Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.
The final frontier? Why we have been ignoring second language attrition, and why it is time we stopped

Abstract Instructed foreign language knowledge – that is, language skills acquired exclusively in the classroom without the benefit of any significant immersion experience – remains a vastly neglected area of studies on language learning in general and language attrition in particular. There is also little consideration of foreign language attrition and maintenance as a problem for policy or pedagogy. The present talk will give an overview of what is and what is not known about second language (L2) attrition at the present time. It will then present the results from a pilot project that will hopefully serve as the foundation for larger studies of instructed L2 attrition in future years.
Towards Low-Cost Classification for Novel Fine-Grained Datasets

Fine-grained categorization is an essential field in classification, a subfield of object recognition that aims to differentiate subordinate classes. Fine-grained image classification concentrates on distinguishing between similar, hard-to-differentiate types or species, for example, flowers, birds, or specific animals such as dogs or cats, and identifying airplane makes or models. An important step towards fine-grained classification is the acquisition of datasets and baselines; hence, we propose a holistic system and two novel datasets, including reef fish and butterflies, for fine-grained classification. The butterflies and fish can be imaged at various locations in the image plane; thus, causing image variations due to translation, rotation, and deformation in multiple directions can induce variations, and depending on the image acquisition device’s position, scales can be different. We evaluate the traditional algorithms based on quantized rotation and scale-invariant local image features and the convolutional neural networks (CNN) using their pre-trained models to extract features. The comprehensive evaluation shows that the CNN features calculated using the pre-trained models outperform the rest of the image representations. The proposed system can prove instrumental for various purposes, such as education, conservation, and scientific research. The codes, models, and dataset are publicly available.
PRZEWLEKŁA CHOROBA NEREK ORAZ WYBRANE ASPEKTY MONITOROWANIA STANU BIORCÓW NERKI

Choroby nerek stanowią wyzwanie dla współczesnej medycyny. Obecnie przewlekła choroba nerek klasyfikowana jest jako „choroba cywilizacyjna”. W związku ze starzeniem się społeczeństwa problem chorób nerek wciąż narasta, ponieważ osoby w wieku podeszłym są grupą szczególnie narażoną na pogorszenie funkcji nerek. Dodatkowo, występowanie chorób współistniejących, takich jak choroby sercowo-naczyniowe, znacząco utrudnia terapię oraz zwiększa śmiertelność chorych. Przewlekła choroba nerek ma charakter postępujący i ostatecznie doprowadza do schyłkowej niewydolności nerek. W tej sytuacji metodą ratującą życie staje się leczenie nerkozastępcze – dializoterapia lub przeszczepienie nerki. Ze względu na ograniczoną podaż narządów do celów transplantacji większość pacjentów ze schyłkową niewydolnością nerek jest leczona dializami. Przeszczepienie nerki jest optymalną metodą leczenia nerkozastępczego — daje najdłuższe przeżycie chorych, najlepszą jakość życia i jest tańsze od dializ. Monitorowanie funkcji nerki przeszczepionej ma na celu zapewnienie jak najdłuższego czasu funkcjonowania „nowego” narządu. Wczesne zdiagnozowanie niezadowalającej czynności przeszczepu oraz podjęcie szybkiej interwencji medycznej ma na celu prewencję utraty graftu. Kontrola takich parametrów jak współczynnik eGFR, stężenie leków immunosupresyjnych czy poziom markerów ryzyka sercowo-naczyniowego stanowi istotną część procesu leczenia.
Prosthesis-Patient Mismatch in Bovine Pericardial Aortic Valves: Evaluation Using 3 Different Modalities and Associated Medium-Term Outcomes

Background— The prevalence of prosthesis-patient mismatch (PPM) and its impact on survival after aortic valve replacement have not been clearly defined. Historically, the presence of PPM was identified from postoperative echocardiograms or preoperative manufacturer-provided charts, resulting in wide discrepancies. The 2009 American Society of Echocardiography (ASE) guidelines proposed an algorithmic approach to calculate PPM. This study compared PPM prevalence and its impact on survival using 3 modalities: (1) the ASE guidelines–suggested algorithm (ASE PPM); (2) the manufacturer-provided charts (M PPM); and (3) the echocardiographically measured, body surface area–indexed, effective orifice area (EOAi PPM) measurement. Methods and Results— A total of 614 patients underwent aortic valve replacement with bovine pericardial valves from 2004 to 2009 and had normal preoperative systolic function. EOAi PPM was severe if EOAi was ⩽0.60 cm2/m2, moderate if EOAi was 0.60 to 0.85 cm2/m2, and absent (none) if EOAi was ≥0.85 cm2/m2. ASE PPM was severe in 22 (3.6%), moderate in 6 (1%), and absent (none) in 586 (95.4%). ASE PPM was similar to manufacturer-provided PPM (P=1.00). ASE PPM differed significantly from EOAi PPM (P<0.001), which identified severe mismatch in 170 (29.7%), moderate in 191 (33.4%), and absent (none) in 211 patients (36.9%). Irrespective of the PPM classification method, PPM did not adversely affect midterm survival (average follow-up, 4.1±1.8 years; median, 3.9 years; range, 0.01–8 years). There were no reoperations for PPM. Conclusions— In patients with normal systolic function undergoing bovine pericardial aortic valve replacement, the prevalence of PPM using the algorithmic-ASE approach was low and correlated well with manufacturer-provided PPM. Independent of the method of PPM assessment, PPM was not associated with medium-term mortality.
Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications

In this study, a method is proposed to detect the presence of olfactory stimuli from Electroencephalogram (EEG) signals to be used in neuromarketing applications. Odor is used in different ways in neuromarketing applications since it stimulates various emotions. Multi-channel EEG signals were recorded from the volunteers while they were subjected to two open boxes of unscented and scented products in succession. After the necessary preprocessing steps, EEG sub-band powers were calculated for 14 EEG channels. These features were classified using machine learning methods, and the EEG segments in which the olfactory stimulus was present were classified. The results show that the proposed method gives successful results with 92% accuracy, 93% precision, 92% recall, and 92% F1-score using the Random Forest classifier.
Predicting Outcomes of the Court of Cassation of Turkey with Recurrent Neural Networks

Natural Language Processing (NLP) based approaches have recently become very popular for studies in legal domain. In this work, the outcomes of the cases of the Court of Cassation of Turkey were predicted with the use of Deep Learning models. These models are GRU, LSTM and BiLSTM which are variants of the recurrent neural network. Models saw only fact description parts of the case decision texts during training. Firstly, the models were trained with the word embeddings that were created from the texts from daily language. Then, the models were trained with the word embeddings that were created from downloaded legal cases from Turkish courts. The results of the experiments on the models are given in a comparative and detailed manner. It is observed based on this study and the past studies that the outcomes of the Court of Cassation can be predicted with higher accuracy than most of the courts that were investigated in previous legal NLP studies. The model which is best at predicting decisions is GRU. The GRU model achieves 96.8% accuracy in the decision prediction task.
Secure SDN Based IoT Network Through Blockchain for Smart Architectures

The ever-increasing application of IoT for smart systems has led to the generation of enormous amounts of continuous and non-disruptive data. The conventional communication protocols seem to fall short and cause congestion in the network whilst dealing with such a huge amount of data. The implementation of Software Defined Networking (SDN) has evolved the solution for communication of these data by manifolds. However, the presence of a centralized controller in SDN not only reduces the effectiveness and speed of communication of data in practical scenarios but also makes the network susceptible to probable security attacks. This work thus proposes a novel framework to ensure the rapid, effective, and secure flow of data packets. The solution integrates blockchain technology with SDN along with the deployment of multiple controllers to ensure the most practically feasible, effective, and secure method for the transfer of data packets. An application scenario is discussed to show the practical feasibility and significance of the proposed framework.
Critical Comments on Emission of Gravitational Potential Energy

Here, within the Scale-Symmetric Physics, the critical remarks about luminal gravitational waves and luminal gravitons are presented. The mechanism of emission of the potential gravitational energy differs very much from the mechanism described within the General Relativity. According to the Scale-Symmetric Physics, (1), during the inflation, the non-gravitating Higgs field (it consists of the superluminal pieces of space and their size is about 10 -64 m) partially transformed into the luminal Einstein spacetime composed of the neutrino- antineutrino pairs. The pairs consist of the binary systems of closed strings (entanglons) which are responsible for the superluminal quantum entanglement of the luminal Einstein- spacetime components. The entanglons have internal helicity and in their interactions with the Higgs field, they transform the chaotic motions of the Higgs-field components (i.e. the chaotic motions of the superluminal pieces of space) into the divergent motions. The collisions of the chaotically and divergently moving superluminal pieces of space produce the gradients in the Higgs field - they are the gravitational fields of the neutrinos i.e. the neutrinos carry the smallest gravitational masses. Such is the true Higgs mechanism. Emphasize once more that neutrinos are the smallest gravitational masses. Neutrinos, due to their internal structure, produce the weakest gravitational fields. Such gravitational fields consist not of luminal gravitons or luminal gravitational waves - they consist of the non- gravitating superluminal pieces of space the Higgs field consists of. We can see that potential gravitational energy consists of the non-gravitating superluminal pieces of space. So what is the mechanism of emission of the potential gravitational energy? Consider a merger of black holes. A merger causes that a part of their gravitational mass decays to the free (i.e. not entangled and/or not confined) luminal Einstein-spacetime components. It causes that local gravitational-mass density of the Einstein spacetime increases (the additional components are the dark energy) i.e. there increases local dynamic pressure - such region starts to expand but with such expansion is not associated a gravitational wavelength. The expansion tries to equalize the dynamic pressure in the Einstein spacetime - it has nothing with gravitational waves. But the expanding region carries the additional Einstein-spacetime components so carries as well associated with them the weakest gravitational fields. It leads to conclusion that the local number density of the non-gravitating superluminal pieces of space decreases i.e. there decreases the gravitational potential energy.
Experiment design with applications in identification for control

The main part of this thesis focuses on optimal experiment design for system identification within the prediction error framework. A rather flexible framework for translating optimal experiment design into tractable convex programs is presented. The design variables are the spectral properties of the external excitations. The framework allows for any linear and finite-dimensional parametrization of the design spectrum or a partial expansion thereof. This includes both continuous and discrete spectra. Constraints on these spectra can be included in the design formulation, either in terms of power bounds or as frequency wise constraints. As quality constraints, general linear functions of the asymptotic covariance matrix of the estimated parameters can be included. Here, different types of frequency-by-frequency constraints on the frequency function estimate are expected to be an important contribution to the area of identification and control. For a certain class of linearly parameterized frequency functions it is possible to derive variance expressions that are exact for finite sample sizes. Based on these variance expressions it is shown that the optimization over the square of the Discrete Fourier Transform (DFT) coefficients of the input leads to convex optimization problems. The optimal input design are compared to the use of standard identification input signals for two benchmark problems. The results show significant benefits of appropriate input designs. Knowledge of the location of non-minimum phase zeros is very useful when designing controllers. Both analytical and numerical results on input design for accurate identification of non-minimum phase zeros are presented. A method is presented for the computation of an upper bound on the maximum over the frequencies of a worst case quality measure, e.g. the worst case performance achieved by a controller in an ellipsoidal uncertainty region. This problem has until now been solved by using a frequency gridding and, here, this is avoided by using the Kalman-Yakubovich-Popov-lemma. The last chapter studies experiment design from the perspective of controller tuning based on experimental data. Iterative Feedback Tuning (IFT) is an algorithm that utilizes sensitivity information from closed-loop experiments for controller tuning. This method is experimentally costly when multivariable systems are considered. Several methods are proposed to reduce the experimental time by approximating the gradient of the cost function. One of these methods uses the same technique of shifting the order of operators as is used in IFT for scalar systems. This method is further analyzed and sufficient conditions for local convergence are derived.
Passive Disposable Microwave Sensor for Online Microplastic Contamination Monitoring

In this paper, non-contact microplastic concentration monitoring is enabled using a sensitivity-enhanced planar microwave sensor. The sensing platform is a tag-reader structure that consists of a dual-resonator tag and a passive microwave reader. The dual-tag structure is combined with a silicon reservoir as a sample container and is energized through the electromagnetic (EM) coupling with the reader. The extremely sensitive area of the tag resonators is exposed to the sample under the test; it creates an exceptional microplastic deposition monitoring with the concentration of the microplastic in the liquid under the test as low as 400K particles/L. The sensor monitors the deposition of microplastic particles in the mixed liquid in a real-time manner and reflects it as the frequency resonance variation in the transmission response.
Miniaturized Dual-Band TM-Mode Dielectric Filter and Its Reconfiguration Capabilities

A new miniaturized C-band dual-band dual-mode filter is reported in this paper using TM mode dielectric resonators. The filter is realized in planar topology to offer easy tuning and assembly for the applications of reconfigurable filters. High isolation between the two channels is obtained through the introduction of an inter-band transmission zero. Also, two asymmetrical transmission zeros are generated to enhance the out-of-band rejections. Both passbands can be tuned either simultaneously at the same time, or independently. A two-pole dual-band dual-mode TM dielectric filter is designed, implemented, and measured with the investigation of its tuning capabilities.
Coverage and Capacity Optimization for 4G LTE Networks Using Differential Evolution

Network planning and optimization is a major issue for telecom carriers. An important question is to determine angles for antenna cells to improve network coverage and capacity. A methodology to optimize coverage of cells is presented. It provides optimization of tilt and azimuth angles of self-defined problematic cells. Improvement of RSRP is performed by optimizing weak and overlapping coverage. An evolutionary algorithm named Differential Evolution is utilized. Experiments show improvements of coverage in a regional area of China.
Impact of Inhibition of Glutamine and Alanine Transport on Cerebellar Glial and Neuronal Metabolism

The cerebellum, or “little brain”, is often overlooked in studies of brain metabolism in favour of the cortex. Despite this, anomalies in cerebellar amino acid homeostasis in a range of disorders have been reported. Amino acid homeostasis is central to metabolism, providing recycling of carbon backbones and ammonia between cell types. Here, we examined the role of cerebellar amino acid transporters in the cycling of glutamine and alanine in guinea pig cerebellar slices by inhibiting amino acid transporters and examining the resultant metabolism of [1-13C]d-glucose and [1,2-13C]acetate by NMR spectroscopy and LCMS. While the lack of specific inhibitors of each transporter makes interpretation difficult, by viewing results from experiments with multiple inhibitors we can draw inferences about the major cell types and transporters involved. In cerebellum, glutamine and alanine transfer is dominated by system A, blockade of which has maximum effect on metabolism, with contributions from System N. Inhibition of neural system A isoform SNAT1 by MeAIB resulted in greatly decreased metabolite pools and reduced net fluxes but showed little effect on fluxes from [1,2-13C]acetate unlike inhibition of SNAT3 and other glutamine transporters by histidine where net fluxes from [1,2-13C]acetate are reduced by ~50%. We interpret the data as further evidence of not one but several glutamate/glutamine exchange pools. The impact of amino acid transport inhibition demonstrates that the cerebellum has tightly coupled cells and that glutamate/glutamine, as well as alanine cycling, play a major role in that part of the brain.
Fundamentals of high accuracy inertial navigation

Part 1 Inertial Navigation: Notation, Coordinate Systems and Units Equations of Motion in a Central Force Gravity Field Inertial Instrumentation Calibration Initial Alignment and Attitude Computation Geodetic Variables and Constants Equations of Motion with General Gravity Model. Part 2 Inertial Navigation with Aids: Inertial Navigation with External Measurements Error Equations for the Kalman Filter State Variable Error Models. Part 3 Accuracy Analysis: Accuracy Criteria and Analysis Techniques Error Equations for Calibration, Alignment and Initialization Evaluation of Gravity Model Error Effects. Appendices: Matrix Inverse Formulas LaPlace Transforms Quaternions Associated Legendre Functions Associated Legendre Function Derivatives Procedure for Generating Gravity Disturbance Realizations Procedure for Generating Specific Force Profile.
Bile acids inhibit human purinergic receptor P2X4 in a heterologous expression system

The human purinergic receptor P2X4 is an ATP-gated nonselective cation channel expressed in a variety of cells, including hepatocytes and cholangiocytes. Ilyaskin et al. find that the bile acids inhibit P2X4 by stabilizing the channel's closed state, likely by interacting with its transmembrane domain.
Biosynthesis, characterization and antibacterial activity of silver nanoparticles by the Arctic anti-oxidative bacterium Paracoccus sp. Arc7-R13

Abstract Silver nanoparticles (AgNPs) have attracted wide interest due to its broad range of applications. This study aims to describe the biosynthesis of AgNPs using an Arctic anti-oxidative bacterium Arc7-R13 and to study its characteristics and antibacterial activity. The biosynthesis of AgNPs was verified using UV–Vis spectrum with the maximum absorption at 416 nm. The morphology of the silver nanoparticles was characterized by TEM and its characterization were investigated by EDX and FTIR. Phylogenetic analysis based on 16S rRNA gene sequence showed that strain Arc7-R13 was affiliated with genus Paracoccus. TEM analysis revealed that the AgNPs synthesized by strain Arc7-R13 were spherical and ellipsoidal in shape with size ranging from 2 to 25 nm. The optimal concentration of AgNO3 and temperature for the biosynthesis were 4 mmol/L and 37 °C, respectively. EDX analysis verified the presence of the element silver in the biosynthesized AgNPs. FTIR analysis revealed that the specific functional groups, OH, CH3 and C≡N, might be responsible for reduction and stabilization of AgNPs. Antimicrobial test showed that the AgNPs had strong antimicrobial activity against all kinds of strains investigated, including Gram-positive Bacillus subtilis, Staphylococcus aureus and Gram-negative Pseudomonas aeruginosa, Escherichia coli.