Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomimetic Design and Topology Optimization of Discontinuous Carbon Fiber-Reinforced Composite Lattice Structures

The ever-increasing requirements for structural performance drive the research and development of lighter, stronger, tougher, and multifunctional composite materials, especially, the lattice structures, heterogeneities, or hybrid compositions have attracted great interest from the materials research community. If it is pushed to the extreme, these concepts can consist of highly controlled lattice structures subject to biomimetic material design and topology optimization (TO). However, the strong coupling among the composition and the topology of the porous microstructure hinders the conventional trial-and-error approaches. In this work, discontinuous carbon fiber-reinforced polymer matrix composite materials were adopted for structural design. A three-dimensional (3D) periodic lattice block inspired by cuttlefish bone combined with computer modeling-based topology optimization was proposed. Through computer modeling, complex 3D periodic lattice blocks with various porosities were topologically optimized and realized, and the mechanical properties of the topology-optimized lattice structures were characterized by computer modeling. The results of this work were compared with other similar designs and experiments to validate the effectiveness of the proposed method. The proposed approach provides a design tool for more affordable and higher-performance structural materials.
Tuberous sclerosis complex‐1 (TSC1) contributes to selective neuronal vulnerability in Alzheimer's disease

Selective neuronal vulnerability of hippocampal Cornu Ammonis (CA)‐1 neurons is a pathological hallmark of Alzheimer's disease (AD) with an unknown underlying mechanism. We interrogated the expression of tuberous sclerosis complex‐1 (TSC1; hamartin) and mTOR‐related proteins in hippocampal CA1 and CA3 subfields.
Adaptive Consensus Clustering for Multiple K-Means Via Base Results Refining

Consensus clustering, which learns a consensus clustering result from multiple weak base results, has been widely studied. However, conventional consensus clustering methods only focus on the ensemble process while ignoring the quality improvement of the base results, and thus they just use the fixed base results for consensus learning. In this paper, we provide an alternative idea to improve the final consensus clustering performance by considering the base results refining. In our framework, we adaptively refine the base results in the process of the ensemble. In more detail, on one hand, we ensemble multiple K-means results to learn the consensus one by considering the consensus and diversity; on the other hand, we apply the consensus result to design a graph filter to learn a more cluster-friendly embedding for refining the base K-means results. In our framework, the consensus learning and base results refining are integrated into one unified objective function so that these two tasks can be boosted by each other. Then we design an effective iterative algorithm to optimize the carefully designed objective function. The extensive experiments on benchmark data sets demonstrate that the proposed method can outperform both the single clustering and the state-of-the-art consensus clustering methods. The codes of this paper are released in http://Doctor-Nobody.github. io/codes/ACMK.zip.
Level-$K$ Reasoning, Deep Reinforcement Learning, and Monte Carlo Decision Process for Fast and Safe Automated Lane Change and Speed Management

This paper presents a decision process model for real-time automated lane change and speed management in highway traffic. The presented algorithm is developed based on level-$K$ game theory to model and predict the interaction between the vehicles. Using deep reinforcement learning, this algorithm encodes and memorizes the past experiences that are recurrently used to reduce the computations and speed up motion planning. Also, we use Monte Carlo Tree Search (MCTS) as an effective tool that is employed nowadays for fast planning in complex and dynamic game environments. This development leverages the computation power efficiently and showcases promising outcomes for maneuver planning and predicting the environment's dynamics. In the absence of traffic connectivity that may be due to either passenger's choice of privacy or the vehicle's lack of technology, this development can be extended and employed in fully-automated vehicles for real-world and practical applications.
Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network

Glioma is the most common form of brain tumor with a high degree of heterogeneity in imaging characteristics, treatment-response, and survival rate. An important factor causing this heterogeneity is the mutation of isocitrate dehydrogenase (IDH) enzyme. The current clinical gold-standard for identifying IDH mutation status involves invasive procedures that involve risk, may fail to capture intra-tumoral spatial heterogeneity or can be inaccessible in low-resource settings. In this study, we propose a deep learning-based method to non-invasively and preoperatively determine IDH status of high- and low-grade gliomas by leveraging their phenotypical characteristics from volumetric MRI scans. For this purpose, we propose a 3D Mask R-CNN-based approach to simultaneously detect and segment glioma as well as classify its IDH status - thus obviating the requirement of any separate tumor segmentation step. The network can operate on routinely acquired MRI sequences and is agnostic to glioma grade. It was trained on patient-cases from publicly available datasets (n = 223) and tested on two hold-out datasets acquired from The Cancer Genome Atlas (TCGA; n = 62) and Washington University School of Medicine (WUSM; n = 261). The model achieved areas under the receiver operating characteristic of 0.83 and 0.87, and areas under the precision-recall curves of 0.78 and 0.79, on the TCGA and WUSM sets, respectively. The model can be used to perform a pre-operative ‘virtual biopsy’ of gliomas, thus facilitating treatment planning, potentially leading to better overall survival.
A complement to the paper “On the Kolář connection” [Arch. Math. (Brno) 49 (2013), 223–240]

“From Corollary 19.8 in [1], we get immediately the following proposition Proposition 1. Let pY : Y → M be an FMm,n-object and pE : E → M be a VBm,n-object, y ∈ Yx, x ∈ M . Let (Γ,Λ,Φ,∆) ∈ Con(Y ) × Conclas(M) × Par(Y ×M E)× Conlin(E). There exists a finite number r = r(Γ,Λ,Φ,∆, y) such that for any (Γ1,Λ1,Φ1,∆1) ∈ Con(Y )×Conclas(M)×Par(Y ×M E)×Conlin(E) we have the following implication (j yΓ1 =j yΓ , j xΛ1 = j xΛ , j yΦ1 = j yΦ , j x∆1 = j x∆) ⇒ A(Γ1,Λ1,Φ1,∆1)(y) = A(Γ,Λ,Φ,∆)(y) . ” One can show that the above proposition is true but it is not an immediate consequence of Corollary 19.8 in [1]. From Corollary 19.8, it follows immediately the following weaker result.
Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.
An automated cervical cancer detection scheme using deeply supervised shuffle attention modified convolutional neural network model

Cervical malignant growth is the fourth most typical reason for disease demise in women around the world. In developing countries, women don’t approach sufficient screening methods because of the costly procedures to undergo examination regularly, scarce awareness and lack of access to the medical centre. Recently, deep learning-based radiomic methods have been introduced in differentiating vessel invasion from non-vessel invasion in Cervical Cancer (CC) by multi-parametric Magnetic Resonance Imaging (MRI). However, this model doesn’t produce sufficient results. In this work, the MRI images are initially pre-processed using bilateral filtering. After pre-processing, the image is segmented by modified U-Net model in order to identify the cancerous region. Extraction of deep semantic information from images by using residual blocks in the processes of contractions and expansions. The last layer of the contracting route uses tightly coupled convolutions in the second phase to speed up feature recycling and feature propagation. It was inferred from the observations that the proposed model was effective as a predictive tool for detecting vessel invasions in preoperative early stages of CC. Proposed model produces 94.00% detection accuracy which is better than the other existing methods.
DISCOURSE ANALYSIS OF QUESTIONS IN TEACHERS’ TALK

The research entitled “Discourse Analysis of Questions in Teachers’ Talk”. This research was focused on investigating the type of questions and the purpose underlying the questions used by the teacher. The research used a qualitative descriptive study by observing classroom interaction specifically in teacher’s questions. The participants in this research were three English teachers from different vocational schools in Garut. The findings showed that the teacher applied various types of questions during teaching and learning with the most frequent use of convergent type of questions. It also was revealed that mostly the purpose underlying the teacher’s questioning was to encourage students’ thinking and focus on the content of the lesson. By the frequent number of convergent questions dominating during teaching and learning, it can be concluded that characteristics of teachers are the same. They still use typically a lower-level type of question. It was then suggested that teacher purposely plans and promotes a higher-level type of question for future classrooms.
IMPLEMENTATION TRANSFER LEARNING CONVOLUTIONAL NEURAL NETWORK FOR POTHOLE DETECTION ON DRONE VIDEO

Pothole is one of the problems that can cause harm to a person or a lot of people and can even cost lives. So a lot of research has been done to detect potholes, especially image-based. This research uses Unmanned Aerial Vehicle (UAV) to get aerial video dataset and train Convolutional Neural Network (CNN) with the dataset. However, instead of doing learning from the beginning, transfer learning can be used to train CNN to recognize the object of a pothole and measure the value of its performance and what the optimal frame rate is. Then the results of this study indicate that the CNN model, ssd_resnet_50_fpn_coco gets an average performance value of 48.90 mAP. And the optimal frame rate with the average highest performance value at a frame rate of 30FPS with a value of 49.43 mAP, followed by 1FPS with a value of 48.36 mAP . Keywords: Performance, Transfer Learning, Convolutional Neural Network, Pothole Detection, Aerial Video.
DISTRIBUTED COMPUTING SYSTEMS BASED ON THE USE OF GRID TECHNOLOGIES

The article describes the use of Grid technology in telecommunication systems and the creation of Grid telecommunication systems. Network service providers are focusing on huge consolidated data centers. Particular attention began to be paid to improving the methods of scheduling data processing tasks. The shortcomings of modern approaches to planning are considered. These include the inability to provide the maximum total priority of tasks performed at individual planning stages in distributed computing systems. The developed planning procedure is described. It overcomes this shortcoming. But at the same time, to reduce the total processing time of tasks in the system in comparison with the planning procedure based on solving the problem of the least coverage. The desire for the maximum sum of priorities of the selected tasks is the main criterion for selecting tasks from the queue, which we will characterize by the importance coefficient. The coefficient of preservation of importance and the coefficient of acceleration of the operation of the Grid system segment are described. The article also proposes a solution to improve the efficiency and quality of task servicing. It is proposed to expand the functionality of distributed telecommunication systems based on the use of computer clusters using Grid technologies.
Re-examining the roles of experience quality at festivals: a comparative analysis using SEM and fsQCA

Purpose The purpose of this study is to reexamine the dimensionality and role of experience quality (EQ) to explain other related factors, namely, perceived value, satisfaction and behavioral intention. Using dual methodological approaches of fuzzy set qualitative comparative analysis (fsQCA) and structural equations modeling (SEM), it seeks to establish the EQ’s construct and predictive validity. Design/methodology/approach Grounded in cross-sectional and online research design, the present study surveyed 881 attendees of an annual music festival (Malakoff) held in Norway and subjected the quantitative data to SEM and fsQCA. Findings Both SEM and fsQCA confirmed the socializing and enjoyment dimensions of EQ, with good validity and reliability, as well as the functional and emotional value dimensions explaining visitors’ perceived value of attending a festival. Both statistical analyses also showed that perceived value-derived satisfaction leads to behavioral intention regarding festival visits. Practical implications This study provides numerous valuable consequences for festival organizers, or marketers that can aid in developing effective strategies to measure visitors’ perceived quality of experience and numerous practical implications for festival organizers or marketers can help in developing effective strategies to measure visitors’ perceived quality of experience and then increase value perceptions, satisfaction and behavioral intentions toward attending festivals. Originality/value To the best of the authors’ knowledge, this study is among the first to adopt a dual-dimensional framework to measure EQ in festivals and establish its utility with two statistical approaches (SEM and fsQCA). It also shows the utility of perceived value, initially developed for adventure tourists, to study visitors’ experience with festivals.
Shaping Curved Surfaces

This dissertation presents a new tool for shaping curved surfaces, the bending operator. The bending operator is an interactive tool intended for use in 3-D sketching. It is based on the idea that bending a surface is equivalent to changing its normal vector field while perturbing its metric as little as possible. The user of this tool specifies a bending operator, which is a surface that indicates how the normals of a target surface should change. The bending algorithm adds the derivatives of the normal vector fields of the bending and target surfaces and integrates this sum to produce a desired normal vector field. The target surface is then reshaped using a variational technique that moves the underlying surface control points to minimize a penalty function of the target surface. After bending, the resulting surface acquires the features of the bending surface while maintaining the general shape of the original target surface. The bending algorithm can perform a wide variety of surface shaping tasks, including bending about a cylinder axis, indenting, twisting, and embossing. The algorithm includes a positioning control used to specify the correspondence between points of the bending operator surface and target surface and a range of action selector used to restrict the bending action to a part of the target surface. The bending operator is intuitive in that a user can easily learn to predict the approximate result of a bending operation without needing a detailed understanding of the algorithm. The algorithm can be applied to any patch type that is based on control points and that is piecewise twice differentiable, including Bezier patches, B-spline patches, and NURBS. The algorithm can also be applied to a non-branching mesh of patches with smoothness constraints. The bending algorithm was implemented in an interactive prototype program using X-windows. This program performs a bending operation in seconds to minutes on a HP-730 workstation depending on the complexity of the target and bending surfaces. The dissertation also includes an outline for a joining algorithm based on variational techniques similar to those used in bending.
From the Lab to the Wild: Affect Modeling via Privileged Information

How can we reliably transfer affect models trained in controlled laboratory conditions (in-vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitro and in-vivo applications defines a core challenge of affective computing. This gap is caused by limitations related to affect sensing including intrusiveness, hardware malfunctions and availability of sensors. As a response to these limitations, we introduce the concept of privileged information for operating affect models in real-world scenarios (in the wild). Privileged information enables affect models to be trained across multiple modalities available in a lab, and ignore, without significant performance drops, those modalities that are not available when they operate in the wild. Our approach is tested in two multimodal affect databases one of which is designed for testing models of affect in the wild. By training our affect models using all modalities and then using solely raw footage frames for testing the models, we reach the performance of models that fuse all available modalities for both training and testing. The results are robust across both classification and regression affect modeling tasks which are dominant paradigms in affective computing. Our findings make a decisive step towards realizing affect interaction in the wild.
The statistical impact of inflation on interest rates

ABSTRACT Understanding the behavior of interest rates is of central importance in finance. This is due to the fact that interest and forward rates serve as underlyings for many fixed income products. Furthermore, interest rate-based quantities may be used as numeraires when it comes to computing present values of future payoffs. An important macroeconomic factor which is likely to trigger interest rates is inflation. In this paper we extend a well-known continuous time interest rate model by incorporating inflation. Finally, we apply a statistical test to real data to explore the goodness-of-fit of the inflation-based model.
Motion characteristics of vertically loaded anchor during drag embedment in layered clay

As a widely used taut-wire mooring system for deepwater platforms, the Vertically Loaded Anchor (VLA) has better performance in bearing capacity, angle adaptability, and deepwater installation than other systems. However, the installation process of the VLA and its motion characteristics are significantly impacted by multi-layered seabed soil. In this paper, the coupled Eulerian‒Lagrangian (CEL) large deformation finite element analysis method has been applied to analyse the continuous penetration of a VLA in nonuniform clay with an interbedded stiff layer. A detailed parametric study has been carried out to explore the trajectory, drag angle, movement direction and drag force of the VLA in layered clay with different embedded depths, thicknesses and undrained shear strength of the stiff layer. The CEL numerical analysis results have been validated by comparison with the analytical solutions from the inverse catenary equation. Excellent agreement has been obtained between the results from the CEL analyses and the analytical solutions. The stiff layer leads to concave and convex shapes on the trend lines of the movement direction angle and drag forces, respectively. The embedded depth of the stiff layer determines where the concave and convex shapes appear on the trend lines, while the thickness affects the sizes of the openings of the shapes. The most decisive parameter, an abrupt variation in the undrained shear strength, causes predominant rotation at the interface of layered clay. It diminishes the final embedment depth and ultimate stable drag force, meaning that the bearing capacity of the VLA severely declined in layered clay.
Rift Valley Fever Virus Primes Immune Responses in Aedes aegypti Cells

The ongoing global emergence of arthropod-borne (arbo) viruses has accelerated research into the interactions of these viruses with the immune systems of their vectors. Only limited information exists on how bunyaviruses, such as Rift Valley fever virus (RVFV), are sensed by mosquito immunity or escape detection. RVFV is a zoonotic phlebovirus (Bunyavirales; Phenuiviridae) of veterinary and human public health and economic importance. We have shown that the infection of mosquitoes with RVFV triggers the activation of RNA interference pathways, which moderately restrict viral replication. Here, we aimed to better understand the interactions between RVFV and other vector immune signaling pathways that might influence RVFV replication and transmission. For this, we used the immunocompetent Aedes aegypti Aag2 cell line as a model. We found that bacteria-induced immune responses restricted RVFV replication. However, virus infection alone did not alter the gene expression levels of immune effectors. Instead, it resulted in the marked enhancement of immune responses to subsequent bacterial stimulation. The gene expression levels of several mosquito immune pattern recognition receptors were altered by RVFV infection, which may contribute to this immune priming. Our findings imply that there is a complex interplay between RVFV and mosquito immunity that could be targeted in disease prevention strategies.
Vector-Angular distance color difference formula in RGB color space

Based on the study of RGB color space and the compares and analysis of several color difference formulas in RGB color space,this paper summarizes three rules of color difference in RGB color space and proposes the conception of the importance of the color component.A new color difference formula in RGB color space is achieved by dynamically adjusting the vector distance and angular between a pair of RGB colors.The dynamic coefficient is weighted by the importance of color component. Color quantization experiment of highly complex color images demonstrates that this new color difference formula’s performance is as better as CIELUV color difference formula while it saves memory and shortens the calculation time.