Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning based Alzheimer's disease early diagnosis using T2w segmented gray matter MRI

Diagnosing Alzheimer's disease at early stage required an effective classification mechanism to differentiate mild cognitive impairment from cognitive normal and AD. In this paper, we used data set collected from ADNI and OASIS. Instead of using the whole volume of the MRI, high informative slices are selected using entropy. The selected slices are pre‐processed by removing unwanted tissues using skull stripping algorithm and extracted gray matter using EICA. In this work, we used CNN model with inception blocks to extract deep features from the GM slices used to predict AD at early stage. The model avoids data leakage by considering all the slices of an MRI as a unit. The model trained with 80% of ADNI subject MRI volumes and tested with the remaining 20% subject MRI volumes, to provide great variance in training and testing data, the model further tested with OASIS data sets. 10‐fold cross‐validation is used to test the model without biasing. The model performance is evaluated using accuracy. The model achieves 98.73%, 100%, 93.72%, and 95.6% of accuracy for differentiating CN‐MCI, CN‐AD, AD‐ MCI and CN‐MCI and AD. At 10‐fold cross‐validation it gives 92.92 ± 3%, 98 ± 2%, 90 ± 4% and 94.9 ± 2% accuracy to differentiate CN‐MCI, CN‐AD, AD‐MCI, and CN‐MCI‐AD using ADNI. We further tested the model with 135 MRI volumes selected from OASIS data set, we achieved 92%, 91.76%, 88.23%, 81.48% of accuracy with CN‐AD, MCI‐AD, CN‐MCI, and three‐way classification. The model gives good accuracy and sensitivity of early AD Diagnosis.
What Is a Tornado Outbreak?: Perspectives through Time

The term “tornado outbreak” appeared in the meteorological literature in the 1950s and was used to highlight severe weather events with multiple tornadoes. The exact meaning of “tornado outbreak,” however, evolved over the years. Depending on the availability of scientific data, technological advancements, and the intended purpose of these definitions, authors offered a diverse set of approaches to shape the perception and applications of the term “tornado outbreak. ” This paper reviews over 200 peer-reviewed publications—by decade—to outline the evolving nature of the “tornado outbreak” definition and to examine the changes in the “tornado outbreak” definition or its perception. A final discussion highlights the importance, limitations, and potential future evolution of what defines a “tornado outbreak. ”
Feasibility of Accelerated 3D T1-Weighted MRI using Compressed-SENSE: Application to Quantitative Volume Measurement of Human Brain Structures

Objective Scan time reduction is crucial for volumetric acquisitions to improve workflow productivity and to reduce motion artifact during MRI procedure. We find out whether Compressed SENSE-4 (CS-4) can be pushed with 3D-turbo-field-echo T1-weighted (3D-TFE-T1W) sequence without compromising subcortical measurements on clinical 1.5T MRI. Materials and methods Thirty-three healthy volunteers (24 females, 9 males) underwent 1.5T MRI equipped with 12-channel coil. 3D-TFE-T1W for whole brain coverage was performed with different acceleration factors including SENSE-2, SENSE-4, CS-4. Freesurfer, FSL’s FIRST, and volBrain packages were implemented for subcortical segmentations. All processed data were assessed using Wilcoxon signed-rank test. A p-value < 0.05 was considered statistically significant. Results In comparison with reference, signal-to-noise ratio (SNR) obtained by CS-4 slightly dropped, in which the maximum SNR drop was detected at Amygdala with value of 10.55%. However, such disadvantage did not affect volume measurements - there were no statically significant differences for all subcortical structures (p < 0.05). They were in strong correlation, in which Pearson’s correlation coefficients were larger than 0.90. Moreover, the effect size scores are mostly in trivial to small ranges with values of 0.07–0.23. Conclusion CS-4 provided sufficient quality of 3D-TFE-T1W images for 1.5T MRI equipped with 12-channel receive coil. Subcortical volumes obtained from CS-4 images were consistent across different post-processing packages.
A review of melting ice adaptation strategies in the glacier tourism context

ABSTRACT Globally, tourism is being deeply impacted by glacial retreat caused by climate change. However, research on stakeholders’ adaptation to climatic change and its threat to the industry on tourism niche is currently inadequate. Thus, through a literature review of 61 glacier peer-reviewed papers, this paper highlights the advancement in research on glacier tourism and provides some basis for understanding stakeholders’ adaptation strategies to climate change. The review shows that glacier tourism research publications increased between 2015 and 2020. It also identifies some impacts of climate change on the glacier tourism industry as well as 27 adaptation strategies to climatic change, which are classified under seven main themes: changes to access, activities, tourism planning, educational activities, temporal substitutions, spatial substitutions, and glacier shrinkage attenuation. We discuss the relevance of the resilience concept in the tourism industry and recommend that tourists’ experiences should be enhanced by applying the findings of research on tourists’ motivations and landscape perception and developing more adaptation-oriented research. The findings suggest that the glacier tourism industry can reduce its vulnerability to climate change through increased collaboration between tourism operators and climate and tourism researchers.
Optimizing influenza vaccine composition: A machine learning approach

We propose a holistic framework based on state‐of‐the‐art methods in machine learning and optimization to prescribe influenza vaccine composition that are specific to a region, or a country based on historical data concerning the rates of circulation of predominant viruses. First, we develop a tensor completion formulation to predict rates of circulation of viruses for the next season based on historical data. Then, taking into account the uncertainty in the predicted rates of circulation of predominant viruses, we propose a novel robust prescriptive framework for selecting suitable strains for each subtypes of the flu virus: Influenza A (H1N1 and H3N2) and B viruses for production. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 11–14% and mortality by 8–11% over vaccine compositions proposed by World Health Organization (WHO) for Northern Hemisphere, and lower morbidity by 8–10% and mortality by 6–9% over vaccine compositions proposed by U.S. Food and Drug Administration (FDA) for United States, and finally, lower morbidity by 10–12% and mortality by 9–11% over vaccine compositions proposed by European Medicines Agency (EMA) for Europe.
The Cr(VI) Stability in Contaminated Coastal Groundwater: Salinity as a Driving Force

Chromium concentrations in seawater are less than 0.5 μg/L, but the Cr(VI) in contaminated coastal groundwater affected by Cr-bearing rocks/ores and/or human activities, coupled with the intrusion of seawater may reach values of hundreds of μg/L. A potential explanation for the stability of the harmful Cr(VI) in contaminated coastal aquifers is still unexplored. The present study is an overview of new and literature data on the composition of coastal groundwater and seawater, aiming to provide potential relationships between Cr(VI) with major components in seawater and explain the elevated Cr(VI) concentrations. It is known that the oxidation of Cr(III) to Cr(VI) and the subsequent back-reduction of Cr(VI) processes, during the transport of the mobilized Cr(VI) in various aquifers, facilitate the natural attenuation process of Cr(VI). Moreover, the presented positive trend between B and Cr(VI) and negative trend between δ53Cr values and B concentration may suggest that seawater components significantly inhibit the Cr(VI) reduction into Cr(III), and provide insights on the role of the borate, [B(OH)4]− ions, a potential buffer, on the stability of Cr(VI) in coastal groundwater. Therefore, efforts are needed toward the prevention and/or minimization of the contamination by Cr(VI) of in coastal aquifers, which are influenced by the intrusion of seawater and are threatened by changes in sea level, due to climate change. The knowledge of the contamination sources, hotspots and monitoring of water salinization processes (geochemical mapping) for every coastal country may contribute to the optimization of agricultural management strategies.
Design of Magnetic Structure for Omnidirectional Wireless Power Transfer

Recently, an omnidirectional wireless power transfer (WPT) has attracted attention as a solution to the limitations of free-positioning and mobility of conventional WPT. However, the efficiency of the omnidirectional WPT is reduced due to the low mutual inductance relative to the transmitter size. Therefore, this article proposed an optimal magnetic structure to improve efficiency without increasing the size of the transmitter. Four magnetic structure candidates (spherical, cubical, crossed bar, three-orthogonal plane core) are presented and their performances are compared using finite element analysis software. A Pareto optimal set is selected using a multiobjective optimization design, and the optimal magnetic structure is determined considering the weight of the objective variable and the Tchebycheff distance. The optimal magnetic structure and omnidirectional WPT prototype are built for performance verification. The experimental results are consistent with the simulation results and demonstrate that the optimal magnetic structure improves the efficiency in the three-dimensional space. When the power transmission distance is 150 mm, the system efficiency is improved up to 18.6%.
Electron Pitch Angle Distributions Around Dipolarization Fronts at the Off Magnetic Equator

The pitch angle distributions of energetic electrons (10 s–200 keV) around the dipolarization front (DF) have been studied extensively. However, the pitch angle distributions of energetic electrons around the DF at the off magnetic equator have been unclear so far. In this study, we present the observations of the DF at the off magnetic equator in the near‐Earth tail. Some properties of the DF at the off magnetic equator are presented. Importantly, the triple‐peaked pitch angle distribution of energetic electrons around the DF is found. Such a new type of distribution is a peak at around 22.5°, 90°, and 157.5°, which is a combinational consequence of betatron acceleration, Fermi acceleration, and magnetic mirror effect. Using an analytical model, we quantitatively reproduce the processes of betatron acceleration, Fermi acceleration, and magnetic mirror effect. These results will further enhance our understanding of the electron dynamics around DFs.
Variable Inverted Pendulum Applied to Humanoid Motion Design

SUMMARY Double inverted pendulum model, stationary or on a cart, is computationally the simplest out of the range of reasonable models used for anthropomorphic robots motion synthesis. However, it is still not sufficient for describing more complex situations. The novel concept of variable double inverted pendulum (VDIP) for static postures and VDIP on cart (VDIPC) for dynamic cases is proposed. It provides a simplified but a sufficiently accurate tool for planning the human-like static and dynamic robot postures. Its variable parameters enable the description of both human static postures and motion dynamics. The variable length of the lower link is essential for the representation of postures attained by bending legs. The studies of a set of static and dynamic postures were used for deducing and verifying the locations of lower and upper joint of a double pendulum and the point masses. To justify the concept, human body and pendulum behaviors are compared taking into account a typical model of the human body. Static analysis was conducted by considering static human postures. Dynamic conditions were analyzed using the data acquired from human motion and thus the VDIPC definition was established. The zero moment point trajectories of the human and of VDIPC were compared, validating the correctness of VDIPC in dynamic situations. The formal description of VDIPC is provided together with the torques equilibrium condition needed for evaluating the dynamic postural stability, with the VDPIC representing the robot configuration. The VDPIC state equations are formulated in a form required by the predictive control method. The paper contributes to the motion synthesis methods of anthropomorphic robots taking into account postural control.
Inhibitors of the hepatitis C virus RNA-dependent RNA polymerase

Infections caused by Hepatitis C Virus (HCV) are a significant world health problem for which novel therapies are in urgent demand. The polymerase of HCV is responsible for the replication of viral RNA. We recently disclosed diketoacids 1 and dihydroxypyrimidine carboxylates 3 as novel, reversible inhibitors of the HCV NS5B polymerase. We report here the further development of 3 into the more potent 5,6-dihydroxy-2-(2-thienyl)pyrimidine-4-carboxylic acids. The structure activity relationship of these inhibitors is discussed in the context of their physicochemical properties, supporting the proposed binding model, which involves pyrophosphate like chelation of the active site Mg-ions. We also report on the discovery and synthesis of two related scaffolds, the N 1-substituted pyrimidones and pyridine-N -oxides.
Variational inference in Bayesian neural network for well-log prediction

We have introduced a Bayesian neural network in quantitative log prediction studies with the goal of improving the petrophysical characterization and quantifying the uncertainty of model predictions. Neural network (NN) methods are gaining popularity in the petrophysics and geophysics communities; however, uncertainty quantification in model predictions is often neglected in the available literature, where the prediction is frequently performed in a deterministic setting. Determination of the uncertainty of the petrophysical model requires the estimation of the posterior distribution of the neural parameters that is generally mathematically intractable; for this reason, we adopt a variational approach to approximate the posterior model of the Bayesian network. To represent the uncertainty, we randomly draw samples from the posterior distribution of neural parameters to predict the model variables given the input data, leading to a learned-ensemble predictor. The proposed approach combines the ability of the NN of extracting hidden relations within the data set that physical relations cannot describe and the probabilistic framework for uncertainty quantification. We apply the proposed method to a well-log data set from the Volve Field, offshore Norway, to predict well logs in intervals where the data are incomplete or missing due to operational issues in the drilling procedure. The proposed approach is validated in intervals where the true data are available but not included in the training process. In the proposed application, the correlation coefficient between predictions and true data is greater than 0.9. In terms of accuracy, the results are comparable to those obtained using a traditional NN approach; however, the proposed method also provides a quantification of the uncertainty in the results, which offers additional information on the confidence in the predictions.
Stay in your lane experiences of children with Special Education Needs and/or Disabilities in two mainstream primary schools in Guyana

ABSTRACT Marginalisation, discrimination and depersonalisation are some of the negative experiences of children with Special Education Needs and/or Disabilities (SEND) in rural mainstream primary schools in Guyana. This paper presents these experiences from qualitative data gathered over six months of ethnographic research in two primary schools in Guyana. The data were analysed using situational analysis as posited by Adele Clarke and interpreted through a poststructuralist lens with the social model of disability theoretical framework. Children with SEND feel valueless and experience anxiety and loneliness in their placement in rural mainstream schools. This paper forms part one of a series presented to highlight institutional discrimination embedded in the practices and discourses which is fuelled by the dominance of the individual deficit model of disability in mainstream schools. The paper also illustrates how teachers’ beliefs’ that disability is biological and they can not cater for such children who are considered unteachable and challenging. The paper further reflects on the collective conscience of mainstream teachers in rural Guyana who feel unsupported in unsuitable classrooms to meet the needs of children with SEND.
Synthesis of silicon nitride from rice husks and sugarcane bagasse ashes for adsorption of flouride ions from aqueous solutions

High concentrations of fluoride in drinking water pose serious problems associated with dental and skeletal fluorosis to humans in several parts of the world. Among various methods used for defluoridation such as electrochemical degradation, ion exchange, precipitation coagulation, adsorption offers an attractive method for the removal of fluoride in terms of cost, simplicity of design and operations. Silicon nitride is a material that is characterized by high mechanical strength, thermal and chemical resistance. It also has high surface area with positive sites emanating from the silamine functional groups. Despite these excellent properties that make it a potential adsorbent for fluoride ions, no reports have been made on its use for this purpose. Therefore, the aim of this study was to synthesize, characterize silicon nitride from rice husk ash (RHA) and sugarcane bagasse ash (SBA) and to determine its adsorption capacity and efficiency for fluoride ions from aqueous media. The silicon nitride was synthesized by hydrothermal synthesis technique at four different temperatures, of 100 o C, 150 o C, 200 o C and 300 o C and labeled, SNA, SNB, SNC and SND respectively. The resultant silicon nitride samples were characterized by X-ray diffraction, X-ray fluorescence (XRF) and FTIR spectroscopy. FTIR spectra suggested that the compounds prepared had similar functional groups. The X-ray diffractogram show that SNA contained 64.6% α-phase and 27.8% β-phase silicon nitride. The adsorptions capacities and efficiencies were determined through batch experiments under various conditions of temperature, pH, adsorbent dose, initial fluoride ions concentration, contact time and shaking speed. The adsorptions capacities of silicon nitride samples were found to be 7.9, 8.9, 13.9 and 49.8 mg/g for SNA, SNB, SNC and SND, respectively. Optimum adsorptions capacities were achieved at pH 2 for SNA, SNB, SNC and pH 5 for SND, contact time of 100 minutes, temperature 25 o C and shaking speed of 600 rpm. The adsorption data best fitted the Langmuir isotherm. This results indicate that the silicon nitride constitute a promising material for the development of an adsorption technology for the removal of fluoride ions from aqueous systems.
Towards End-to-End Deep Learning Performance Analysis of Electric Motors

Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in physics and engineering. Recent works on DL-assisted analysis showed enormous potential of CNN applications in electrical engineering. This paper explores the possibility of developing an end-to-end DL analysis method to match or even surpass conventional analysis techniques such as finite element analysis (FEA) based on the ability of CNNs to predict the performance characteristics of electric machines. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. Studied CNNs show over 90% accuracy for an analysis conducted under a minute, whereas a FEA of comparable accuracy required 200 h. It is also shown that training CNNs to predict multidimensional outputs can improve CNN performance. Multidimensional output prediction with data-driven methods is further discussed in context of multiphysics analysis showing potential for developing analysis methods that might surpass FEA capabilities.
Forced Interactions with Sheriff Deputies Over Time and Their Influence on Stigma and Self Identities among Individuals Convicted of Sex Crimes

This paper examined the perceptions of convicted sex offenders and their interactions with law enforcement over time. Specifically, we focused on how formal interactions influenced stigma management and self-identity transformation. For decades, scholars have proposed that identities and behaviors often result from interactions with others. Sex offender registration and notification laws force interactions between registrants and police agents for years, if not a lifetime. Given that desistance from sex offending is dependent on prosocial identity transformation, we analyzed interviews with 63 registrants to uncover how interactions with police promote or inhibit identity transformation over time. Our findings suggested interactions with police can influence the internalization of a “sex offender” label, can reaffirm non-offender role identities, but mostly have little to no effect on personal identity transformation over time.
Interband optical absorption obtained by pseudopotential method for type-II InAs/GaSb SL photodetectors

We have calculated interband optical absorption for InAs/GaSb based type-II superlattice (SL) structures. The empirical pseudopotential method (EPM) has been used as an alternative to the k.p method since it is less sophisticated while providing similar results in the mid wavelength infrared range and long wavelength infrared range atmospherics bands for comparison. EPM results show that the bandgap wavelengths of SLs have been predicted with the underestimating of 0.4 µm. This corresponds to an uncertainty of less than 0.3 monolayer in the layer width. The theoretical estimation is comparable with the uncertainty of the layer width during the growth process. Heterostructures or SLs with their ternary and quaternary alloys can be calculated by this method to identify electronic and optical parameters for both intersubband and interband applications.
An LSTM model for extracting hierarchical relations between words for better topic modeling

Often when dealing with text data, there exists valuable information that determines the relationship between the words encountered in the corpus. The type of relationship which is sought after is the “has-a” and “is-a” relationship, with which one can build a hierarchical representation of words. Since each language has its own set of rules and syntax, extraction of the relationships ultimately boils down to understanding the syntax of the particular language and using relevant features in the process. The paper presents a machine-learning model for understanding the language syntax and deducing the relationships between the words encountered. To be specific, a sequence modeling approach if followed, where the model receives a sequence of words and makes use of the various properties of the words to build a hierarchical graph. The algorithm described will be independent of the language, and the model should be versatile enough to be trained for different languages. In addition, the paper also describes how this information can be used to build better topic models, given a corpus of text.