Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging—more specifically, magnetic resonance imaging (MRI)—has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.
Silicon nitride directional coupler interferometer for surface sensing

Abstract. A silicon nitride directional coupler (DC) used to create a biosensing device is presented. The DC detects changes in the refractive index of the cladding (nclad) as changes in the relative output intensity. The DC length (L), nclad-dependent sensitivities of the DC, and preferred dimensions of the single-mode DC waveguides are obtained through numerical simulations. The performance of the DC is evaluated through end-fire coupling measurements. The intensities measured after varying the nclad using air, water, and glycerol solutions agree well with the fitting for a wide range of L values between 60 and 600 μm, i.e., corresponding to 6 to 60 times the coupling length. The bulk refractive index sensitivity was investigated using glycerol solutions of different concentrations and was found to be 18.9 optical intensity units per refractive index unit (OIU/RIU). Biotin/streptavidin bindings were detected with a sensitivity of 60 OIU/RIU and a detection limit of 0.13 μM, suggesting the feasibility of the DC for immunosensing.
Development of Photo-Polymerization-Type 3D Printer for High-Viscosity Ceramic Resin Using CNN-Based Surface Defect Detection

Due to the high hardness and brittleness of ceramic materials, conventional cutting methods result in poor quality and machining difficulties. Additive manufacturing has also been tried in various ways, but it has many limitations. This study aims to propose a system to monitor surface defects that occur during the printing process based on high-viscosity composite resin that maximizes ceramic powder content in real time using image processing and convolutional neural network (CNN) algorithms. To do so, defects mainly observed on the surface were classified into four types by form: pore, minor, critical, and error, and the effect of each defect on the printed structure was tested. In order to improve the classification efficiency and accuracy of normal and defective states, preprocessing of images obtained based on cropping, dimensionality reduction, and RGB pixel standardization was performed. After training and testing the preprocessed images based on the DenseNet algorithm, a high classification accuracy of 98% was obtained. Additionally, for pore and minor defects, experiments confirmed that the defect surfaces can be improved through the reblading process. Therefore, this study presented a defect detection system as well as a feedback system for process modifications based on classified defects.
Investigating large-scale Granger causality analysis in presence of noise and varying sampling rate

Large-scale Granger causality (lsGC) analysis quantifies multivariate voxel-resolution connectivity in resting-state functional MRI (fMRI) unlike commonly used multivariate approaches that estimate connectivity at a coarse resolution. We investigate the effect noise and repetition time (TR) of fMRI signals have on the ability of lsGC to capture true connectivity and compare with traditionally used multivariate Granger causality analysis (mvGC). To this end, we use realistic fMRI simulations, generated with varying TR and noise levels, for fifty-node simulations. LsGC produces directed connectivity graphs, represented as connectivity matrices which we compare with the known ground truth of the simulations with the Area Under the receiver operating characteristic Curve (AUC) as a measure of agreement. The best AUC with lsGC was 0.957 while the least was 0.835 at TR = 3 s. Our results show that lsGC performs much better than mvGC approaches for both noise levels and different TR. An interesting finding with lsGC was that at higher sampling rate, corresponding to TR < 2 s increase in noise did not significantly reduce performance. However, as with increasing TR beyond 2 s, the effects of noise in the system is no longer negligible. Our results indicate that if the TR is sufficiently small, the performance of lsGC is not hindered greatly by noise levels. However, at higher TR, the deterioration of performance due to high TR is compounded by higher noise levels, indicating that improvements in TR may be more beneficial in extracting accurate lsGC connections.
A Comparative Analysis of Video Summarization Techniques

: Video summarization special field of signal processing which includes pre-processing of video sets, their contextual segmentation, application-specific feature extraction & selection, and identification of dissimilar frame sets. Various variety of machine learning models are proposed by researchers to design such summarization methods, and each of them varies in terms of their functional nuances, application-specific advantages, deployment specific limitations
Neural Network-Based Adaptive Sliding Mode Control for T-S Fuzzy Fractional Order Systems

This brief investigates the adaptive sliding mode control based on the radial basis function (RBF) neural network for T-S fuzzy fractional order systems. The RBF neural network is used to approximate the nonlinearities and external disturbances. Then, the switching control term is represented as a proportional integral control format to reduce the chattering phenomenon. The conditions of the sliding mode controller are given to ensure the stability of the control system. Finally, the efficiency of the conditions is demonstrated by a permanent magnet synchronous motor (PMSM) example, i.e., it shows that the designed controller can stabilize T-S fuzzy fractional order systems.
Comparative Analysis of Numerical Solutions of Advection-Diffusion Equation

Abstract. The advection diffusion equation is one of the most popular and convenient equations in calculating the transport of energy and materials in flux areas. In this paper, one-dimensional advection-diffusion equation is solved using the finite difference, fourth order finite difference, finite volume, and differential quadrature methods in explicit condition. The quantitative comparative analysis involved two hypothetical cases and one experimental study. The results of the numerical solutions for the hypothetical cases are compared against the analytical solution. The experimental data are also simulated by the methods. The comparative analysis results revealed that the differential quadrature method performs as good as the analytical solution for the hypothetical cases. All the methods but the finite difference showed comparable performance in simulating the experimental data. Keywords: Advection-diffusion equation, Numerical methods, Explicit solution Ozet. Tasinim-yayilim denklemi, akim alanlarinda enerji ve malzemelerin tasiniminin hesaplanmasinda kullanilan en populer ve kullanisli denklemlerden biridir. Bu makalede, bir boyutlu tasinim-yayilim denkleminin, sonlu fark, dorduncu mertebeden sonlu farklar, sonlu hacim ve diferansiyel kuadratur yontemleri kullanilarak acik cozumleri yapilmistir. Kantitatif karsilastirmali inceleme, iki varsayimsal vaka ile bir deneysel calisma icermektedir. Varsayimsal vakalar icin sayisal cozumlerin sonuclari analitik cozum ile karsilastirilmistir. Deneysel veriler de yontemlerle simule edilmistir. Karsilastirmali inceleme sonuclari, diferansiyel kuadratur yonteminin varsayimsal durumlarda analitik cozum kadar iyi performans gosterdigini ortaya koymustur. Cozumde kullanilan tum yontemler deneysel verilerin simulasyonu icin karsilastirmaya deger performans gostermistir. Anahtar Sozcukler: Tasinim-yayilim denklemi, Sayisal yontemler, Acik cozum
The Hidden Subgroup Problem and Non-interactive Perfect Zero-Knowledge Proofs

The Hidden Subgroup Problem (HSP) generalises many problems that are candidates to be NP-intermediate. It was shown that the decision version of HSP belongs to the zero-knowledge complexity class HVPZK and that, if the size of the group is known, it also belongs to NISZK. We show that whenever we can sample uniformly at random elements of the group and of a set, with the same size of the group, that contains the image of the function that hides the subgroup, the problem is in NIPZK1 (i.e. NIPZK with perfect completeness). As a second contribution, we show that NIPZK1 has a complete promise problem that is a restricted version of a complete promise problem for the NIPZK class.
Needs and demands for mHealth cardiac health promotion among individuals with cardiac diseases: a patient-centred design approach

Abstract Aims Cardiovascular diseases are one of the main contributors to disability and mortality worldwide. Meanwhile, risk factors can be modified by lifestyle changes. mHealth is an innovative and effective way to deliver cardiac health promotion. This study aims to examine the needs and demands regarding the design and contents of an mHealth intervention for cardiac health promotion among individuals with cardiac diseases. Different clusters were determined and analysed in terms of the intention to use an mHealth intervention. Methods and results A cross-sectional study was conducted via a web-based survey. Three hundred and four individuals with coronary artery diseases (CADs) and/or congestive heart failure (CHF) were included in the data analysis. Descriptive statistics were applied to evaluate needs and demands regarding an mHealth intervention. A k-medoids cluster analysis was performed. Individuals with CAD and CHF favoured an mHealth intervention that supports its users permanently and is easily integrated into everyday life. Handheld devices and content formats that involve active user participation and regular updates were preferred. Three clusters were observed and labelled high, moderate, and low burden, according to their psychometric properties. The high burden cluster indicated higher behavioural intention towards use of an mHealth intervention than the other clusters. Conclusion The results of the study are a valuable foundation for the development of an mHealth intervention for cardiac health promotion following a user-centred design approach. Individuals with cardiac diseases report positive attitudes in the form of high usage intention regarding mHealth. Highly burdened individuals report a high intention to use such interventions.
The Effect of Korean Celebrity Endorsement on Perceived Product Quality and Brand Loyalty: Case of Indonesian Skincare Brand in DKI Jakarta

According to Indonesian Cosmetic Association, the beauty industry in Indonesia has rapidly increased as the number of brands varied. To attract more customers, many local brands adopt a celebrity endorsement strategy with Korean stars. In recent years’ Korean celebrity endorsement has brought the eye of the public and has benefited businesses in increasing the awareness from fans and sometimes even selling out. However, it becomes a concern whether this strategy can bring the intended result and bring actual buying behavior. The purpose of this research is to examine the impact of celebrity endorsement on perceived product quality and brand loyalty among 100 young-adult women exposed to Somethinc skincare brand and NCT who have previously purchased the product in DKI Jakarta. This study uses quantitative analysis with Partial Least Square Equation Modeling (PLS-SEM) to analyze the data. The result of this study shows that Celebrity Endorsement shows a positive relationship with Perceived Product Quality. AndPerceived Product Quality positively affects Brand Loyalty. This study provides empirical evidence that the above-mentioned relationships do exist, and companies can strategically leverage the facts to their advantage and drive customer perceptions of quality and loyalty through the promotion of appropriate celebrity endorsements.
Localization of Myocardial Infarction From 2D-VCG Tensor With DSC-Net

Myocardial infarction (MI) can cause acute and permanent damage to the myocardial muscle. Vectorcardiogram (VCG) is formed by the time-varying coordinates of cardiac electrical activity in space. According to different infarct locations, the ring of VCG in the three orthogonal planes has pathological morphological changes. Yet the existing algorithms only extract the pathological information of three-lead VCG signals, but they do not fully consider the correlation information between different orthogonal planes. We proposed a depthwise separable convolution network (DSC-Net) for automatic MI localization from 2D-VCG tensor. Using the orthogonality between the lead axes, we first combine the three leads in pairs to form a 2D-VCG, and then construct a 2D-VCG tensor that captures the correlation information between leads. DSC-Net extracts spatial features related to MI obtained in 2D-VCG before Softmax is applied to classify MIs. The proposed method was validated on the benchmark Physikalisch Technische-Bundesanstalt dataset, which includes VCG of 11 types of MI. We demonstrated, with the cardiac electrical activity spatial features obtained from the 2D-VCG tensor, that the accuracy of 11 categories of MI and normal is higher than 99.92%. The proposed model effectively realized the localization of MI with competitively high accuracy for all 11 categories.
Recent Advances in Functional Nanomaterials for Diagnostic and Sensing Using Self-Assembled Monolayers

Functional nanomaterials have attracted attention by producing different structures in any field. These materials have several potential applications, including medicine, electronics, and energy, which provide many unique properties. These nanostructures can be synthesized using various methods, including self-assembly, which can be used for the same applications. This unique nanomaterial is increasingly being used for biological detection due to its unique optical, electrical, and mechanical properties, which provide sensitive and specific sensors for detecting biomolecules such as DNA, RNA, and proteins. This review highlights recent advances in the field and discusses the fabrication and characterization of the corresponding materials, which can be further applied in optical, magnetic, electronic, and sensor fields.
Digital Twin: a Hybrid Approach for Structural Health Monitoring

– The scope of this work is to illustrate the advantages that can be obtained in the context of structural health monitoring (SHM) when data-driven and model-based approaches are combined through the construction of a numerical twin of the structure. While the strategy isn’t entirely novel per se, the use of wholly integrated technologies and software developed by the same company for all parts of the workﬂow, preventing data loss and ensuring interoperability, is where the originality lies. ASDEA S.r. l. provides products designed to perform each part in the SHM cycle, spanning from data acquisition to alert emission and damage management. The paper describes how the pieces are put together inside the coherent environment provided by the STKO software, initially designed as a powerful interface to the OpenSees solver for ﬁnite element methods (FEM). Data is acquired through a network of MonStr sensors (produced by ASDEA Hardware), managed using artiﬁcial intelligence (AI). The data is then exposed to near-real-time analysis to obtain an accurate picture of the structural conditions, and the numerical model is updated continuously to reﬂect present conditions. When anomalies are detected by the AI-based classiﬁer, they are compared to the output provided by the FEM analysis to ensure reliability.
Generation Method of Multi-Regional Photovoltaic Output Scenarios-Set Using Conditional Generative Adversarial Networks

The uncertainty of photovoltaic (PV) output power has an increasing impact on power balance with the increase of installed capacity. The construction of day-ahead PV output scenarios-set is an important basis for the stochastic optimal scheduling of the power system. For the uncertainty modeling of multi-regional day-ahead PV output, a scenarios-set generation method based on improved conditional generation adversarial network (CGAN) is proposed. This method learns the potential spatio-temporal characteristics of the output power of PV clusters distributed in different regions by convolutional neural networks. Moreover, a mapping relationship between the input PV prediction results and the output scenarios-set is established. Thereafter, the scenarios-set with correlation characteristics for day-ahead multi-regional PV clusters is generated simultaneously. By comparing with the traditional Latin hypercube sampling (LHS) method, the results of the proposed method show the comprehensive advantages in terms of the uncertainty range and the spatial correlation coefficient.
Analysis of Kernel Redundancy for Soft Error Mitigation on Embedded GPUs

The use of state-of-the-art commercial processors such as graphical processing units (GPUs) is becoming increasingly common in the New Space industry to ensure high performance and power efficiency. However, commercial GPUs are not designed to operate in a harsh environment, and therefore, different protection techniques need to be applied to mitigate the effects of radiation, including those produced by single events. This article assesses the effectiveness of redundant kernel execution on tightly constrained embedded GPUs under proton irradiation, with results suggesting a significant improvement in the silent data corruption (SDC) cross Section without penalizing the stability of the whole system. In addition, the posterior error analysis shows that the CPU is the source of the majority of the events, which are mainly dominated by functional interrupts.
The evolution of medieval /ü/ and its dialectal variation

The medieval high, front, round vowel /u/ was phonetically realized in Greek until the 10 th century A.D. After that time a sound change took place, resulting in the phonetic realization of underlying /u/ with two variants, namely as [i] in most dialects and as [u] in the dialects of Old Athenian Complex. We claim that the underlying /u/ could not be realized anymore due to a phonological change that resulted in the promotion of the markedness constraint *[+front, +round], which is now undominated and forbids the realization of front, rounded vowels in the Greek phonological system. The variation among the output vowels [i] and [u] can be accounted for on the basis of co-phonologies or multiple grammars, namely by a single ranking where a set of unranked constraints obtain, giving multiple outputs. In the course of language evolution, however, it is the crucial ranking of the one constraint over the other that forces the activation of a distinct output vowel in every dialectal group.
Ocean literacy in Brazilian formal education: A tool for participative coastal management

Abstract Ocean literacy (OL) proposes to include ocean and marine environment-related content in school curricula. Such a topic has been deemed effective for citizens to develop actions and attitudes towards the health of marine ecosystems. This study aimed to verify the presence and frequency of OL principles and concepts in the Brazilian high school curriculum at the federal (National Curricular Parameters-PCN) and regional (Rio de Janeiro - Curricular Reference-RC) levels. Both PCN and RC contained OL-related content. Moreover, Biology and Geography were the subjects with the highest numbers of OL concepts, both in the PCN (26 and 27, respectively) and in the RC-RJ documents (28 and 24, respectively), while OL concepts were very little represented in History subject. A Mann–Whitney U-test did not indicate statistically significant differences in the number of concepts between PCN and RC-RJ (p = 0.54). A principal component analysis discriminated the documents according to subjects, regardless of their origin (federal or regional). These results provide an unbiased assessment of the relationship between the curriculum and OL in a strongly affected area (Rio de Janeiro State coastal zone [CZ]). Therefore, these results provide valuable support for managers seeking to promote effective CZ management practices and public compliance.
Miniaturized spectrometers using integrated optics

In this work, two different integrated-optics based spectrometer designs are presented. The first one is called interleaved arrayed waveguide grating (AWG) spectrometer provides large bandwidth (i.e. 30 nm) and high resolution (i.e. 0.1 nm) for a compact size (i.e. 2.4 cm × 3 cm). The second spectrometer is called ultra-high resolution Fourier transform (FT) spectrometer provides 1 pm of resolution for only 2 cm × 0.5 cm (1 cm2) device size at 1.3 μm. For the interleaved AWG spectrometer, the primary AWG has narrow closely spaced passbands (that equal the final desired channel spacing) that repeat N times in the desired wavelength range, using the frequency-cyclic nature of the AWG. The channel spacing of the secondary AWGs should be equal to the free spectral range (FSR) of the primary AWG. In this configuration, the FSR of the secondary AWGs defines the FSR of the overall configuration whereas the channel spacing (resolution) of the primary AWG defines the overall system resolution. The ultra-high resolution FT spectrometer is formed by sequentially-activated 60 Mach-Zehnder interferometers that are connected to photodetectors through very-low-loss beam combiners based on two-mode interference. The long optical delays are provided by tapping the propagating light out at certain locations on the optical waveguides by using electro-optically-controlled directional couplers. A design example with a spectral resolution of 500 MHz (~1 pm) and bandwidth of 15 GHz is presented for a device size of only 2 cm × 0.5 cm (1 cm2).
The algebra ℋ

We construct a combinatorial generalization of the Leray models for hyperplane arrangement complements. Given a matroid and some combinatorial blowup data, we give a presentation for a bigraded (commutative) diﬀerential graded algebra. If the matroid is realizable over C , this is the familiar Morgan model for a hyperplane arrangement complement, embedded in a blowup of projective space. In general, we obtain a cdga that interpolates between the Chow ring of a matroid and the Orlik–Solomon algebra. Our construction can also be expressed in terms of sheaves on combinatorial blowups of geometric lattices. As a key technical device, we construct a monomial basis via a Gr¨obner basis for the ideal of relations. Combining these ingredients, we show that our algebra is quasi-isomorphic to the classical Orlik–Solomon algebra of the matroid.