Miracle-dz/UniFAR-FTUs huggingface dataset:
A learning based 3D reconstruction method for point cloud

In recent years, with the rapid development of computer technology, 3D reconstruction technology has gradually entered people’s lives. 3D reconstruction is one of the more popular research directions in the field of computer vision. Three-dimensional reconstruction mainly studies the process of how to obtain three-dimensional information based on single-view or multi-view reconstruction. With the growing demand for computers to automatically obtain three-dimensional information of the surrounding environment, the practical application requirements for three-dimensional reconstruction technology are also getting higher and higher. Therefore, how to quickly and efficiently reconstruct 3D models has become an important research topic in the field of computer vision. In view of this practical application problem, this paper starts from the perspective such as Structure from Motion,Clustering Multi-view Stereo,Patch-based Multi-View Stereo and other aspects, and use the learning-based method to match the sift feature points. In this paper, we mainly research on several key technologies in the 3D reconstruction process.
VSIE Method with Genetic Algorithm for Optimizing RCS Reduction From Composite Targets

In this paper, an optimization technique based on volume surface integral equation (VSIE) method and genetic algorithm (GA) is proposed for the radar cross section (RCS) reduction from composite metallic and dielectric (CMD) targets. This VSIE scheme utilizes the tetrahedral/triangular elements for volume (surface) modeling, and the SWG/RWG basis functions for volume/surface current modeling. The solutions of VSIE for the electromagnetic scattering of CMD targets is integrated into a genetic algorithm (GA) optimization technique to obtain the best optimal composite coating by optimizing the permittivity parameters. In contrast to the traditional VSIE, our scheme, which is reported for the first time in VSIE-GA, shows much more flexible, and requires less computational time without loss of solution accuracy. Numerical results are given to demonstrate the accuracy, efficiency, and flexibility of this proposed VSIE-GA method.
Microwave Interferometry Measurements of Yeast Cell Suspension and Sediment Process

A microwave interferometry based coplanar waveguide sensor is proposed for cell dynamic process monitoring applications. Combining the ease of microfluidics integration and impedance modeling from coplanar waveguide with high sensitivity and wide frequency range from interferometer structure, the designed setup has proved its potential for biological applications. Based on characteristic of interferometer and sensor modeling, the algorithm for extraction of the material under test complex permittivity from the S-parameters changes has been developed. Measurement and calculation of 2 mg/ml yeast cell suspension have been carried out to validate the setup, which agreed well with reference data in literature. Furthermore, monitoring of yeast cell sediment process was performed to lay the groundwork for future cell growth monitoring.
ATM Inhibitor Suppresses Gemcitabine-Resistant BTC Growth in a Polymerase θ Deficiency-Dependent Manner

Patients with advanced biliary tract cancer (BTC) inevitably experience progression after first-line, gemcitabine-based chemotherapy, due to chemo-resistance. The genetic alterations of DNA damage repair (DDR) genes are usually determined in BTC tumors. In this study, we found that the POLQ mRNA levels are downregulated and the ataxia-telangiectasia mutated (ATM) inhibitor AZD0156 was more sensitive in gemcitabine-resistant BTC sublines than in the parental cell lines. The knockdown of DNA polymerase θ does not affect cell proliferation, but its combination with the ATM inhibitor facilitated cell death in gemcitabine-resistant and gemcitabine-intensive BTC cells. Moreover, in the DNA damage caused by photon, hydrogen peroxide, or chemotherapy drugs, synthetic lethal interactions were found in combination with ATM inhibition by AZD0156 and DNA polymerase θ depletion, resulting in increased DNA damage accumulation and micronucleus formation, as well as reduced cell survival and colony formation. Collectively, our results reveal that ATM acts as a potential target in gemcitabine-resistant and DNA polymerase θ-deficient BTC.
Machine learning techniques for detecting motor imagery in upper limbs

Nowadays, the human machine interfaces have increased the applications for improving the quality of life in injured people. In spite of the progress in the field, new strategies are important to contribute to solve new problems. This proposal shows the employing of feature extraction in time and frequency domains. Three machine learning techniques as KNN, SVM and Random Forest were used to detect motor imagery from EEG signals. Comparison for feature extraction and the employed detection models were analyzed to find the best election in an application for close-open fist in hands. The results achieved more than 90% in accuracy for both approaches, showing as the frequency domain is preferable for feature extraction and the employment of the KNN classifier as best strategy for the present demand.
The Theory of Economic Nonequilibrium in Application to the Regional Disparities Problems: The Impact of Coronacrisis

This the article considers the main postulates of the theory in relation to the regional economic space. Based on statistical data, the author confirms the hypothesis that different regions of the Russian Federation are in a state of recession and inflation gaps. For recession gap price level is stable above the equilibrium, for inflation, on the contrary, the price level stable below the equilibrium.
Experimental Implementation of Collective Motion based on Swarm Robotic Control

Collective behavior is now observed commonly in animal groups, while there is still a giant gap between theoretical research and implementation on the on real swarm robotic systems. Collective motion is a kind of consensus behavior in which robots show self-organized aggregation and alignment. Different from the shape formation of a swarm of robots in a fixed speed, the collective motion requires each robot to change its both linear and angular speed spontaneously. We adapt a decentralized algorithm which has the similar interaction method with animal group to the collective motion of swarm robotic control, considering the physical limits of the robots. Using e-puck robots, we show that the algorithm can make the robots aggregate and align with each other automatically on the condition of a bounded circle scenario.
Cooling System Design and Thermal analysis of a PMSM for Rail Transit

In the field of rail transit, permanent magnet synchronous motor (PMSM) is widely used due to high power density, high efficiency and small volume. Temperature is an important factor affecting motor performance, so it is necessary to analyze the temperature of the motor. In this paper, the thermal analysis considering thermal contacts and the cooling system design for a permanent magnet synchronous motor are investigated. The calculation methods of heat source and heat transfer coefficient are introduced. The finite element method (FEM) is used for temperature analysis. In the simulation model, the copper and insulation of the winding are simplified into an equivalent material. Finally, the cooling effects of axial direction and circumferential direction are compared.
Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network

Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder’s perception. The crude oil price changes every minute, and millions of shares ownerships are traded everyday. The market price for commodity such as crude oil is influenced by many factors including news, supply-and-demand gap, labour costs, amount of remaining resources, as well as stakeholders’ perception. Therefore, various indicators for technical analysis have been utilized for the purpose of predicting the future crude oil price. Recently, many researchers have turned to machine learning approached to cater to this problem. This study demonstrated the use of RNN-LSTM networks for predicting the crude oil price based on historical data alongside other technical analysis indicators. This study aims to certify the capability of a prediction model built based on the RNN-LSTM network to predict the future price of crude oil. The developed model is trained and evaluated against accuracy matrices to assess the capability of the network to provide an improvement of the accuracy of crude oil price prediction as compared to other strategies. The result obtained from the model shows a promising prediction capability of the RNN-LSTM algorithm for predicting crude oil price movement.
Design and Development of Machine Learning Technique for Software Project Risk Assessment - A Review

Accurate assessment of software project risk is amongst the key activities in a software project. It directly impacts the time and cost of software projects. This paper presents a literature review of designing & developing machine learning techniques for software project risk assessment. The results of the review have concluded prominent trends of machine learning approaches, size metrics, and study findings in the growth and advancement of machine learning in project management. Besides that, this research provides a deeper insight and an important framework for future work in the software project risk assessment. Furthermore, we demonstrated that the assessment of project risk using machine-learning is more efficient in reducing a project's fault. It also increases the probability for the software project's prediction and response, provides a further way to reduce the probability chances of failure effectively and to increase the software development performance ratio.
A Model-View-ViewModel (MVVM) Application Framework for Hearing Impairment Diagnosis - Design and Features

Around 466 million people worldwide (over 5% of the world's population) have disabling hearing loss, and out of these 34 million are children. Estimates suggest that by 2050, over 900 million people worldwide will have disabling hearing loss. The annual global cost of unaddressed hearing loss amounts to US$ 750 billion. Early detection of hearing loss can reduce its impact on an individual's life in addition to saving a huge cost. This paper, the first in a series of three papers, presents the design and features of an open-source application framework for hearing impairment diagnosis. The framework is built using the Model-View-ViewModel (MVVM) pattern which separates the development of graphical user interface (GUI) from the development of business and back-end logic. Some of the benefits of the MVVM pattern include reusable components, independent development of GUI and business or back-end logic, flexibility to modify GUI without having to change business or back-end logic, ease of unit testing, and reduced maintenance overhead. The proposed framework along with the open-source code makes it possible to easily extend the application functionality thus enabling other researchers and practitioners to develop their own versions of hearing loss diagnosis applications. The proposed software was evaluated by an otolaryngologist and found to be very beneficial in assisting a clinician to reach a hearing impairment diagnosis conclusion more methodically, swiftly and accurately.
Student Dropout Prediction: A KMUTT Case Study

Higher education is a key factor in Thai national development. Promoting educational quality and improving the quality of learners are challenging for our government. One of the most common educational problems is a university dropout problem which has a negative impact not only on the economic level but also on the personal level of students. Then, in this study, we focus on the study of factors affecting undergraduates’ educational status and to create binary classification models for predicting their educational status whether will be Dropout or Other (i.e., other statuses which are not dropout) when they were studying at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand. By applying the principles of data mining and machine learning techniques, we first collect data from the KMUTT internal data sources, i.e., Registrar’s Office and KMUTT Library After that we investigate the completeness and the quality of the data, as well as we, investigate which features are meant to improve the accuracy and applicability of our models. Particularly, the data used in our analysis are from 13,714 undergraduate students in 7 academic years which are from the academic year 2012-2013 to 2019-2020. We develop 3 classifiers based on a decision tree, random forest, and gradient boosting classification. The results show that the prediction accuracy of gradient boosting, decision tree and random forest models are 93%, 92%, and 92% respectively. Moreover, we found that the top 5 important features are student’s academic year, high-school GPA, channels of university admission, student’s faculty, and gender. In summary, the model constructed using gradient boosting outperforms the others with the most accuracy and recall but the random forest model can outperform the others with the most dropout status precision.
FISCAL-MONETARY CO-ORDINATION IN INDIA: AN ASSESSMENT

3.1 This chapter provides an assessment of fi scal-monetary co-ordination in India, covering institutional developments and empirical trends and analysis. The evidence in the chapter suggests that while several institutional changes have helped bring about improved fi scal-monetary co-ordination and moderated the fi scal dominance of monetary policy, new challenges have surfaced that impinge on monetary policy effi cacy.
On the Direct Voltage Control Concept of Grid Connected Power Converters

With the increasing number of sustainable sources integrated into the electrical grid, the demand for new and improved control concepts for grid connected power converters to ensure grid stability is growing. One control concept developed to face future challenges is the direct voltage control (DVC). Within this paper, the DVC is analysed and classified as a grid-supporting control concept. It is pointed out, that the new approach is still based on the classical current control with the only change in the replacement of the PI controller by high-pass washout filters. The modified current control is investigated and an alternative approach improving the dynamic performance is recommended. The results are verified by setting up and analysing the transfer function as well as by time domain simulations.
Discovery of 12O—A Novel Oral Multi-Kinase Inhibitor for the Treatment of Solid Tumor

A novel series of pyrimidine-benzotriazole derivatives have been synthesized and evaluated for their anticancer activity against human solid tumor cell lines. The most promising molecule 12O was identified for its excellent antiproliferative activities, especially against the SiHa cell line with IC50 value as 0.009 μM. Kinase inhibition assay assessed 12O was a potential multi-kinase inhibitor, which possessed potent inhibitory activities against cyclin-dependent kinases (CDKs) and fms-like tyrosine kinase (FLT) with IC50 values in the nanomolar range. Molecular docking studies illustrated that the introduction of triazole moiety in 12O was critical for CDKs inhibition. In addition, 12O inhibited cancer cell proliferation, colony-formation, and cell cycle progression and provoked apoptotic death in vitro. In an SiHa xenograft mouse model, a once-daily dose of compound 12O at 20 mg/kg significantly suppressed the tumor growth without obvious toxicity. Taken together, 12O provided valuable guide for further structural optimization for CDKs and FLT inhibitors.
A Flexible Transport Layer Protocol Architecture for Handover in a Vehicular VLC Network

Recent research works have focused on the feasibility of using the multipath-transmission control protocol (MPTCP) in order to optimize network throughput and latency. In this work, we propose a novel architecture using MPTCP for a vehicular visible light communications (VLC) network to improve the performance in terms of network outage duration and throughout. Two relevant MPTCP schedulers and an MPTCP tool are selected to analyze VLC performance during the handover. The results show that the proposed system offers low-outage duration handover of 24 ms and high data throughput of 125 Mbps using "Redundant" and "Default" schedulers, respectively.
Step-Up Series-Resonant DC-DC Converter with Switched Mode Rectifier Operating at Fixed Switching Frequency

This paper studies the feasibility of voltage step-up implementation of the series-resonant dc-dc converters employing the switched mode rectifier. The semi-active rectifier utilized at the output side of the converter enables the boost regulation of the input voltage. The proposed solution provides an alternative to the existing topologies where twice the number of high-voltage switches is used at the expense of an additional diode. Also, the converter is controlled at a fixed switching frequency close to the resonant frequency. As a result, it requires small resonant inductance that could be integrated into the isolating transformer. The steady-state analysis of the lossless converter is provided. The theoretical results are verified using a 200 W prototype. The experimental results match the theoretical results well despite ignoring the converter losses in the analysis.
Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user’s transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.
Comparison of three advanced oxidation processes for degradation of textile dyes

This paper examines the use of three advanced oxidation processes in degrading the textile dye, Reactive Red 235. The dye was oxidised using ozonation, standard Fenton's reagent (Fe2+/H2O2) and a modified photo-Fenton's process (UV/Fe oxalate/H2O2) under a variety of conditions. The effects of initial dye concentration, initial pH and NaCl concentration were studied for each process. Each process was found to have different optimum conditions as determined by the underlying reaction mechanisms.
Nodal conduction block: A unifying concept

A newly introduced term, “axonal conduction block,” brought a confusion in the electrodiagnostic diagnosis of Guillain‐Barrè syndrome (GBS). I am proposing the term “nodal conduction block” for “axonal conduction block. ” This unifying concept of nodal conduction block will accommodate both the traditional concept of demyelination as well as the new concept of nodopathy in the “axonal form of GBS,”, making the practice of electrodiagnosis much easier.
Cell Type-Specific Adhesion and Migration on Laser-Structured Opaque Surfaces

Cytocompatibility is essential for implant approval. However, initial in vitro screenings mainly include the quantity of adherent immortalized cells and cytotoxicity. Other vital parameters, such as cell migration and an in-depth understanding of the interaction between native tissue cells and implant surfaces, are rarely considered. We investigated different laser-fabricated spike structures using primary and immortalized cell lines of fibroblasts and osteoblasts and included quantification of the cell area, aspect ratio, and focal adhesions. Furthermore, we examined the three-dimensional cell interactions with spike topographies and developed a tailored migration assay for long-term monitoring on opaque materials. While fibroblasts and osteoblasts on small spikes retained their normal morphology, cells on medium and large spikes sank into the structures, affecting the composition of the cytoskeleton and thereby changing cell shape. Up to 14 days, migration appeared stronger on small spikes, probably as a consequence of adequate focal adhesion formation and an intact cytoskeleton, whereas human primary cells revealed differences in comparison to immortalized cell lines. The use of primary cells, analysis of the cell–implant structure interaction as well as cell migration might strengthen the evaluation of cytocompatibility and thereby improve the validity regarding the putative in vivo performance of implant material.
Functional disconnection between subsystems of the default mode network in schizophrenia

Abstract Background A dysfunctional default mode network (DMN) has been reported in patients with schizophrenia. However, the stability of the deficits has not been determined across different stages of the disorder. Methods We examined the functional connectivity of the DMN subsystems of 125 patients with first-episode schizophrenia (FES) or recurrent schizophrenia (RES), compared to that of 82 healthy controls. We tested the robustness of the findings in an independent cohort of 158 patients and 39 healthy controls. We performed resting-state functional connectivity analysis, and examined the strength of the connections within and between the three subsystems of the DMN (core, dorsal medial prefrontal cortex [dMPFC], and medial temporal lobe [MTL]). We also analyzed the connectivity correlations to symptoms and illness duration. Results We found reduced connectivity strength between the core and MTL subsystems in schizophrenia patients compared to controls, with no differences between the FES and RES patient groups; these findings were validated in the second sample. Schizophrenia patients also showed a significant reduction in connectivity within the MTL and between the dMPFC−MTL subsystems, similarly between FES and RES groups. The connectivity strength within the core subsystem was negatively correlated with clinical symptoms in schizophrenia. There was no significant correlation between the DMN subsystem connectivity and illness duration. Conclusions DMN subsystem connectivity deficits are present in schizophrenia, and the homochronicity of their appearance indicates the trait-like nature of these alterations. The DMN deficit may be useful for early diagnosis, and MTL dysfunction may be a crucial mechanism underlying schizophrenia.