Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumers' Use of Smartphone Technology for Travel and Tourism in a COVID Era

Mobile phone technology has become a necessary component for today's travellers. Information and communication technologies (ICTs) have substantially affected tourism and hospitality consumers over the past two decades. Mobile technologies such as smartphones, tablets, and mobile applications have become travellers' primary access to information. This study focuses on mobile technologies such as smartphones and mobile applications (apps) and consumers' use of mobile technology when travelling. A scoping review following PRISMA guidelines was used to answer the research question; "How do tourism consumers use mobile technologies for travel and tourism during the COVID era? " This study will identify and analyse any relationships, patterns, trends, and gaps in the literature. Peer-reviewed journal articles from the COVID era (2020 to 2022) were included in this study. Articles were sourced using the keywords listed below. The full articles were imported into NVivo, and the main themes and subthemes were extracted from the data and reported using an inductive qualitative thematic analysis. The results from this study identified "food" as the main theme and "food delivery" as the most frequent subtheme. Food, tourism, transportation, Fourth Industrial Revolution (4IR), Hotel Operations, and Shopping were the top 6 themes. The 4IR is changing how smartphone consumers use their devices for travel and tourism. In the COVID Era, Smartphone technology has been recognised as a solution to maintaining safe distancing and contactless transactions. This research will benefit tourism operators and policymakers to remain competitive in an ever-changing environment during the COVID era
NeuroScrub+: Mitigating Retention Faults Using Flexible Approximate Scrubbing in Neuromorphic Fabric Based on Resistive Memories

Neuromorphic computation-in-memory fabric based on emerging nonvolatile memories considered an attractive option to accelerate neural networks (NNs) in hardware as they provide high-performance, low-power, and reduced data movement. Although nonvolatile resistive memories (NVMs) offer many benefits, they are susceptible to data retention faults, where previously stored data are not retained after a certain amount of time due to external influence. These faults are more likely to happen unidirectional and severely impact the inference accuracy of the hardware implementation of NNs since the synaptic weights stored in the NVMs are subject to retention faults. In this work, we propose an approximate scrubbing technique for NVM-based neuromorphic fabric to mitigate unidirectional retention faults with virtually zero storage overhead depending on the definition of scrub area for multilayer perceptron (MLP) and convolutional NNs (CNNs). The training of the NNs is adjusted accordingly to meet the requirements of the proposed approximate scrubbing scheme. On different benchmarks, the proposed scrubbing approach can improve the inference accuracy up to 85.51% for MLP and 87.76% for CNN over the expected device operational time with negligible storage overhead.
New Spectrally Constrained Sequence Sets With Optimal Periodic Cross-Correlation

Spectrally constrained sequences (SCSs) play an important role in modern communication and radar systems operating over non-contiguous spectrum. Despite numerous research attempts over the past years, very few works are known on the constructions of optimal SCSs with low cross-correlations. In this paper, we address such a major problem by introducing a unifying framework to construct unimodular SCS families using circular Florentine rectangles (CFRs) and interleaving techniques. By leveraging the uniform power allocation in the frequency domain for all the admissible carriers (a necessary condition for beating the existing periodic correlation lower bound of SCSs), we present a tighter correlation lower bound and show that it is achievable by our proposed SCS families including multiple SCS sets with zero correlation zone properties.
An Explicit Method for Inverse Reconstruction of Equivalent Current Dipoles and Quadrupoles

The purpose of the neuromagnetic inverse problem is to reconstruct primary neural current from measured MEG data. It is known that this inverse problem is ill-posed: uniqueness of the solution to the inverse problem is not guaranteed without a priori assumptions on the current source (Fokas et al. (2004)), and, even when using the source model that can be uniquely reconstructed, the obtained solution changes very sensitively depending on the noise contained in MEG data. Thus, employment of a stable algorithm is highly required in order that MEG becomes a non-invasive brain monitoring tool with not only high temporal resolution but also high spatial resolution. Basically, conventional methods are categorized into two groups: parametric approaches and imaging approaches. See the detailed list of references in Baillet et al. (2001). The former methods assume that the current source can be represented by a relatively small number of equivalent current dipoles (ECDs). This source model is shown to be uniquely reconstructed from radial MEG data, except the radial component of the dipole moment, when the head is assumed to consist of concentric spheres. The usual algorithm for this source model is the non-linear least-squares method that minimizes the squared error of data and the forward solution. An advantage of this algorithm is that the parametric description allows us the accurate estimation of the center position of the activated region. However, the problems of this algorithm are: 1) it requires an initial solution, 2) it requires an iterative computing of forward solution, 3) it is often trapped by the local minimum of the cost function, which gives a solution far from the true one, 4) estimation of the number of ECDs is difficult, and 5) spatial extent of the source is not considered. The secondmethods, imaging approaches, assume that there exist current dipoles at the nodes of artificial meshes on the cerebral surface, and solve a system of linear equations for the dipole moments at the fixed positions. An advantage of this method is that it can describe the spatial distribution of the neural current. However, the problems of this algorithm are: 1) the solution is not unique, 2) adding a regularization term often gives a unique but over-smoothed solution, 3) choice of the regularization parameters strongly affects the solution. Recently, a method with the multipolar representation of the source has been developed that incorporates some of the advantages of the above twomethods, and has attracted considerable attention (Irimia et al. (2009); Jerbi et al. (2002; 2004); Nara (2008a); Nolte et al. (1997; 2000)). In this model, instead of expressing the current source by an equivalent current dipole, 6
Tibetan Pastoralism under Climate and Socio-economic Change: Carbon Footprint Assessment and beyond - Research Plan and Preliminary Results

: As the “Third Pole” of the world, the Tibetan Plateau is especially vulnerable to anthropogenic environmental disturbances and climate change variations. With an alpine grassland dominated landscape, nomadic grazing is one of the major anthropogenic activities on the Tibetan Plateau. In this study, we present a research plan with preliminary results that aim to quantify the human carbon footprint in a typical Tibetan nomadic area (Namtso village, Damsung County) through semi-structured interviews and on-site fieldwork, complementary to using existing data, which may suffer from restrictions and inaccuracies due to the special ecological conditions of the Tibetan Plateau and the nomadic characteristic of the inhabitants. After obtaining a detailed first-hand inventory list of the activities of human and grazing animals, the method of systems process analysis and life cycle analysis can be used to calculate the annual carbon footprint of a nomadic grazing unit, as well as the water footprint, energy footprint, and land footprint of the nomadic activities on the Tibetan Plateau. The obtained results will provide a comprehensive dataset and analysis of Tibet’s nomadic pastoralist activities, which is conducive to a better understanding of the linkages between the ecological and economic activities on the Earth’s third pole and global climate change.
Remarks on the stability of a class of TCP-like congestion control models

This note addresses the global asymptotic stability of a class of nonlinear dynamical systems including delays in their representations. These systems are encountered in the modelling of the congestion control mechanisms for the Internet [F. Kelly, 2001], [S. Deb, et al., 2002]. The analysis makes use of some appropriate Lyapunov-Krasovskii functionals. The derived conditions are simple, and easy to check.
Analisis Kepuasan Pelanggan Terhadap Kualitas Pelayanan di PT. X.

Kepuasan pelanggan memiliki peran penting dalam mencapai kesuksesan suatu organisasi yang menawarkan produk ataupun layanan jasa. Hal ini dikarenakan kepuasan pelanggan adalah kunci suatu usaha dapat terus berjalan maka dari itu pemilik usaha perlu memenuhi ekspektasi para pelanggan terhadap pelayanan yang diberikan. Sehingga dapat disimpulkan bahwa meraih kepuasan pelanggan adalah dengan memberikan pelayanan yang berkualitas. Penelitian ini bertujuan untuk mengetahui bagaimana tingkat kepuasan pelanggan melalui beberapa atribut kualitas pelayanan di PT. X. Responden pada penelitian ini adalah para pelanggan di PT. X dan ditentukan dengan disproportionate stratified random sampling. Variabel dependen dalam tinjauan ini adalah kepuasan pelanggan sedangkan variabel independen adalah 5 komponen nilai yang terdiri dari responsive, reliability, tangible, assurance dan emphaty. Pengumpulan data dilakukan dengan pengamatan, wawancara serta menyebarkan kuesioner dan diolah dengan menggunakan metode Service Performance (SERVPERF). Hasil dari penelitian ini menyatakan tingkat kepuasan pelanggan terhadap kualitas sebesar 82,185% dan terdapat 3 atribut pelayanan utama yang dapat mempengaruhi kepuasan pelanggan yaitu atribut kemampuan karyawan dalam menjelaskan masalah, kemampuan karyawan dalam memenuhi kebutuhan pelanggan serta atribut kecepatan karyawan dalam pemberian solusi
Normal Inverse Gaussian Features for EEG-Based Automatic Emotion Recognition

Electroencephalography (EEG)-based emotion recognition is crucial in the domain of human–computer interaction (HCI), which gained significant attention in recent years. However, the nonstationarity and chaotic nature of the EEG signals pose challenges and restrict the state-of-the-art techniques from precisely identifying distinct emotional states from the EEG data and hence offer limited emotion recognition performance. To capture the underlying nonlinear characteristics of EEG, this study employed a novel local mean decomposition (LMD) algorithm, which decomposes EEG signals into product functions (PFs). Further PFs are modeled by normal inverse Gaussian (NIG) probability density function (pdf) parameters. Thus, these pdf features are fed to an optimized Adaboost classifier developed with the help of a cross-validation (CV) approach. The novelty of the work lies in the NIG modeling of LMD-domain PFs to identify specific emotions from the EEG signals. The significance of the NIG parameters is illustrated by qualitative, pictorial, and statistical analyses. To assess the efficiency of the proposed approach, intensive experiments are conducted on open-source datasets, Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED), SEED-IV, and Database for Emotion Analysis of Physiological Signals (DEAP). The emotion recognition performance is evaluated in terms of heat maps, receiver operating characteristics (ROCs), and accuracy. The proposed emotion recognition system outperformed the state-of-art methods and achieved a maximum accuracy of 97.3%, 98%, and 98.6% with the CV approach and 93.23%, 94.87%, and 95.58% for the cross-subject validation (CSV) approach using DEAP, SEED, and SEED-IV datasets, respectively.
Aplicação da Análise de Sentimento para Avaliar Mensagens Significativas em um Ambiente Colaborativo: um estudo de caso no ambiente Collabora

Este artigo apresenta a aplicação da análise de sentimentos em uma base de dados que contém mensagens em chats de atividades realizadas por alunos no objeto virtual de aprendizagem colaborativa Collabora. A implementação das etapas para avaliar as mensagens significativas foi em linguagem Python com o uso de duas ferramentas de análise de sentimentos (NTLK e spaCy). Os dados processados pelas ferramentas foram comparados entre eles e aplicados ao cálculo de colaboração, reformulado do trabalho de Ishikawa (2018), a fim de criar uma métrica a partir das mensagens com sentimentos. O resultado permitiu verificar que o sentimento total das mensagens do grupo ou do aluno, podem refletir no comprometimento do aluno com a disciplina ou exercício proposto.
Host Immune Responses to Arthritogenic Alphavirus Infection, with Emphasis on Type I IFN Responses

Arthritogenic alphaviruses, such as Ross River virus, chikungunya virus and O’nyong-nyong virus, cause endemic disease globally and are a major public health concern. The hallmarks of arthritogenic alphavirus disease are debilitating pain, and potentially chronic inflammation of the muscles, thus influencing quality of life. The type I IFN response is a major component of the innate immune response against arthritogenic alphaviruses, and is essential in inhibiting viral replication and dissemination. Type I IFNs are induced during early stages of infection and are essential for the activation of the antiviral innate immune response. They also link the innate immune response and the activation of adaptive immunity. This review focuses on the host immune response, particularly that involving type I IFN, in arthritogenic alphavirus disease.
Assessing the phenotypic variation, heritability and genetic advance in bread wheat (Triticum aestivum L.) candidate lines grown under rainfed semi-arid region of Algeria

The genetic improvement of any breeding population largely depends upon the magnitude of genetic variability present. This study was carried out to estimate parameters of the genetic variation among 13 quantitative traits of bread wheat evaluated at INRAA-Setif institute (Algeria) during the 2020–2021 crop season in a rainfed environment. 34 genotypes including four control checks were planted in a randomized complete block design with three replications. Genotypes showed significant variations for almost all the studied traits. Proline content, spikes weight, and grain yield exhibited a high genotypic coefficient of variation along with moderate to high heritability coupled with a high genetic gain, suggesting the implication of additive gene action. The number of spikes, spikes weight, and thousand kernel weight were significantly and positively correlated with grain yield at both phenotypic and genotypic levels. Path analysis results showed that spikes weight is an important route through which most of the measured traits influenced indirectly grain yield. Lines L1, L20, L28, L16, and L18 exhibited a sizeable grain yield advantage, which suggests they are potential candidates for future release and could be incorporated into the wheat breeding programs as parents to improve yield in the rainfed environments of Algeria
Endometrial Cancer-Adjacent Tissues Express Higher Levels of Cancer-Promoting Genes than the Matched Tumors

Molecular alterations in tumor-adjacent tissues have recently been recognized in some types of cancer. This phenomenon has not been studied in endometrial cancer. We aimed to analyze the expression of genes associated with cancer progression and metabolism in primary endometrial cancer samples and the matched tumor-adjacent tissues and in the samples of endometria from cancer-free patients with uterine leiomyomas. Paired samples of tumor-adjacent tissues and primary tumors from 49 patients with endometrial cancer (EC), samples of endometrium from 25 patients with leiomyomas of the uterus, and 4 endometrial cancer cell lines were examined by the RT-qPCR, for MYC, NR5A2, CXCR2, HMGA2, LIN28A, OCT4A, OCT4B, OCT4B1, TWIST1, STK11, SNAI1, and miR-205-5p expression. The expression levels of MYC, NR5A2, SNAI1, TWIST1, and STK11 were significantly higher in tumor-adjacent tissues than in the matched EC samples, and this difference was not influenced by the content of cancer cells in cancer-adjacent tissues. The expression of MYC, NR5A2, and SNAI1 was also higher in EC-adjacent tissues than in samples from cancer-free patients. In addition, the expression of MYC and CXCR2 in the tumor related to non-endometrioid adenocarcinoma and reduced the risk of recurrence, respectively, and higher NR5A2 expression in tumor-adjacent tissue increased the risk of death. In conclusion, tissues proximal to EC present higher levels of some cancer-promoting genes than the matched tumors. Malignant tumor-adjacent tissues carry a diagnostic potential and emerge as new promising target of anticancer therapy.
Analisis Sentimen Pada Review Skincare Female Daily Menggunakan Metode Support Vector Machine (SVM)

Penampilan menarik merupakan hal yang diinginkan oleh sebagian perempuan, terutama perawatan kulit. Saat ini tingkat kepedulian merawat kulit sudah tinggi pada perempuan. Sebelum melaksanakan perawatan kulit hal yang utama yaitu mengetahui jenis kulit, untuk membantu perempuan agar lebih mudah mengetahui jenis produk skincare yang cocok pada kulit sehingga penelitian ini membahas mengenai analisis sentimen review skincare pada website kecantikan female daily. Metode yang digunakan pada penelitian ini adalah Support Vector Machine (SVM). SVM merupakan machine learning yang sudah banyak digunakan pada penelitian-penelitian sebelumnya dan memberikan hasil yang maksimal. Penelitian ini mendapatkan hasil akurasi sebanyak 87% dengan recall sebesar 90%, precision sebesar 84,90%, dan f1 score sebesar 87,37%.
Global diversity and adaptations of avian eggshell thickness indices

The amniote eggshell is a fundamental aspect of the embryo life-support system, protecting it from UV light, microbes and mechanical damage, while regulating gas exchange and providing calcium for growth. The thickness of eggshells is highly diverse across modern birds and in ﬂ uences multiple eggshell functions, yet the selective pressures driving eggshell thickness have not been clearly identi ﬁ ed. Here, we use a global dataset of avian eggshell thickness indices for 4260 ( > 41%) avian species to assess trends in egg-shell thickness across the phylogeny, as these indices are strongly correlated with direct measures of eggshell thickness and are non-destructive to the sample. We analysed the dataset within a phylogenetic framework to assess the relative importance of climatic and ecological explanations for variation in eggshell thickness indices. The distribution of avian eggshell thickness indices across species was found to be primarily driven by phylogenetic relatedness, in addition to evolutionary processes that do not match a Brownian model of evolution. Across modern birds, thicker eggshells were more prevalent in species (1) with precocial young, (2) which exhibit a scavenger-based diet, (3) which primarily feed on vertebrates or plants (excluding nectivores, seed and fruit specialists) and (4) which breed in open habitats. Thicker eggshells found in species with precocial young probably enable higher rates of calcium removal for the more advanced embryo development. Excessive light transmission through the shell damages developing embryos, while too little light transmission can impede development. Eggs in shaded habitats experience low light exposure, and thus thinner shells are more prevalent in these environments potentially to increase light transmission through the shell. Overall, variation in eggshell thickness indices appears to be driven largely by phylogeny, with certain life-history traits linked to embryo growth rate, calcium content of their food, and the need to mitigate excessive light transmission through the shell
Benefits of Patient-Specific Reconstruction Plates in Mandibular Reconstruction Surgical Simulation and Resident Education

Poorly contoured mandibular reconstruction plates are associated with postoperative complications. Recently, a technique emerged whereby preoperative patient-specific reconstructive plates (PSRP) are developed in the hopes of eliminating errors in the plate-bending process. This study’s objective is to determine if reconstructions performed with PSRP are more accurate than manually contoured plates. Ten Otolaryngology residents each performed two ex vivo mandibular reconstructions, first using a PSRP followed by a manually contoured plate. Reconstruction time, CT scans, and accuracy measurements were collected. Paired Student’s t-test was performed. There was a significant difference between reconstructions with PSRP and manually contoured plates in: plate-mandible distance (0.39 ± 0.21 vs. 0.75 ± 0.31 mm, p = 0.0128), inter-fibular segment gap (0.90 ± 0.32 vs. 2.24 ± 1.03 mm, p = 0.0095), mandible-fibula gap (1.02 ± 0.39 vs. 2.87 ± 2.38 mm, p = 0.0260), average reconstruction deviation (1.11 ± 0.32 vs. 1.67 ± 0.47 mm, p = 0.0228), mandibular angle width difference (5.13 ± 4.32 vs. 11.79 ± 4.27 mm, p = 0.0221), and reconstruction time (16.67 ± 4.18 vs. 33.78 ± 8.45 min, p = 0.0006). Lower plate-mandible distance has been demonstrated to correlate with decreased plate extrusion rates. Similarly, improved bony apposition promotes bony union. PSRP appears to provide a more accurate scaffold to guide the surgeons in assembling donor bone segments, which could potentially improve patient outcome and reduce surgical time. Additionally, in-house PSRP can serve as a low-cost surgical simulation tool for resident education.
Análise e síntese de sistemas LPV polinomiais homogêneos usando funções de Lyapunov dependentes de sucessivos instantes de tempo

Rodrigues, Luis Antonio. Analysis and synthesis of homogeneous polynomially LPV systems using path-dependent Lyapunov functions. Campinas: School of Mechanical Engineering, University of Campinas. This work investigates stability and H∞ performance of discrete-time linear parameter varying systems. Sufficient conditions for stability analysis, H∞ performance analysis and synthesis of both robust and gain-scheduled static output feedback controller are provided. It is assumed that the system matrices have a homogeneous polynomial dependence of arbitrary degree on the time-varying parameters. Thus, a homogeneous-polynomially parametrization method for affine LPV systems is proposed. The parameters are assumed to vary inside a polytope and to have known bounds on their rates of variation. The geometric properties of the polytopic domain are exploited to derive a finite set of LMIs that take into account the bounds on the rates of variation of the scheduling parameters. The LMI conditions are obtained using a quadratic in the state Lyapunov function with a homogeneous polynomial dependence on the scheduling parameters at successive instants of time. The proposed techniques are applied to an LPV model of a vibroacoustic setup. Comparisons with numerical results found in literature show the benefits of the proposed approach.