Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired morphological processing: insights from multiple sclerosis

ABSTRACT Multiple Sclerosis (MS) is a neurological disease characterised by damage affecting large bundles of white matter fibres. Morphological segmentation of complex words (e.g. walked) into stems (walk) and suffixes (∼ed) is thought to depend on intact white matter. We tested the hypothesis that Arabic speaking patients with MS may lose the ability to segment morphologically complex words in a primed lexical decision task using word pairs that shared either a root and a semantic relationship (+R + S, e.g. “AnzAl”–“nuzwl” lowering-landing), a root without semantics (+R–S, e.g. “rtAbp”–“trtyb” monotony-tidying up),a semantic relationship (–R + S, e.g. “xyr”–“nEmp” good-grace), or a phonological relationship (–R + Phon, e.g. “mEdn”–“mEAnd” mineral-stubborn). While healthy controls showed priming by root regardless of semantics and inhibition by phonology, the patients showed facilitation by semantics (+R + S and –R + S), and inhibition by phonology (–R + Phon). These findings are used to adjudicate three contending models of lexical processing.
Role of the score for the targeting of atrial fibrillation (STAF) combined with D-dimer in screening ischemic stroke patients with atrial fibrillation

Background We aim to explore the effect of the score for the targeting of atrial fibrillation (STAF) combined with the serum D-dimer (DD) level in screening acute ischemic stroke patients with atrial fibrillation (AF). Methods This study is a retrospective case observation study. This study consecutively selected patients with acute ischemic stroke who were hospitalized in the Department of Neurology at Zhuhai Hospital Affiliated with Jinan University from February 2019 to February 2021. Venous blood was drawn from all patients within 24 hours of hospitalization for DD detection. In accordance with the medical records, the patients were classified into an AF group and a non-AF group and were scored according to the STAF standard. A combined test method was used to estimate the diagnostic screening value of the STAF combined with the DD value for acute ischemic stroke patients with AF.
Game of Power Allocation on Networks: Balancing Equilibrium

This paper defines and studies a special kind of equilibrium termed as "balancing equilibrium" which arises in the power allocation game in \cite{allocation}. The equilibrium has both unique mathematical property and realistic implications. In the equilibrium, all countries in antagonism have to use all of their own power to counteract the received threats, and the threat made to each adversary only equalizes the threat received from that adversary. This paper establishes necessary and sufficient conditions for countries' power in order for this equilibrium to exist in games on different signed graphs. It also links the existence of this equilibrium in PAGs on structurally balanced graphs to the Hall's Maximum Matching problem and the Max Flow problem.
About Incentives Distribution

In this paper we study the problem of distributing a given amount among a set of agents, according to their performance in a set of issues. In contrast with multi-issue allocation situations where each agent claims some of the amount, in this model the distribution must take into account how each agent behaves on some issues. We characterize a family of solutions for this problem using the well-known additivity, equal treatment and efficiency properties, adapting them to our context. If we want a solution that satisfies two additional properties, reduction and proportional, another problem arises: we need to look for a system of prices for a unity of each issue. Also, we apply classical bargaining theory techniques for the problem and some results are shown.
Factors Affecting Malaysia Tourists’ Destination Loyalty Behavior: A Case Study of Hatyai, Thailand

Retaining the loyal visitors to revisit is highly crucial for generating the revenue in tourism destination as well as reducing the marketing expenses, this empirical study has been designed to primarily explore factors that motivate Malaysia tourists to revisit Hatyai and to identify the key factors that affect Malaysia Tourists Destination loyalty behavior. The results of this study indicated that “destination attractiveness” “social interaction” and “Cost/Price/Value” were the most important destination attributes and travel motives for malaysia tourists revisit to Hatyai. Concerning Malaysia tourists destination loyalty behavior, the result reveals that “destination attractiveness” “social interaction” and “price/value/cost” predicts Malaysia tourists’ destination loyalty behavior with R2 = 58.9%.
Phase extraction based on iteration algorithm with crossed fringes in phase measuring deflectometry

In phase measuring deflectometry, two orthogonal sinusoidal fringe patterns are separately projected on the test surface and the distorted fringes reflected by the surface are recorded, each with a sequential phase shift. Then the two components of the local surface gradients are obtained by triangulation. It usually involves some complicated and time-consuming procedures (fringe projection in the orthogonal directions, accurate phase shifting). To avoid the complex process, a novel phase extraction algorithm with crossed fringes is presented in this paper. It is based on a least-squares iterative process. Both a numerical simulation and a preliminary experiment are conducted to verify the validity and performance of this algorithm. Experimental results obtained by our method are shown, and comparisons between our experimental results and those obtained by the traditional phase-shifting algorithm and between our experimental results and those measured by the Fizeau interferometer are made.
Rotating Manipulation of Flower Stick Based on Only Consideration of Kinematic Constraint

これまでに,ロボット技術を用いて器用なタスクを工学的に 実現しようと試みたロボットの運動制御に関する研究が行われ ており [1],ロボットジャグリングのような巧みな運動を工学的 に実現する非把持マニピュレーションに関する研究も進められ ている [2]. 一般的に,非把持マニピュレーションは把持形態に よるマニピュレーション法 [3]では不向きな高速・効率性や簡便 性が期待されるが,運動学的拘束と動力学的拘束を同時に考慮 する kinodynamic な制約を満たさなければならない典型例で あり,その制約条件の難しさに起因し,これまでに実現された 操作例は限定的である. 本論文では,操作対象の状態量をロボットの制御周期レベル で取得可能な計測器と計測結果に基づいたフィードバック入力 を実現可能なロボットハードウェアを前提とし,運動学的拘束条 件のみを考慮した非把持マニピュレーション法を提案する. 提 案手法により,これまでの従来研究には見られない,よりダイ ナミックで挑戦的な操作タスクであるフラワースティックのプ
Indexing and Segmentation of Video Contents: A Review

Increasing video content on Internet day by day has increased the challenge of searching and finding the relevant topic for effective navigation inside video by users mostly in very long lecture video. One such way is the segmentation of specific video to get features like indexing, searching etc. In this paper we are going to discuss for indexing and segmentation inside a video. The model is based upon the extraction of text from the speech and uses them to segmentation and index the specific text inside video.
General Information Conditioned by a Variable Event

The aim of this paper is to present, by axiomatic way, an idea about the general conditional information of a single, fixed fuzzy set when the conditioning fuzzy event is variable. The properties of this conditional information are translated in a system of functional equations. Some classes of solutions of this functional system have been found.
A Novel Cellular User Offloading via UAV-Borne IRS

Unmanned Aerial Vehicles (UAVs) can be leveraged to perform cellular user offloading in dynamic hotspot scenarios and enable the unserved users of overloaded gNodeB (gNB) use the unutilized bandwidth of a neighbouring underloaded gNB. To reach the blocked users and to minimize the co-channel interference between offloaded users and other users, we propose using UAVs mounted with two Intelligent Reflecting Surface (IRS) panels. We formulate the problem to minimize the total Service Level Agreement (SLA) violations cost by jointly optimizing the location of the UAV, user association, phase shifts of the IRS elements, transmit power allocation, and bandwidth allocation. Further, we propose an efficient heuristic solution to the formulated problem and show its effectiveness using numerical simulations. Through our simulations, we show that the total SLA violation cost decreases by up to 17.18% and spectral efficiency increases by up to 6.66% by using 400 elements in each IRS.
Negative income tax and universal basic income in the eyes of Aiyagari

We compare two welfare programs: the universal basic income (UBI) and negative income tax (NIT). Under a linear income tax system, we show that (i) the NIT can replicate the allocation of the UBI exactly by providing an identical marginal effective tax schedule, and (ii) the budget of the NIT is always smaller than that of the UBI. According to our quantitative model, which is calibrated to approximate the income and wealth distributions in the United States, the optimal UBI is to pay everyone 7.2% of the average income. We also show that the NIT can achieve a similar average welfare with a much smaller budget (2.3% of the GDP) by providing a subsidy that is generous to the very poor and quickly phases out as income increases.
Unconventional precipitation and martensitic transformation behaviour of Ni-rich NiTi alloy fabricated via laser-directed energy deposition

ABSTRACT In this study, we report an unconventional precipitation and martensitic transformation behaviour of directly aged Ni-rich NiTi alloys fabricated via laser-directed energy deposition (LDED). Ni4Ti3 particles precipitate uniformly under all ageing conditions and no traditional multiple-step martensitic transformations are observed. We conclude this unique behaviour to the intrinsic characteristics of the LDED technique, which are metastable microstructures and high residual stresses. On the one hand, these features make grain boundaries no longer a fevered location for precipitation and, on the other hand, significantly suppress the martensitic transformation when ageing at low temperatures (300°C/400°C). As the aging temperature increase (500°C), residual stresses release significantly, accompanied by the growth of Ni4Ti3 precipitates from several nanometres to 452 ± 181 nm with increased interparticle spacing. At the same time, reverse martensitic transformations change from two-step (B19′ → R → B2) to single-step (B19′ → B2).
Eléments de théorie spectrale : le Laplacien sur un ouvert borné

Dans ces notes de cours nous fournissons les elements de base concernant la theorie spectrale des operateurs auto-adjoints en dimension infinie, avec un accent sur les operateurs a resolvante compacte. En particulier, nous etudions en details l'operateur Laplacien sur un ouvert borne et discutons les divers conditions au bord (Dirichlet, Neumann, Robin, periodique, Born-von Karman).
Adaptive fuzzy synchronization for a class of fractional-order neural networks

In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.
A health-promoting role of exclusive breastfeeding on infants through restoring delivery mode-induced gut microbiota perturbations

The establishment of human gut microbiota in early life is closely associated with both short- and long-term infant health. Delivery mode and feeding pattern are two important determinants of infant gut microbiota. In this longitudinal cohort study, we examined the interplay between the delivery mode and feeding pattern on the dynamics of infant gut microbiota from 6 weeks to 6 months post-delivery in 139 infants. We also assessed the relationship between infant respiratory infection susceptibility and gut microbial changes associated with delivery mode and feeding pattern. At 6 weeks postpartum, the composition and structure of gut microbiota of cesarean section-delivered (CSD) infants differed from those of vaginally delivered (VD) infants, with decreased Bacteroides and Escherichia-Shigella and increased Klebsiella, Veillonella, and Enterococcus. At 6 months postpartum, these delivery mode-induced microbial shifts were restored by exclusive breastfeeding, resulting in similar gut microbial profiles between VD and CSD infants who were exclusively breastfed (P = 0.57) and more variable gut microbial profiles between VD and CSD infants who were mixed fed (P < 0.001). We identified that the VD-associated genera were enriched in healthy infants, while the CSD-associated genera were enriched in infants who suffered from respiratory infections. Our findings indicate that exclusive breastfeeding may play a health-promoting role by reducing infant respiratory infection susceptibility through the restoration of gut microbiota perturbations caused by cesarean section.
The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering

Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD$^{\mathcal{A}}$, a parameter-efficient score \emph{adaptation} model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component -- which only increases the number of model parameters by $0.03\%$ -- is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD$^{\mathcal{A}}$ produces significantly more accurate results than current state-of-the-art methods, improving from $34.4$ to $35.1$ Mean Reciprocal Rank values averaged across all datasets and query types while using $\leq 30\%$ of the available training query types. We further show that CQD$^{\mathcal{A}}$ is data-efficient, achieving competitive results with only $1\%$ of the training complex queries, and robust in out-of-domain evaluations.
Extracting Salient Features for EEG-based Diagnosis of Alzheimer's Disease Using Support Vector Machine Classifier

ABSTRACT Alzheimer's disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.
Overcoming mathematical anxiety to promote progress in mathematics during undergraduate engineering studies at university

Mathematics (math) is one of the logical and practical academic disciplines, which is one of the basic competencies in engineering. Despite that, some students have difficulty to learn math, which is determined by several cognitive and emotional factors. Math anxiety is one of the emotional factors that causes learning difficulties in math. Many students experience anxiety, fear, tension, or discomfort when faced with math, what affect students’ achievement. Math anxiety can be caused by several factors: lack of appropriate math knowledge, learning strategies, application of math in real life, limited exam time, lack of specific materials as well as personality type, lack of confidence, and stereotypes related to math. It is already proved, that achievement in math increases when anxiety is controlled. Therefore, to promote progress in math during engineering studies at university, the aim is to identify the most important factors that cause mathematics anxiety, as well as existing experiences in overcoming them. To develop the methodology of this study, a study of the scientific literature on math anxiety is performed, considering the above-mentioned math learning difficulties. In the case of math anxiety three factors are analysed: student’s prior mathematical experience as well as learning strategies at pre-university and university education level. The article looks at three approaches to learning math that are commonly used by students in their studies of math at university: (1) The use of ICT tools and math software to understand the concepts of math and solve independent work tasks; (2) The link between teaching of math and real/ everyday life; (3) Cooperative learning during additional math classes. The effectiveness of these approaches is characterised using the data from the quality monitoring of the math study process at the authors’ universities and students’ survey results as well as math teachers focused interviews.
Cephalometric Assessment of the Brain Ventricular System Index Using Computerised Tomography in Sokoto North-Western, Nigeria

Aim: To determine the normal brain ventricular system index of Nigerians using computerized tomography in Sokoto North-Western Nigeria. Study Design: Retrospective cross- sectional. Place and Duration of Study: Department of Anatomy Usmanu Danfodiyo University Sokoto and Department of Radiology Usmanu Danfodiyo University Teaching Hospital Sokoto, between January, 2020 and January, 2022. Methodology: One hundred and seventy-eight normal brain CT scans of subjects from the Radiology Department, whose ages ranged from 1 to 70 years, were recruited for this study. The Bi-frontal and Bi-occipital indices were calculated as ratios of measured distances of the frontal and occipital horns of the lateral ventricles and the transverse intracranial diameter, at the same level, respectively. The brain ventricular system index was then determined by a ratio of the Bi-frontal index and Bi-occipital index. Results: The largest brain ventricular system indexes were 7.83cm and 7.68cm in males and females respectively and these were observed between the age of 2-4years for both sexes. While, the smallest brain ventricular system indexes were 7.47cm in males and 7.29cm in females, which were observed in age group 61-70years for both sexes. Conclusion: This study was conducted to determine the Ventricular Brain System Index and it relation with sex and age in our environment.
Sustainable and Elastic Carbon Aerogel by Polydimethylsiloxane Coating for Organic Solvent Absorption and Potential Application for Sensors (Infections, Environmental, Wearable Sensors, etc.)

Carbon aerogel is a promising material in various applications, such as water treatment, insulators, catalysts, and sensors, due to its porosity, low density, conductivity, and good chemical stability. In this study, an inexpensive carbon aerogel was prepared through lyophilization and post-pyrolysis using waste paper. However, carbon aerogel, in the form of short belts, is randomly entangled without a crosslinking agent and has weak mechanical properties, thus limiting its applications, which would otherwise be various. In this paper, a novel strategy is proposed to fabricate a PDMS-coated carbon aerogel (Aerogel@PDMS). Benefiting from microwave heating, precise PDMS coating onto the carbon frame was able to be carried out in a short amount of time. PDMS coating firmly tied the carbon microstructure, maintaining a unique aerogel property without blocking its porous structure. FE-SEM, RAMAN, XPS, and FT-IR were all used to confirm the surface change in PDMS coating. Compressible stability and water contact angle measurement showed that Aerogel@PDMS is a perspective organic solvent absorbent due to its good resilience and its hydrophobicity, and, as a result, its organic solvent absorption capacity and repeated absorption were evaluated, ultimately suggesting a promising material in oil clean-up and pollution remediation in water. Based on our experimental results, we identified elastic carbon aerogels provided by a novel coating technology. In the future, then, the developed carbon/PDMS composite can be examined as a promising option for various applications, such as environmental sensors, virus sensors, and wearable sensors.