Integrált (botanikai és zoológiai) konzerváció-ökológiai kutatások gyepek természetközeli állapotának visszaállítására, biodiverzitásának megőrzésére és növelésére = Integrated botanical and zoological research in conservation ecology to restore, conserve and increase grassland biodiversity

A biodiverzitás-védelem egyik jelentős feladata a fogyatkozó fajok számára megfelelő élőhelyek létesítése. Kutatásunkban a természetes növény- és állatfajok visszatelepülését vizsgáltuk Európa legnagyobb területű, szántókon végzett gyeprekonstrukciós programjában. A tervek szerint kiépítettük mintavételi helyeinket és elvégeztük a terepi felméréseket. Elkészítettük a vizsgált 7000 hektáros táj élőhelytérképeit és kialakítottuk a projekt egységes adatbázisát. Az adatok alapján értelmeztük a gyeprekonstrukció első három évében zajló ökológiai folyamatokat. Kimutattuk, hogy kevés fajt tartalmazó magkeverékek vetése is jelentősen felgyorsíthatja a növényzet visszatelepülését a felhagyott lucernaföldeken. Hajdani gabona- és napraforgó-földeken a gyeptakaró lassabban alakul vissza és a gyomok visszaszorítására további kezelések (legeltetés, kaszálás) szükségesek. A természetes gyepekre jellemző ízeltlábú fajok a rekonstrukciót követő második évre váltották fel a szántókra jellemző tágtűrésű fajokat. A madarak közül a rekonstrukció elsősorban a mezőgazdasági területekhez kötődő, Európa-szerte fogyatkozó fajoknak kedvezett. A tapasztalatok alapján áttekintő tanulmányban bemutattuk a tájszintű élőhely-rekonstrukció előnyeit, gyakorlati kivitelezési lehetőségeit és költségeit. A pályázat révén 9 impakt faktoros és 7 hazai cikket jelentettünk meg, 12 előadást tartottunk nemzetközi konferenciákon és lefektettük egy nemzetközi szinten is versenyképes kutatócsoport alapjait. | The creation or restoration of habitats suitable for declining species is an important task in biodiversity conservation. We studied the recolonisation of natural plant and animal species in the largest grassland restoration programme in Europe. We established permanent plots and carried out field surveys as planned. We prepared the habitat maps of the 7000-ha landscape and developed a unified database for the project. We interpreted the ecological processes occurring in the first three years after restoration based on the data collected. We showed that restoration using low-diversity seed mixtures can considerably accelerate the recolonisation of natural vegetation on former alfalfa fields. On former wheat and sunflower fields, grass cover forms slower and further management (grazing, mowing) is necessary to control weeds. Arthropod species characteristic to natural grasslands replaced generalist species characteristic to croplands by the second year after restoration. Within birds, the restoration primarily favoured those species of agricultural areas that are declining all over Europe. Based on the experience gained, we demonstrated the benefits, practical implementation and costs of landscape-scale restoration in Europe in a review. As a result of the project, we published 9 papers in ISI-listed journals, 7 papers in Hungarian journals, presented 12 talks at international conferences and laid the foundations of an internationally competitive research group

Integrált konzervációökológiai kutatások a tájszintű biológiai sokféleség fejlesztésére és az ökoszisztéma-szolgáltatások felmérésére = Integrated studies in conservation ecology to conserve landscape biodiversity and to assess ecosystem services

Egy év alatt 8 cikk jelent meg/került elfogadásra ISI folyóiratokban (összes IF: 16,615), 3 magyar dolgozattal, 1 PhD értekezéssel, 3 OTDK-dolgozattal, 6 szakdolgozattal együtt. A botanikai eredmények szerint a gyom-dominancia csökkent és a célfajok borítása nőtt az évek során. A magvetés felgyorsította a másodlagos szukcessziót a szik- és löszgyepek felé, mivel a vetett fűfajok hatékonyan visszaszorították a gyomokat, habár ez a hatás változott a helyek és a fűmagkeverék szerint. Egy új kísérletben a szénaráhordással kombinált magvetés még jobban felgyorsította a gyepek kialakulását a gyomok még erőteljesebb visszaszorítása révén. Zoológiai eredményeink szerint a restauráció hatása állatcsoportonként változik. Több ízeltlábú csoportban csökkenő összes fajszámot és abundanciát, de az élőhely-specialisták gyarapodását találtuk. Három új vizsgálatban kimutattuk, hogy a (i) méh-együttesek a virággazdag, gyomos területeken a legfajgazdagabbak, majd a generalisták eltűnésével az ötödik évben szinte csak a természetes gyepek fajaiból álltak, (ii) kétéltűek fajszáma a régebbi gyepesítéseken magasabb, mint a fiatalabbakon, és (iii) kisemlősöket csak a kezelés, míg a restauráció nem befolyásolta, mert több egyed volt a nem-kezelt, mint a kaszált vagy legelt gyepeken, ahol a növényzet alacsony, a predációs nyomás pedig magas volt. Eredményeink szerint az ökoszisztéma-szolgáltatásokat végző fajok védelme gondosan tervezett, finoman hangolt poszt-restaurációs intézkedéseket igényel. | We published 8 papers in ISI-journals (total IF: 16.615), 3 Hungarian papers, 1 PhD dissertation, 3 student papers (OTDK) and 6 MSc-theses in 1 year. Botanical results showed that the dominance of weeds decreased and the cover of target species increased with years. Seed sowing accelerated secondary succession towards alkali steppes and loess grasslands because the sown grasses effectively suppressed short-lived weeds, although this effect differed by site history and seed mixture. In a new field experiment, we found that adding hay accelerated the development of grasslands even further, with higher weed suppression than in seed sowing only. Zoological results showed varying responses to restoration in animal taxa. Trends in several arthropods showed decreasing total richness and abundance but increasing numbers of habitat specialists. In 3 new studies, we found that (i) bee assemblages were richest in flower-rich, weedy fields and with the disappearance of generalists almost exclusively contained species of natural grasslands by year 5, (ii) the species richness of amphibians was higher in older than in younger restorations, and (iii) small mammals were affected only by management but not by restoration, with more individuals in non-managed than in mowed or grazed fields with lower vegetation and higher predation pressure. Our results show that post-restoration management requires carefully designed actions fine-tuned to address specific ecosystem service providers

Predictor-corrector interior-point algorithm based on a new search direction working in a wide neighbourhood of the central path

We introduce a new predictor-corrector interior-point algorithm for solving P_*(κ)-linear complementarity problems which works in a wide neighbourhood of the central path. We use the technique of algebraic equivalent transformation of the centering equations of the central path system. In this technique, we apply the function φ(t)=√t in order to obtain the new search directions. We define the new wide neighbourhood D_φ. In this way, we obtain the first interior-point algorithm, where not only the central path system is transformed, but the definition of the neighbourhood is also modified taking into consideration the algebraic equivalent transformation technique. This gives a new direction in the research of interior-point methods. We prove that the IPA has O((1+κ)n log⁡((〖〖(x〗^0)〗^T s^0)/ϵ) ) iteration complexity. Furtermore, we show the efficiency of the proposed predictor-corrector interior-point method by providing numerical results. Up to our best knowledge, this is the first predictor-corrector interior-point algorithm which works in the D_φ neighbourhood using φ(t)=√t

Unified approach of primal-dual interior-point algorithms for a new class of AET functions

We propose new short-step interior-point algorithms (IPAs) for solving P_* (κ)-linear complementarity problems (LCPs). In order to define the search directions we use the algebraic equivalent transformation technique (AET) of the system which characterizes the central path. A novelty of the paper is that we introduce a new class of AET functions. We present the complexity analysis of the IPAs that use this general class of functions in the AET technique. Furthermore, we also deal with a special case, namely φ(t)=t^2-t+√t. This function differs from the ones used in the literature in the sense that it has inflection point. It does not belong to the class of concave functions determined by Haddou et al. Furthermore, the kernel function corresponding to this AET function is neither eligible nor self-regular kernel function. We prove that the IPAs using any member φ of this new class of AET functions have polynomial iteration complexity in the size of the problem, bit length of the integral data and in the parameter κ. Beside this, we also provide numerical results that show the efficiency of the introduced methods

Unified Approach of Interior-Point Algorithms for P_*(\kappa )-LCPs Using a New Class of Algebraically Equivalent Transformations

We propose new short-step interior-point algorithms (IPAs) for solving P_*(\kappa ) P ∗ ( κ ) -linear complementarity problems (LCPs). In order to define the search directions, we use the algebraic equivalent transformation (AET) technique of the system describing the central path. A novelty of the paper is that we introduce a whole, new class of AET functions for which a unified complexity analysis of the IPAs is presented. This class of functions differs from the ones used in the literature for determining search directions, like the class of concave functions determined by Haddou, Migot and Omer, self-regular functions, eligible kernel and self-concordant functions. We prove that the IPAs using any member \varphi φ of the new class of AET functions have polynomial iteration complexity in the size of the problem, in starting point’s duality gap, in the accuracy parameter and in the parameter \kappa κ

New predictor-corrector interior-point algorithm with AET function having inflection point

In this paper we introduce a new predictor-corrector interior-point algorithm for solving P_* (κ)-linear complementarity problems. For the determination of search directions we use the algebraically equivalent transformation (AET) technique. In this method we apply the function φ(t)=t^2-t+√t which has inflection point. It is interesting that the kernel corresponding to this AET function is neither self-regular, nor eligible. We present the complexity analysis of the proposed interior-point algorithm and we show that it's iteration bound matches the best known iteration bound for this type of PC IPAs given in the literature. It should be mentioned that usually the iteration bound is given for a fixed update and proximity parameter. In this paper we provide a set of parameters for which the PC IPA is well defined. Moreover, we also show the efficiency of the algorithm by providing numerical results

Considering PKI Safety Impact on Network Performance During V2X-based AD/ADAS Function Development Processes

In this research, we examine the impact of PKI on vehicle safety and thus make suggestions for further improvements to V2X-based safety application design processes. In the first step, we introduce the novel methodological background of characterizing the safety impact of the network performance metrics on the V2X-based automotive applications. Following this, we investigated two cases: with and without Public Key Infrastructure (PKI) authorization, to identify the potential safety effect if the V2X device is unprepared for the additional computational overhead caused by the authentication framework-related processes. Based on our results, we can identify the operational domain of a specific V2X-based application that can be used safely

Autonomous neural information processing by a dynamical memristor circuit

Analog tunable memristors are widely utilized as artificial synapses in various neural network applications. However, exploiting the dynamical aspects of their conductance change to implement active neurons is still in its infancy, awaiting the realization of efficient neural signal recognition functionalities. Here we experimentally demonstrate an artificial neural information processing unit that can detect a temporal pattern in a very noisy environment, fire a single output spike upon successful detection and reset itself in a fully unsupervised, autonomous manner. This circuit relies on the dynamical operation of only two memristive blocks: a non-volatile Ta$_2$O$_5$ device and a volatile VO$_2$ unit. A fading functionality with exponentially tunable memory time constant enables adaptive operation dynamics, which can be tailored for the targeted temporal pattern recognition task. In the trained circuit false input patterns only induce short-term variations. In contrast, the desired signal activates long-term memory operation of the non-volatile component, which triggers a firing output of the volatile block.Comment: 11 pages, 6 figure

Classics in a new perspective: gluten as a special food safety and analytical challenge

In the last couple of decades, the nutritional role and perception of gluten became controversial. In one hand, gluten proteins play a central role in determining the baking quality of wheat and other cereals. On the other hand, hypersensitivity reactions triggered by gluten in susceptible individuals have become subjects of growing interest. Of these gluten-related disorders, with an estimated global prevalence of 1%, the most important one is celiac disease (CD), which is an autoimmune disorder accompanied by villous atrophy. CD can manifest in a wide range of symptoms, its only treatment option is a lifelong gluten-free (GF) diet. To support compliance to this diet, current EU legislation maximizes the gluten-content of products sold with a GF label in 20 mg/kg. It necessitates accurate quantification of gluten in this low concentration range. The method-of-choice for this purpose is the immunoanalytical-based ELISA (enzyme-linked immunosorbent assay). However, validation of different ELISA methods and the comparability of their results and, consequently, the reliability of the data they provide is problematic. The major goal of this paper is to introduce the analytical and protein chemistry issues behind this problem and the efforts to improve the conditions of the methodology. We are also including the special role of oats in the GF diet in an attempt to provide the widest possible overview of the food safety and analytical challenges represented by gluten

EASY-APP : An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model