Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and Application of the Calculation Method of River Roughness Coefficient with Vegetation

The roughness coefficient is a comprehensive parameter reflecting river resistance, which is widely used in the planning and design of river regulation and flood control projects. In recent years, as the upstream water conservancy and hydro-power projects have been put into operation, the frequency of low flow in the middle and lower reaches has increased, and the frequency of flood flow has decreased. All kinds of vegetation in the river floodplain grow luxuriantly, which causes a change in the river resistance and roughness coefficient. The present study was carried out with theoretical analysis and laboratory tests. A formula for the roughness coefficient calculation was derived based on the momentum equilibrium equation and momentum exchange between the vegetation layer and upper layer. The relationship between the depth-averaged velocity within the vegetation layer and depth-averaged velocity of the whole flow was analyzed. The reliability of the formula was verified by a large amount of previous experimental data. Based on the derived formula, the variation law of the roughness coefficient with vegetation density, vegetation height, and water depth were obtained. For the emerged vegetation flow, the Manning coefficient tended to increase with the increase in the vegetation density and water depth. For the submerged vegetation flow, the Manning coefficient showed a trend of decreasing with the increase in the water depth and increased with the increase in the vegetation height. Finally, the derived formula was applied in the Yueyang reach of the Yangtze River and the Duliujian River. The study can be applied in the fields of water level-flow discharge relationship analysis and the water surface line calculation of vegetated rivers.
Local Demagnetization Detection in Six-Phase Permanent Magnet Synchronous Machines

The partial demagnetization process in surface-mounted permanent-magnet synchronous motors (SMPMSMs) negatively affects the performance of an electric drive, reducing the rated torque and the efficiency of the machine. This article presents a comprehensive analysis of the rotor demagnetization caused by an overcurrent event for a six-phase SMPMSM. Multiphase drives may become a key solution for safety-critical applications because they allow one to increase the intrinsic reliability of electric systems and monitor the health state of electric motors and power converters. This article proposes a diagnostic algorithm for the online detection of rotor demagnetization faults that also considers the magnetization direction of the magnets. The effects of the parallel or radial magnetization of the rotor magnets are analyzed for multiphase machines. The technique quantifies the demagnetization degree to estimate the fault severity and corrects the angular position of the encoder to ensure maximum motor performance under postfault conditions. Experimental tests have been carried out on two identical six-phase PMSMs differing only in the size of the rotor magnets to emulate the effect of trailing-edge demagnetization. The experimental results confirm the effectiveness and robustness of the proposed solution.
Multiscale Characteristics of Different-Source Perturbations and Their Interactions for Convection-Permitting Ensemble Forecasting during SCMREX

The multiscale characteristics of initial conditions (ICs), model physics (MO), and lateral boundary conditions (LBCs) perturbations, as well as their interactions, are investigated for an experimental convection-permitting ensemble prediction system used in southern China, with a focus on 32 12-h forecasts collected over a half-month period of the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. Forecast perturbations differed from each other in terms of magnitude, evolution, and vertical distribution between components at different scales. The meso-β-scale MO perturbations show faster growth and saturation, as well as larger magnitude than the meso-α-scale ones, especially in the presence of moist convection. For IC perturbations, damping was present for nonprecipitation variables, while rapid growth and saturation occurred for precipitation. Adding LBCs perturbations to IC or MO perturbations caused linear impacts, which caused consistent, although small, perturbation increments. Nonlinear impacts of adding MO perturbations to IC perturbations were evident and were intrinsic to the predictability limits of IC and MO perturbations, which were closely related to moist dynamics. These nonlinear impacts had the most significant effects on meso-β-scale precipitation perturbations and can effectively improve precipitation prediction. These results highlight the importance of developing multiscale and multisource perturbation methods that reasonably include the interactions among different-scale perturbations from different sources, especially the nonlinear impacts of adding MO perturbations to IC perturbations.
On the Geselowitz formula in biomagnetics

A detailed analysis of the Geselowitz formula for the magnetic induction and for the electric potential fields, due to a localized dipole current density, is provided. It is shown that the volume integral, which describes the contribution of the conductive tissue to the magnetic field, exhibits a hyper-singular behaviour at the point where the dipole source is located. This singularity is handled both via local regularization of the volume integral as well as through calculation of the total flux it generates. The analysis reveals that the contribution of the primary dipole to the volume integral is equal to the one third of the magnetic field generated by the primary dipole while the rest is due to the distributed conductive tissue surrounding the singularity. Furthermore, multipole expansion is introduced, which expresses the magnetic field in terms of polyadic moments of the electric potential over the surface of the conductor.
Factors That Influence Sustained Release from Hot-Melt Extrudates

Hot-melt extrusion is a well-established tool in the pharmaceutical industry, mostly implemented to increase the solubility of poorly soluble drugs. A less frequent application of this technique is to obtain formulations with extended release. This study investigated the influence of polymer choice, drug loading, milling and hydrodynamics on the release of a model drug, flurbiprofen, from sustained-release hot-melt extrudates with Eudragit polymers. The choice of polymer and degree of particle size reduction of the extrudate by milling were the two key influences on the release profile: the percentage release after 12 h varied from 6% (2 mm threads) to 84% (particle size <125 µm) for Eudragit RL extrudates vs. 4.5 to 62% for the corresponding Eudragit RS extrudates. By contrast, the release profile was largely independent of drug loading and robust to hydrodynamics in the dissolution vessel. Thus, hot-melt extrusion offers the ability to tailor the release of the API to the therapeutic indication through a combination of particle size and polymer choice while providing robustness over a wide range of hydrodynamic conditions.
The Structural Changes of Frontal Subregions and Their Correlations with Cognitive Impairment in Patients with Alzheimer's Disease.

BACKGROUND The frontal lobe is affected by Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, we still lack sufficient understanding of subregion atrophy in the frontal cortex, and the relationship between subregions volume and cognitive decline in AD or MCI remains unclear. METHODS This study enrolled 434 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 150 cognitively normals (CN), 187 subjects with MCI, and 97 patients with AD. The gray matter of frontal regions and subregions was divided based on the BNA-246 atlas and its volume was measured by voxel-based morphometry (VBM). Analysis of covariance was performed to compare the differences in frontal regions and subregions volume. Then, receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to analyze the discriminative ability of subregion volume to distinguish the three groups. In addition, we investigated the association of subregion volume with Mini-Mental State Examination (MMSE) score and Alzheimer's Disease Assessment Scale-Cognitive Behavior section (ADAS-cog) scores with age, gender, education, and the estimated total intracranial volume (eTIV) as covariates. RESULTS In addition to the regions of frontal lobe atrophy found in previous studies, atrophy of the precentral gyrus (PrG) and some of its subregions were found in MCI. The volume of the right dorsal area 9/46 (MFG_7_1) was the best index to differentiate AD from CN, with an AUC value of 0.7. Moreover, we found that some subregions are associated with cognition in patients with MCI and AD. CONCLUSIONS Frontal lobe atrophy in MCI is more extensive than we assumed. In addition, the volume of right MFG_7_1 has the potential to distinguish AD from CN.
Scaled consensus for MASs with compound noises over Markovian switched topologies

This article aims to address the stochastic scaled consensus issue in almost surely and mean square sense of stochastic multiagent systems (SMASs) with compound noises in a Markovian switching setting. At first, based on stochastic approximation technique and nearest‐neighbor interaction rules, the stochastic scaled consensus controller is built for SMASs in presence of multiplicative and additive noise by designing a time‐varying gain. Meanwhile, some consensus criterions of SMASs are addressed if the union of the graph and time‐varying gain fulfill some mild requirements. Furthermore, the scaled consensus means that the agents' state variables tend to a prescribed proportion rather than the common value, so this behavior can be seen as non‐convergent behavior. The difference with most of excellent literatures is that the coexistence of noncooperative behavior and multiplicative noise generates that it is not easy to transform the multiplicative noise term into the form of an error equation, which means that the Lyapunov methods cannot be directly utilized in our article. To cope with this, we first demonstrate the boundedness for each agent's state, and hence the convergence can be achieved. Finally, in order to reveal the efficiency of our proposed protocol, the corresponding example is given in the simulation part.
Semilinear and quasilinear stochastic evolution equations in Banach spaces

In this thesis, we investigate the Cauchy problem for the quasilinear stochastic evolution equation \begin{equation*} \begin{cases} u(t)=[{-}A(u(t))u(t)+f(t)]\operatorname{dt}+B(u(t))dW(t),\quad t\in [0,T],\\ u(0)=u_0 \end{cases} \end{equation*} in a Banach space $ X. $ In the first part of the thesis, we concentrate on the parabolic situation, i.e. we assume that $ {-}A(u(t)) $ is for every $ t $ a generator of an analytic semigroup and that $ A(u(t)) $ has a bounded $ H^{\infty} $-calculus. Under a local Lipschitz assumption on $ u\mapsto A(u) $ we prove existence and uniqueness of a local strong solution up to a maximal stopping time that can be characterised by a blow-up alternative. We apply our local well-posedness result to a second order parabolic partial differential equation on $ \mathbb{R} ^d $, to a generalised Navier-Stokes equation describing non-Newtonian fluids and to a convection-diffusion equation on a bounded domain with Dirichlet, Neumann or mixed boudary conditions. In the last situation, we can even show that the solution exists globally. In the second part of the thesis, we go to a special hyperbolic situation. We look at a Maxwell equation on a domain $ D $ with perfect conductor boundary condition in chiral media with a nonlinear retarded material law, i.e. we consider $$ A(u)u(t)=-Mu(t)+|u(t)|^qu(t)-\int_{0}^{t}G(t-s)u(s)\operatorname{ds}. $$ Here, $ M(u_1,u_2)=(\operatorname{curl} u_2,-\operatorname{curl} u_1)^T $ is the Maxwell operator on $ L^{2}(D)^3\times L^{2}(D)^3 $. To solve this equation we apply a refined version of the monotonicity approach using the spectral multipliers of the Hodge-Laplace operator, which is a componentwise Laplace operator with boundary conditions comparable to those of $ M^2. $ We show existence and uniqueness of a weak solution $ u $ in the sense of partial differential equations and under stronger assumptions we prove that $ u $ is a strong solution, i.e. $ Mu(t,x) $ exists almost surely for almost all $ t\in[0,T] $ and $ x\in D $.
Immigrants’ Experiences and Perceptions of COVID-19 Information in Norway

When the COVID-19 virus hit the world, immigrants were overrepresented among those infected. In Norway, throughout the pandemic, there were far higher infection rates in people of Somali and Pakistani descent than in the rest of the population. The first aim of this study is to explore the experiences and perceptions of immigrants from Somalia and Pakistan living in Oslo regarding the different sources of COVID-19 information that they referred to at the beginning of the pandemic. The second aim is to suggest and discuss measures to improve practice in the event of a new pandemic. Four semi-structured focus-group interviews were conducted with a total of 27 first-generation immigrants from Somalia and Pakistan. The results showed that the immigrants found various COVID-19 information sources to be important. These sources are further presented in three categories: (1) COVID-19 information from the authorities through mass communication; (2) family, friends, and local environments as sources of information; (3) information from religious communities and volunteer resource personnel. We conclude that the participants were active users of available information from different sources and channels. Stigmatization made a negative contribution and religion made a positive contribution to coping and to trust in the authorities.
An Analysis of Limiting Cases for the Metal Oxide Film Growth Kinetics Using an Oxygen Defects Model Accounting for Transport and Interfacial Reactions

Abstract This work was motivated by the need to understand the passivation of metal surfaces to provide resistance against chemical degradation, given that corrosion is a major limiting factor in the operational lifetime of metals and their alloys. In this study, a unified analysis for an oxide growth model was presented. The oxide growth model was consistent with the literature and accounted for the transport of oxygen defects through a growing oxide film, as well as the electrochemical reactions of oxygen defects at the metal/oxide and oxide/environment interfaces. A linear potential profile across the oxide film was assumed. The model was analyzed for different rate limiting steps in the physicochemical process and perturbation techniques were utilized when necessary. The investigation yielded the well-known linear, parabolic, logarithmic and integral rate laws and the conditions that led to these rate laws were discussed.
Predicting Closed Price Time Series Data Using ARIMA Model

Closed price forecasting plays a main rule in finance and economics which has encouraged the researchers to introduce a fit model in forecasting accuracy. The autoregressive integrated moving average (ARIMA) model has developed and implemented in many applications. Therefore, in this article the researchers utilize ARIMA model in predicting the closed time series data which have been collected from Amman Stock Exchange (ASE) from Jan. 2010 to Jan. 2018. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.
Real-space phase field investigation of evolving magnetic domains and twin structures in a ferromagnetic shape memory alloy

A real-space phase field model combining Landau–Lifshitz–Gilbert equation and time-dependent Ginzburg–Landau equation is developed to investigate the evolution of ferromagnetic domains and martensitic twin structures in a ferromagnetic shape memory alloy at different lengthscales. Both domain and twin structures are obtained by simultaneously solving for minimization of magnetic, elastic, and magnetoelastic coupling energy terms via a nonlinear finite element method. The model is applied to simulate magneto-structural evolution within a nanoparticle and a bulk single-crystal of the alloy Ni2MnGa, which are subjected to mechanical strains. It is shown that a nanoparticle contains magnetic vortex structures within a single twin variant, whereas for a bulk crystal both 90° and 180° domain structures are present within multiple twin variants.
Improving Indonesian seniors’ digital resilience and quality of life through the Digital Academy for Seniors program

Digital literacy education is essential for everyone, including seniors to sustain their quality of life. The Digital Academy for Seniors aims to instill digital skills among seniors through a non-formal learning program. This qualitative research aimed to provide a comprehensive description of how the program can develop digital resilience and improve the quality of life of seniors. This study concluded that the program can improve the knowledge and attitudes of seniors in using digital media safely. Both of these are important assets for seniors to develop their digital resilience so that they can take advantage of digital media to support their higher quality social lives. These new findings are significant because they differ from previous research that focused on the limitations of seniors in using digital media. The program has demonstrated that anyone can learn digital literacy and benefit from it, regardless of perceived limitations.
Exploring Deep Learning Models Aimed at Favorable Optimization and Enhancement of Fiber Optic Sensor’s Performance

We present a deep learning (DL)-assisted extrapolation approach for modeling a function that maximizes the performance of a fiber optic sensor (in terms of the figure of merit, i.e., FOM) under the variation of design parameters. We exploit a presumption that FOM should be treated as a relative entity. The recurrent neural network (RNN) framework is used to build the models that treat the data sequentially and relatively detect the hidden patterns. The objective of the adopted methodology is to forecast the best probable value of FOM for a particular combination of design parameters considered under finer resolutions. Our proposed model displays promising results on the test set and in the extrapolation corresponding to a subsequence length ( ${l}$ ) value of 35. Specifically, the sequential–sequential (Seq2Seq)-attention model’s performance on the test metrics is root-mean-square error (RMSE) = 2.12 ± 0.62, mean absolute error (MAE) = 0.54 ± 0.16, and symmetric mean absolute percentage error (SMAPE) = 0.92 ± 0.18. To verify the generality of our model for finer resolution data, we conducted tests for different values of wavelength. The results show that the model can capture the pattern and momentum of the FOM but marginally struggles to keep track of its magnitude for finer resolution data. However, the predictions for the training resolution are adequate. The processing time to predict FOM is nearly 1 s (68 ms/step), at least 100 times lesser than traditional simulation techniques. Our proposed network is relatively robust, converges quickly, has fewer parameters, and performs well on the test dataset. The analysis enables highly efficient and cost-effective optimization and enhancement of the fiber optic sensor’s performance.
Diabetic prediction using fuzzy back propagation and analysis

As the amount of data is increasing, it has become merely impossible to carry out the manual analysis of data. As there is enough variety of data crisp data set is not so practical, hence the need to process the fuzzy data is necessary. Data mining algorithms helps in analyzing and predicting the huge amount of data, with very less human effort. This project aims at implementation of enhanced fuzzy back propagation neural network with triangular membership function and its comparative analysis with neural network, Naive Bayes. The project aims at predicting the states of diabetic. As diabetic is a serious problem and need to be predicted. We have eight indicators which determine the state of the protein. The best algorithm found was Naïve Bayes with an accuracy of 89% followed by fuzzy ANN with 86% accuracy and standard ANN had 82% accuracy. The study was done in java using Java Servlet packages and SQL database for storage. A precise and accurate prediction is necessary as sometimes there can human errors which lead to poor diagnosis of data and sometimes false report, so accurate prediction is required.
Comparative Analysis of Text Vectorization Methods

The paper considers methods of vectorization of textual properties of natural language in the context of the task of intellectual text analysis. The most common methods of statistical analysis of feature extraction and methods that taking into account the context are analyzed. The work describes the above types of text embeddings and their most common variations and implementations. Their comparative analysis was performed, which showed the relationship between the type of task of intellectual text analysis and the method showing the best metrics. The topology of the neural network, which is the basis for solving the problem and obtaining metrics, is described, and implemented. The comparative analysis was carried out using the relative time analysis of the theory of algorithms and classification metrics: accuracy, f1-score, precision, recall. The classification metrics are taken from the results of building a neural network model using the described framing methods. As a result, in the task of analyzing the tonality of the text, the statistical method of framing based on n-grams of character sequences turned out to be the best.
WHERE DO TREE TERMS COME FROM

Terms for 'tree' and other general biological categories such as 'grass', 'vine', 'fish' and 'bird' are relatively recent additions to languages. These broad categories have probably developed in response to most individuals being removed from daily intimate contact with the world of plant and animals as societies have increased in size and complexity. We suggest that languages of thousands of years ago either lacked 'tree' or encoded it only as a low salience category by widening the referential application of a term for 'wood. ' In this manner many of the world's languages acquired 'wood'/'tree' polysemy. As salience of 'tree' grows with increases in societal complexity, languages tend to lose 'wood'/'tree' polysemy by developing separate terms for each referent.