Miracle-dz/UniFAR-FTUs huggingface dataset:
Alzheimer's disease diagnosis via 5-layer Convolutional Neural Network and Data Augmentation

OBJECTIVES: Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious onset and one of the biggest challenges in geriatrics. Because the cause of the disease is unknown and there is currently no cure, AD Early diagnosis is particularly important. METHODS: In this paper, we built a 5-layer convolutional neural network based on deep learning technology. We used six data augmentation methods to increase the size of the training set. Batch normalization and dropout techniques are also used, which are respectively associated with the convolutional layer and the fully connected layer, Form convolution batch normalization (CB) and dropout fully connected (DOFC) block respectively. RESULTS: Our 5-layer CNN has shown excellent results on the training set, a sensitivity of 94.80%, a specificity of 93.98%, a precision of 94.04% and an accuracy of 94.39%, and has good performance compared with several other state-of-the-art methods. CONCLUSION: In terms of classification performance, our method performs better than 8 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer’s disease.
Noxious Mechanosensation

This article introduces a number of critical features of mechanical transduction and neuronal sensory coding, with particular reference to mechanical pain. High threshold mechanoreceptors (HTMRs) in skin are the first relay point for the transduction of noxious mechanical force into the nervous system, and their properties determine how and when nociceptive signal is relayed up to the central nervous system. The article describes the structure and physiology of the mechano-nociceptors and explains the peripheral molecular mechanisms of transduction leading to mechanical pain. In addition, this article also describes a set of computer models of HTMRs and relevant ion channels representing structural and biophysical features of the biological prototype. A better understanding of HTMRs’ function may lead to the development of novel therapeutic pathways in the treatment of chronic mechanical pain.
Identifikasi dan Keragaman Genetik Bakteri Asam Laktat dari Tapai Singkong berdasarkan Sekuen Gen 16S rRNA

Bakteri asam laktat (BAL) adalah kelompok bakteri gram-positif yang tidak membentuk spora dan dapat memfermentasikan karbohidrat untuk menghasilkan asam laktat. BAL berperan penting pada fermentasi bahan pangan dan penting bagi kesehatan. Tapai merupakan salah satu jenis pangan fermentasi tradisional Indonesia yang mengandung BAL. Namun, informasi tentang BAL dari Tapai masih terbatas. Oleh karena itu, tujuan penelitian ini adalah untuk mengidentifikasi dan mengkaji keragaman genetik BAL pada Tapai berdasarkan sekuen gen 16S ribosomal ribonucleic acid (rRNA). Media DeMan Rogosa and Sharp Agar (MRSA) digunakan untuk menumbuhkan BAL. Pasangan primer 63f dan 1387r digunakan untuk mengamplifikasi sekuen gen 16S rRNA. Sebayak 31 isolat BAL telah berhasil diisolasi. Berdasarkan sekuen gen 16S rRNA, 31 isolat BAL tersebut terdiri atas Lactobacillus fermentum (21 isolat), Weissella cibaria (3 isolat), W. confusa (6 isolat), dan W. paramesenteroides (1 isolat) dengan similaritas 95%-100%. Pohon filogenetik yang dikonstruksi berdasarkan sekuen gen 16S rRNA dapat menunjukkan hubungan keragaman BAL hingga tingkat spesies, namun tidak dapat membedakan hingga tingkat strain.
Estimation of the Catastrophic Risk using Mixture Models

⎯ Indonesia is one of the countries in the world that is susceptible to various types of natural disasters such as earthquakes, floods, and other natural disasters. These events do not occur often, yet can result massive financial loss. This risk of loss is referred to be catastrophic risk since it impacts not only the individual but also the government while also posing a threat to insurance companies if they do not have sufficient resources to make a payment of claims. However, due to the complexity and uncertainty of natural hazards, measuring this risk is quite challenging. This study employs a method for estimating catastrophic risk in Indonesia based on the Value-at-Risk (VaR) of total loss from natural disasters. A key issue for estimating VaR is to fit an appropriate distribution. Extreme value distribution, such as Generalized Pareto Distribution (GPD) has been used to assess the tail behavior of extreme loss. However, this distribution provides no information about the central behavior that may affect the estimation of the model parameter in GPD. Therefore, this paper employs mixture models that combine the parametric form of loss distributions such as gamma, Weibull, and lognormal distribution with GPD. The results reveal that VaR calculations differ significantly depending on the mixture model and confidence level used. In addition, the lognormal-GPD model is chosen as the best model that fits data best with the highest value of Log-likelihood.
Structure and Stress Field of the Lithosphere Between Pamir and Tarim

The Pamir plateau protrudes ∼300 km between the Tajik‐ and Tarim‐basin lithosphere of Central Asia. Whether its salient location and shape are caused by forceful indentation of a promontory of Indian mantle lithosphere is debated. We present a new local‐seismicity and focal‐mechanism catalog, and a P‐wave velocity model of the eastern part of the collision system. The data suggest a south‐dipping Asian slab that overturns in its easternmost segment. The largest principal stress at depth acts normal on the slab and is orientated parallel to the plate convergence direction. In front (south) of the Asian slab, a volume of mantle with elevated velocities and lined by weak seismicity constitutes the postulated Indian mantle indenter. We propose that the indenter delaminates and overturns the Asian slab, underthrusts the Tarim lithosphere along a compressive transform boundary, and controls the location and shape of the Pamir plateau.
New results on egalitarian values for games with a priori unions

ABSTRACT Several extensions of the equal division value and the equal surplus division value to the family of games with a priori unions have been proposed by Alonso-Meijide et al. [On egalitarian values for cooperative games with a priori unions. TOP. 2020;28:672–688]. In this study, we provide new axiomatic characterizations of these values. Furthermore, using the equal surplus division value in two steps, we propose a new coalitional value. The balanced contributions and quotient game properties generate a different modification of the equal surplus division value.
A molecular structure-informed viscoelastic constitutive model for natural rubber materials

This paper presents a molecular structure-informed viscoelastic constitutive equation that adopts the Doi–Edward’s tube model with coarse-grained molecular dynamics simulation and primitive path analysis. Since this model contains polymer physics-related parameters directly obtained from molecular simulations, it can reflect molecular information in predictions of the viscoelastic behavior of elastomers, unlike other empirical models. The proposed incremental formulations and constitutive stiffness matrix were implemented into implicit finite element analysis codes as a user-supplied material model and viscoelastic properties (storage, loss modulus, and tanδ ) were calculated from the constitutive equation. While obtaining polymer dynamics parameter of the molecular system, a relationship between self-diffusivity coefficient ( Dc ) and the polymerization degree of the polymer was confirmed. Furthermore, a series of parametric studies showed that increase of the primitive path length (L) and decrease of Dc have led to the strengthening of moduli and decrease of tanδ peak. Moreover, under the same condition, the shift of tanδ peak to low-frequency domain was observed, which implies a decline in free volume in the molecular system and an increase in the glass transition temperature.
‘Consciousnessoids’: clues and insights from human cerebral organoids for the study of consciousness

Abstract Human cerebral organoids (HCOs) are an in vitro three-dimensional model of early neural development, aimed at modelling and understanding brain development and neurological disorders. In just a few years, there has been a rapid and considerable progress in the attempt to create a brain model capable of showcasing the structure and functions of the human brain. There are still strong limitations to address, including the absence of vascularization that makes it difficult to feed the central layers of organoids. Nevertheless, some important features of the nervous system have recently been observed: HCOs manifest electrical activity, are sensitive to light stimulation and are able to connect to a spinal cord by sending impulses that make a muscle contract. Recent data show that cortical organoid network development at 10 months resembles some preterm babies’ electroencephalography (EEG) patterns. In the light of the fast pace of research in this field, one might consider the hypothesis that HCOs might become a living laboratory for studying the emergence of consciousness and investigating its mechanisms and neural correlates. HCOs could be also a benchmark for different neuroscientific theories of consciousness. In this paper, I propose some potential lines of research and offer some clues and insights so as to use HCOs in trying to unveil some puzzles concerning our conscious states. Finally, I consider some relevant ethical issues regarding this specific experimentation on HCOs and conclude that some of them could require strict regulation in this field.
Inferential statistics are descriptive statistics

There has been much discussion of a "replication crisis" related to statistical inference, which has largely been attributed to overemphasis on and abuse of hypothesis testing. Much of the abuse stems from failure to recognize that statistical tests not only test hypotheses, but countless assumptions and the entire environment in which research takes place. Honestly reported results must vary from replication to replication because of varying assumption violations and random variation; excessive agreement itself would suggest deeper problems, such as failure to publish results in conflict with group expectations or desires. Considerable non-replication is thus to be expected even with the best reporting practices, and generalizations from single studies are rarely if ever warranted. Because of all the uncertain and unknown assumptions that underpin statistical inferences, we should treat inferential statistics as highly unstable local descriptions of relations between assumptions and data, rather than as generalizable inferences about hypotheses or models. And that means we should treat statistical results as being much more incomplete and uncertain than is currently the norm. Rather than focusing our study reports on uncertain conclusions, we should thus focus on describing accurately how the study was conducted, what data resulted, what analysis methods were used and why, and what problems occurred.
An Effective Feature Extraction Mechanism for Intrusion Detection System

SUMMARY The development of an e ﬃ cient detection mechanism to determine malicious network tra ﬃ c has been a critical research topic in the ﬁeld of network security in recent years. This study implemented an intrusion-detection system (IDS) based on a machine learning algorithm to periodically convert and analyze real network tra ﬃ c in the campus environment in almost real time. The focuses of this study are on determining how to improve the detection rate of an IDS and how to detect more non-well-known port attacks apart from the traditional rule-based system. Four new features are used to increase the discriminant accuracy. In addition, an algorithm for balancing the data set was used to construct the training data set, which can also enable the learning model to more accurately reﬂect situations in real environment.
Detecting and Counting People without Mask with Deep Neural Network

In this work, we develop a real-time computer vision system to detect people and judge whether each person is wearing a mask or not. We construct a 2-stage algorithm based on deep convolutional neural networks, where the masks are treated as objects in an image. Furthermore, in order to improve the accuracy of recognizing masks when the human face occupies a large area of the image, we adopt the dilated convolution algorithm to solve this problem. Based on the recent research of COVID-19 for infection danger, this system can send dangerous signal level 1-3 due to the proportion of masked people in the captured image. Due to the report of infection danger criteria, this system can send an alarm of three levels with the borders at 20% and 50% of the people without masks in the area, which can notify people in the area as a safe, a little dangerous, or a particularly dangerous situation.
An Automatic Planning-Based Attack Path Discovery Approach from IT to OT Networks

With the convergence of IT and OT networks, more opportunities can be found to destroy physical processes by cyberattacks. Discovering attack paths plays a vital role in describing possible sequences of exploitation. Automated planning that is an important branch of artificial intelligence (AI) is introduced into the attack graph modeling. However, while adopting the modeling method for large-scale IT and OT networks, it is difficult to meet urgent demands, such as scattered data management, scalability, and automation. To that end, an automatic planning-based attack path discovery approach is proposed in this paper. At first, information of the attacking knowledge and network topology is formally represented in a standardized planning domain definition language (PDDL), integrated into a graph data model. Subsequently, device reachability graph partitioning algorithm is introduced to obtain subgraphs that are small enough and of limited size, which facilitates the discovery of attack paths through the AI planner as soon as possible. In order to further cope with scalability problems, a multithreading manner is used to execute the attack path enumeration for each subgraph. Finally, an automatic workflow with the assistance of a graph database is provided for constructing the PDDL problem file for each subgraph and traversal query in an interactive way. A case study is presented to demonstrate effectiveness of attack path discovery and efficiency with the increase in number of devices.
Modified T-piece jet nebulizer with high-flow nasal cannula in a severe COVID-19 patient with intensive care unit delirium

© 2021 Indian Chest Society | Published by Wolters Kluwer Medknow in the T-piece, and it has been connected to the nebulizer machine [Figure 1]. We set a flow rate of 40 L/min with fractional inspired oxygen of 0.6. Nebulization has been carried for over a period of 10 min and the T-piece has been disconnected from HFNC circuit and nasal cannula interface once the nebulization is completed. Throughout the procedure, the patient is resting and not irritable as there was no provocating factor, and nebulization has been carried successfully. She was started on tablet haloperidol 10 mg daily at night and then her delirium symptoms were relieved within 1 week. Although studies are not in large number when coming to nebulization with HFNC, some studies showed the advantage of nebulization carried out with HFNC. Reminiac et al.[3] in their study successfully carried out nebulization with HFNC using a mesh nebulizer and compared it with a jet nebulizer with face mask interface and found that nebulization through HFNC is noninferior to jet nebulizer with face mask interface. Imitazione et al.[4] found in their study that for those patients who had nebulization through HFNC recorded a significant improvement in quality of life. Valencia-Ramos et al.[5] concluded in their study that the use of a nebulizer in line with HFNC therapy showed a heightened level of comfort and satisfaction in patients with bronchiolitis.
Ab initio modeling of Raman and infrared spectra of calcite

Calcite is the most thermodynamically stable polymorphic phase of the CaCO3 crystal. It is widely used in modern optical instruments operating in the infrared and visible wavelengths of electromagnetic radiation. In particular, due to its anisotropic properties, calcite is used in polarization optics devices. Ab initio quantum mechanical modeling of the Raman and infrared spectra of calcite makes it possible to better understand the structure and nature of the chemical bonds of the compound, and find the optimal conditions for the effective use of unique properties of calcite in photonics tools. The calculations are performed within the framework of the density functional theory in the CRYSTAL program, using the “hybrid” B3LYP functional and the all-electronic bases of the STO-6G, PO-TZVP and BSD atomic orbitals of the Gaussian type. The obtained results for the elastic constants, Raman and infrared spectra of calcite agree satisfactorily with the available experimental data for the basic sets of POB-TZVP and BSD.
Performance Analysis on Popularity Based, Content Based and Collaborative Filtering Utilizing Recommendation Framework

In today's computerized world, it has become an irritating undertaking to locate the substance of one's loving in an interminable assortment of substance that are being devoured like art, education, media and so on. Then again there has been a developing development among the computerized substance suppliers who need to connect the same number of clients on their administration as feasible for the most extreme time. A tune proposal is significant in our public activity because of its highlights, for example, in building smart cities recommending a lot of melodies to clients dependent on their advantage, or the popularity of the tunes. In this paper we are proposing a song suggestion framework that can prescribe song to another client just as the other existing clients. We use Popularity Based, Content Based separating, and Collaborative Filtering, which is a blend of application of communication systems, to develop a framework that gives progressively exact proposals concerning melodies.
The concept of research and training simulator for testing car assistive devices for physically disabled drivers

Presented are the assumptions and a description of the work carried out as part of the action on the development of a project to assist the prevention of social exclusion of people with disabilities. The developed research and training simulator for a car with a weight of up to 3.5 tons will allow the study of the suitability of various solutions enabling the driving of a motor vehicle for people with upper and lower limb dysfunctions. This simulator equipped with a driver’s cockpit attached to the motion platform, platform, image projection system and application will be tested with the participation of 20 people.
Teachers’ Beliefs on Students’ Home Language as a Resource in EFL Classroom

This is the age of multilingual turn. It advocates the inclusion of indigenous languages that the children bring to school from home as resource. At present, use of students’ home language as a resource in teaching and learning of a new language has become a vexing issue. The teachers’ beliefs on any issue determine the types of teaching learning activities that they use in the classroom. In this context, this paper aims at exploring the Nepalese English language teachers’ beliefs regarding the use of students’ home language in English as a foreign language (EFL) classroom, and why or how they form their respective beliefs. For this, I collected data from five ‘Grade ten’ English language teachers of different community schools in Kathmandu, taking in-depth interview with them. The data were analyzed qualitatively creating themes. The analysis shows that the English language teachers regard home language as a resource in EFL classroom, and they do so on the basis of their own theoretical understanding and practical experience in teaching.
South African citizens’ self-assessed knowledge about the fourth industrial revolution

This study assessed the levels of knowledge of South African citizens about the fourth industrial revolution (4IR) to determine how organizations can assimilate the implications of 4IR to leapfrog the development of the country. The findings show that knowledge was higher among citizens with better socio-economic status, higher education, with in excess of R6 500 (US$464.29) household income per month, are employed full-time, and have access to uncapped internet data or could afford to spend more than R150 (US$10.72) on data per month (more than 1 GB). There were low levels of knowledge among unemployed citizens (excluding tertiary institution students and unemployed graduates) and entrepreneurs. This was concerning as these groups are the main target for the country’s economic development and growth. The study recommends increasing knowledge sharing about the 4IR and related technologies and raising awareness or advocating appropriate skills for future careers. This study serves as a baseline for the policymakers and other development agencies or stakeholders to effectively set a national agenda to respond to opportunities and challenges emerging with the 4IR.
Refugee-background students and the institutional responsibility of schools: a narrative discourse analysis of US education news media

ABSTRACT Necessitated by a heightened focus on global migration and its impact on educational realities across the globe, this study examined how educational experiences of students with refugee backgrounds are characterized in US educational news outlets. 385 articles from three online education news sites in the US were examined using a narrative-discursive approach. Analysis demonstrated how mutually dependent narrative patterns constructed students as resilient survivors, while schools were positioned as safe havens designed to support them. We show how these narrative patterns were accomplished through the use of common discursive features, positioning refugee student populations and the institutional responsibility of schools in bounded ways. We consider these findings in light of how broader discourses visible in news media narratives shape and reflect the reality of refugee-background students’ educational experiences in the US.
Mu rhythm suppression over sensorimotor regions is associated with greater empathic accuracy

Abstract When people encounter others’ emotions, they engage multiple brain systems, including parts of the sensorimotor cortex associated with motor simulation. Simulation-related brain activity is commonly described as a ‘low-level’ component of empathy and social cognition. It remains unclear whether and how sensorimotor simulation contributes to complex empathic judgments. Here, we combine a naturalistic social paradigm with a reliable index of sensorimotor cortex-based simulation: electroencephalography suppression of oscillatory activity in the mu frequency band. We recruited participants to watch naturalistic video clips of people (‘targets’) describing emotional life events. In two experiments, participants viewed these clips (i) with video and sound, (ii) with only video or (iii) with only sound and provided continuous ratings of how they believed the target felt. We operationalized ‘empathic accuracy’ as the correlation between participants’ inferences and targets’ self-report. In Experiment 1 (US sample), across all conditions, right-lateralized mu suppression tracked empathic accuracy. In Experiment 2 (Israeli sample), this replicated only when using individualized frequency-bands and only for the visual stimuli. Our results provide novel evidence that sensorimotor representations—as measured through mu suppression—play a role not only in low-level motor simulation, but also in higher-level inferences about others’ emotions, especially when visual cues are crucial for accuracy.