Wyniki wyszukiwania dla: CORPORATE BANKRUPTCY PREDICTION
-
CSR in Polish SMEs- from perception to infatuation. Examples of socially engaged firms from Pomeranian Region
PublikacjaDespite the increasing worldwide awareness of the importance of social issues in con-temporary business management, Polish small and medium-sized enterprises seem to re-main in the group of followers, certainly not leaders, of the corporate social responsibility (CSR) movement, and this despite important dissemination efforts by numerous institu-tions. The aim of this paper is to present the bumpy road to the (still incomplete)...
-
Tworzenie wartości dla akcjonariuszy w świetle nadzoru korporacyjnego
PublikacjaDuży odsetek spółek publicznych w Polsce nie tworzy wartości dla akcjonariuszy. Zaprezento-wano wyniki badań dotyczących wpływu struktur własności na efektywność działania spółek pu-blicznych. Badania te nie były w stanie wyjaśnić w wystarczającym stopniu zjawiska destrukcji warto-ści. Starano się wykazać, że jedną z przyczyn tego zjawiska jest transfer wartości. Zalicza się go do głównych problemów nadzoru korporacyjnego. Szczególna...
-
Elimination of impulsive disturbances from archive audio files – comparison of three noise pulse detection schemes
PublikacjaThe problem of elimination of impulsive disturbances (such as clicks, pops, ticks, crackles, and record scratches) from archive audio recordings is considered and solved using autoregressive modeling. Three classical noise pulse detection schemes are examined and compared: the approach based on open-loop multi-step-ahead signal prediction, the approach based on decision-feedback signal prediction, and the double threshold approach,...
-
Efficient uncertainty quantification using sequential sampling-based neural networks
PublikacjaUncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based...
-
CSR and innovation or CSR as non-technological innovation
PublikacjaThis chapter investigates the relationship between Corporate Social Responsibility (CSR) and innovation process. There is an ongoing discussion in the literature regarding this relationship, in particular on its one- or bidirectional nature and possible antecedents or mediating factors. This research addresses this gap and aims to provide more conceptual clarity and synthesize the different types of relationships between CSR and...
-
On ship roll resonance frequency
PublikacjaThe paper deals with the problem of modeling of rolling motion under a variety of excitation parameters. Special emphasis is put on the analysis and prediction of the frequency of the resonant mode of rolling, since it is often an essential issue in terms of motion of a ship related to her safety against capsizing or excessive amplitudes of roll. The research is performed for both free rolling and excited rolling and it is based...
-
Elimination of clicks from archive speech signals using sparse autoregressive modeling
PublikacjaThis paper presents a new approach to elimination of impulsivedisturbances from archive speech signals. The proposedsparse autoregressive (SAR) signal representation is given ina factorized form - the model is a cascade of the so-called formantfilter and pitch filter. Such a technique has been widelyused in code-excited linear prediction (CELP) systems, as itguarantees model stability. After detection of noise pulses usinglinear...
-
New semi-causal and noncausal techniques for detection of impulsive disturbances in multivariate signals with audio applications
PublikacjaThis paper deals with the problem of localization of impulsive disturbances in nonstationary multivariate signals. Both unidirectional and bidirectional (noncausal) detection schemes are proposed. It is shown that the strengthened pulse detection rule, which combines analysis of one-step-ahead signal prediction errors with critical evaluation of leave-one-out signal interpolation errors, allows one to noticeably improve detection results...
-
Trim Optimisation - Theory and Practice
PublikacjaForce Technology has been working intensively with trim optimisation tests for almost last 10 years. Focus has primarily been put on the possible power savings and exhaust gases reduction. This paper describes the trim optimisation process for a large cargo vessel. The physics behind changed propulsive power is described and the analyses in order to elaborate the optimum trimmed conditions are presented. Different methods for prediction...
-
Global Surrogate Modeling by Neural Network-Based Model Uncertainty
PublikacjaThis work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically...
-
Chemistry and Nanochemistry 2022.
Kursy OnlineThe course consists of lectures (15 x 2 hours) and laboratories (5 x 3 hours).The goal of this course is to teach general chemistry and adequately apply it to nano-size systems, their synthesis and analysis. An emphasis is laid on an analysis of electronic structure of molecules and prediction of resulting properties and reasons of consequent behaviour in chemical reactions. The course also encloses laboratory classes, where the...
-
Antecedents and Consequences of Brand Loyalty
PublikacjaThe objective of this paper is to review, systematize, and summarize empirical research on the antecedents and consequences of brand loyalty. The literature review has identified five categories of antecedents to brand loyalty associated with consumer, brand, social, corporate and relational factors. The type of loyalty formed varies according to the combination of various antecedents, with premium loyalty being considered the...
-
Traffic Noise Analysis Applied to Automatic Vehicle Counting and Classification
PublikacjaProblems related to determining traffic noise characteristics are discussed in the context of automatic dynamic noise analysis based on noise level measurements and traffic prediction models. The obtained analytical results provide the second goal of the study, namely automatic vehicle counting and classification. Several traffic prediction models are presented and compared to the results of in-situ noise level measurements. Synchronized...
-
TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA
PublikacjaThe paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results...
-
Impact of digital technologies on reliability of risk forecasting models - case study of enterprises in three global financial market regions
PublikacjaThis chapter focuses on the evaluation of impact of ICT on reliability of financial risk forecasting models. Presented study shows how the development of ICT can improve the effectiveness of such models. Determining a firm’s financial risk is one of the most interesting topics for investors and decision-makers. The multifaceted goal of the presented research is to separately estimate five traditional statistical and five soft computing...
-
Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
-
An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
-
Nowe wyzwania, nowe rozwiązania. Jak przedsiębiorstwo branży IT odnajduje się w erze VUCA?
PublikacjaZmienność, niepewność, złożoność i niejednoznaczność, określane akronimem VUCA (volatility, uncertainty, complexity, ambiguity) towarzyszą funkcjonowaniu każdego przedsiębiorstwa. Jak wykorzystać te nieodłączne cechy otoczenia przedsiębiorstw jako szanse dla ich rozwoju to wy-zwania stojące przed nimi, a zarazem pytanie badawcze artykułu. Celem artykułu jest wykazanie, na przykładzie przedsiębiorstwa branży IT, że analiza zmienności,...
-
Wojciech Wyrzykowski dr hab.
OsobyWojciech Wyrzykowski jest pracownikiem Katedry Finansów na Wydziale Zarządzania i Ekonomii Politechniki Gdańskiej. Jest autorem 70 publikacji naukowych w tym 5 monografii oraz współautorem 7 monografii. Najistotniejsze z nich oddające zainteresowania naukowe autora to m.in.: Podatkowe uwarunkowania rozwoju przedsiębiorczości w Polsce, Podatki w Polsce – zarys wykładu, Księgi, ewidencje i rejestry podatkowe małych przedsiębiorców,...
-
Jacek Krenz dr hab. inż. arch.
OsobyUr. 11 maja 1948 w Poznaniu – polski architekt, malarz, profesor Wydziału Architektury Politechniki Gdańskiej, Wydziału Architektury, Inżynierii i Sztuki Sopockiej Szkoły Wyższej, W latach 70. członek Grupy Kadyńskiej, skupiającej plastyków i architektów związanych z Międzynarodowymi Plenerami "Ceramika dla architektury" w Kadynach. W roku 1980 uzyskał stopień doktora, a w 1997 – doktora habilitowanego. Prowadzi badania na temat...
-
Wireless Networks as an Infrastructure for Mission-Critical Business Applications
PublikacjaDespite the dynamic growth of wireless network systems, their pres-ence in business-support infrastructure has been limited. In the article we provide an overview of generic corporate network architecture and examine usefulness of available wireless network solutions in such systems. Following this overview we analyze new wireless network architecture which currently undergoes standardization process - wireless mesh. It can result...
-
Computational collective intelligence for enterprise information systems
PublikacjaCollective intelligence is most often understood as a kind of intelligence which arises on the basis of a group (collective) of autonomous unites (people, systems) which is taskoriented. There are two important aspects of an intelligent collective: The cooperation aspect and the competition aspect (Levy 1997). The first of them means the possibility for integrating the decisions made by the collective members for creating the decision of...
-
I love to write and create. Can I earn money doing it? Entrepreneurial process of bloggers
PublikacjaNew technologies and a new way of looking at the life of future generations, open up new perspectives of entrepreneurial activities. One of them is to run a blog. Although the first blogs appeared many years ago and earning money from running them is also known to bloggers, little research is devoted to this form of entrepreneurship. The analysis of this phenomenon was carried out using the netnography method, which is not common...
-
Factors affecting the conclusion of an arrangement in restructuring proceedings: evidence from Poland
PublikacjaThe EU Restructuring Directive (2019/1023) requires Member States to provide a preventive restructuring framework for financially distressed entities that remain viable or are likely to readily restore economic viability. The first step to a successful restructuring is the approval of an arrangement between the debtor and creditors. The main research objective of the article is to identify factors affecting the conclusion of an...
-
Andrzej Chybicki dr inż.
OsobyZ wykształcenia informatyk, absolwent Wydziału Elektroniki, Telekomunikacji i Informatyki Politechniki Gdańskiej, doktor nauk technicznych w dziedzinie informatyka specjalizujący się w przetwarzaniau danych przestrzennych w rozproszonych systemach informatycznych. Ukierunkowany na wykorzystywanie osiągnięć i wiedzy zakresu prowadzonych badań w przemyśle. Współpracował z szeregiem podmiotów przemysłu informatycznego, geodezyjnego...
-
Aerodynamic shape optimization by variable-fidelity computational fluid dynamics models: a review of recent progress
PublikacjaA brief review of some recent variable-fidelity aerodynamic shape optimization methods is presented.We discuss three techniques that—by exploiting information embedded in low-fidelity computationalfluid dynamics (CFD) models—are able to yield a satisfactory design at a low computational cost, usu-ally corresponding to a few evaluations of the original, high-fidelity CFD model to be optimized. Thespecific techniques considered here...
-
ADAPTIVE PREDICTIONS OF THE EURO/ZŁOTY CURRENCY EXCHANGE RATE USING STATE SPACE WAVELET NETWORKS AND FORECAST COMBINATIONS
PublikacjaThe paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day- ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space...
-
Multi-Criteria Knowledge-Based Recommender System for Decision Support in Complex Business Processes
PublikacjaIn this paper, we present a concept of a multi-criteria knowledge-based Recommender System (RS) designed to provide decision support in complex business process (BP) scenarios. The developed approach is based on the knowledge aspects of Stylistic Patterns, Business Sentiment and Decision-Making Logic extracted from the BP unstructured texts. This knowledge serves as an input for a multi-criteria RS algorithm. The output is prediction...
-
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
-
System do prototypowania bezprzewodowych inteligentnych urządzeń monitoringu audio-video
PublikacjaW komunikacie przedstawiono system prototypowania bezprzewodowych urządzeń do monitoringu audio-video. System bazuje na układach FPGA Virtex6 i wielu dodatkowych wspierających urządzeniach jak: szybka pamięć DDR3, mała kamera HD, mikrofon z konwerterem A/C, moduł radiowy WiFi, itp. Funkcjonalność systemu została szczegółowo opisana w komunikacie. System został zoptymalizowany do pracy pod kontrolą systemu operacyjnego Linux, zostały...
-
Akcelerator predykcji wewnątrzramkowej H.264 do kompresji obrazu w sensorach wizyjnych
PublikacjaW komunikacie przedstawiono konfigurowalny cyfrowy akcelerator predykcji wewnątrzramkowej przeznaczony dla enkodera wideo standardu H.264. Akcelerator realizuje predykcję typu „intra” dla makrobloków luminancji o wymiarach 4x4 i 16x16. Akcelerator wstępnie zaimplementowano w układzie FPGA, gdzie został on pomyślnie zweryfikowany, a następnie zaimplementowano go w układzie ASIC w technologii UMC 90 nm. Szczegółowe wyniki testów...
-
Akcelerator predykcji wewnątrzramkowej H.264 do kompresji obrazu w sensorach wizyjnych
PublikacjaW artykule przedstawiono konfigurowalny cyfrowy akcelerator predykcji wewnątrzramkowej przeznaczony dla enkodera wideo standardu H.264. Akcelerator realizuje predykcję typu „intra” dla makrobloków luminancji o wymiarach 4x4 i 16x16. Akcelerator wstępnie zaimplementowano w układzie FPGA, gdzie został on pomyślnie zweryfikowany, a następnie zaimplementowano go w układzie ASIC w technologii UMC 90 nm. Szczegółowe wyniki testów akceleratora...
-
Performance of Noise Map Service Working in Cloud Computing Environment
PublikacjaIn the paper a noise map service designated for the user interested in environmental noise subject is presented. It is based on cloud computing. Noise prediction algorithm and source model, developed for creating acoustic maps, are working in cloud computing environment. In the study issues related to noise modeling of sound propagation in urban spaces are discussed with a special focus on road noise. Examples of results obtained...
-
New generation of analytical tests based on the assessment of enzymatic and nuclear receptor activity changes induced by environmental pollutants
PublikacjaAnalytical methods show great potential in biological tests. The analysis of biological response that results from environmental pollutant exposure allows: (i) prediction of the risk of toxic effects and (ii) provision of the background for the development of markers of the toxicants presence. Bioanalytical tests based on changes in enzymatic activity and nuclear receptor action provide extremely high specificity and sensitivity....
-
On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublikacjaWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
-
CSR and small business from the international and national perspective
PublikacjaCorporate and social responsibility is nowadays quite a popular topic among large companies but thanks to numerous popularization activities undertaken by national and international institutions, also small entrepreneurs become more and more interested in exploring this fairly new approach. This paper outlines the main differences between the implementation of the CSR approach in large companies and in SMEs, which are not able...
-
It is not OK but it works – unproductive entrepreneurship, the case of Poland
PublikacjaThe concept of unproductive entrepreneurs was introduced to science by Baumol, who pointed out the differences in business output between countries. Unproductive behaviour of entrepreneurs is often a consequence of ineffective institutions used by entrepreneurs for rent seeking. The aim of this article is to examine subjective norms (S.N.) and attitudes regarding specified types of unproductive entrepreneurship, which in many cases...
-
Mathematical Modeling of Ice Dynamics as a Decision Support Tool in River Engineering
PublikacjaThe prediction of winter flooding is a complicated task since it is affected by many meteorological and hydraulic factors. Typically, information on river ice conditions is based on historical observations, which are usually incomplete. Recently, data have been supplemented by information extracted from satellite images. All the above mentioned factors provide a good background of the characteristics of ice processes, but are not...
-
The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process
PublikacjaThis paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in...
-
Implementation of Extended Kalman Filter with Optimized Execution Time for Sensorless Control of a PMSM Using ARM Cortex-M3 Microcontroller
PublikacjaThis paper addresses the implementation and optimization of an Extended Kalman Filter (EKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex-M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of EKF estimator was reduced from 260.4 μs to 37.7 μs without loss of accuracy. To further...
-
O MNIEJ LUB BARDZIEJ SŁODKIM CUKRZE, CZYLI KILKA UWAG O ODPOWIEDZIALNOŚCI SPOŁECZNEJ FIRM RODZINNYCH I PRZEDSIĘBIORSTW SPOŁECZNYCH
PublikacjaW artykule, przyjmując za punkt wyjścia teorię interesariuszy Freemana, autorki podjęły się refleksji na temat specyfiki odpowiedzialności społecznej firm rodzinnych i przedsiębiorstw społecznych. Przedsiębiorstwa społeczne, uznawane są powszechnie za ucieleśnienie pełnej, wręcz doskonałej formy odpowiedzialności społecznej. Z kolei przedsiębiorstwa rodzinne, charakteryzują się szczególną paletą interesariuszy oraz specyficznymi...
-
On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublikacjaThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
-
ANALYZING TITLES OF ECONOMY NEWS TO UNDERSTAND IMPACT OF COVID-19 ON ECONOMICAL SITUATION
PublikacjaCovid-19 affected the whole world in a short time, causing serious panic and uncertainty in society. Because it was an unprecedented disease, the medical community has worked hard to find out how to deal with it, and it continues to do so. The rapid spread of the disease, the shortage of hospital capacity and the increase in deaths drove the whole world to a closure, so to speak. In this time period, life in the world came to a...
-
Long-term hindcast simulation of sea level in the Baltic Sea
Dane BadawczeThe dataset contains the results of numerical modelling of sea level fluctuations over a period of 50 years (1958-2007) in the Baltic Sea. A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997). The hydrodynamic model...
-
The Factors Affecting Group Identity of Cluster Structures
PublikacjaThe paper provides a new approach to cluster analysis, basing on a sociologically rooted concept of identity. The authors state that identity in cluster structures is formed by two main groups of factors – uncontrollable or slightly controllable factors (identity mix) and factors that can be fully controlled by a cluster initiative (corporate identity mix). It means that the cluster coordinator is able to consciously build the...
-
Survival time prognosis under a Markov model of cancer development
PublikacjaIn this study we look at a breast cancer data set of women from Pomerania region collected in year 1987-1992 in the Medical University of Gdańsk. We analyze the clinical risk factors in conjunction with Markov model of cancer development. We evaluate Artificial Neural Network (ANN) survival time prediction via a simulation study.
-
Things You Might Not Know about the k-Nearest Neighbors Algorithm
PublikacjaRecommender Systems aim at suggesting potentially interesting items to a user. The most common kind of Recommender Systems is Collaborative Filtering which follows an intuition that users who liked the same things in the past, are more likely to be interested in the same things in the future. One of Collaborative Filtering methods is the k Nearest Neighbors algorithm which finds k users who are the most similar to an active user...
-
An Ontology-based Contextual Pre-filtering Technique for Recommender Systems
PublikacjaContext-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort...
-
Pose-Invariant Face Detection by Replacing Deep Neurons with Capsules for Thermal Imagery in Telemedicine
PublikacjaAbstract— The aim of this work was to examine the potential of thermal imaging as a cost-effective tool for convenient, non- intrusive remote monitoring of elderly people in different possible head orientations, without imposing specific behavior on users, e.g. looking toward the camera. Illumination and pose invariant head tracking is important for many medical applications as it can provide information, e.g. about vital signs, sensory...
-
Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublikacjaPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...