recovery of páramo vegetation. The consistently high δ13C and C/N values observed between AD ~700 and 1100 suggest an expansion of Muhlenbergia, a native C4 grass growing near shore, suggesting that the interval between AD ~700 and 1100, broadly corresponding to the Terminal Classic Drought and Medieval Climate Anomaly, was characterized by a decrease in effective moisture and temperature.
Classification of Abnormal Conditions: A Data-driven Aid for the Selection of Abnormal Operating Procedures in NPPs

Abnormal Operating Procedures (AOPs) are provided in nuclear power plants (NPPs), which are procedures that specify operator actions to restore normal operating conditions following a transient or abnormalities. The selection of the appropriate AOPs is decided by the combination of multiple alarms which need to be analyzed by the NPP operator to identify the actual abnormality that occurred. However, multiple alarms from multiple systems often occur at the same time during an incident, making it difficult for the operator to select a correct response efficiently in a time-critical situation. Furthermore, quick system recovery to normal condition before the trip is important since any delay may result to a condition that will degenerate to the emergency situation thereby leading to the unwanted shutdown of the plant. Therefore, considering the fact that plant recovery to normal condition before reactor trip occurs is paramount and the operator’s state of condition in time-critical situations in case of multiple alarms analysis, the benefits of an aid to assist operator in knowing the actual plant condition in time thereby selecting the appropriate response procedures cannot be overemphasized. In addition, owing to the fact that operator depends only on the alarm systems in selecting AOPs, the symptoms from process parameters are also significant in selecting proper AOPs. In this regards, we proposed a data-driven based pattern detection and classification method that concatenates the symptoms from the process sensors (analog signals) and the alarm signals (digital signals) together for effective identification and classification of abnormities in the event of abnormal situations in NPP. The proposed method is validated using simulation analog data from the Multi-dimensional Analysis for Reactor Safety (MARS-KS) simulator and artificially generated alarm digital data for the case of abnormalities concerning steam generator tube in NPP. The proposed method employing four classification models, Linear Discriminant Analysis (LDA), Classification and Regression Tree (CART), Support Vector Machine (SVM), and Random Forest (RF), are trained, and their performance are evaluated on the test set. The proposed method utilizing RF model performed excellently with 100% performance on the test set over the proposed method utilizing other classification models. The excellent results obtained from the case study suggest that the proposed model is a promising approach for aiding the selection of AOPs in the event of abnormal conditions and minimizing the operators’ burden in identifying the actual plant status during abnormal situations. Keyword: AOPs, Alarm’s digital signals, Symptom’s analog signals, Abnormality Classification, NPP
Microbial and process performance in anaerobic digestion of palm oil mill effluent under normal and over loading in hybrid reactor

Influence of suspended solid (SS) and oil and grease (O&G) in palm oil mill effluent (POME) under normal and overload feeding on microbial characteristics and reactor performances was studied in anaerobic hybrid sludge bed-fixed film reactor (AHR). The normal condition was achieved in feeding operation of TCOD, SS and O&G concentration in the range of 10-20 g/l, 5.011.0 g/l and 1.0-2.3 g/l, respectively. The process stability and performance were in pH 7, TVA/Alk < 0.4, and methane yield 0.21 lCH4/gCODremoved. The shocked load operation was occurred in 12.5 gSS/l and reflected by pH < 6.0 with TVA/Alk 0.5-0.7 and dropping in methane yield. Under normal and shocked load not only showed differences in performance and stability but also microbial characteristics. The results in this study illustrated that microbial activity of nonmethanogens and methanogens directly reflected in process stability and performance efficiency more than microbial population.
THE EFFECT OF CLIMATE CHANGE ON THE COST OF DAIRY FARMS IN TURKEY; CASE STUDY OF THRACE REGION

In Turkey, studies investigating the economic effects of climate change on agriculture are limited and there is little or no research that focuses on livestock production as regards changes in climate. In this context, the main objective of this research is to analyze the impacts of climate change on dairy cattle farms in terms of costs, according to different future scenarios and farms scales. Interviews were conducted with 140 dairy farmers in Thrace Region. To analyze the change in costs, the model developed by Calil et al. (2012) was used. According to the results of cost analysis, it was determined that climate change will lead to a 10-50% cost increase on dairy farms by the year 2044. The heat stress is responsible for 48-71% of the increase in the cost of production, where 24-52% is due to an increase in feed prices. Based on the outcome of this research, it was suggested that agricultural extension activities should be carried out in order for farms to get adapted to climate change. Agricultural supports and investment projects should be related to climate change.
The Empirical Analysis of Shanghai Composite Index based on GARCH Model

Because of the financial field, the GARCH model has a wide range of applications in the simulation of financial time series volatility and the measurement of financial risk. Therefore, based on the GARCH model, the assumption of normal distribution is used to measure the accuracy of VAR values, and the failure rate of VAR values is detected. The results show that the daily yield of Shanghai stock index has the characteristics of "peak thick tail". The correlation test of the daily yield series of Shanghai stock index shows that the Eviews index has a strong correlation. Through the GARCH model to eliminate relevance, to yield modeling, and effectively predict the Shanghai Composite Index VAR value.
Learning defect classifiers for textured surfaces using neural networks and statistical feature representations

Detecting surface defects is a challenging visual recognition problem arising in many processing steps during manufacturing. These defects occur with arbitrary size, shape and orientation. The challenges posed by this complexity have been combated with very special, runtime intensive and hand-designed feature representations. In this paper we present a machine vision system which uses basic patch statistics from raw image data combined with a two layer neural network to detect surface defects on arbitrary textured and weakly labeled image data. Evaluation on an artificial dataset with more than 6000 examples in addition to a real micro-cold forming process showed excellent classification results. © 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha
NDT_A_212528 1951..1956

Alina Wilkowska Andrzej Rynkiewicz Joanna Wdowczyk Jerzy Landowski Wiesław Jerzy Cubała 1Department of Psychiatry, Faculty of Medicine, Medical University of Gdansk, Gdańsk, Poland; 2Department of Cardiology and Cardiosurgery, Faculty of Medicine, University of Warmia and Mazury, Olsztyn, Poland; 3First Department of Cardiology, Faculty of Medicine, Medical University of Gdansk, Gdańsk, Poland Background: Post-myocardial depression is a highly prevalent condition worsening the course and prognosis of coronary artery disease. One of the possible pathogenetic factors is dysregulation of the autonomous nervous system, resulting in heart rate variability reduction. Methods: Twenty two patients hospitalised due to a first myocardial infarction were included. The Beck Depression Inventory (BDI) was used to rate the severity of their depressive symptoms. Results: Depressive symptomatology, defined as BDI ≥10, was present in 36.3% of the patients. Increase in heart rate variability (HRV) was observed in both groups during the first 6 months after the myocardial infarction. The HRV was significantly lower in the depressed group compared to patients without depression. Conclusion: Presence of depression after the myocardial infarction (MI) is associated with a significant decrease of the time domain HRV measure SDNN (standard deviation of all normal RR intervals) and with its slower increase during at least a three months period.
Development of an Improved Data-Driven Diagnostic Platform for Process Plants: Case Study of Feedwater Heater Leakage

The degradation conditions of process plant components such as Feedwater Heater (FWH) can have a significant effect on the performance of the overall plant efficiency depending on the associated functions of those components. In this regard, there is need for a more robust equipment condition assessment technique that will provide a diagnostic system to detect equipment failures early enough to warn operators in advance of impending failures and to inform maintenance personnel about the nature of the failure. Several data-driven techniques to diagnose the degradation conditions of process plant components have been proposed. However, the actual performance of the data-driven methods when applied to the real-time environment after training depends largely on the quality of data used for training, as actual plant data are noisy. This paper seeks to improve the quality of data used for degradation assessment by introducing a bilateral filter. Bilateral filter is used as a smoothing and prediction technique to preserve the edges in the signal variables in order to eliminate the noise for effective signal predictions. Bilateral filters are applied to both training data and test data with dynamic filter bandwidths in order to extract multiple features thereby make it possible to extract the interest and relevant features from the data and to generate different patterns for each filter bandwidth. That is, multiple features can be obtained. Then, we developed a diagnostic model using Support Vector Machine (SVM) as a classification method. With the diagnostic platform created, the proposed methodology has been validated by applying it to internal leakage diagnosis of Feedwater Heater (FWH) in Nuclear Power Plants (NPPs). Keyword: Data-Driven Diagnostics, Bilateral Filter, Feedwater Heater
University of Groningen Par1b induces asymmetric inheritance of plasma membrane domains via LGN-dependent mitotic spindle orientation in proliferating hepatocytes Slim,

The development and maintenance of polarized epithelial tissue requires a tightly controlled orientation of mitotic cell division relative to the apical polarity axis. Hepatocytes display a unique polarized architecture. We demonstrate that mitotic hepatocytes asymmetrically segregate their apical plasma membrane domain to the nascent daughter cells. The nonpolarized nascent daughter cell can form a de novo apical domain with its new neighbor. This asymmetric segregation of apical domains is facilitated by a geometrically distinct ‘‘apicolateral’’ subdomain of the lateral surface present in hepatocytes. The polarity protein partitioning-defective 1/microtubule-affinity regulating kinase 2 (Par1b/MARK2) translates this positional landmark to cortical polarity by promoting the apicolateral accumulation of Leu-Gly-Asn repeat-enriched protein (LGN) and the capture of nuclear mitotic apparatus protein (NuMA)–positive astral microtubules to orientate the mitotic spindle. Proliferating hepatocytes thus display an asymmetric inheritance of their apical domains via a mechanism that involves Par1b and LGN, which we postulate serves the unique tissue architecture of the developing liver parenchyma. Citation: Slim CL, Lázaro-Diéguez F, Bijlard M, Toussaint MJM, de Bruin A, et al. (2013) Par1b Induces Asymmetric Inheritance of Plasma Membrane Domains via LGN-Dependent Mitotic Spindle Orientation in Proliferating Hepatocytes. PLoS Biol 11(12): e1001739. doi:10.1371/journal.pbio. 1001739 Academic Editor: David Pellman, Dana-Farber Cancer Institute, United States of America Received April 11, 2013; Accepted November 5, 2013; Published December 17, 2013 Copyright: 2013 Slim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: No specific funding was received for this work. Competing Interests: The authors have declared that no competing interests exist. Abbreviations: fps, frames per second; MDCK, Madin-Darby canine kidney; PA, polarity axis; SA, spindle axis; SEM, standard error of the mean; shRNA, short hairpin RNA. * E-mail: s.c. d.van. ijzendoorn@umcg.nl
Poor sanitation and child mortality outcomes in Zimbabwe

While studies in developing countries have examined the role of maternal and socio-demographic factors on child mortality, the role of poor sanitation (open defecation) on child mortality outcomes in rural communities of sub-Saharan Africa has received less attention. This study sought to examine the link between poor sanitation and child mortality outcomes in rural Zimbabwe. The analysis uses data from four rounds of the nationally representative Demographic and Health Survey for Zimbabwe conducted in 1994, 1999, 2005/06, and 2010/11. Using propensity score matching, we find that children living in households with no toilet facilities are 2.43 percentage points more liable to be observed dead by the survey date, 1.3, and 2.24 percentage points more likely to die before reaching the age of one and five years respectively. We also examined the possible differences in survival among female and male children. Our results indicate that male children are more liable to be observed dead by the survey date than female children. Also, female children have a slight survival advantage over boys during the under-five period. Our results suggest the need for more investments in basic sanitary facilities in Zimbabwe’s rural areas to mitigate the potential devastating impacts of poor sanitation on child survival.
Internal Energy Build-up in MALDI

The present paper reports detailed studies on the internal energy of matrixassisted laser desorbed ions using delayed extraction MALDI-TOF and atmosphericpressure MALDI. We used benzylpyridinium cations as internal energy probes. Our study reveals three distinct contributions to internal energy build-up in vacuumMALDI (classical MALDI-TOF), each one having different effects on ion fragmentation. Some fragments are formed before ion extraction (i.e. maximum 100 ns after the laser impact), and are therefore well resolved and sharp in the MALDITOF MS scan. This prompt fragmentation can have two origins: (i) in-plume thermal activation, presumably always present, and (ii) in-plume chemical reactions with hydrogen radicals. In addition to these promptly formed fragments, a broad peak slightly offset to higher masses could be detected, corresponding to fragments formed after the extraction has started. A third source of internal energy for MALDI ions is therefore (iii) the extraction-induced collisional activation of the ions with the neutral components of the plume. These three contributions are difficult to quantify in vacuum-MALDI, because of the combined influence of several parameters (nature of the matrix, spot-to-spot variability, total laser exposure, delay time, acceleration voltage) on extraction-induced fragmentation. Atmospheric pressure MALDI has two advantages for comparative studies of analyte fragmentation. First, extraction-induced fragmentation is absent, and only the contribution of early plume activation remains. Second, the reproducibility is far better than in vacuum-MALDI. AP-MALDI is therefore expected to shed a new light on the early steps of the MALDI process. † Present address : Mass Spectrometry Laboratory, Chemistry Department, University of Liège, Institut de Chimie, Bat. B6c, B-4000 Liège, Belgium.