Miracle-dz/UniFAR-FTUs huggingface dataset:
A pilot study on a novel gesture-based tongue interface for robot and computer control

People with complete tetraplegia who are paralyzed from the neck down require alternative computer and robot interfaces based on the remaining functionalities. The inductive tongue-computer interface (ITCI) consists of two small touchpads that are manipulated by the tongue and can provide individuals with tetraplegia the possibility to control computers, robots, and other electronic devices. The tongue gesture can be used to expand the vocabulary of the ITCI and provide more control commands. In this study for the first time, a tongue gesture recognition method was introduced that can identify a set of six gestures with 94.3% accuracy and a set of 23 gestures with 72.3% accuracy. Furthermore, a robot interface was developed using the tongue gestures and it was compared with a previously tested method based on soft buttons on the ITCI. Four able-bodied participants performed a pouring water task with the two control methods when visual feedback from the interface was presented on a screen and then without the visual feedback. Task completion time for the gesture-based control and the button-based control did not differ. However, gesture-based control provides more control commands that can be used for interfacing other systems.
Consumer Behaviour of Purchasing Biofortified Food Products

In light of increasing attention on biofortified products from the institutional, scientific and industrial worlds, we investigate consumer knowledge, consumer choice, the relationship between consumer choice and lifestyles, willingness to pay, and factors influencing consumer groups regarding biofortified foods. Complicating the matter is the lack of a clear definition of biofortified foods in the last proposal of the Codex Alimentarius Commission dated 26 November 2018. Research has shown the importance of market information and variables related to lifestyle, socio-demographic characteristics, knowledge of nutritional principles, and diet, to understanding consumers’ purchase and consumption choices regarding biofortified products. Our research shows that at present the potential consumer of biofortified food products is generally confused and uninformed, conditions that, even when there is a high willingness to pay, limit purchases of biofortified products. Even in the absence of a concise definition and clear labelling at a globally recognized level, in Italy biofortified products are increasingly widespread (products biofortified with selenium, iodine, etc.), confirming consumer demand for this category of product.
Wavelet Packet VS Backpropagation for localization and Classification PCG Signals

Phonocardiography signals (PCG) show the enrolment of sounds and distortions generated from cardiac auscultation. Cardiac signal analysis is crucial for the diagnosis of various diseases. In previous years, there are several groups and different techniques, freshness, and methods have been proposed to analyze the cardiac signal. Auscultation is a manner in which a stethoscope is used to hearken to the cardiac sound. Structural trouble of the heart is often reflected in the cardiac sound created and Listening to the heart’s sound helped doctors diagnose and predict diseases. Whilst a cardiac sound examine via Listening is appropriate as a scientific tool, it is tough to analysis PCG signals in the time(T) or frequency(F) scale. The PCG signals have many benefits over conventional Listening, in that they may be rebuilt and analyzed for time(T) and frequency(F) information. Using a wavelet packet transform(WPT). Where the signal is decomposed and rebuild without the first-rate loss of data within the signal content. Reconstruction mistakes can be thoughtfulness an important piece of information in classifying the pathological severity of phonocardiography signals. In this paper we will focus on how to choose the level and the mother wave of the wave to cry out so that it is appropriate to analyze the cardiac signal in good mathematical and analytical ways to train the neural network (error backpropagation) on it. It will be explained in the following details.
Investigation of The Effect of Climate Change on Extreme Precipitation: Tekirdağ Case

This study examines the potential impacts of climate change on extreme precipitation in a specific location, Tekirdag, Turkey. Trends in rainfall extremes for (1963-2015 period) observed data of 5, 10, 15, 30 minutes and 1, 2, 3, 4, 5, 6, 8, 12, 18, 24 hours are determined by using 1:1 straight line method and Mann-Kendall trend test. Also, daily (24h) future projections for Tekirdag region are assessed and bias corrected with Quantile Mapping method for the 2015-2050 period. Subsequently, observed and bias corrected daily (24h) time series are used for the Generalized Extreme Value analyses to quantify the potential changes with respect to observation period. Most of the observed time series show increasing trend tendency. Considering the projected data driven analyses results; for shorter return periods results show smaller variations while variability increase with the increasing return period. Depending on the models and Representative Concentration Pathways, there are different results for the future extreme rainfall; yet all results indicate an increasing extreme daily rainfall magnitude at Tekirdag Province.
Complete mitochondrial genomes and phylogeny of two rare whale species: True’s (Mesoplodon mirus: True 1913) and Sowerby’s (Mesoplodon bidens: Sowerby 1804) beaked whales

Abstract Beaked whales of the genus Mesoplodon are rarely encountered and information on their basic biology is lacking. We sequenced the complete mitochondrial genomes of True’s (Mesoplodon mirus) and Sowerby’s (Mesoplodon bidens) beaked whales to high depth (>120X). The length of the de novo assembled genomes was 16,342 bp (M. mirus) and 16,347 bp (M. bidens), with GC compositions of 37.7 and 39.2%, respectively. A maximum-likelihood phylogeny supports the hypotheses that M. mirus is most closely related to Mesoplodon europaeus, and that M. bidens is the basal species of the genus. These data provide molecular resources that will aid in further understanding population genetics and evolution in this rare and poorly understood group of cetaceans.
Research on the travel characteristics of online car hailing based on spatio-temporal data

User travel characteristics are the representation of user portrait in travel aspect. Travel characteristics analysis can provide support for subsequent enterprises to improve travel services and make strategic decisions. In order to explore the characteristics of users' car-hailing travel, this paper analyzes the car-hailing order data and historical weather data. First of all, in order to understand the overall rules of user travel, the overall travel characteristics were visualized, which mainly included the average daily order volume, order time distribution and POI type of destination analysis. The ConvLSTM model was used to build a dual-input neural network by introducing external factors, and other models were used for horizontal comparison. The prediction results showed that the constructed model could learn the time and space characteristics well, and had the best performance in each evaluation index.
Development of Photo-Induced Persulfate-Based Processes for Efficient Application in Water Treatment

55 LÜHIKOKKUVÕTE . 57 Appendix . 59 Paper I . 61 Paper II . 73 Paper III . 87 Paper IV . 99 CURRICULUM VITAE . 113 ELULOOKIRJELDUS . 115
Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online Work

Current reputation systems in online (labor) markets are overly positive and unidimensional. This article presents a new reputation framework that combines human input with machine learning to provide dynamic, multidimensional, and skill-set-specific quality assessments. The framework significantly outperforms current reputation systems. By providing more representative reputation scores, the framework helps workers to differentiate, employers to make informed decisions, and the market to improve its recommendation algorithms and understand the supply distributions across different dimensions. The framework generalizes in other contexts where reputation systems are overly positive and unidimensional. The framework highlights how combining human input with advanced machine learning techniques can augment intelligence by creating the necessary conditions for humans to make informed decisions. Such systems have the potential to increase efficiency and outcome quality precisely because they intelligently differentiate workers. The deployment of the proposed intelligence augmentation framework in different types of online platforms could have implications for workers, employers, businesses, and the future of work.
Observed changes in the temperature dependence response of surface ozone under NOx reductions

Abstract. Due to the strong temperature dependence of surface ozone concentrations (O3), future warmer conditions may worsen ozone pollution levels despite continued efforts on emission controls of ozone precursors. Using long-term measurements of hourly O3 concentrations co-located with NOx concentrations in stations distributed throughout Germany, we assess changes in the climate penalty, defined as the slope of ozone-temperature relationship during the period 1999–2018. We find a stronger temperature sensitivity in the urban stations over the southwestern regions, especially in the first period of the study (1999–2008). We show a decrease in the climate penalty in most of stations during the second period of the study (2009–2018), with some exceptions (e.g. Berlin) where the climate penalty did not show significant changes. To examine the impacts of NOx reductions on the O3 sensitivity to temperature, we propose a statistical approach based on generalized additive models (GAMs) to describe ozone production rates, inferred from hourly observations, as a function of NOx and temperature, among other variables relevant during the O3 production. We find lower O3 production rates during the second period (2009–2018) at most stations and a decreasing sensitivity to temperature, pointing out that lowering NOx concentrations resulted in decreasing O3 production rates. However, we also observe changes in the shape of the function representing the O3-temperature relationship, which indicate that NOx reductions alone can not explain the changes in the temperature dependence of O3. Our analysis would suggest that decreasing NOx concentrations are not the only factor causing the observed changes in the climate penalty factor.
How does coopetition affect radical innovation? The roles of internal knowledge structure and external knowledge integration

Purpose Building on the resource- and knowledge-based views, this paper aims to explore how coopetition affects radical innovation and the roles of knowledge structure and external knowledge integration in the relationship between coopetition and radical innovation. Design/methodology/approach This study proposes a research model to examine the mediating role of external knowledge integration on the coopetition-radical innovation link, where the mediation is moderated by the firm’s knowledge structure (including component knowledge and architectural knowledge). The authors use regression and bootstrapping to test the proposed model with survey data from 241 Chinese technology firms. Findings This study finds that coopetition positively affects radical innovation and the effect is fully mediated by external knowledge integration. Additionally, component knowledge negatively moderates the coopetition-external knowledge integration link and architectural knowledge positively moderates this relationship. Further, the mediating effect of external knowledge integration is also moderated by component knowledge and architectural knowledge. Practical implications Firms should engage in coopetition to promote radical innovation. Further, it is necessary for firms to appropriately manage coopetition according to their internal knowledge structure. Originality/value This study explains why scholars have different ideas about the relationship between coopetition and radical innovation by exploring the mediating role of external knowledge integration and the moderating effect of knowledge structure. Firms possess increased possibilities for knowledge leakage and partner opportunism with high levels of component knowledge, which will reduce the positive effect coopetition on external knowledge integration; thus, they are less likely to realize radical innovation. Instead, firms possess increased opportunities for resource sharing with high levels of architectural knowledge, thus improving the positive effect coopetition on external knowledge integration and they are more likely to achieve radical innovation.
Optimized DNN Classification Framework based on Filter Bank Common Spatial Pattern (FBCSP) for Motor-imagery-based BCI

The Brain-Computer Interface (BCI) is a kind of communication channel between the brain and the outside world that is not dependent on the nervous system. The recent improvement of deep neural network (DNN) in a classification task lead several researchers to apply DNN methods for BCI motor imagery (MI) classification. However, DNN based classification methods for MI classification either record low accuracy or take a long time to classify MI signal due to the high dimensional nature of the MI signal. This issue prevents the DNN based MI systems from being deployed in a real application. An ideal DNN classification framework for the MI classification was proposed in this study, which can be trained with better accuracy in a short period. To address the long DNN training time and the challenges of low classification accuracy, a new method was proposed that uses the FBCSP method to reduce the data dimension and increase class discrimination by extracting the best subject-specific features of the CSP from raw EEG data as input into the DNN. The DNN is designed with a few layers, and several DNN hyperparameters have been evaluated. A configuration that has performed better in terms of minimum training time and good classification accuracy has been selected. To investigate the accuracy of the proposed DNN methods called FBCSP-DNN; first, we use traditional approaches such as LDA, SVM, and KNN to create a baseline; second, we choose some DNN studies used in other studies that have been applied to the same dataset used in this study; third, we look at the use of transfer learning, a network pre-training methodology for one data collection of some subjects and then fine-tuning for another to improve some subjects with low accuracy; finally, the results obtained using the proposed were compared to traditional methods and other competing DNN methods used in other studies. The performance of the proposed FBSCP-DNN method is evaluated using BCI competition IV dataset 2a. The results show that the proposed method (81.43%) is superior to the traditional classification methods (SVM: 72.37%; KNN: 61.06%; LDA: 72.07%). We also compared the proposed DNN method and other DNN methods proposed by other studies using the same dataset. The proposed method outperforms other studies in terms of accuracy of the MI classification for within-subjects and cross-subjects, which improved by 6% and 9% respectively. The FBCSP-DNN classification framework proposed in this paper has the advantages of short training time and high classification accuracy, which ensures reliability for the practical application of BCI-motor imagery systems.
Development, testing and therapeutic applications of ketone esters (KE) for CNS oxygen toxicity (CNS‐OT); i.e., hyperbaric oxygen (HBO2)‐induced seizures

We previously described the effect of KEs, non‐ionized precursors of ketone bodies, in delaying seizures in rats breathing HBO2 at 5 ATA. We hypothesize that oral administration of specific KEs (esters of acetoacetate) will cause a rapid and sustained ketosis that confers neuroprotection against CNS‐OT in rats. Male rats (n = 53) were implanted with radio‐transmitters to measure diaphragmatic electromyogram (dEMG), ECG and EEG. ≥ 7 days post‐surgery, rats were fasted for 12h and administered intragastrically with 1 of 2 KEs (10g/kg), including R,S‐1,3‐butanediol acetoacetate monoester (mKE) or R,S‐1,3‐butanediol acetoacetate di‐ester (dKE) and placed into a hyperbaric chamber and pressurized to 5 ATA O2. Latency to seizure (LS) was measured from the beginning of maximum level of HBO2 until the onset of increased EEG activity and/or tonic‐clonic twitches. Blood was drawn from 18 animals and levels of glucose, pH, pO2, pCO2, β‐hydroxybutyrate, acetoacetate and acetone were analyzed. KEs caused a rapid and sustained ketosis and delayed LS by ~227% (mKE) and ~574% (dKE) compared to control (water). In conclusion, KEs mimic the mild therapeutic ketosis produced by ketogenic diets and confer a significant neuroprotective effect by delaying CNS‐OT (ONR).
Tunable Plasmon-Induced Transparency through Bright Mode Resonator in a Metal–Graphene Terahertz Metamaterial

The combination of graphene and metamaterials is the ideal route to achieve active control of the electromagnetic wave in the terahertz (THz) regime. Here, the tunable plasmon-induced transparency (PIT) metamaterial, integrating metal resonators with tunable graphene, is numerically investigated at THz frequencies. By varying the Fermi energy of graphene, the reconfigurable coupling condition is actively modulated and continuous manipulation of the metamaterial resonance intensity is achieved. In this device structure, monolayer graphene operates as a tunable conductive film which yields actively controlled PIT behavior and the accompanied group delay. This device concept provides theoretical guidance to design compact terahertz modulation devices.
NONLOCAL PROBLEM FOR A MIXED TYPE FOURTH-ORDER DIFFERENTIAL EQUATION WITH HILFER FRACTIONAL OPERATOR

Abstract: In this paper, we consider a non-self-adjoint boundary value problem for a fourth-order differential equation of mixed type with Hilfer operator of fractional integro-differentiation in a positive rectangular domain and with spectral parameter in a negative rectangular domain. The mixed type differential equation under consideration is a fourth order differential equation with respect to the second variable. Regarding the first variable, this equation is a fractional differential equation in the positive part of the segment, and is a secondorder differential equation with spectral parameter in the negative part of this segment. A rational method of solving a nonlocal problem with respect to the Hilfer operator is proposed. Using the spectral method of separation of variables, the solution of the problem is constructed in the form of Fourier series. Theorems on the existence and uniqueness of the problem are proved for regular values of the spectral parameter. For sufficiently large positive integers in unique determination of the integration constants in solving countable systems of differential equations, the problem of small denominators arises. Therefore, to justify the unique solvability of this problem, it is necessary to show the existence of values of the spectral parameter such that the quantity we need is separated from zero for sufficiently large n. For irregular values of the spectral parameter, an infinite number of solutions in the form of Fourier series are constructed. Illustrative examples are provided.
Shoot induction and plant regeneration from cotyledon segments of the muskmelon variety "hógolyó"

Cotyledonary segments of the casaba type muskmelon variety "Hogolyo" were used to induce organogenesis. Fifty different hormone combinations were applied to enhance the induction of shoot formation on the edge of the segments. The phases of organogenesis were followed with light- and scanning electron microscope. Shoot induction was achieved with high frequency. The shoots were transferred to hormone free media for root induction. The rooted plantlets were planted out to soil. NAA was feasible and the method can be applied in transformation experiments.
Applications of Human Biometrics in Digital Image Processing

Digital image processing is the trending topic of research in recent time and big amount of research work related to Biometric features is done and currently it achieved good amount of accuracy. Biometric features is used for security, verification and recognition purpose. This paper is a showcase of how security systems can be developed by using biometric features of human like face, fingerprint and iris, etc. It can be used for the purpose of identification, recognition and Authentication and it is also applicable for making software for image preparation in bioscience laboratories that make use of scanned or digitally photographed images. The widespread use of such image processing techniques using photography and microscope imaging across the natural science with particular attention being paid to research in cell and molecular bioscience. This paper is a review of various methods trending to the field of biometric applications on biotechnologies.
Characteristics of Beta Waveform Shape in Parkinson’s Disease Detected with Scalp Electroencephalography

Neural activity in the beta frequency range (13-30 Hz) is excessively synchronized in Parkinson’s Disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between beta phase and broad-band gamma amplitude (i.e., phase amplitude coupling) are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that nonsinusoidal features of beta oscillation shape also characterize PD. Here we show that these features of beta waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, beta oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p=0.006, p=0.003 respectively). We also showed that beta oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion-criteria increased the effect size of our findings. Together our findings suggest that novel ways of measuring beta synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings.
Military and Civilian Field–Related Factors in Estonian Reservists’ Military Service Readiness

The military service readiness of reserve soldiers is an essential question for countries that rely mostly on reserve units for their defense or other operational needs. This study examines how forms of capital related to the military and civilian fields are associated with the military service readiness of reservists. Survey data were obtained from 396 Estonian reservists during the 2018 Hedgehog exercise. Reservists’ military service readiness (RMSR) was studied first with respect to military field–related factors, and in the next phase civilian field linked factors are included. In a military field, cultural, social, and symbolic capital have a positive influence on RMSR. However, if civilian field–related social and economic capital is included, some military capitals would lose their effect. Study outcomes are discussed considering Pierre Bourdieu’s theory of the forms of capital.