Miracle-dz/UniFAR-FTUs huggingface dataset:
It Is Time for a Multidisciplinary Rehabilitation Approach: A Scoping Review on Stomatognathic Diseases in Neurological Disorders

Patients affected by neurological disorders can develop stomatognathic diseases (SD) related to decreased bite force and quality of mastication, bruxism, severe clicking and other temporomandibular disorders (TMD), which deeply affect patients’ swallowing, masticatory and phonation functions and, therefore, their quality of life. The diagnosis is commonly based on medical history and physical examination, paying attention to the temporomandibular joint (TMJ) range of movements, jaw sounds and mandibular lateral deviation. Diagnostic tools such as computed tomography and magnetic resonance imaging are used instead in case of equivocal findings in the anamnesis and physical evaluation. However, stomatognathic and temporomandibular functional training has not been commonly adopted in hospital settings as part of formal neurorehabilitation. This review is aimed at describing the most frequent pathophysiological patterns of SD and TMD in patients affected by neurological disorders and their rehabilitative approach, giving some clinical suggestions about their conservative treatment. We have searched and reviewed evidence published in PubMed, Google Scholar, Scopus and Cochrane Library between 2010 and 2023. After a thorough screening, we have selected ten studies referring to pathophysiological patterns of SD/TMD and the conservative rehabilitative approach in neurological disorders. Given this, the current literature is still poor and unclear about the administration of these kinds of complementary and rehabilitative approaches in neurological patients suffering from SD and/or TMD.
A Cross-Modal Generative Adversarial Network for Scenarios Generation of Renewable Energy

Scenarios data of renewable energy resources plays an essential role in the study of mitigating the risk in the power system due to their intermittent nature. Existing researches have mainly focused on how to generate high-quality time series based scenarios with corrupted or missing observed data. However, the multimodality of renewable energy resources is largely ignored from the perspective of the spatial-temporal correlation which can significantly strengthen the renewable energy prediction and optimization. To make full use of these multi-modal data and further improve the data quality of scenarios, a cross-modal Generative Adversarial Network (cGAN) model is proposed with the setting of two spatial-temporal transformer models. In cGAN, the task of scenarios generation is formulated as a probability approximation problem. Then, a cross-modal scenarios generation framework is constructed to process data from multi-modal observations. Theoretically, it can generate infinite number of uncertain scenarios data via the repeated random noise sampling technique. Extensive experiments are carried out at a NREL photovoltaic power dataset and a real-world wind power dataset, respectively. The results validate the state-of-the-art performance of cGAN, even though the validation dataset is missing at random. Moreover, the results also show that the setting of spatial-temporal transformer clearly explains the contributions of different modal data, and stabilize the training process of cGAN. Finally, a stochastic day-head economic dispatch is also studied to show the practical value of cGAN.
Threshold behaviour in photodetachment into a final state with large negative polarizability

In a recent experiment, Lindahl et al. (Lindahl A. O. et al., Phys. Rev. Lett., 108 (2012) 033004) observed a new threshold behavior in the photodetachment of the potassium negative ion when the residual atom is left in the state. Unfortunately the resonance close to threshold made it more difficult to apply the semi-classical model that was developed to describe the threshold behavior. In this paper we present a study of the threshold behavior of the partial cross-section in the photodetachment of the sodium negative ion. The experiment was conducted using a collinear beams apparatus where the detection was performed using a resonance ionization scheme. We observe the same threshold behavior as in the previous experiment, but without any obfuscating resonance. The model derived in the previous paper is found to fit the observed cross-section up to 35 meV above threshold. The present experiment clearly demonstrates the threshold behaviour for the fundamental process of a free electron moving in the field of an atom with a large negative polarizability.
Assessment and grading of papilledema using optical Coherence Assessment and grading of papilledema using optical Coherence Tomography Tomography

Background : There is a strong positive signi ﬁ cant correlation between grades of papilledema by optical coherence tomography (OCT) and grades of papilledema by the modi ﬁ ed Frisen scale. Aim : To identify and correlate the role of coherent optical tomography imaging of the head of the optic nerve with the clinical grading of papilledema using Frisen scale via digital fundus photographs. Patients and methods : This was a randomized study in which 40 patients were selected from the patients attending the outpatientneurologyandophthalmologyclinicsofAl-AzharUniversityHospitalsinCairobetweenAugust2021andJuly2022. Results : The current study showed that the papilledema was distributed according to OCT into 10 (25%) patients with stage 1, 26 (65%) with stage 2, and four (10%) with stage 3. The papilledema was distributed according to modi ﬁ ed Frisen scale into 10 (25%) patients with stage 1, 22 (55%) with stage 2, four (10%) with stage 3, and four (10%) with stage 4. There was a highly signi ﬁ cant association and correlation between grades of papilledema by OCT and grades of papilledema by the modi ﬁ ed Frisen scale ( P < 0.001). Conclusion : Clinical staging may be supplemented by current OCT measurement. OCT corresponds favorably with clinical staging of optic nerve images for lower-grade anomalies. OCT processing algorithms of thickness of the retinal nerve ﬁ ber layer frequently fail with higher grades.
The Past Contribution and Future Fate of Genetic Variants under Climate Change in an Island Population of Musa itinerans

Genetic variation within species is crucial for sessile species to adapt to novel environments when facing dramatic climate changes. However, the debate continues whether standing ancestral variation adaptive to current environmental variability is sufficient to guarantee future suitability. Using wild banana Musa itinerans, we investigated the relative contribution of standing ancestral variation versus new mutations to environmental adaptation and inferred their future fate. On the continental island of Taiwan, local populations immigrated from the Southeast Asian continent during the ice age and have been isolated since then. This allows the classification of genetic variants into standing ancestral variation (polymorphic in Taiwan and the continent) and new mutations (polymorphic only in Taiwan). For temperature-related variables where Taiwan is mainly within the ancestral climatic range, standing ancestral variation had a slightly stronger association than new mutations. New mutations were more important for precipitation-related variables, where northeastern Taiwan had much more winter rainfall than most of continental Southeast Asia. Upon future climate change, new mutations showed higher genetic offset in regions of abrupt transition between allele frequency and local environments, suggesting their greater spatial heterogeneity of future vulnerability.
Automated prediction of wet-snow avalanche activity in the Swiss Alps

Abstract Wet-snow avalanches are triggered by the infiltration of liquid water which weakens the snowpack. Wet-snow avalanches are among the most destructive avalanches, yet their release mechanism is not sufficiently understood for a process-based prediction model. Therefore, we followed a data-driven approach and developed a random forest model, depending on slope aspect, to predict the local wet-snow avalanche activity at the locations of 124 automated weather stations distributed throughout the Swiss Alps. The input variables were the snow and weather data recorded by the stations over the past 20 years. The target variable was based on manual observations over the same 20-year period. To filter out erroneous reports, we defined the days with wet-snow avalanches in a stringent manner, selecting only the most extreme active or inactive days, which reduced the size of the dataset but increased the reliability of the target variable. The model was trained with weather variables and variables computed from simulated snow stratigraphy in 38$^\circ$ slopes facing the 4 cardinal directions. While model development and validation were done in nowcast mode, we also studied model performance in 24-hour forecast mode by using input variables computed from a numerical weather prediction (NWP) model. Overall, the performance was good in both nowcast and forecast mode (f1-score around 0.8). To assess model performance beyond the stringent definition of wet-snow avalanche days, we compared model predictions to wet-snow avalanche activity over the entire Swiss Alps, based on the raw data over 8 winters. We obtained a Spearman correlation coefficient of 0.71. Hence, our model represents a step toward the application of support tools in operational wet-snow avalanche forecasting.
Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics

Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.
Surface reconstruction from imperfect point models

This thesis presents work on surface reconstruction from imperfect point models. Here "imperfections" refer to the cases such as missing information about point nomials, unknown level of noise, irregular sampling, and/or non-manifold surfaces, resulting in incorrect reconstruction results using existing approaches. Due to the diversity of point acquisition approaches (laser range finders, computer vision methods, etc), imperfections occur often in practice and pose a great challenge to surface reconstruction algorithms. In this thesis, several novel algorithms, targeted towards different imperfections, are proposed. To recover the geometry and the texture of insufficiently sampled regions, we exploit user knowledge about the planarity and the symmetry of man-made objects in indoor scenes and fill the holes in a natural way. After that, there might still be some remaining holes. We then develop a hole filling algorithm as a post processing module to complete the resulting mesh. This algorithm interpolates the interior of a hole based on its vicinity. It can also be used as an independent module for mesh repairing. In addition, we propose a distance-field based algorithm to handle very noisy points without point normals. A global distance field is constructed by propagating the information from interior/exterior regions in a hierarchical and adaptive way. A mesh is extracted later as the zero-set surface of the distance field. Furthermore, a generalized approach is introduced to naturally handle both manifold and non-manifold surfaces, including both non-orientable surfaces and surfaces with boundaries. The input points are first voxelized, enabling easy classification of the local shape for each point. We then locate non-manifold regions (junctions and boundaries) and take special care to mesh them. We also propose to enforce the regularity of the resulting mesh reconstructed from point models. Using a nearly isometric point parameterization, we are capable of controlling the shape of output triangles. The aforementioned algorithms cover a wide range of imperfections and we believe they could handle many problems encountered in practice.
Early adoption of Industry 4.0 technologies in the agricultural sector: A phenomenological analysis

ABSTRACT The adoption of Industry 4.0 technologies is generating an expansion of new capabilities in traditional sectors such as agriculture, giving rise to the AgTech phenomenon. However, the speed of adoption varies according to the characteristics of farmers and the relationships between them and a set of actors within an ecosystem that can favor or hinder innovation. The literature recognizes the importance of early adopters in the process of technology diffusion. Therefore, this article aims to characterize the profile of early adopters of Industry 4.0 technologies and to identify the main drivers and obstacles in the process of early adoption in the agriculture. A phenomenological study was carried out in the region of Mar y Sierras in Argentina, and eight actors were interviewed. The findings allow the identification of key actors, considered as referents, outside the companies that offer or demand the technologies, who favor the early adoption. Emphasis is placed on the adoption of technologies that meet the real needs of the sector and the region to make their impact visible and generate a spillover effect among laggard adopters. This work highlights the different modalities of linkages between actors for the adoption and diffusion of new technologies. Finally, although the region is not a pioneer in the adoption of AgTechs, it is recognized that there is an institutional density that encourages the early adoption of technologies, which presents an opportunity for public policy makers to develop strategies and join public–private articulation efforts to dynamize the ecosystem.
Orbital and sub-orbital space tourism: motivation, constraint and artificial intelligence

Purpose There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups. Design/methodology/approach A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior. Findings Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics. Originality/value This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.
Proof about Weakly Universal State Contracting Networks

Recurrent networks that have transfer functions that fulfill the Lipschitz continuity with L = 1, may be echo state networks if certain limitations on the recurrent connectivity are applied. Initially it has been shown that it is sufficient if the largest singular value of the recurrent connectivity Λ is smaller than 1. Here it is investigated under which it still can be shown that the network is an echo state network if Λ is equal to 1. It turns out that for certain transfer functions indeed the network is still an echo state network. This work is closely related to experimental results outlined in Mayer [2014].
Cerebral Projection of Mirrored Touch via sLORETA Imaging

Touch is one of the primary communication tools. Interestingly, the sensation of touch can also be experienced when observed in another person. Due to the system of mirror neurons, it is, in fact, being mapped on the somatosensory cortex of the observer. This phenomenon can be triggered not only by observing touch in another individual, but also by a mirror reflection of the contralateral limb. Our study aims to evaluate and localize changes in the intracerebral source activity via sLORETA imaging during the haptic stimulation of hands, while modifying this contact by a mirror illusion. A total of 10 healthy volunteers aged 23–42 years attended the experiment. The electrical brain activity was detected via scalp EEG. First, we registered the brain activity during resting state with open and with closed eyes, each for 5 min. Afterwards, the subjects were seated at a table with a mirror reflecting their left hand and occluding their right hand. The EEG was then recorded in 2 min sequencies during four modifications of the experiment (haptic contact on both hands, stimulation of the left hand only, right hand only and without any tactile stimuli). We randomized the order of the modifications for each participant. The obtained EEG data were converted into the sLORETA program and evaluated statistically at the significance level of p ≤ 0.05. The subjective experience of all the participants was registered using a survey. A statistically significant difference in source brain activity occurred during all four modifications of our experiment in the beta-2, beta-3 and delta frequency bands, resulting in the activation of 10 different Brodmann areas varying by modification. The results suggest that the summation of stimuli secured by interpersonal haptic contact modified by mirror illusion can activate the brain areas integrating motor, sensory and cognitive functions and further areas related to communication and understanding processes, including the mirror neuron system. We believe these findings may have potential for therapy.
Categorical Abstract Algebraic Logic: Local Characterization Theorems for Classes of Systems

Let ℒ = ⟨F, R, ρ⟩ be a system language. Given a class of ℒ-systems K and an ℒ-algebraic system A = ⟨SEN,⟨N,F⟩⟩, i.e., a functor SEN: Sign → Set, with N a category of natural transformations on SEN, and F:F → N a surjective functor preserving all projections, define the collection K A of A-systems in K as the collection of all members of K of the form 𝔄 = ⟨ SEN,⟨N,F⟩,R 𝔄 ⟩, for some set of relation systems R 𝔄 on SEN. Taking after work of Czelakowski and Elgueta in the context of the model theory of equality-free first-order logic, several relationships between closure properties of the class K, on the one hand, and local properties of K A and global properties connecting K A and K A′, whenever there exists an ℒ-morphism ⟨ F,α⟩ : A → A′, on the other, are investigated. In the main result of the article, it is shown, roughly speaking, that K A is an algebraic closure system, for every ℒ-algebraic system A, provided that K is closed under subsystems and reduced products.
Structure Optimization of 12β-O-γ-Glutamyl Oleanolic Acid Derivatives Resulting in Potent FXR Antagonist/Modulator for NASH Therapy

The farnesoid X receptor (FXR) plays a crucial role in regulating the metabolism of bile acids, lipids, and sugars. Consequently, it is implicated in the treatment of various diseases, including cholestasis, diabetes, hyperlipidemia, and cancer. The advancement of novel FXR modulators holds immense importance, especially in managing metabolic disorders. In this study, a series of oleanolic acid (OA) derivatives bearing 12β-O-(γ-glutamyl) groups were designed and synthesized. Using a yeast one-hybrid assay, we established a preliminary structure–activity relationship (SAR) and identified the most potent compound, 10b, which selectively antagonizes FXR over other nuclear receptors. Compound 10b can differentially modulate the downstream genes of FXR, including with the upregulation of the CYP7A1 gene. In vivo testing revealed that 10b (100 mg·Kg−1) not only effectively inhibits lipid accumulation in the liver but also prevents liver fibrosis in both BDL rats and HFD mice. Molecular modeling indicated that the branched substitution of 10b extends into the H11–H12 region of FXR-LBD, possibly accounting for its CYP7A1 upregulation, which is different from a known OA 12β-alkonate. These findings suggest that 12-glutamyl OA derivative 10b represents a promising candidate for the treatment of nonalcoholic steatohepatitis (NASH).