Miracle-dz/UniFAR-FTUs huggingface dataset:
Internship Management System (Planning Phase)

This research seeks to develop an internship management system (ISM) at the university level that plays a crucial role as an intermediary platform between internship stakeholders, which are interns, academic supervisors, and field supervisors. The system development life cycle methodology was followed to develop the internship system, which consists of four phases, i.e., planning, analysis, design, and implementation. This study shows only one phase, which is the planning phase.
Centralized Multi-Robot Collaborative LiDAR SLAM Utilizing Loop Closure Selection

Multi-robot systems have attracted much attention due to their high efficiency and robustness. Multi-robot Simultaneous Localization and Mapping (SLAM) is the cornerstone of collaboration, such that each robot can localize and build a map of the workspace. One of the key challenges of this problem lies in utilizing the shared information between robots adequately so that the system can retain the correct information and reject the outliers. Here we propose a centralized multi-robot collaborative LiDAR SLAM framework for robots, each only equipped with a 2D LiDAR without the need for additional sensors. With each robot able to run simple LiDAR odometry onboard, a central server with greater computational capacity collects the information from robots, optimizes their poses globally, and updates their poses through communication. For a large number of loop closures in multi-robot SLAM systems, it is catastrophic to build wrong data associations between pose nodes in the global factor graph. We resort to a loop closure selection method to ensure the correctness and consistency of global optimization. Throughout the verification process on the real Automated Mobile Robot (AMR) platform, the framework exhibits better performance on localization and mapping.
Hierarchical Domain Prior CycleGAN: Translated Satellites ISAR Image from the Optical Domain

Despite the deep learning has shown revolutionary success in classification tasks, the performance is determined by the training data. Therefore, image augmentation and generation are necessary for the tasks that are lack of sufficient training samples. This paper focuses on generating Inverse Synthetic Aperture Radar (ISAR) images from the optical counterparts, in particular, for the classification of the satellite tasks. We propose a hierarchical domain prior CycleGAN (HDP-CycleGAN) to achieve an image translation between the ISAR and optical satellite images, as such providing synthetic training samples for deep classification models. The proposed HDP-CycleGAN constitutes a physical feature projection consistency for ISAR scattering distribution feature extraction and a classification-consistency to integrate the identifying feature with the generative model. Extensive simulations validate that the obtained ISAR images have better visible-authenticity and training-effectiveness than the existing image translation alternatives.
Detection method for driver's gaze direction based on improved VGG network

Gaze detection is the basis for driver state monitoring and intent recognition as well as vision-based human-vehicle interaction. This paper proposed an appearance-based gaze direction vector detection model, taking the eye image, head posture and pupil center position as sample data and the gaze direction vector as sample labels, and using the improved VGG deep learning network for training to obtain the driver's gaze direction vector, and completing the gaze direction detection of the driver's gazing at different target regions. In the real car environment, we carried out experiments to recognize 6 different regions of the windshield, and the results show that the gaze direction detection method proposed in this paper is basically unaffected by changes in head posture and driving environment illumination, and the average detection accuracy for 10 drivers is 96.53%.
PIN Diodes Integrated Switchable Trans-Receiver Short Range Radar Antenna for Automotive Radar Applications

A Short Range Radar (SRR) antenna for automotive applications is proposed in this paper. Substrate Integrated Waveguide (SIW) slot array antenna at 24 GHz is designed to operate in SRR mode. The slots are designed in a 2x4 arrangement in the SIW slot antenna array, where lx2 Y junction power divider is implemented in which each arm of the power divider feeds the lx4 slotted array. Here, the same SIW-slot radiator can act as the transmitting and receiving antenna based on the status (ON/OFF) of the two RF PIN diodes, placed in the ground plane of the antenna. To obtain the proposed transceiver functionality of-the-shelf DSG9500-000 RF PIN diodes are used in this work. The designed transceiver yields a maximum gain of 7.72 dBi and 4 dBi in in the receiving and the transmitting mode respectively. The proposed idea finds application in the field of short range radar which focuses on parking assistance, pre-crash sensing, blind spot detection, obstacle detection.
A lumped-element directional coupler with arbitrary output amplitude and phase distributions

A directional coupler with arbitrary output amplitude and phase distributions is proposed. Design equations and experimental results are presented and compared with numerical simulations. During the presentation, the use of the coupler as a building block for a Butler matrix will be shown. The proposed coupler achieves a significant size reduction over conventional couplers. It is five times smaller than a classical branchline coupler, and may provide an attractive solution for applications that require size reduction.
A Flexible Crosstalk-Free Piezoresistive Pressure Sensor Array Based on Interdigital-Electrode for High-Precision Pressure Mapping

Flexible piezoresistive pressure sensor arrays have become an attractive field of research due to their extensive use in human-machine interface, human posture monitoring, healthcare, and other scenarios. However, the inherent crosstalk issue always affects the sensing performance of piezoresistive sensor arrays. In this study, a flexible crosstalk-free pressure sensor array with an interdigital-electrode layout structure is demonstrated. The device mainly consists of an interdigital-electrode (IDE) array on a flexible printed circuit (FPC) board, pressure-sensitive Velostat films, and current-regulating diodes. The combination of IDE and diodes allows a simplified sensor structure with an excellent ability to pattern crosstalk-free pres-sure distributions. The performance of a single sensing unit has been fully characterized, and a high spatial resolution pressure sensor array of $9\times 14$ sensing units is successfully fabricated and tested. As examples of pressure mapping, the proposed sensor array has been used to measure the pressure distribution of foot stepping, palm pressing, and sitting with high precision.
THE MECHANISM OF ACTION OF 3‐DEAZAURIDINE IN TUMOR CELLS SENSITIVE AND RESISTANT TO ARABINOSYLCYTOSINE *

Deazauridine inhibited growth of tumor cells in culture and in culture and in vivo; this agent was significantly more effective against L1210/AraC than against the parent sensitive line. Inhibition of growth of tumor cells in culture was prevented by uridine and cytidine and was partially alleviated by deoxycytidine, but not by deoxyuridine or thymidine. DeazaUR inhibited nucleic acid synthesis but not protein synthesis in tumor cells in culture; deoxycytidine alleviated inhibition of nucleic acid synthesis. The labeling of pyrimidine ribonucleotides by 6-14C-orotic acid was inhbited by deazaUR. DeazaUR treatment of tumor cells in culture resulted in increased uptake of cytidine-3H into RNA, whereas uridine-3H uptake into RNA was inhibited. Labelling of DNA by uridine-3H/ and cytidine-H was inhibited by deazaUR. Pools of CMP, CDP, and CTP decreased markedly during deazaUR treatment of L1210 cells in culture and in vivo. These observations in growing cells pointed to deazaUR inhibition of the synthesis of cytidylic acid. Deazauridine 5'-triphosphate was found to be an inhibitor of the synthesis of CTP from UTP catalyzed by enzyme preparations from L1210 cells. This observation is in agreement with those of McPartland et al.19 that deazaUTP inhibited CTP synthetase purified from calf liver. Deazauridine treatment of L1210 cells in culture stimulated the uptake of deoxycytidine-3H into DNA while inhibiting the uptake of 3H-labeled deoxyuridine, thymidine, deoxyadenosine, and deoxyguanosine. Intracellular pools of dCTP were decreased by deazauridine treatment in L1210 cells in culture and in vivo. Deazauridine 5'-diphosphate inhibited the enzymatic reduction of pyrimidine ribonucleoside 5'-diphosphates to the corresponding deoxyribonucleotides. These results are consistent with the view that deazauridine, after its uptake and intracellular phosphorylation, strongly inhibits the formation of CTP. This is considered to be the primary metabolic effect of the analog. A secondary effect appears to be an inhibition of dCTP formation.
Detection of the Elite Structure in a Virtual Multiplex Social System by Means of a Generalised K-Core

Elites are subgroups of individuals within a society that have the ability and means to influence, lead, govern, and shape societies. Members of elites are often well connected individuals, which enables them to impose their influence to many and to quickly gather, process, and spread information. Here we argue that elites are not only composed of highly connected individuals, but also of intermediaries connecting hubs to form a cohesive and structured elite-subgroup at the core of a social network. For this purpose we present a generalization of the -core algorithm that allows to identify a social core that is composed of well-connected hubs together with their ‘connectors’. We show the validity of the idea in the framework of a virtual world defined by a massive multiplayer online game, on which we have complete information of various social networks. Exploiting this multiplex structure, we find that the hubs of the generalised -core identify those individuals that are high social performers in terms of a series of indicators that are available in the game. In addition, using a combined strategy which involves the generalised -core and the recently introduced -core, the elites of the different ’nations’ present in the game are perfectly identified as modules of the generalised -core. Interesting sudden shifts in the composition of the elite cores are observed at deep levels. We show that elite detection with the traditional -core is not possible in a reliable way. The proposed method might be useful in a series of more general applications, such as community detection.
Online Data-Driven Event-Triggered Control for Heterogeneous Nonlinear Platoons Under Communication Constraints

In this study, we propose an online learning data-driven event-triggered control approach for the synthesis of control in heterogeneous nonlinear platoons under communication constraints. The system is estimated using a Gaussian process(GPs) regression based on a squared exponential(SE) kernel, while the controller design is implemented through feedback linearization. Furthermore, event-triggered conditions are constructed to guarantee system stability. Through simulation comparisons with time-triggered control utilizing fixed sampling, the superiority of the proposed method is demonstrated.
Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m2, 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height.
Assessing the Propagation of Magnetic Nanoparticles in a Microfluidic Channel and their Behavior at the Suspension-Hydrogel Interface for On-Chip Modeling of Organs and Tissues

Oncotheranostic drugs based on magnetic nanoparticles (MNPs) are a promising method for the treatment and diagnosis of cancer. Control of the parameters and properties of MNPs is important both at all stages of their synthesis and during direct use in clinical practice. This work considers a microfluidic approach to assessing the diffusion properties of magnetic nanoparticles of various types. Using image analysis in the channel, the diffusion coefficients of ferromagnetic nanoparticles and magnetic liposomes were determined. Also, using microfluidic systems, the behavior of these particles at the suspension-hydrogel interface in the presence of a guiding magnetic field in agarose, collagen and polyacrylamide gel was studied. The considered approach is promising for the creation of organs-on-a-chip, which allow preliminary testing of magnetotheranostic drugs under conditions close to real samples.
Research of Crowd Density Estimation Algorithm Based on CAN

Population estimation refers to the use of various methods and tools to estimate the number of people in a specific area or location, which has broad significance in multiple fields. It helps to better understand population behavior, make wise decisions, and prevent emergency situations. This article introduces a crowd density estimation algorithm based on an improved Context AwareNet (CAN). Using the classic vgg-16 network structure and the ShanghaiTech dataset, the function of estimating the number of people in the target image is achieved. After the training and testing of the network model, it is found that the effect of void convolution is not as good as that of ordinary convolution, and the model has serious errors for low-density test images. By optimizing the parameters, improving the network structure, and modifying the data set, two crowd density estimation models for different types of people are generated. By combining the two models, the identification accuracy of low density people is improved and the generalization ability of the algorithm is enhanced without affecting the accuracy rate of the algorithm.
Deterministic algorithms for the Lovász Local Lemma

The Lovász Local Lemma [5] (LLL) is a powerful result in probability theory that states that the probability that none of a set of bad events happens is nonzero if the probability of each event is small compared to the number of events that depend on it. It is often used in combination with the probabilistic method for non-constructive existence proofs. A prominent application is to <i>k</i>-CNF formulas, where LLL implies that, if every clause in the formula shares variables with at most <i>d</i> ≤ <i>2<sup>k</sup> / e</i> other clauses then such a formula has a satisfying assignment. Recently, a randomized algorithm to efficiently construct a satisfying assignment was given by Moser [13]. Subsequently Moser and Tardos [14] gave a randomized algorithm to construct the structures guaranteed by the LLL in a very general algorithmic framework. We address the main problem left open by Moser and Tardos of derandomizing these algorithms efficiently. Specifically, for a <i>k</i>-CNF formula with <i>m</i> clauses and <i>d</i> ≤ <i>2<sup>k/(1+ε)</sup> / e</i> for some ε ε (0, 1), we give an algorithm that finds a satisfying assignment in time <i>Õ(m</i><sup>2(1+1ε)</sup>). This improves upon the deterministic algorithms of Moser and of Moser-Tardos with running times <i>m</i>Ω(<i>k</i><sup>2</sup>) and <i>m</i>Ω(<i>k</i>·1/ε) which are superpolynomial for <i>k</i> = ϖ(1) and upon other previous algorithms which work only for <i>d</i> ≤ 2<sup><i>k</i>/16</sup> / <i>e</i>. Our algorithm works efficiently for the asymmetric version of LLL under the algorithmic framework of Moser and Tardos [14] and is also parallelizable, i.e., has polylogarithmic running time using polynomially many processors.
THESIS FOR THE DEGREE OF LICENTIATE OF ENGINEERING

The air flow through a generator has been numerically investigated with different rotor and stator designs to give a better understanding of the flow for cooling purposes. A simple rotor design has been chosen to start with, and modified through a set of changes, where stepwise modifications have been imposed to the design of the rotor and the stator. The flow properties for all cases have been compared to each other to see the effect of each parameter change on the flow inside the machine. The flow is predicted with the OpenFOAM solverMRFSimpleFOAM , which uses the Frozen Rotor concept. This means that there is no actual mesh movement in this study, but instead, the rotating regions in the domain are provided with source terms that account for rotation. The choice of the turbulence model for solving the air flow in the generator was based on a study of the turbulence models and validation test cases. All incompressible RAS turbulence models in OpenFOAM1.5. x were studied and the implementations were compared to the original models. Many of the mentioned turbulence models were tested on a backward-facing step test case. The numerical results were compared to the experimental data and the most appropriate turbulence model was chosen. Details on turbulence model studies are discussed in Appendix A. A laminar Couette flow between two concentric cylinders was run and the numerical velocity and pressure distributions between the cylinders were compared to the analytical resutls. More details on the validation test cases are found in Appendix B.
NUMERICAL INVESTIGATION ON MECHANICAL PROPERTIES OF POLYMER COMPOSITES REINFORCED WITH MXENE NANOSHEETS

was obtained modulus of 0.33 ± 0.03 TPa. For our investigation these MXene properties were used: Young’s Modulus of 0.33 TPa; Tensile Strength of 20 MPa; Tensile Elongation (at break) 4.5 %. The interaction effects between nanofillers and surrounding polymer matrix were taken into account, as this interface has a significant influence on the strength of nanocomposites [6]. Here, we used several approaches for the investigation of interface effect on nonocomposite properties. The inverse micromechanical modelling method [7] based on experimentally obtained macro mechanical properties of composite was used to clarify the mechanical properties of Epoxy/MXene interface layer. of of the homogenization The DIGIMAT™ and of and the
User-Specific Hand Modeling from Monocular Depth Sequences

This paper presents a method for acquiring dense nonrigid shape and deformation from a single monocular depth sensor. We focus on modeling the human hand, and assume that a single rough template model is available. We combine and extend existing work on model-based tracking, subdivision surface fitting, and mesh deformation to acquire detailed hand models from as few as 15 frames of depth data. We propose an objective that measures the error of fit between each sampled data point and a continuous model surface defined by a rigged control mesh, and uses as-rigid-as-possible (ARAP) regularizers to cleanly separate the model and template geometries. A key contribution is our use of a smooth model based on subdivision surfaces that allows simultaneous optimization over both correspondences and model parameters. This avoids the use of iterated closest point (ICP) algorithms which often lead to slow convergence. Automatic initialization is obtained using a regression forest trained to infer approximate correspondences. Experiments show that the resulting meshes model the user's hand shape more accurately than just adapting the shape parameters of the skeleton, and that the retargeted skeleton accurately models the user's articulations. We investigate the effect of various modeling choices, and show the benefits of using subdivision surfaces and ARAP regularization.
What's Next?: Exploring Machine Learning-Based Approaches to Content Suggestions Using IMDb Movie Reviews

In today's data-driven world, people often rely on online reviews to decide what to watch next. Streaming services, such as Netflix and Disney+, apply machine learning approaches in recommending films based on viewing history. Sentiment analysis refers to discerning and classifying the sentiment or opinion expressed by users towards various services, such as movies, product concerns, or events. This analysis involves categorizing the sentiment as positive, negative, or neutral. This study aims to create a system that utilizes sentiment analysis to evaluate movie reviews obtained from IMDb to assist viewers in making informed decisions when selecting films that suit their preferences. Sentiment analysis (SA) simplifies the process of opinion summarization by extracting the sentiments the reviewer conveys. The algorithms employed for accuracy rate classification include the Logistic Regression model, Linear Support Vector Machines, and Multinomial Naive Bayes. The study yielded a precision rate of 0.91 from the analysis of 6,157 data evaluations, suggesting a substantial presence of negative resistance. A data splitting technique was employed, where 25% of the dataset comprising 12,500 reviews was allocated for training purposes. The level of accuracy attained was 90.288%.
Heart disease diagnosis using extreme learning based neural networks

Heart disease is the leading cause of death in Indonesia based on 2010 Hospital Information System (SIRS) Report. Early detection and treatment of heart disease will reduce the patient mortality rate. Therefore, implementation of artificial neural networks (ANN) technique in diagnosing heart disease have been widely used and reached good accuracy. Beside of that, there are disadvantages in implementation of ANN technique, such as a long training process, many parameters have to be tuned, the obtained solution potentially get stuck in local minima, and activation function must be differentiable. We implemented Extreme Learning Machine (ELM) which is fast, simple tuning, and better generalization model learning algorithm. It has better performance than backpropagation ANN, Support Vector Machine (SVM), and decision tree. The results indicate that the ELM model has potentially implemented to help medical professional in diagnosing heart disease.