Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioinspired Crocodile Skin-Based Flexible Piezoelectric Sensor for Three-Dimensional Force Detection

The demand for flexible pressure sensors is increasing with the rapid development of wearable smart devices, dexterous manipulators, and human–machine application interfaces. Despite the development of flexible sensors capable of effectively sensing multidimensional forces, the challenge of directly restoring information from objects in contact remains significant. Inspired by the tactile sensory organ of crocodile skin, this study reports a 3-D force sensing sensor, mainly composed of barium titanate polyvinylidene difluoride and silicone, to form a piezoelectric elastomer layer. Under normal and shear loads, the sensing mechanism relies on the change in output voltage between the semi-spherical electrodes and the piezoelastomer. An interlocking structure detects different directions of shear force. The sensor exhibits excellent directional resolution, with a normal force sensitivity of 20.42 ± 2 mvN−1 in the pressure range of 0.1–1.5 N and directional sensing sensitivity of 4.40, 5.29, 4.44, and 4.84 mVN−1 for the four faces, respectively. The sensor also exhibits high linearity. Further demonstrations included testing the sensor in molds over various rough surfaces. The sensor provided timely feedback to distinguish between different surface roughnesses and force directions—expected to be a self-powered wearable device for future human–computer interaction or personalized identification applications.
CRISPR interference to evaluate modifiers of C9ORF72-mediated toxicity in FTD

Treatments for neurodegenerative disease, including Frontotemporal dementia (FTD) and Amyotrophic lateral sclerosis (ALS), remain rather limited, underscoring the need for greater mechanistic insight and disease-relevant models. Our ability to develop novel disease models of genetic risk factors, disease modifiers, and other FTD/ALS-relevant targets is impeded by the significant amount of time and capital required to develop conventional knockout and transgenic mice. To overcome these limitations, we have generated a novel CRISPRi interference (CRISPRi) knockin mouse. CRISPRi uses a catalytically dead form of Cas9, fused to a transcriptional repressor to knockdown protein expression, following the introduction of single guide RNA against the gene of interest. To validate the utility of this model we have selected the TAR DNA binding protein (TDP-43) splicing target, stathmin-2 (STMN2). STMN2 RNA is downregulated in FTD/ALS due to loss of TDP-43 activity and STMN2 loss is suggested to play a role in ALS pathogenesis. The involvement of STMN2 loss of function in FTD has yet to be determined. We find that STMN2 protein levels in familial FTD cases are significantly reduced compared to controls, supporting that STMN2 depletion may be involved in the pathogenesis of FTD. Here, we provide proof-of-concept that we can simultaneously knock down Stmn2 and express the expanded repeat in the Chromosome 9 open reading frame 72 (C9ORF72) gene, successfully replicating features of C9-associated pathology. Of interest, depletion of Stmn2 had no effect on expression or deposition of dipeptide repeat proteins (DPRs), but significantly decreased the number of phosphorylated Tdp-43 (pTdp-43) inclusions. We submit that our novel CRISPRi mouse provides a versatile and rapid method to silence gene expression in vivo and propose this model will be useful to understand gene function in isolation or in the context of other neurodegenerative disease models.
Quadcopter Guidance Law Design using Deep Reinforcement Learning

This paper examines the quadcopter-target inter-ception problem and proposes a deep reinforcement learning-based approach to solve this problem. The quadcopter-target interception problem is formulated by constructing an Markov Decision Process (MDP) which consist of states, possible actions, transition probabilities and real-valued reward function. The relative position, velocity and angle information between the quadcopter and the target is used when the agent selects the appropriate actions to intercept the target. Permissible acceleration commands are defined as the action space and closing velocity is used in the definition of real-valued reward function. The proposed algorithm is compared with the True Proportional Navigation (TPN) and Model Predictive Control (MPC) algorithms. Numerical simulation results confirm that proposed approach is a suitable solution for the quadcopter guidance problem.
Editorial: Physical exercise related to student's academic performance

COPYRIGHT © 2023 Honório, Batista, Santos and Vandoni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Physical exercise related to student’s academic performance
An Intelligent VGG-16 Framework for Brain Tumor Detection Using MRI-Scans

Although it is a difficult undertaking, using Convolutional Neural Networks (CNN) to detect brain tumors from MRI images has the potential to significantly improve patient outcomes. This article aims to properly diagnose various forms of brain tumor diseases by training a deep learning model with a huge dataset of MRI scans. A promising method in the realm of medical imaging is the VGG-16 model for the identification of brain tumors using MRI data. A popular deep convolutional neural network for image recognition applications is the VGG-16 which is proposed in this article. This system has performed remarkably well in identifying brain tumors from MRI data. The VGG-16 model can correctly diagnose many forms of brain cancers, including glioblastoma, meningioma, and pituitary tumors. It was trained on a sizable dataset of MRI images. Even when the tumors are tiny or situated in intricate regions of the brain, the model has demonstrated good sensitivity and specificity in identifying them. The VGG-16 model has a lot of potential for precise and early brain tumor diagnosis, which might improve patient health, and outcomes and perhaps save lives.
Comparative Study of Linearized Analysis Frameworks for Mid-Rise TDCs

The time-to-digital converter (TDC) that is used as the phase detector in a digital phase locked loop (DPLL) exhibits a quantization nonlinearity. It is crucial to perform an accurate linearized estimation for its effective gain and power of quantization error (QE) to facilitate the linearity analysis of DPLLs at a system level. This paper compares and contrasts two state-of-the-art linearized analysis frameworks, i.e., a minimum mean square error (MMSE) estimator and a small-signal approximation, whilst taking into account the interaction between the characteristics of the TDC and the statistical properties of the input time jitter. Behavioral simulation results confirm the accuracy of the MMSE estimation method and invalidate the other one. The verified method can deliver accurate linearized analysis for any quantizer where the input signals have known probability distributions.
Zone Wise Random Space Vector Based Spread Spectrum PWM For Improving Inverter Non-Linearities

This paper illustrates the randomization of inactive vectors with fixed switching frequencies in different spatial regions inside the sector for compensating the inverter's non-linearity, such as the current ripple and dead time effects, which are not discussed in existing literature. It improves the harmonic spreading of discrete switching frequency components into continuous ones. Within every segment of the spatial vector diagram, the perimeters of the spatial domains for each sequence are adjusted as necessary through the randomization of the dormant vectors. To exhibit the superiority of the novel scheme, the voltage and current spectra are presented along with a current ripple analysis of different regions in terms of stator flux ripple.
Spectrométrie de réflexion diffuse en infrarouge moyen : apports à l'analyse des poudres et des interfaces

La reflexion diffuse du rayonnement infrarouge moyen est souvent consideree comme etant la somme de deux composantes : la reflexion de surface et la reflexion de volume. Dans le cas de poudres constituees de particules de diametre moyen superieur a la longueur d'onde, nous avons degage les principaux phenomenes optiques caracterisant la reflectance diffuse, suivant la nature des interfaces formees dans ces milieux discontinus. La reflectance ainsi que le volume reel de diffusion de poudres de materiaux non absorbants ont ete caracterises en fonction de la granulometrie moyenne et de l'indice de refraction. En accord avec les theories statistiques (melamed, simmons,. . . ), une forte proportion de la lumiere est piegee par reflexion totale a l'interieur des particules supposees spheriques et de surface rugueuse (diffuseur lambertien). La reflectance de ces poudres passe ainsi par un maximum lorsque l'indice de refraction croit. Le volume concerne par la diffusion est tres superieur a celui defini par la surface eclairee par le faisceau incident. Ceci s'explique par le transit de la lumiere de particules en particules par reflexion totale frustree aux interfaces de contact du reseau cubique simple qu'elles forment apres un leger tassement.
Defending Your Mobile Fortress: An In-Depth Look at on-Device Trojan Detection in Machine Learning: Systematic Literature Review

Mobile app trojans are becoming an increasingly serious threat to personal information security. They can cause severe damage by exposing sensitive and personally-identifying information to malicious actors. This paper’s contribution is a comprehensive review of the attack vectors for trojan attacks, and ways to eliminate the risks posed by attack vectors and generate settlement automatically. As such, such attacks must be prevented. In this study, we explore to find how to detect the trojan attack in detail, and the way that we know in machine learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. We review literature from several publications and analyze the use of machine learning for on-device trojan detection. This review provides evidence for the effectiveness of machine learning in detecting such threats. The current trend shows that signature-based analysis using various metadata, such as permission, intent, API and system calls, and network analysis, are capable of detecting trojan attacks before and after the initial infection
EfficientNetB3-Adaptive Augmented Deep Learning (AADL) for Multi-Class Plant Disease Classification

Plant diseases can significantly impact agricultural productivity if not promptly identified and treated. Traditional plant disease classification methods are often challenging and time-consuming, making the identification of diseases a challenging task. This paper aims to bridge research gaps and address challenges in existing methodologies by proposing an efficient, effective multi-class plant disease classification approach. The research explores the application of pre-trained deep convolutional neural networks (CNNs) in this classification task, utilizing an open dataset comprising 52 categories of various diseases and healthy plant leaves. This study evaluated the performance of pre-trained deep CNN models, including Xception, InceptionResNetV2, InceptionV3, and ResNet50, paired with EfficientNetB3-adaptive augmented deep learning (AADL) for precise disease identification. Performance assessment was conducted using parameters such as batch size, dropout, and epoch counts, determining their accuracy, precision, recall, and F1 score. The EfficientNetB3-AADL model outperformed the other models and conventional feature-based methods, achieving a remarkable accuracy of 98.71%. This investigation highlights the potential of the EfficientNetB3-AADL model in offering accurate, real-time disease diagnostics in agricultural systems. The findings suggest that transfer learning and augmented deep learning techniques enhance the accuracy and performance of the model.
Detection of False Data Injection in Smart Water Metering Infrastructure

Smart water metering (SWM) infrastructure collects real-time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-tier approach of stateless and stateful detection, reducing false alarms without significantly sacrificing the attack detection rate. We validate our approach using real-world water usage data of 92 households in Alicante, Spain for varying attack scales and strengths and prove that our method limits the impact of undetected attacks and expected time between consecutive false alarms. Our results show that even for low-strength, low-scale deductive attacks, the model limits the impact of an undetected attack to only C0.2199375 and for high-strength, low-scale camouflage attack, the impact of an undetected attack was limited to C1.434375
Dendrite growth and spacing in succinonitrile-acetone alloys

These Ecole polytechnique federale de Lausanne EPFL, n° 615 (1986)Laboratoire de simulation des materiaux Reference doi:10.5075/epfl-thesis-615Print copy in library catalog Record created on 2005-03-16, modified on 2017-05-10
THEORETICAL MODEL FOR VIRTUAL REALITY JOURNALISM RESEARCH (MVRJR)

ABSTRACT – Virtual reality (VR) journalism promises to impact the cognitive, affective and behavioral faculties of audiences. However, research on VR journalism does not have its own theoretical frameworks. Therefore, this article proposes a model, the MIPRV, which integrates the Uses and Gratifications Theory (U&G), Affordances, and the Expectation-Confirmation Model (ECT) to analyze the three stages of the ‘journey’ (separation, immersion and return) that a user experiences when exposed to a VR news story. The model achieves, in addition to rereading in light of the ethical and normative principles of journalism several of the concepts associated with VR (e.g., presence, embodiment, empathy, etc.), formulating affordances (e.g., storyliving) and gratifications/effects unique to non-fiction stories (e.g., news values and framing, authenticity of the news events and content credibility, knowledge and understanding of the event). RESUMO – O jornalismo de realidade virtual (RV) promete impactar, mais ou menos, as faculdades cognitivas, afetivas e comportamentais das audiências. No entanto, a pesquisa sobre jornalismo de realidade virtual não possui quadros teóricos próprios. Portanto, este artigo propõe um modelo, o MIPRV, que integra Usos e Gratificações (U&G), Potencialidades Tecnológicas da mídia (affordances, em inglês) e o Modelo de Confirmação de Expectativas (ECT, em inglês) para analisar as três etapas da ‘jornada’ (separação, imersão e retorno) que experimenta um usuário quando exposto a uma notícia de RV. O modelo consegue, além de reler à luz dos princípios éticos e normativos do jornalismo, vários dos conceitos associados à RV (por exemplo, presença, corporificação, empatia, etc.), formular potencialidades tecnológicas (por exemplo, storyliving) e gratificações/efeitos exclusivo para histórias de não ficção (por exemplo, valores e enquadramento da notícia, autenticidade do fato e credibilidade do conteúdo, conhecimento e compreensão do evento). RESUMEN – El periodismo de realidad virtual (RV) promete impactar, más o menos, las facultades cognitivas, afectivas y conductuales de las audiencias. Su investigación, empero, carece de modelos teóricos propios. En este artículo se propone un modelo, el MIPRV, que integra las teorías de Usos y Gratificaciones (U&G), de Potencialidades tecnológicas de los medios (affordances, en inglés) y el Modelo de Confirmación de Expectativas (MCE) para analizar las tres etapas del ‘viaje’ (separación, inmersión y retorno) que experimenta un usuario al exponerse a una historia periodística de RV. El modelo consigue, además de releer a la luz de los principios éticos y normativos del periodismo varios de los conceptos asociados a la RV (ej.: presencia, encarnación, empatía, etc.), formular potencialidades tecnológicas (ej.: narrativa viva) y gratificaciones/efectos exclusivos de los relatos de no ficción (ej.: valores y encuadres informativos, autenticidad del acontecimiento noticioso y credibilidad en el contenido, conocimiento y comprensión del acontecimiento).
Relação entre a idade, o risco de queda e o nível de confiança no equilíbrio corporal com a função dos canais semicirculares

RESUMO Objetivo verificar a relação entre o risco de queda, o nível de confiança nas atividades que envolvem equilíbrio e a idade com os valores de ganho e simetria dos canais semicirculares (CSCs), por meio do Vídeo Teste do Impulso Cefálico (vHIT), em pacientes com disfunção vestibular periférica. Método estudo transversal, composto por 12 indivíduos submetidos à avaliação funcional por meio da Activities-specific Balance Confidance Scale (ABC Scale) e do Dynamic Gait Index (DGI) e avaliação vestibular com o vHIT. Os resultados foram comparados por meio do Coeficiente de Correlação de Spearman. Resultados observou-se predomínio do gênero feminino (75%), com média de idade de 59 anos e 8 meses. A média de escore no DGI foi de 19,70 pontos e 43,65% na ABC Scale, característico de baixo nível de confiança. Observou-se correlação de grau moderado entre os parâmetros idade com o DGI, DGI e ABC Scale com os valores de ganho e simetria dos CSCs do vHIT. Conclusão Observaram-se relações entre o maior risco de queda em pacientes com hipofunção vestibular e idade avançada e entre o baixo nível de confiança para realizar atividades diárias diante da assimetria de ganho dos CSCs.
Down the Tubes: Financial Distress, Bankruptcy, and Industrial Water Pollution

How do firms’ efforts to prevent harm to third parties change as they approach, enter, and then exit bankruptcy? To help answer this, I investigate a panel of roughly 350 US firms, all of which declare bankruptcy and are regulated under the Clean Water Act (CWA) for pollutants they release into public waterways. As these firms approach bankruptcy, the rate at which they release pollutants beyond their permitted limit increases by 50%. Once firms file for bankruptcy, their compliance dramatically improves, returning to a baseline from well before they filed for bankruptcy. I explore mechanisms that may explain this pattern and conclude that the most likely causes are a nexus of moral hazard problems and financing frictions affecting thinly capitalized firms. These findings lend support to a range of policy interventions, such as changes in the priority of bankruptcy claims, that can help to alleviate moral hazard problems for thinly capitalized firms. Furthermore, the dramatic compliance improvements that occur after firms file for bankruptcy point to a less-recognized role of corporate bankruptcy in helping to prevent public harms that can occur as a result of firms operating while in significant financial distress. This suggests an added public benefit to bankruptcy policies that encourage firms to file for bankruptcy early, and an added detriment to policies and practices that seek to delay firms’ resolution of financial distress.
TSM-CV: Twitter Sentiment Analysis for COVID-19 Vaccines Using Deep Learning

The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our goal is to use the deep learning (DL) technique to understand and elucidate their feelings. Due to the advancement of IT and internet facilities, people are socially connected to explain their emotions and sentiments. Among all social sites, Twitter is the most used platform among consumers and can assist scientists to comprehend people’s opinions related to anything. The major goal of this work is to understand the audience’s varying sentiments about the vaccination process by using data from Twitter. We have employed both the historic (All COVID-19 Vaccines Tweets Kaggle dataset) and real (tweets) data to analyze the people’s sentiments. Initially, a preprocessing step is applied to the input samples. Then, we use the FastText approach for computing semantically aware features. In the next step, we apply the Valence Aware Dictionary for sentiment Reasoner (VADER) method to assign the labels to the collected features as being positive, negative, or neutral. After this, a feature reduction step using the Non-Negative Matrix Factorization (NMF) approach is utilized to minimize the feature space. Finally, we have used the Random Multimodal Deep Learning (RMDL) classifier for sentiment prediction. We have confirmed through experimentation that our work is effective in examining the emotions of people toward the COVID-19 vaccines. The presented work has acquired an accuracy result of 94.81% which is showing the efficacy of our strategy. Other standard measures like precision, recall, F1-score, AUC, and confusion matrix are also reported to show the significance of our work. The work is aimed to improve public understanding of coronavirus vaccines which can help the health departments to stop the anti-vaccination leagues and motivate people to a booster dose of coronavirus.
Leveraging Virtualization Technology in Teaching Cyber Security Courses in Sub-Saharan Africa

Teaching and learning can be effective and enjoyable if the course is well-packaged and presented. This was the experience of students and teachers participating in our hands-on Cyber Security course. Effective teaching of Cyber Security requires practical demonstration of attack patterns and their related countermeasures. This implies that outstanding and modern laboratories must be in place to teach and learn the necessary principles and practices effectively. Unfortunately, newly established universities in Sub-Saharan Africa need more funds for establishing and maintaining such laboratories. However, several researchers have shown that virtualization technology (VT) can enhance available but limited physical computing laboratories as an alternative to complex physical laboratories with huge procurement costs. This paper proposes an ad hoc and mobile laboratory for teaching practical principles in Cyber Security. The report aims to demonstrate the effectiveness of virtualization technology in teaching Cyber Security in newly established universities in Sub-Saharan Africa with zero to minimal computer laboratory infrastructure.
A Novel Push-Pull Input Buffer for Wideband ADCs with Improved High-Frequency Linearity

In this paper, we present a novel input buffer based on the push-pull architecture for an 11-bit 2-GS/s 8x time-interleaved ADC. The proposed buffer features an auxiliary follower which is used to drive the body terminals of the main push-pull output transistors, removing their non-linear contribution at the output node, with a negligible overhead in terms of power consumption. The ADC features a 0.9-V reference voltage and requires an input common-mode voltage of 0. 45V. The proposed buffer, designed in a 28-nm CMOS technology, achieves better than 73-dB Spurious-Free Dynamic Range (SFDR) in the 1-GHz Nyquist bandwidth, which is up to 6.3dB better than the conventional push-pull topology, burning 38mW from +1.8/-1V supplies, including the bias circuit.