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Noise profiling for speech enhancement employing machine learning models
PublicationThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks
PublicationAge prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...
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Material characterisation of biaxial glass-fibre non-crimp fabrics as a function of ply orientation, stitch pattern, stitch length and stitch tension
PublicationDue to their high density-specific stiffnesses and strength, fibre reinforced plastic (FRP) composites are particularly interesting for mobility and transport applications. Warp-knitted non-crimp fabrics (NCF) are one possible way to produce such FRP composites. They are advantageous because of their low production costs and the ability to tailor the properties of the textile to the reinforcement and drape requirements of the application....
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Hey student, are you sharing your knowledge? A cluster typology of knowledge sharing behaviours among students
PublicationKnowledge Sharing (KS) is crucial for all organisations to better face current and future challenges. It is justifiable to assume that after graduation, students will have to face the coming challenges at societal and business levels, and that they will need the adequate KS skills to do so. Though the importance of KS is established, the understanding of how students pass on their knowledge is still fragmented and underdeveloped....
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Applying the Lombard Effect to Speech-in-Noise Communication
PublicationThis study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting;...
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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks
PublicationHandwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated graphomotor analysis method for the dynamic electronic representation of the handwritten signature authentication was researched. The developed algorithms are based on dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the...
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Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
PublicationArtificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained...
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Clothes Detection and Classification Using Convolutional Neural Networks
PublicationIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud
PublicationAirborne laser scanning (ALS) provides survey results in the form of a point cloud. The ALS point cloud is a source of data used primarily for constructing a digital terrain model (DTM). To generate a DTM, the set of ALS observations must be first subjected to the point cloud processing methodology. A standard methodology is composed of the following stages: acquisition of the ALS data, initial processing (including filtration),...
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Application of multivariate statistics in assessment of green analytical chemistry parameters of analytical methodologies
PublicationThe study offers a multivariate statistical analysis of a dataset, including the major metrological, “greenness” and methodological parameters of 43 analytical methodologies applied for aldrin determination (a frequently analyzed organic compound) in water samples. The variables (parameters) chosen were as follows: metrological (LOD, recovery, RSD), describing the “greenness” (amount of the solvent used, amount of waste generated)...
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Enabling Deeper Linguistic-based Text Analytics – Construct Development for the Criticality of Negative Service Experience
PublicationSignificant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights...
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Data regarding a new, vector-enzymatic DNA fragment amplification-expression technology for the construction of artificial, concatemeric DNA, RNA and proteins, as well as biological effects of selected polypeptides obtained using this method
PublicationApplications of bioactive peptides and polypeptides are emerging in areas such as drug development and drug delivery systems. These compounds are bioactive, biocompatible and represent a wide range of chemical properties, enabling further adjustments of obtained biomaterials. However, delivering large quantities of peptide derivatives is still challenging. Several methods have been developed for the production of concatemers –...
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Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data
PublicationObjective monitoring of the real estate value is a requirement to maintain balance, increase security and minimize the risk of a crisis in the financial and economic sector of every country. The valuation of real estate is usually considered from two points of view, i.e. individual valuation and mass appraisal. It is commonly believed that Automated Valuation Models (AVM) should be devoted to mass appraisal, which requires a large...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublicationThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
PublicationCOVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a...
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Efficiency of Artificial Intelligence Methods for Hearing Loss Type Classification: an Evaluation
PublicationThe evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as golden standard for assessing auditory function. If the presence of hearing loss is determined, it is possible to differentiate between three types of hearing loss: sensorineural, conductive, and mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone...
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OCENA KLIMATU PRACY W ZESPOLE WIELOKULTUROWYM
PublicationArtykuł dotyczy tematyki zespołów wielokulturowych, które ze względu na specyfikę globalnego rynku odgrywają coraz ważniejszą rolę we współczesnych organizacjach. Skoncentrowano się na kształtowaniu klimatu pracy zespołowej, który pozwala wykorzystać potencjał zespołu zróżnicowanego kulturowo, podkreślając potencjalne utrudnienia i ich źródła. Zaprezentowano wyniki badań kwestionariuszowych, których celem było sprawdzenie, na ile...
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Wieloobszarowa rozmyta regulacja PIλDµ mocy reaktora jądrowego
PublicationWartykule przedstawiono wieloobszarowy regulator rozmyty z lokalnymi regulatorami PIλDµ niecałkowitego rzędu. Regulator ten ostał zaprojektowany do sterowania mocą reaktora jądrowego typu PWR (Pressurized Water Reactor). Do syntezy wieloobszarowego regulatora PIλDµ wykorzystano model matematyczny reaktora PWR o parametrach skupionych obejmujący procesy generacji i wymiany ciepła oraz efektów reaktywnościowych. Nastawy lokalnych...
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Wieloobszarowa rozmyta regulacja PIλDμ mocy reaktora jądrowego
PublicationW artykule przedstawiono wieloobszarowy regulator rozmyty z lokalnymi regulatorami PIλDμ niecałkowitego rzedu. Regulator ten został zaprojektowany do sterowania mocą reaktora jądrowego typu PWR (Pressurized Water Reactor). Do syntezy wieloobszarowego regulatora PIDμ wykorzystano model matematyczny reaktora PWR o parametrach skupionych obejmujący procesy generacji i wymiany ciepła oraz efektów reaktywnościowych. Nastawy lokalnych...
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Absencja chorobowa - szacunek niewytworzonego PKB
PublicationKoszty pośrednie spowodowane absencją chorobową bądź prezenteizmem pracowników są sporadycznie uwzględniane w polskich analizach ekonomicznych. Nie szacuje się wielkości utraconego PKB na skutek chorób, przedwczesnych zgonów, długiego dochodzenia do zdrowia oraz nie uwzględnia w wycenach farmakoekonomicznych. Brakuje jednolitej metodologii obliczania kosztów pośrednich. Celem opracowania jest oszacowanie, ile gospodarka narodowa...
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Standardy IEEE wspierające koncepcje Ethernet End-to-End
PublicationW pracy wykazano, że ostatnie prace komitetów IEEE 802.1 i 802.3 znacznie przybliżyły realizację koncepcji Ethernet End-to-End. Wskazano, że o ile problemy skalowalności zostały w dużej mierze rozwiązane, o tyle inne zagadnienia związane m.in. z niezawodnością i zarządzaniem wymagają jeszcze dużego wysiłku standaryzacyjnego. Jednakże niektóre przedstawione w pracy propozycje IEEE są już na ukończeniu i możliwość realizacji sieci...
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Porównanie heurystyk dla problemu szeregowania zadań czasowo-zależnych o wspólnym podstawowym czasie wykonywania
PublicationW pracy rozważany jest następujący, jednoprocesorowy problem szeregowania zadań czasowo-zależnych. danych jest n+1 zadań o czasach wykonywania postaci pi = a + bisi, gdzie si oznacza czas rozpoczęcia wykonywania i-tego zadania, a > 0, bi > 0, i = 0, 1, ..., n. wszystkie zadania są niepodzielne i dostępne w chwili t0 = 0. należy znaleźć harmonogram minimalizujący łączny czas zakończenia. w pracy przedstawiono algorytm, który, o...
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Aspekty metodyczne w przeprowadzaniu badań naukowych - badania ilościowe
PublicationPrzedmiotem badań są aspekty metodyczne w przeprowadzaniu badań naukowych, w tym skupienie uwagi na badaniach o charakterze ilościowym. Jako problem badawczy przyjęto: jakie są aspekty metodyczne badań naukowych, z podziałem na ich poszczególne rodzaje. Wynikające z praktyki przeprowadzania badań naukowych, aspekty metodyczne powodują, że nie zawsze można skorzystać z badań tych typowo ilościowych,...
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Weryfikacja algorytmów MPPT dla modułów fotowoltaicznych w warunkach przesłonięcia
PublicationIntensywny rozwój technologii powoduje obniżenie ceny modułów fotowoltaicznych i dedykowanych przetwornic. Podstawą opłacalności jest wysoka sprawność całego układu na którą składają się sprawności modułów, przetwornic oraz algorytmu śledzenia maksymalnej mocy (MPPT - Maximum Power Point Tracking). Znane i stosowane algorytmy mają MPPT sprawności od ok. 95 do 99%, o ile ogniwa mają identyczne parametry i są jednakowo nasłonecznione....
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Algorytmy MPPT dla modułów fotowoltaicznych w warunkach przesłonięcia
PublicationIntensywny rozwój technologii powoduje obniżenie ceny modułów fotowoltaicznych i dedykowanych przetwornic. Podstawą opłacalności jest wysoka sprawność całego układu na którą składają się sprawności modułów, przetwornic oraz algorytmu śledzenia maksymalnej mocy (MPPT - Maximum Power Point Tracking). Znane i stosowane algorytmy mają MPPT sprawności od ok. 95 do 99%, o ile ogniwa mają identyczne parametry i są jednakowo nasłonecznione....
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Rating mathematical models for first-pass of tracer in pCT lung studies
PublicationThis paper presents a comparison of model based on the Gauss function and the most commonly used Gamma-variate model in perfusion computed tomography (pCT) lung studies. It also verifies whether used model affects value of blood volume parameter. Three mean concentration-time curves were created from actual pCT measurements: arterial input function, blood vessels in lungs and lung parenchyma. On the basis of these mean curves we...
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublicationBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Impact of Visual Image Quality on Lymphocyte Detection Using YOLOv5 and RetinaNet Algorithms
PublicationLymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better...
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Tax rates for the payroll and profit tax for financial institutions Israel 2002-2015
Open Research DataThe following dataset presents the historical tax rates for the payroll and profit tax paid by financial institutions (non-VAT taxation) in Israel. The data presented in the dataset concerns 2002-2015.
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The TPO oxidation profile of CeO2/10wt.%Co with BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Co. The samples of nanoCeO2 impregnated with BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.
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The TPO oxidation profile of CeO2/10wt.%Mn with BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Mn. The samples of nanoCeO2 impregnated with BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.
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The TPO oxidation profile of CeO2/10wt.%Ni with BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Ni. The samples of nanoCeO2 impregnated with BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.
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The TPO oxidation profile of CeO2/10wt.%Cu with BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Cu. The samples of nanoCeO2 impregnated with BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.
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The TPO oxidation profile of CeO2/10wt.%Fe with BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Fe. The samples of nanoCeO2 impregnated with BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.
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Database of the minimal sets of Lefschetz periods for Morse-Smale diffeomorphisms of a connected sum of g real projective planes.
Open Research DataMorse–Smale diffeomorphisms, structurally stable and having relatively simple dynamics, constitute an important subclass of diffeomorphisms that were carefully studied during past decades. For a given Morse–Smale diffeomorphism one can consider “Minimal set of Lefschetz periods”, which provides the information about the set of periodic points of considered...
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Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives
PublicationLong-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are...
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Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data
PublicationThis paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical models that can identify consumers in a difficult financial situation that may lead to consumer bankruptcy. In the face of the current global pandemic crisis, the future of household finances...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublicationMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift
PublicationWhile recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e.g. weather or lighting conditions not considered during training), they may produce an erroneous prediction. Therefore, it is desired that such a model will be able to reliably predict its confidence measure. In this work, uncertainty estimation for the task...
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Towards application of uncertainty quantification procedure combined with experimental procedure for assessment of the accuracy of the DEM approach dedicated for granular flow modeling
PublicationThere is a high demand for accurate and fast numerical models for dense granular flows found in many industrial applications. Nevertheless, before numerical model can be used its need to be always validated against experimental data. During the validation, it is important to consider how the measurement data sets, as well as the numerical models, are affected by errors and uncertainties. In this study, the uncertainty quantification...
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Urban scene semantic segmentation using the U-Net model
PublicationVision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities...
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Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging
PublicationIn the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...
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Investigating Feature Spaces for Isolated Word Recognition
PublicationMuch attention is given by researchers to the speech processing task in automatic speech recognition (ASR) over the past decades. The study addresses the issue related to the investigation of the appropriateness of a two-dimensional representation of speech feature spaces for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and timefrequency signal representation...
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Going all in or spreading your bet: a configurational perspective on open innovation interaction channels in production sectors
PublicationUsing different interaction channels within open innovation partnerships holds the potential to enhance the chance of success in production sectors. However, our comprehension of how open innovation partnerships are affected by varying combinations of interaction channels, and how this corelates with their level of open innovation output, remains limited. There are discrepancies in the current literature regarding the individual...
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Learning sperm cells part segmentation with class-specific data augmentation
PublicationInfertility affects around 15% of couples worldwide. Male fertility problems include poor sperm quality and low sperm count. The advanced fertility treatment methods like ICSI are nowadays supported by vision systems to assist embryologists in selecting good quality sperm. Computer-Assisted Semen Analysis (CASA) provides quantitative and qualitative sperm analysis concerning concentration, motility, morphology, vitality, and fragmentation....
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THERMION-C2S_10 Ionic thermoelectri effect in the phase transition in Cu2Se
Open Research DataThe dataset contains results of measurements of the ionic thermoelectric effect in copper selenide with Cu1.99Se and Cu1.8Se compositions. X-Ray diffraction data, SEM images and EDX spectra of the samples are also in the dataset.
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Increasing the conductivity of v2o5-teo2 glass by crystallization: structure and charge transfer studies
Open Research DataThis is the dataset concerning the publication titled: Increasing the conductivity of V2O5-TeO2 glass by crystallization: structure and charge transfer studies. In this dataset raw data and origin project concerning this article can be found.
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The TPO oxidation profile of CeO2/10wt.%Cu without BCD - prereduced
Open Research DataThe dataset includes the TPO oxidation profiles of CeO2/10wt.%Cu. The samples of nanoCeO2 impregnated without BCD-assisted precursor solution (betacyclodextrin). The dataset includes oxidation profile of the prereduced sample.