Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrap Estimation of Long-Range Dependence in Arfima Processes

Long-memory or long-range dependence (LRD) denotes the property of a time series to exhibit a significant dependence between very distant observations. The presence of long-memory is established in diverse fields, for example, in hydrology, finance, network traffic, psychology, etc. From a practical point of view an important issue is the estimation of LRD parameters, especially the estimation of the Hurst parameter proposed in [19]. The Hurst parameter, usually referred as the H parameter, indicates the intensity of LRD.Various methods for estimating the H parameter in a time series data are available some of which are described in [3]. They are validated by appealing to an asymptotic analysis supposing that the sample size of the time series converges to infinity. Taqqu et al. [37] investigated empirical performance of nine methods for estimating the LRD in simulated autoregressive fractionally integrated moving average (ARFIMA) processes for a fairly large sample size of 10000 terms.The bootstrap is a general statistical procedure for statistical inference, originally introduced by Efron [11] that generally yields better estimations in finite sample sizes, than the classical methods. In this paper, we consider a bootstrap technique, which uses blocks composed of cycles, to estimate the H parameter in ARFIMA processes. The bias and the root mean square error (RMSE) of five estimators and their corresponded bootstrap estimators are obtained by a Monte Carlo study.
KEANEKARAGAMAN DAN DOMINANSI SERANGGA PARASITOID TELUR BERDASARKAN FASE PERTUMBUHAN TANAMAN PADI

The insect egg parasitoid is a potential agent of biological control for pest, especially concerning the development of technology alternative pest control. The existing species of egg parasitoids in rice agroecosystems needs is known for their use as pest control an effective and efficient. The research aimed to determine of diversity, dominance, and parasitization levels for species of egg parasitoids in rice agroecosystems based on plant growth. The location research and sampling of parasitoids were done in Matangkuli and Syamtalira Aron sub-districts, North Aceh district, and Kuta Malaka sub-districts, Aceh Besar district, Province Aceh. The sampling was done in the vegetative stage from 20 to 40 and generative from 70 to 90 days after planting (DAT). Groups of eggs collected from the sampling location were then observed and identified at the Laboratory of Plant Pests and Diseases, Faculty of Agriculture, Malikussaleh University, Aceh. The results showed that egg parasitoids found in the rice agroecosystem form Order Hymenoptera, superfamily Chalcidoidea, species Ooencyrtus malayensis (Encyrtidae) and Tetrastichus schoenobii (Eulophidae). Superfamily Platygasteroidae, species Telenomus triptus (Scelionidae) and Hadronotus leptocorisae (Scelionidae). The number of individuals and diversity of egg parasitoids were low in vegetative than generative. The evenness index in both growth stages belongs to high. Species of T. triptus and T. rowani were dominant egg parasitoids found in the rice agroecosystem in both growth stage. The level of parasitization to parasitoid T. triptus, T. rowani, and Te. schoenobii is generally known as > 50%, species O. malayensis and H. leptocorisae less than 50%
The Contribution of Sleep Texture in the Characterization of Sleep Apnea

Obstructive sleep apnea (OSA) is multi-faceted world-wide-distributed disorder exerting deep effects on the sleeping brain. In the latest years, strong efforts have been dedicated to finding novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep, we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focusing on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical information to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration. Machine learning approaches may reinforce our understanding of this complex multi-level pathology, supporting patients’ phenotypization and easing in a more tailored approach for sleep apnea.
Entropie topologique et représentation de Burau

Let f be an orientation-preserving homeomorphism of the disk D, P a finite invariant subset and [f] the isotopy class of f in D\P. We give a non trivial lower bound of the topological entropy for maps in [f], using the spectral radius of some specializations in GL(n,C) of the Burau matrix associated with [f] and we discuss some examples.
Was Fixing This Really That Hard? On the Complexity of Correcting HTN Domains

Automated modeling assistance is indispensable to the AI planning being deployed in practice, notably in industry and other non-academic contexts. Yet, little progress has been made that goes beyond smart interfaces like programming environments. They focus on autocompletion, but lack intelligent support for guiding the modeler. As a theoretical foundation of a first step towards this direction, we study the computational complexity of correcting a flawed Hierarchical Task Network (HTN) planning domain. Specifically, a modeler provides a (white) list of plans that are supposed to be solutions, and likewise a (black) list of plans that shall not be solutions. We investigate the complexity of finding a set of (optimal or suboptimal) model corrections so that those plans are (resp. not) solutions to the corrected model. More specifically, we factor out each hardness source that contributes towards NP-hardness, including one that we deem important for many other complexity investigations that go beyond our specific context of application. All complexities range between NP and Sigma-2-p, rising the hope for efficient practical tools in the future.
IGF2BP1—An Oncofetal RNA-Binding Protein Fuels Tumor Virus Propagation

The oncofetal RNA-binding protein IGF2BP1 has been reported to be a driver of tumor progression in a multitude of cancer entities. Its main function is the stabilization of target transcripts by shielding these from miRNA-mediated degradation. However, there is growing evidence that several virus species recruit IGF2BP1 to promote their propagation. In particular, tumor-promoting viruses, such as hepatitis B/C and human papillomaviruses, benefit from IGF2BP1. Moreover, recent evidence suggests that non-oncogenic viruses, such as SARS-CoV-2, also take advantage of IGF2BP1. The only virus inhibited by IGF2BP1 reported to date is HIV-1. This review summarizes the current knowledge about the interactions between IGF2BP1 and different virus species. It further recapitulates several findings by presenting analyses from publicly available high-throughput datasets.
A New Model for Text Coherence Evaluation Using Statistical Characteristics

Background and Objectives: Discourse coherence modeling evaluation becomes a critical but challenging task for all content analysis tasks in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entity-based and graph-based models are engaging in semantic and linguistic concepts of a text. It means that the problem cannot be solved very well and these methods are only very limited to available word co-occurrence information in the sequential sentences within a short part of a text. One of the greatest challenges of the above methods is their limitation in long documents coherence evaluation and being suitable for documents with low number of sentences. Methods: Our proposed method focuses on both local and global coherence. It can also assess the local topic integrity of text at the paragraph level regardless of word meaning and handcrafted rules. The global coherence in the proposed method is evaluated by sequence paragraph dependency. According to the derived results in word embeddings, by applying statistical approaches, the presented method incorporates the external word correlation knowledge into short and long stories to assess both local and global coherence, simultaneously. Results: Using the effect of combined word2vec vectors and most likely n-grams, we show that our proposed method is independent of the language and its semantic concepts. The derived results indicate that the proposed method offers the higher accuracy with respect to the other algorithms, in long documents with a high number of sentences. Conclusion: Our current study, comparing our proposed method with BGSEG method showed that the mean degree of coherence evaluation 1.19 percent improvement. The results in this study also indicate improvement results are much more in larger texts with more sentences.
All-Optical Tunable Filter With a Graphene-Buried Long-Period Waveguide Grating

We demonstrate low-power all-optical tunable filters with long-period gratings formed in graphene-buried polymer waveguides, where the heat generated by graphene's absorption of the TE-polarized pump light serves to tune the TM-polarized resonance band. Our experimental gratings designed for operation at ∼1550 nm and pumped at 980 nm, which contain ∼6-mm long graphene, achieve a tuning efficiency of approximately –0.7 nm/mW, which corresponds to a ∼20-nm blue shift in the resonance wavelength with a pump power lower than ∼30 mW. Our grating can also serve as an all-optical switch when operated at a fixed wavelength, which can provide an extinction ratio larger than 20 dB with a pump power lower than 30 mW at a response time of ∼2.0 ms. The graphene-buried grating platform can be further explored for the development of a wide range of all-optical control devices.
Coffee-Associated Endophytes: Plant Growth Promotion and Crop Protection

Simple Summary Several hundreds of endophytic microbes are found to be associated with coffee plants. Endophytes are mostly recognized as symbionts and offer many benefits to plants. Several studies in different laboratories and natural environments show that endophytes are responsible for enhancing coffee plant growth and minimizing various pest damages. This shows that coffee endophytes can be sustainable alternatives to chemical pesticides. In the coffee beverage industry, using coffee endophytes is more advantageous as they improve the aroma and flavor of the beverage. Limited studies have been carried out in this regard; nevertheless, in this article, we highlighted the importance of coffee endophytes based on the available study findings. Abstract Endophytic microbes are a ubiquitous group of plant-associated communities that colonize the intercellular or intracellular host tissues while providing numerous beneficial effects to the plants. All the plant species are thought to be associated with endophytes, majorly constituted with bacteria and fungi. During the last two decades, there has been a considerable movement toward the study of endophytes associated with coffee plants. In this review, the main consideration is given to address the coffee-associated endophytic bacteria and fungi, particularly their action on plant growth promotion and the biocontrol of pests. In addition, we sought to identify and analyze the gaps in the available research. Additionally, the potential of endophytes to improve the quality of coffee seeds is briefly discussed. Even though there are limited studies on the subject, the potentiality of coffee endophytes in plant growth promotion through enhancing nitrogen fixation, availability of minerals, nutrient absorption, secretion of phytohormones, and other bioactive metabolites has been well recognized. Further, the antagonistic effect against various coffee pathogenic bacteria, fungi, nematodes, and also insect pests leads to the protection of the crop. Furthermore, it is recognized that endophytes enhance the sensory characteristics of coffee as a new field of study.
Human-Inspired Control of Dual-Arm Exoskeleton Robots With Force and Impedance Adaptation

Humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. Traditional robots perform tasks independently without considering their interactions with the external environment, which leads to poor flexibility and adaptability. Comparatively, humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. In order to solve the problems of human–robot security and adaptability to unknown environment, a human-inspired control with force and impedance adaptation is proposed to interact with unknown environments and exhibit this biological behavior on the developed dual-arm exoskeleton robots. First, we propose a computationally model utilizing the sampled surface electromyogram (sEMG) signals to calculate the human arm endpoint stiffness and define a co-contraction index to describe the dynamic behaviors of the muscular activities in the tasks. Then, the obtained human limb impedance stiffness parameters and the sampling position information are transferred to the slave arm of the exoskeleton as the input variables of the controller in real-time. In addition, a variable stiffness observer is used here to compensate for the errors of the calculated stiffness by sEMG signals. The experimental studies of human impedance transfer control have been conducted to show the effectiveness of the developed approach. Results of the experimental suggest that the proposed controller can achieve human motor adaptation and enable the subjects to execute a skill transfer control by a dual-arm exoskeleton robot.
Compliance with Iron-Folic Acid Supplementation Among Pregnant Women in the Bekwai Municipality, Ghana

: Globally, 40% of pregnant women are affected by anaemia. Anaemia, low haemoglobin level in the bloodstream, has many negative effects on pregnancy. The World Health Organisation recommends supplementation with iron and folic acid to prevent the incidence of anaemia in pregnancy. In Ghana, iron and folic acid supplementation has been through Ante Natal Clinics. Despite this intervention, Anaemia in Pregnancy prevalence in Bekwai has been above 37% for three years, higher than the national average of 25%. This study found out the compliance level and determining factors among ANC clients. This facility-based quantitative descriptive cross-sectional study was carried out in two sub-districts using multistage sampling. Data was collected using structured questionnaire. Data entered into SPSS was exported to Stata version for analysis. Demographic and obstetric data were analysed into frequencies and percentages. Bivariate and multivariate analyses were conducted to identify factors affecting compliance. Compliance was low (38.8%), despite high knowledge (90.3%). Gravidity (p-value 0.025 AOR= 5.88, 95% CI= 2.214-15.542) and level of knowledge (P-Value 0.01, AOR 2.89, 95%CI= 0.592-12.10) positively affected compliance. It was recommended that health education at ANC be improved, and adherence partners introduced to help prevent forgetfulness.
A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.
In vivo imaging of hepatocellular carcinoma using a glypican-3-binding peptide based probe

Hepatocellular carcinoma (HCC) has been the third most common cause of cancer-related death worldwide. Glypican-3 (GPC3) is a heparin sulfate proteoglycan linked to the cell membrane by a glycosyl-phosphatidylinositol anchor (GPI) and is expressed by 75% of all hepatocellular carcinomas but undetectable in healthy liver tissue or liver with focal lesions. What’s more, GPC3 has been gradually applied in clinical applications as a specific indicator for the early detection and prognosis of HCC. As GPC3 can also regulate many pathways in HCC pathogenesis including Wnt, Hh and Yap signaling, it has been shown that GPC3 knockdown can inhibit HCC growth, reinforcing the important roles of GPC3 in HCC development. For HCC early detection, we designed a peptide targeting GPC3 that allows to establish a fluorescent dyes-labeled probe. Firstly, according to the structure of the GPC3 antibody GC33 and the positive peptide reported in the literature, we generated a peptide consisting of twelve amino acids named 12P that may bind to GPC3 with tight binding ability and specificity. In vitro testing, we utilized FCM and laser confocal microscopy to verify its specificity of targeting to the high expression cells of GPC3. What’s more, we linked 12P with a near infrared dye to verify its in vivo targeting ability. All results indicated that 12P possessed potent binding capacity which could be used as a targeting module in GPC3 detection probe.
Transport of micro-bubbles in turbulent shear flows

The dynamics of micro-bubbles, which are typical in many industrial applications, is addressed by means the Direct Numerical Simulations (DNS) of two prototypal flows, namely a homogeneous shear flow and a fully developed pipe flows. This preliminary study has a two-fold purpose. The homogenous turbulent shear flow is useful to characterize the bubble dynamics in terms of their eventual clustering properties which is expected to be controlled by the Stokes number. The time history of the fluid pressure experienced by the bubbles during their evolution is recorded and successively employed to force the Rayleigh-Plesset equation [1]. The ensuing data are used to address a posteriori the bubble diameter statistics in view of bubble collapse induced by strong and intermittent turbulent pressure fluctuations. The turbulent pipe flow simulations serve to address the bubble dynamics in wall bounded flows. Here the bubbles are observed to accumulate in the near-wall region with different intensity depending on the bubble dimensions.
Local Polya fluctuations of Riesz gravitational fields and the Cauchy problem

We consider a pseudodifferential equation of parabolic type with a fractional power of the Laplace operator of order $\alpha\in(0;1)$ acting with respect to the spatial variable. This equation naturally generalizes the well-known fractal diffusion equation. It describes the local interaction of moving objects in the Riesz gravitational field. A simple example of such system of objects is stellar galaxies, in which interaction occurs according to Newton's gravitational law. The Cauchy problem for this equation is solved in the class of continuous bounded initial functions. The fundamental solution of this problem is the Polya distribution of probabilities $\mathcal{P}_\alpha(F)$ of the force $F$ of local interaction between these objects. With the help of obtained solution estimates the correct solvability of the Cauchy problem on the local field fluctuation coefficient under certain conditions is determined. In this case, the form of its classical solution is found and the properties of its smoothness and behavior at the infinity are studied. Also, it is studied the possibility of local strengthening of convergence in the initial condition. The obtained results are illustrated on the $\alpha$-wandering model of the Lévy particle in the Euclidean space $\mathbb{R}^3$ in the case when the particle starts its motion from the origin. The probability of this particle returning to its starting position is investigated. In particular, it established that this probability is a descending to zero function, and the particle "leaves" the space $\mathbb{R}^3$.
Book review: YJ Doran, The Discourse of Physics: Building Knowledge through Language, Mathematics and Image

liveliness, the writing is a model of clarity and there is an impressive synthesis of a large body of research. The downside of this accommodation, however, is that this book has little to say about theory or method, and generalises at the expense of subtlety. Moreover, the body of research covered is that of (human) communication research, a field that is dominant in the United States. The almost complete neglect of work on politeness, and more particularly impoliteness, is a shame. Of course, one might argue that much work on (im)politeness neglects that of human communication research, though perhaps not to the same extent. Nevertheless, this is where the book might have interest for the linguist: we can rapidly find out how human communication research has tackled the notion of incivility and be furnished with a contextually richer vision of our work on the language of incivility/impoliteness.