Miracle-dz/UniFAR-FTUs huggingface dataset:
A binary relation inference network for constrained optimization

This thesis proposes a new parallel computing network--a binary relation inference network. The network topology is set up to match conventional optimization procedures. The network transforms some optimization problems (shortest path, transitive closure, assignment, etc.) into network convergence problems and divides the work load evenly among all processors. Link weights are known. It can operate in synchronous discrete-time, asynchronous discrete-time, and continuous-time domains. Inference network algorithms are derived directly from problem formulation, and are effective for a large range of problem sizes. The inference network consists of interconnected components; each component is composed of a unit, a number of sites attached to the unit, and links from the unit to sites on other units. Each unit is a simple computational engine. The topology of the inference network matches naturally to many optimization problems, since its deduction sites produce candidates to compete for the optimum and its decision-making units select the optimum from the site values. Either directly or through a transformation mapping to a systolic structure, discrete inference network algorithms can be implemented on a commercially available parallel processing facility, such as the Connection Machine. Analog inference network can be built using integrated circuits which solve problems efficiently in the continuous-time domain. In this thesis, mathematical analysis and numerical simulation were conducted for the synchronous, asynchronous, and analog inference network. Various applications has been discussed. The inference network has shown to solve the all-pair shortest path problem efficiently in various updating schemes. The inference network algorithm for the transitive closure problem parts derived straightforwardly from the formulation of the problem and the topology of the inference network. Efficient continuous-time solution was obtained from a non-synchronized logical circuit. The convergence of the network for the shortest path or transitive closure problems was shown to be independent of the problem size. It was demonstrated that the inference network can solve the assignment problem in a way similar to the Hopfield net's solution to the traveling salesman problem. However, the former was shown to converge for all test cases with problem size up to 128. (Abstract shortened by UMI.)
Economic viability of large-scale floating solar pv system in Nigeria: a case study of the ikang river project

The shortage of livable land, rising energy utilization, and environmental repercussions of fossil fuels are nurturing the development of renewable energy projects in the aquatic environment [1]. The oceans obtain 70% of the worldwide primary energy resource (radiation from the sun) [2]. As we know, the world’s economy is hugely reliant on fossil fuel carriers and these fossil fuel reserves are limited and expected to run out by the next century [3]. The rising demand for energy globally and the world’s economic situation with the use of available resources rationally have propelled the transition to alternative energy [4], [5]. Its goal is for sustainable development and helping approaches in the search for optimally new strategies to utilize the technologies available [6]. Among the different types of renewable energy, photovoltaic (PV) solar energy is proving reliable. Although, it has not reached adequate development, efforts are being made in PV technology research towards lower industrialized A R T I C L E I N F O ABST RACT
Sawdust-Based Concrete Composite-Filled Steel Tube Beams: An Experimental and Analytical Investigation

Incorporating waste byproducts into concrete is an innovative and promising way to minimize the environmental impact of waste material while maintaining and/or improving concrete’s mechanical characteristics and strength. The proper application of sawdust as a pozzolan in the building industry remains a significant challenge. Consequently, this study conducted an experimental evaluation of sawdust as a fill material. In particular, sawdust as a fine aggregate in concrete offers a realistic structural and economical possibility for the construction of lightweight structural systems. Failure under four-point loads was investigated for six concrete-filled steel tube (CFST) specimens. The results indicated that recycled lightweight concrete performed similarly to conventional concrete when used as a filler material in composite steel tube beams. The structural effects of sawdust substitution on ultimate load and initial stiffness were less substantial than the relative changes in the material properties, and the ultimate capacity of the tested samples decreased moderately as the substitution percentage of sawdust increased. Moreover, the maximum load capacity was observed to decrease by 6.43–30.71% for sawdust replacement levels between 5% and 45.1% across all tested samples. Additionally, when using lightweight concrete with 5% sawdust, the moment value of the CFST sample was reduced by 6.4%. Notably, the sawdust CFST samples exhibited a flexural behavior that was relatively comparable to that of the standard CFST samples.
Decomposition and Nutrient Release from Larix olgensis Stumps and Coarse Roots in Northeast China 33-Year Chronosequence Study

Stumps and coarse roots form an important C pool and nutrient pool in a Larix olgensis (Larix olgensis Henry) plantation ecosystem, and their decomposition processes would affect nutrient cycling dynamics of the overall Larix olgensis plantation. We studied the decomposition and release of nutrients from stumps and coarse roots that were cleared 0, 6, 16, 26 and 33 years ago in Northeast China. The stumps and coarse roots were divided into stump discs (SD), stump knots (SK), coarse roots (>10 cm in diameter) (CR1), medium-coarse roots (5–10 cm in diameter) (CR2) and fine-coarse roots (2–5 cm in diameter) (CR3). During the entire 33-year study period, SK, CR1, CR2 and CR3 lost 87.37%, 96.24%, 75.76% and 91.98% of their initial mass, respectively. The average annual decomposition rate (k) was 0.068 for SD, 0.052 for SK, 0.092 for CR1, 0.068 for CR2 and 0.066 for CR3. After 33 years of decomposition, CR3 lost 5% of its initial C, CR2 lost 2%, and SK accumulated 1%, indicating slow C release. The N residues in SK, CR1, CR2 and CR3 were 186%, 109%, 158% and 170%, respectively. Coarse roots released P significantly faster than SD and SK, with 13% of the initial P released in CR1. SD and SK release cellulose, hemicellulose and lignin faster than coarse roots. The results show that Larix olgensis stumps and coarse roots could contribute to soil fertility recovery and serve as a long-term nutrient reservoir for forest vegetation.
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model.
The Effect of Rest Redistribution on Kinetic and Kinematic Variables During the Countermovement Shrug

Supplemental Digital Content is Available in the Text. Abstract Meechan, D, McMahon, JJ, Suchomel, TJ, and Comfort, P. The effect of rest redistribution on kinetic and kinematic variables during the countermovement shrug. J Strength Cond Res 37(7): 1358–1366, 2023—This study compared the effects of rest redistribution (RR) on kinetic and kinematic variables during the countermovement shrug (CMS). Twenty-one male subjects (age 27.2 ± 3.3. years, height 1.78 ± 0.07 m, body mass 77.2 ± 10.6 kg, relative 1 repetition maximum (1RM) power clean [PC] 1.22 ± 0.16 kg·kg−1) performed the CMS using 140% of 1RM PC with 3 traditional sets of 6 repetitions (TS), 9 sets of 2 repetitions with RR [45 s rest after 2 repetitions] (RR45), and 6 sets of 3 repetitions with RR [72 s rest after 3 repetitions] (RR72). There were no significant or meaningful differences (p > 0.05, g = 0.00–0.15) between set configurations for any variables for the average of the 18 repetitions. There were no significant (p > 0.05) or meaningful (g = 0.00–0.14) differences for configuration and configuration × set for peak (PF) and mean force (MF), peak velocity (PV), impulse, phase duration, peak velocity decline, peak velocity maintenance, and rating of perceived exertion. There was significantly greater (p = 0.034) albeit small (g = 0.15) difference for mean velocity (MV) during TS compared with RR72. There were no significant or meaningful differences (p > 0.05, g = 0.00–0.09) between sets for PF, MF, PV, MV, impulse, and duration across TS, RR45, and RR72. Rest redistribution protocols did not result in greater kinetics or kinematics during the CMS compared with TS, when total rest time was equated. Thus, shorter more frequent rest periods during the CMS may not be required to maintain force-time characteristics.
Mechanical Design of a Biped Robot FORREST and an Extended Capture-Point-Based Walking Pattern Generator

In recent years, many studies have shown that soft robots with elastic actuators enable robust interaction with the environment. Compliant joints can protect mechanical systems and provide better dynamic performance, thus offering huge potential for further developments of humanoid robots. This paper proposes a new biped robot. The new robot combines a torque sensor-based active elastic hip and a spring-based passive elastic knee/ankle. In the first part, the mechanical design is introduced, and in the second part, the kinematics and dynamics capabilities are described. Furthermore, we introduce a new extended capture-point-based walking pattern generator that calculates footstep positions, which are used as input for the controller of our new biped robot. The main contribution of this article is the novel mechanical design and an extended walking pattern generator. The new design offers a unique solution for cable-driven bipeds to achieve both balancing and walking. Meanwhile, the new walking pattern generator can generate smooth desired curves, which is an improvement over traditional generators that use a constant zero-moment-point (ZMP). A simple cartesian controller is applied to test the performance of the walking pattern generator. Although the robot has been built, all experiments regarding the pattern generator are still simulated using MATLAB/Simulink. The focus of this work is to analyze the mechanical design and show the capabilities of the robot by applying a new pattern generator.
Preliminary Research on the Impact of Bilingual Teaching on Current Technical and Vocational Education in Taiwan

: Based on the blueprint for bilingual education as a national policy, teachers in technical and vocational schools are preparing, in full swing, to teach subjects in English. However, most teachers in the institutions do not have the skills and backgrounds to implement English-mediated instruction (EMI). Therefore, it is necessary to cultivate talents and popularize programs in technical and vocational education and educate talents to have bilingual ability. In addition, international mobility and internship experience in English is also required. The current environment needs to be improved for bilingual education, promoting that bilingual teaching is beneﬁcial to students and teachers. Even though thus puts pressure on them for learning and teaching in technical and vocational schools, bilingual education is the future direction of education in Taiwan.
Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.
NoSQL Databases in Kubernetes

With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn’t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and the available solutions for running databases in Kubernetes. Most of the current research is focused towards SQL-like databases in K8s even though the DNA of NoSQL distributed databases is more aligned with K8s. With no research being done with NoSQL databases, this project outlines the process behind setting up two famous NoSQL databases in K8s: MongoDB and Cassandra. The project also shows a representative viewpoint of the performance comparison between them using the YCSB benchmark. The project lays a foundation around the setup of these databases using K8s Operators and their benchmarking. The goal of the project is to describe the advantages of having databases in K8s, provide developers a clear path for setup and provide insights on basic benchmark performance. NoSQL Databases in Kubernetes
Identification of ApbHLH1 as a Partner Interacting with ApMYB1 to Promote Anthocyanin Biosynthesis during Autumnal Leaf Coloration in Acer palmatum

Anthocyanin biosynthesis determines the leaf color of Acer palmatum as a widely-planted landscape tree. Previously, ApMYB1 has been characterized as a positive regulator of anthocyanin biosynthesis. To further elucidate the mechanism of leaf coloration, the present study identified a basic helix-loop-helix (bHLH) transcription factor (ApbHLH1) through the phylogenetic analysis of 156 putative bHLH proteins in Acer palmatum and eight reference bHLHs which were known to be involved in the anthocyanin biosynthesis of selected plants. Protein structure comparison showed that ApbHLH1 has a conserved bHLH domain, and its N-terminal contains an MYB-interacting region. The expression of ApbHLH1 in leaves was found to not be correlated with anthocyanin contents either in green, semi-red leaves or during leaf autumnal senescence when anthocyanin content increased. ApbHLH1 expression in detached leaves was induced by exogenous senescence-promoting chemicals, including H2O2, SA, MeJA, ACC and ABA, with certain durations. In particular, either high light or low temperature induced ApbHLH1 expression significantly, and combination of high light and low temperatures seemed more effective in inducing ApbHLH1 expression. Luciferase complementation imaging assays confirmed the physical interaction between ApbHLH1 and ApMYB1, which could be abolished by either the truncating MYB-interacting region of ApbHLH1 or the deleting bHLH interacting domain of ApMYB1. The transient expression of ApbHLH1 could not induce anthocyanin production, while the co-expression of ApbHLH1 and ApMYB1 resulted in a higher accumulation of anthocyanins compared to the expression of ApMYB1 alone in tobacco leaves. Collectively, our results revealed that ApbHLH1 participated in leaf coloration through binding with ApMYB1 and enhancing the ApMYB1 function of promoting anthocyanin biosynthesis during leaf autumnal reddening in Acer palmatum. ApbHLH1 could have the potential for breeding color-leafed plants through co-transformation with ApMYB1.
Twitter Bot Detection using NLP and Graph Classification

Twitter Bot Detection using NLP and Graph Classification by Warada Jayant Kulkarni Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial users and are useful for various tasks. Unfortunately, bots do often disseminate inaccurate news and low-quality information [1]. Discriminating bots from real users is a difficult task to perform just based on content. The aim of this project is to explore and expand techniques for bot detection. A novel approach is proposed by combining text features of the news and the graph structure of the diffusion of the specific news on the OSM. In our methodology, we first perform feature extraction using Natural Language Processing techniques, like the pre-trained models, BERT [2] and spaCy [3] on the news. Next, we create diffusion graphs of the news posted by bots, and news posted by real users based on how it is propagated through the OSM. We model our problem as a graph classification one and apply graph convolutional network approaches to solve it. We use the Cresci-2017 [4] Twitter dataset for our experiments, which contains real and bot users and their tweets. We expand this dataset by performing dataset augmentation using web-scrapping to fetch additional tweets and user data to create the graphs. We analyze the graphs that we constructed based on news from bots and from real users’ graphs. An experimental comparison between the existing techniques and the proposed methodology will be performed to better understand the scope for optimization and improvement.
PRBM_A_411412 1975..1986

Background: The link between stress and Internet addiction (IA) has been confirmed. However, the mechanism underlying the correlation is poorly understood. Thus, the current study proposed a moderated mediation model to test the mediating role of anxiety and the moderating role of self-control (SC) in the link between stress and IA. Methods: Eight hundred and sixty-one Chinese college students (Mage = 20.62 years; SD = 1.58; male = 47.7%) were required to complete an online questionnaire package, including a depression-anxiety-stress scale, a self-control scale, and an Internet addiction test. The PROCESS macro developed based on SPSS was used to test the moderated mediation model. Results: When controlling for gender and age, the results revealed that anxiety partially mediated the link between stress and IA. Specifically, the more stressed college students are, the higher their anxiety level is, and the more likely they are to become addicted to the Internet. Additionally, the direct and indirect links between stress and IA were all moderated by SC. SC buffered the effect of stress on anxiety and anxiety on IA but enhanced stress on IA. Conclusion: These findings emphasized the predictor role of stress on IA and provided insights on intervening in college students’ excessive Internet use behaviors for educators, such as reducing anxiety levels and improving self-control abilities.
Analysis of Preprocessing Techniques for Missing Data in the Prediction of Sunflower Yield in Response to the Effects of Climate Change

Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of sunflower crops, we identified datasets that are not very large and have many missing values, generating a low-performance regression model. In this paper, our aim is to study and compare several approaches for missing-value imputation in order to improve our regression model. In our experiments, we compare nine imputation methods, using mean values, similar values, interpolation (linear, spline, pad), and prediction (linear regression, random forest, extreme gradient boosting regressor, and histogram gradient boosting regression). We also employ four unsupervised outlier removal algorithms and their influence on the regression model: isolation forest, minimum covariance determinant, local outlier factor and OneClass-SVM. After preprocessing, the obtained datasets are used to build regression models using the extreme gradient boosting regressor and histogram gradient boosting regression, and their performance is compared. The evaluation of the models shows an increased R2 from 0.723 when removing instances with missing data, to 0.938 for imputation using Random Forest prediction and OneClass-SVM-based outlier removal.
Vérification de systèmes hiérarchiques par raffinement

This article treats the specification and verification by model-checking the reactive hierarchical systems. The model used is the hierarchical automata which exploits the decomposition of a state in to a set of acyclic automata. In order to deal with the problem of the state explosion induced by the model-checking, we propose to use the technique of refinement. The contribution of this paper is to define the conditions of refinement between hierarchical automata. This refinement involves the refinement of their models which are Kripke structures. This property is necessary to preserve the properties of linear temporal logic on the Kripke structures. The properties are verified only on the abstract model with small size.