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Found 6525 publications

New data on terrestrial gastropods (Gastropoda: Cyclophoroidea) from mid-Cretaceous Burmese amber, with descriptions of two new species in the genus Euthema (Diplommatinidae)

J. M. Bichain Barna Páll-Gergely Imre Szenti M. Szabó None Kokavecz

Publication Name: Zoosystematica Rossica

Publication Date: 2025-01-01

Volume: 34

Issue: 2

Page Range: 336-353

Description:

Mid-Cretaceous Burmese amber preserves an exceptionally diverse assemblage of operculate land snails. Here, two new diplommatinid species are described: Euthema convexispira Bichain et Páll-Gergely, sp. nov. and Eu. torokzselenszkyi Páll-Gergely et Szabó, sp. nov. Newly found specimens of two previously known species, Hirsuticyclus canaliculatus Yu, 2022 and Cretadiostoma caperatum Yu, Zhuo et Páll-Gergely, 2023, are described and illustrated; the specimen of the former is characterised by a distinctive spiral operculum, while that of the latter provides additional data on the morphology of the aperture and the proportions of the shell whorls. Furthermore, the article reports three specimens that are tentatively attributed here to the species Euthema cf. annae Balashov, 2021 and to the genera Euthema Yu, Wang et Pan, 2018 and Pulchraspira Yu, Salvador et Jarzembowski, 2021.

Open Access: Yes

DOI: 10.31610/ZSR/2025.34.2.336

Intelligent total transportation management system for future smart cities

Daniel Rohacs Anita Boros Dinh Dung Nguyen József Rohács

Publication Name: Applied Sciences Switzerland

Publication Date: 2020-12-02

Volume: 10

Issue: 24

Page Range: 1-31

Description:

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Open Access: Yes

DOI: 10.3390/app10248933

Delay propagation in a real life railway network controlled by a fuzzy logic rule base

Tibor Héray Gabor Rozsa Márk Farkas Laszlo T. Koczy

Publication Name: 10th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics Cinti 2009

Publication Date: 2009-12-01

Volume: Unknown

Issue: Unknown

Page Range: 423-433

Description:

This paper considers a real-life railway timetable related problem, where a set of interconnected railway junction points form a railway network, which is essentially a directed graph with corresponding vertex and edge capacities representing railway tracks and railway platforms.The dynamic behavior of this model is driven by a timetable. Unforeseen weather and other external effects may contribute to delays in the timetable, which alters the behavior of the whole system. In this scenario a hierarchical fuzzy system is proposed that can suggest a possible outgoing delay for each train by evaluating a set of fuzzy rule bases using two input data. The first proposal does not take into account the possible propagation of delay in the whole railway network. In this article a negative feedback is applied on the hierarchical fuzzy system.The fuzzy sets are optimized by an evolution based global search metaheuristics.

Open Access: Yes

DOI: DOI not available

Evaluating AI-driven credit scoring models versus traditional statistical techniques

Neha Parashar Jewel Kumar Roy Pradip Sanatkumar Padhye Varsha Shriram Nerlekar Sushil Kumar Gupta Dipak Santram Vakrani

Publication Name: Discover Artificial Intelligence

Publication Date: 2026-12-01

Volume: 6

Issue: 1

Page Range: Unknown

Description:

This study evaluates AI credit-scoring models against standard statistical models using a real-life data set with 1000 loan applications. The main research question is about whether machine-learning methods are more valuable in terms of predictive accuracy, interpretability and stability to changes in the process of macroeconomic deterioration as compared to traditional methods. The four model variants were developed: the logistic regression and decision tree as examples of the classical ones, and XGBoost gradient-boosting ensemble and multilayer perceptron neural networks as examples of AI-based alternatives. The methodological engine was R (v4.3.1), which established a 70:30 train-test strata union, inner cross-validation and harmony spatial search to tune the hyperparameter. The findings show that XGBoost produces an optimal balanced accuracy cumulative gain curve (cumulative gain, CGC: 0.89; area under the receiver operating characteristic, AUC: 0.89) that is trumped by the neural network (CGC: 0.87; AUC: 0.87) and succeeded by the logistic regression (CGC: 0.76; AUC: 0.76). The SHAP analysis shows that the amount of credit, duration of loan, and age are central predictors. During stress-test simulation, XGBoost is stable in making predictions (AUC = 0.83) compared to a severe decline in logistic-regression results (AUC = 0.68). The results therefore justify the claim that the state-of-the-art AI tools have better predictive potential and robust interpretability, thus rising as viable alternatives to the systems in vogue in modern finance organisations.

Open Access: Yes

DOI: 10.1007/s44163-025-00772-1

Impact of Big Four Audit Firms on Environmental Disclosure in China: Critical Role of Governance and Ownership Structure

Irum Shahzadi Najaf Iqbal Lei Zhou Chen Liu Irum Shahzadi Nabiha Jamil Wenzhu Ji

Publication Name: Politicka Ekonomie

Publication Date: 2025-01-01

Volume: 73

Issue: 5

Page Range: 810-838

Description:

As the corporate world has recently been increasingly held accountable for its non-green behaviour, corporate environmental disclosures (CED) are crucial in informing the relevant stakeholders. Among the factors influencing CED, firm auditors play a pivotal role. Our research investigates the impact of Big Four audit firms on corporate environmental disclosure in China. With unique institutional factors and the most significant carbon emissions globally, China provides an exciting and compelling ground for studying this relationship. For this purpose, we use annual firm-level data of A-listed shares on the Shenzhen and Shanghai stock exchanges and employ the Poisson and negative binomial regression models for empirical analyses. Contrary to the common belief that Big Four audit firms lead to improved disclosure quality, we find that they negatively affect environmental disclosure in China. We also investigate the moderating role of corporate governance and ownership structure in this relationship. A high ratio of state ownership, male directors on the board and institutional investor holding worsen the disclosure quality. However, a high ratio of independent directors mitigates this issue. These findings open new avenues for further research and can guide future policy decisions regarding environmental disclosure in Chinese firms. The Big Four audit firms should be more stringent in their operations and supervise public firms regarding environmental disclosures.

Open Access: Yes

DOI: 10.18267/j.polek.1482

OWA operators in the insurance industry

István Harmati D. Fülep Krisztián Koppány Norbert Kovács

Publication Name: Journal of Infrastructure Policy and Development

Publication Date: 2024-01-01

Volume: 8

Issue: 13

Page Range: Unknown

Description:

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

Open Access: Yes

DOI: 10.24294/jipd8015

Optimization of strategic level performance measurement and decision making using artificial neural network

János Botzheim Péter Földesi Csaba Tápler Péter Németh

Publication Name: 2012 IEEE International Technology Management Conference Itmc 2012

Publication Date: 2012-11-01

Volume: Unknown

Issue: Unknown

Page Range: 93-97

Description:

In our paper we propose a new method for the strategic level performance measurement and decision making by presenting two case studies performed in 2011. We proposed a computational intelligence method to establish connection between basic operational level data and important strategic level indicators. In the first case study this indicator is related to performance measurement. In the second case study the indicator is the contribution margin of the given company. After the introduction we present the process of programming and learning with actual company data. Finally an evaluation of the results is presented based on the program runs. © 2012 IEEE.

Open Access: Yes

DOI: 10.1109/ITMC.2012.6306360

Characterization of Model Uncertainty Features Relevant to Model Predictive Control of Lateral Vehicle Dynamics

Gábor Rödönyi Dániel Pup Ádám Kisari Zsombor Vigh Alexandros Soumelidis József Bokor

Publication Name: 2020 23rd IEEE International Symposium on Measurement and Control in Robotics Ismcr 2020

Publication Date: 2020-10-15

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The information about a system's dynamics represented by measurement data sets are often confined to regions of restricted operations where the system is not sufficiently excited for model identification purposes. Experiments performed in closed-loop with safety constraints allow only for reduced order modeling. In the paper, a set of low order models are identified from real experimental data of the lateral dynamics of an electric passenger car. Low order models are advantageous for on-line computation in model-based control, though uncertainty due to neglected dynamics may deteriorate control performance and constraint satisfaction. The effect of uncertainty is analyzed by controller cross-validation where a controller designed based on one model is evaluated on other models playing the role of the true system. This method allows us to qualify not only model-controller pairs, but to determine the properties of input data and model uncertainty, which lead to more useful data sets, more robust and better performing controllers than the others.

Open Access: Yes

DOI: 10.1109/ISMCR51255.2020.9263745

Efficacy of Advanced Robotic and Virtual Therapy in the Treatment of Acute and Subacute Stroke Patients: a Feasibility Study

Tamás Haidegger József Tollár Bence Csutorás Nándor Prontvai Blanka Törő Szilvia Kóra Petra Kós Barbara Kopácsi Veronika Keresztesy Júlia Kutas Dóra Kozma

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2025-01-01

Volume: 22

Issue: 10

Page Range: 83-102

Description:

The aim of this study is to compare the effectiveness of advanced robotic therapy, virtual therapy and conventional physiotherapy in the rehabilitation of patients with acute and subacute stroke. In addition, we aim to further demonstrate the importance of an early mobilisation exercise program in the management of acute stroke patients. Participants (first-time ischaemic stroke patients in acute or subacute stages) were randomly divided into three equal groups (n=10 persons/group): a robot-assisted early mobilisation + virtual reality therapy (ROB+VR) group, a robot-assisted early mobilisation + conventional physiotherapy (ROB+FIZ) group, and a conventional physiotherapy (CON) group. Each group performed a 3-week-long training program (1 hour/session, 5 days/week). The results were measured before and after the exercise. The primary outcome measure used was the modified Rankin Scale (mRS) which indicates the severity of disability in daily activities and measures the degree of independence of the individual. The secondary outcomes were measured by the EuroQoL 5 dimensions questionnaire 5 levels version (EQ-5D-5L), the Berg Balance Scale (BBS), the Barthel Index (BI), and the 6-minute walking test (6mWT). All the three groups showed improvements in most outcomes. The scores for BI, EQ-5D-5L, and mRS showed significant improvements in quality of life. Comparing the groups, the ROB+VR group showed the greatest improvement in the scores in almost all of the tests. From the test scores, the 6-minute walk test had the highest improvement at the end of the therapy (94.58% improvement). Our results show that early robotic mobilization, followed by a movement program, combined with a virtual reality therapy, significantly improve both the speed and quality of rehabilitation after a stroke.

Open Access: Yes

DOI: 10.12700/APH.22.10.2025.10.6