Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifunctional Iron Oxide Magnetic Nanoparticles for Biomedical Applications: A Review

Due to their good magnetic properties, excellent biocompatibility, and low price, magnetic iron oxide nanoparticles (IONPs) are the most commonly used magnetic nanomaterials and have been extensively explored in biomedical applications. Although magnetic IONPs can be used for a variety of applications in biomedicine, most practical applications require IONP-based platforms that can perform several tasks in parallel. Thus, appropriate engineering and integration of magnetic IONPs with different classes of organic and inorganic materials can produce multifunctional nanoplatforms that can perform several functions simultaneously, allowing their application in a broad spectrum of biomedical fields. This review article summarizes the fabrication of current composite nanoplatforms based on integration of magnetic IONPs with organic dyes, biomolecules (e.g., lipids, DNAs, aptamers, and antibodies), quantum dots, noble metal NPs, and stimuli-responsive polymers. We also highlight the recent technological advances achieved from such integrated multifunctional platforms and their potential use in biomedical applications, including dual-mode imaging for biomolecule detection, targeted drug delivery, photodynamic therapy, chemotherapy, and magnetic hyperthermia therapy.
The role of diabetes self-care education and practice in the management of type 2 diabetes mellitus (T2DM)

A – study design, B – Data Collection, C – Statistical Analysis, D – Data Interpretation, E – Manuscript Preparation, F – literature search, G – Funds Collection Background. diabetes self-care is yet to be incorporated into diabetes management policy, hence the need to establish its practice as an essential catalyst for good glycemic control in the management of diabetes patients. Objectives. This study was aimed at determining the role of diabetes self-care education (DSME) and practice in the management of T2DM patients. Material and methods. A hospital-based study was conducted on 120 adults with T2DM who had been presenting at the clinic for at least two months. The study participants were divided into two groups. Group 1 was the test group and had DSME, while group 2 served as the control. Data was collected using a questionnaire, and clinical parameters of glycemic control were measured both pre-and post-intervention period. Results. A significant relationship was found between diabetes education, diabetes self-care and glycemic control. This was demon strated by the significant difference in the proportion of those who achieved good FPG from 28.3% pre-intervention to 51.7% post--intervention ( p = 0.001); 2HPP from 30% to 46.7% ( p = 0.060) and HbA 1c from 53.3% to 85% ( p < 0.001) in the test group; while that of the control was FPG 18.3% to 30% ( p = 1.355), 2HPP 11.7% to 26.7% ( p = 0.369) and HbA 1c 43.3% to 53.3% ( p = 0.804). This showed a significant improvement in the glycemic control of the test group compared to the control group. Conclusions. We conclude that good knowledge and practice of diabetes self-care is relevant to the achievement of good glycemic control.
On Quality Assurance Mechanisms in Engineering Education: A Case Study of Purdue University

The ABET EC2000 reform has had a range of different impacts on U.S. universities and colleges as they work to meet accreditation standards. This paper uses Purdue University as a case study of these processes. The quality assurance mechanisms in engineering education at Purdue are deeply analyzed and presented based on data from interviews with 10 key stakeholders involved with ABET accreditation at Purdue. All stakeholders were also asked to share their views about other internal quality assurance mechanisms and current efforts to assure the quality of engineering education. The interviews were systematically analyzed using qualitative coding procedures, including inductive and deductive coding. The main data analysis tool was NVIVO 10.0. This paper finds that Purdue, as a research-oriented university, has set up a relatively well-integrated internal quality assurance system of engineering education. KEYWORD: quality assurance; engineering education; ABET; accreditation; EC2000; continuous quality improvement
Optimization of Training Dataset Size for Predicting Homogenized Macroscopic Cross-Sections using Deep Neural Network

During the last decade, Artificial Neural Networks (ANN) became a solid instrument for solving complex and widespread problems such as image recognition or defect (anomaly) detection. At the same time, since ANNs are essentially Machine Learning (ML) algorithms, they are being successfully applied for numerical data prediction and analysis. For example, we used ANN-based surrogate models for accelerating our in-house reactor analysis codes as well as reactor simulation methodologies. Thus, we applied a Convolutional Neural Network (CNN) to the problem of reactor design parameters estimation [1]. Later, we found that using an ANN for whole-core parameters prediction was not as promising as building a surrogate ANN model for homogenized macroscopic cross-section (XS) generation [2]. That study led us to development of a brand-new nodal diffusion code system – RAST-AI. The establishment of the RAST-AI code system is briefly discussed in [3]. To train a neural network model that can predict XS data required by RAST-AI, we needed to significantly optimize our data preparation workflow. Since the idea of RAST-AI is to work with a free-range geometry, we would have to deal with way too many free-range parameters, such as Fuel Assembly (FA) layout, each Fuel Pin (FP) fuel configuration and enrichment, various operation parameters such as boron concentration (BOR), fuel temperature (TFU) change due to power change, moderator temperature (TMO), burnup, etc. Even the FP configuration alone can create a significant problem, since each 16x16 FA contains 236 FP, which can be generalized to 32 unique FP positions as a result of octant symmetry. This could theoretically lead to over 2·10 unique FA configurations in case of having all unique FP compositions. Though it sounds like a tremendously complex problem, it is something that ANNs are known to successfully deal with. Thus, a typical image recognition problem consists of theoretically infinite number of each class images, which can only be reduced to finite (though, still extremely large) number if we restrict the pixel size of images to some reasonably small value. Therefore, if we generalize the training data as much as possible, we would be able to train a model that can detect underlying dependencies between the input and output and make a prediction with reasonable accuracy. In our last publication [4], we attempted to generalize the data preparation for an ANN in terms of the required training dataset size. We found that for a general case prediction, the training dataset size can be greatly reduced. In this paper, we are discussing how this result and methodology was applied to the problem of macroscopic XS generation.
AN INTELLIGENT CONTROL USING SLIDING MODE VARIABLE STRUCTURE AND ITS APPLICATION

An intelligent control using sliding mode variable structure has been analyzed and designed in this paper. It is insensitive to uncertainties including parameter variations and external load disturbance. To overcome the two main problems with sliding mode variable structure, a fuzzy neural networks sliding mode controller is investigated. Simulation and experimental results show that the controller provides high performance dynamic characteristics and robustness.
A Review of Modeling Approaches for Understanding and Monitoring the Environmental Effects of Marine Renewable Energy

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME.
Impact of China’s OFDI to the Greater Mekong Subregion (GMS)

Abstract The study aims to investigate the impact of China's outward foreign investment (OFDI) in Cambodia, Laos, Myanmar, Vietnam, and Thailand (CLMVT). The motivation behind this paper is to examine a pivotal role in determining the macroeconomic factors in these five hosting countries as the “neighboring model” of China. Using panel data for China's outward to her five neighboring countries in the Greater Mekong Subregion (GMS) for the period 2007–2019. This paper uses two different panel specifications models named correlation from classical statistics and Bayesian statistics where empirical results of this research qualify that China's OFDI is the main factor to have a positive influence on the macroeconomic factors in CLMVT. Highlights The GMS region is currently challenging the general FDI theory from the “neighboring model” of China’s going global” strategy using OFDI as a pioneer for success for the small developing country namely, CLMVT. Many studies showed that the implementation of the “Belt and Road” strategy will help China increase investment in countries along the route, which will further promote the implementation of the “on going” strategy since 2013. We use panel data for China's outward to her five neighboring countries in the Greater Mekong Subregion (GMS) for the period 2007–2019, measures the potential of China’s outward foreign direct investment by using two different panel specifications models named correlation from classical statistics and Bayesian statistics. This research implements the core concept of Bayes’ theorem. This theorem allows us to use a priori beliefs of probability to combine with evidence that it can be found (update every economic situation in CLMVT countries) then this method will have a new prediction of the posterior probability distribution. The posterior probability distribution will be received from the simulation algorithm once again. It would be calculated from the scope of every scenario that can be happening based on our belief in the future. The Bayesian correlation testing still confirms that the FDI inflow from China per GDP of CLMVT countries has the most play important role to drive the macroeconomic of these five countries’ economy.
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning

Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning s set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is sample-inefficient. In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning. In a weakly supervised manner, we propose a spatial transform autoencoder (STAE) to learn an interpretable and controllable representation in which different parts correspond to different object attributes (shape, color, position). Due to the high controllability of the representations, STAE can simply recombine and recode the representations to generate unseen goals for agents to practice themselves. The manifold structure of the learned representation maintains consistency with the physical position, which is beneficial for reward calculation. We empirically demonstrate that DR-GRL significantly outperforms the previous methods in sample efficiency and policy generalization. In addition, DR-GRL is also easy to expand to the real robot.
Synthesis and Biological Evaluation of Chromen-2-One and Chromen-2-Imine Derivatives Bearing Aryldiazenyl Moiety as Expected Antimicrobial Agents

Abstract In an attempt to develop new antifungal and antibacterial agents; new series of chromen-2-one and chromen-2-imine derivatives bearing an aryldiazenyl group were synthesized. 2-Chromenones were synthesized through ring closure of 5-aryldiazenylsalicylaldehyde derivatives by ethyl acetoacetate, diethylmalonate and hippuric acid. 2-Chromenimines were synthesized through ring closure of 5-aryldiazenylsalicylaldehydes by malononitrile, cyanoacetamide and cyanoacetamide derivatives. The Knoevenagel reaction between 5-(aryldiazenyl)salicylaldehydes and ethyl cyanoacetate produced coumarin or iminocoumarin depends on the nature of the aromatic ring substituent. The antimicrobial activity for the synthesized chromen-2-one and chromen-2-imine derivatives were evaluated.
Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is a difficult, tedious and a time-consuming task. The limitations of manual sleep stage scor- ing have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diag- nosis and treatment of related sleep disorders. In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes utilizing the Twin Convolutional Model FTC2, including restless leg syndrome, insomnia, based on an algorithm which is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.
Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data

Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. As more vehicles are equipped with GPS devices, remote sensing data can be collected and used to conduct data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS data from Shenzhen. The experimental results show that the proposed model outperforms both the classic time series prediction model and the deep learning model at different scales.
Distributed Frequency Divider for Power System Bus Frequency Online Estimation Considering Virtual Inertia From DFIGs

In this paper, a distributed frequency divider is proposed to estimate power system bus frequency with a limited number of PMUs while considering the inertial contributions from double-fed induction generators (DFIGs). The key idea is to reformulate the original frequency divider by modeling the contributions of DFIGs with inertia emulation and external regional power system through Thevenin equivalents. The distributed frequency divider is general and able to estimate local bus frequencies in a distributed manner. Besides, only the knowledge of the rotor speeds of synchronous generator, boundary bus frequencies and terminal bus frequencies of DFIGs as well as the admittance matrix are required to estimate the frequencies at all buses. This is drastically different from existing approaches that require system observability by PMUs to monitor all bus frequencies. Numerical results carried out on the IEEE 39-bus and modified 118-bus power systems with DFIGs demonstrate and effectiveness and robustness of the proposed method.
In Silico Study of the Effectiveness of 16S rRNA Gene as a Universal Genetic Marker to Identify Closely Related Burkholderia Spp. in Panicle Blight of Rice

The 16S rRNA gene is a housekeeping genetic marker that is available in almost all bacterial species and it is used in bacterial phylogeny and taxonomy studies. In many studies, the 16S rRNA gene is used in identification of certain bacterial species. Being a less conserved genetic marker, certain studies found it is a useful tool to infer the genome-wide similarity levels among the closely related prokaryotic organisms. Thus, this study aimed to compare the variation in the 16S rRNA partial region of Burkholderia spp. that infect the panicle of rice from eight different geographical areas. 58 sequences with total of 688 base pairs (bp) of 16S rRNA gene in B. glumae and B. gladioli were retrieved from public database based on several countries namely United State, Panama, Ecuador, Thailand, China, India, Korea and Malaysia. Then, the data sequences were analysed and validated using MEGAX and ABGD software respectively. The result of phylogenetic tree confirmed that B. glumae and B. gladioli were species that present in the panicle blight of rice. However, Data Analysis in Molecular Biology and Evolution (DAMBE) and Automatic Barcode Gap Discovery (ABGD) software were not able to detect substitution saturation and divergence between B. glumae and B. gladioli respectively based on the 58 sequences of the 16S rRNA partial region. Hence, it proves that 16S rRNA gene is an ineffective genetic marker to be used to differentiate the closely related species of bacteria from similar genus.
Uber Versus Trains? Worldwide Evidence from Transit Expansions

There is a contentious debate on whether ride-hailing complements or substitutes public transportation. We address this question using novel data and an innovative identification strategy. Our identification strategy relies on exogenous variation in local transit availability caused by rail expansions. Using proprietary, anonymized trip data from Uber for 35 countries, we use a dynamic difference-in-differences strategy to estimate how transit expansions affect local Uber ridership in 100 m distance bands centered on the new train station. Our estimates compare Uber ridership within a distance band before and after a train station opens relative to the next further out distance band. Total effects are obtained by aggregating relative effects at all further distance bands. We find that a new rail station opening increases Uber ridership within 100 m of the station by 60%, and that this effect decays to zero for distances beyond 300 m. This sharp test implies Uber and rail transit are complements.
Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost

A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors’ previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3–3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below −25 dB on boresight and below −18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced.
Research of Roundness Error Visual Evaluation System

In order to realize the visual evaluation of roundness error,the system program language is adopted with LabVIEW by using the least square and minimum area of two evaluation methods.According to the established mathematical model,the roundness error visual evaluation based on virtual instrument system is constructed,the system has data acquisition,storage,analysis and display results.It is low manufacturing cost,easy to use and good function expansion.
Toxicological Information Literacy Protects Human Health

ABSTRACT Chemicals are used extensively in modern societies. Evidence-based, updated safety information is necessary for using chemicals correctly and for protecting the health of humans and environment. This paper describes safety information life cycle of chemicals and highlights why it is important that individuals need to acquire critical toxicological information literacy skills to identify reliable information. Six competence standards of toxicological information literacy are defined according to the principles of the Association of College and Research Libraries. We also discuss the reliability of open access information sources.