Miracle-dz/UniFAR-FTUs huggingface dataset:
The Nanostructure of Polymer-Active Principle Microparticles Produced by Supercritical CO2 Assisted Processing

Traditional and supercritical CO2 assisted processes are frequently used to produce microparticles formed by a biopolymer containing an active principle to improve the bioavailability of the active principle. However, information about the internal organization of these microparticles is still scarce. In this work, a suspension of dextran + Fe3O4 nanoparticles (model system) and a solution of polyvinylpyrrolidone (PVP) + curcumin were used to produce spherical microparticles by supercritical CO2 processing. Periodic dynamic light scattering measurements were used to analyze the evolution of the microparticles dissolution, size, and size distribution of the guest active principle in the polymeric matrix. It was found that curcumin was dispersed in the form of nanoparticles in the PVP microparticles, whose size largely depended on its relative concentration. These results were validated by transmission electron microscopy and scanning electron microscopy of the PVP microparticles and curcumin nanoparticles, before and after the dissolution tests.
TECHNOLOGY ACCEPTANCE OF OPTIMIZATION IN SEARCH ENGINE (INTERNET) AMONG OPTIMIZER IN INDONESIA

There is new and vivid phenomenon among entrepreneurs and practitioners in Indonesia called OPTIMIZer. OPTIMIZer is a person who learn Optimization skill. There are 22 branches of Optimization skill, such as Optimization in search engine, social media (facebook, twitter, etc.), mobile devices (Blackberry, Android, IOs), community web, web TV. All of them are technologies which born from Internet which is an innovation in entrepreneurship context. The aim of this paper is to investigate what factors that affect OPTIMIZer in accepting the technology (optimization skill in search engine). OPTIMIZers play an important role in today’s Indonesian entrepreneurial, because they become a ‘bridge’ between the entrepreneurs, targeted consumers, and reality of Information Technology which always changing. To reveal the factors of acceptance and understand the constructs and relationship among them, data collection from interviewing six key informants (Founders of Optimization skill, Mentor, OPTIMIZers) being anayzed using multiple case studies. Previous research from related context such as Entrepreneurship, SEO (Search Engine Optimization), and Technology Acceptance will be reviewed. The expected contribution is to give fresh insight about new concept of nowdays entrepreneurship, and also to fill the knowledge gap for technology acceptance in entrepreneurship context related in Information Technology field.
The Role of ABUS in The Diagnosis of Breast Cancer

Abstract Breast cancer, which is the most common cancer in women, is a major problem both in Poland and worldwide. Mammography remains the primary screening method. However, the sensitivity of mammographic screening is lower in women with dense glandular breasts due to tissue overlap and the effect of the glandular tissue obscuring the tumor and the fact that tumors and glandular tissue show similar X-ray absorption. Consequently, other methods are being sought to increase breast cancer detection rates. Currently, the most common and used methods are ultrasonography, magnetic resonance imaging and advanced mammographic methods (digital breast tomosynthesis and contrast-enhanced spectral mammography). Despite many advantages and superiority over mammography in dense breasts, they also have many disadvantages. Ultrasound is operator-dependent and the other techniques are expensive or not widely available. The Automated Breast Ultrasound Service (ABUS) technique appears to be a good option in terms of both effectiveness and lower cost.
Seabed Modelling by Means of Airborne Laser Bathymetry Data and Imbalanced Learning for Offshore Mapping

An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data. The prepared data set was used to train the Multi-Layer Perceptron (MLP) neural network used for classifying the point cloud. Data balancing contributed to significantly increasing the accuracy of classification. The best overall classification accuracy achieved varied from 95.8% to 97.0%, depending on the oversampling algorithm used, and was significantly better than the classification accuracy obtained for unbalanced data and data with downsampling (89.6% and 93.5%, respectively). Some of the algorithms allow for 10% increased detection of points on the objects compared to unbalanced data or data with simple downsampling. The results suggest that the use of selected oversampling algorithms can aid in improving the point cloud classification and making the airborne laser bathymetry technique more appropriate for seabed mapping.
Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

Recently, hybrid wind/PV microgrids have gained great attention all over the world. It has the merits of being environmentally friendly, reliable, sustainable, and efficient compared to its counterparts. Though there has been great development in this issue, the control and energy management of these systems still face challenges. The source of those challenges is the intermittent nature of both wind and PV energy. On the other hand, a new intelligent control technique called Brain Emotional Learning-Based Intelligent Controller (BELBIC) has garnered more interest. This paper proposes the control and energy management of hybrid wind/PV microgrids using a BELBIC controller. To design the system, simple power and energy analyses were proposed. The proposed microgrid was modeled and simulated using MATLAB. The responses of the energy system were tested under two different types of disturbances, namely step and ramp disturbances. These disturbances are applied to the wind speed, the irradiation level of the PV, and the load power. The results indicate that the AC load voltage and frequency are steady with negligible transients against the previous disturbance. In addition, the performance is better than that of the classical PI controller. Also, energy management acts perfectly to compensate for the intermittence and disturbances of the wind and PV energies. On the other hand, the system robustness against model parameters uncertainties in the microgrid parameters are studied.
Information Quality of Investment Services with Social Media

ABSTRACT With the increasing use of social media to provide investment information, it is still unclear what information characteristics customers value and thus enhance long-term relationships between the customers and financial consultants. This study examined the attributes of information quality and hedonic motivation that could influence social media use by customers to receive investment information from investment consultants. A sample of 460 investors in the Stock Exchange of Thailand (SET) was collected to test the proposed hypotheses. The study finds that investors’ use of social media to receive investment information and interact regularly resulted in positive relationships with investment consultants. The results highlighted the aspect of fit for use and context-specific for information quality. Some attributes were considered valuable and more influential than others from the investors’ point of view for this investment decision context.
STRUCTURAL MODELING OF OVONIC MATERIALS

The problem of phase-change mechanism at the atomic scale in ovonic materials is still challenging. Various models have been developed, but no definite conclusions have been drawn up to day. The old and new points of view on the phase change mechanism are reviewed. The structural modeling achieved recently in the group of research of the author is presented and compared to the results from literature. A general mechanism of transition from amorphous to crystalline state and back, on the case of typical phase-change material GeSb2Te4 is advanced.
The Effect of Parenting Experience During Childhood on The Later Awareness of Children’s Rights: The Mediating Effects of Self-Esteem and Emotional Intelligence

A large body of literature has shown the close relationship between parents’ parenting attitudes and their parenting experiences during their own childhood. However, it is hard to find either theoretical or empirical studies explaining why and how such close relationships develop. This study fills this gap. To do so, we extensively review the related theories and hypothesize that mothers’ parenting experiences during their own childhood affect their later level of awareness of children’s rights through the mediation effects of two psychological factors: self-esteem and emotional intelligence. Applying structural equation modeling to a vast dataset acquired by surveying 1,011 Korean mothers we provide evidence that strongly supports the main hypothesis. We also find that this relationship holds in a roundabout way: while there is no direct relationship between mothers’ parenting experiences during childhood and their current level of awareness of children’s rights, the effects of the former on the latter are indirectly mediated by self-esteem and emotional intelligence. These findings may imply that psychological programs for parents can improve their emotional intelligence and self-esteem and thus improve their practices regarding children’s rights since psychological factors can change over one’s lifetime.
Design conversations and informal chats: ‘situated’ professional development, community, and casual tutors in undergraduate architectural design education

ABSTRACT This paper argues that in undergraduate architectural design education, professional learning and the formation of casual academics as university educators occur while ‘situated’ in tutorials through design conversations with students and as-needed chats with colleagues. For casual academics, such disciplinary, routine, and informal activities develop their agency, construct/reconstruct their personal knowledge about teaching, build shared knowledge, and foster supportive communities. Given the discontinuous contractual engagements of casual academics in universities, understanding the nature and characteristics of their teaching conversations makes visible their teaching knowledge, expertise, and professional development needs as university educators.
A rough set theory and deep learning based predictive system for gender recognition using audio speech

Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.
Assessing Changes in the Value of Forest Ecosystem Services in Response to Climate Change in China

Ecosystem services are the basis of human survival and development and play an irreplaceable role in maintaining the dynamic balance of the earth’s life support system and environment. This study evaluated the annual forest ecosystem service values (ESVs) and their spatial evolution characteristics from 2001 to 2020 in China and revealed the impact of climatic factors as well. The equivalent factor method was applied to calculate ESVs in combination with net primary productivity (NPP) calculated by the CASA model. The water conservation results based on the InVEST model and equivalent factor method were compared to test the reliability of the method. From 2001 to 2020, the annual forest ESVs ranged from RMB 9.17 trillion to 10.81 trillion, with an average of RMB 9.99 trillion in China. The forest ESVs increased from the northwest to the southeast regions of China with the lowest values of less than RMB 3 million per square kilometer and relatively high values of more than RMB 9 million per square kilometer. In the past 20 years, the forest ESVs have shown a significant increasing trend, especially in the Northeast Forest Region and Southeast Forest Region. The ESVs have decreased only in very few forest areas such as Cuona and Motuo counties on the southern edge of Tibet Province in Southwest China and Pingtung and Kaohsiung counties in southern Taiwan. The mean annual forest ESV was necessarily higher in the recent 10 years (2011–2020, RMB 10.43 trillion) than in the previous 10 years (2001–2010, RMB 9.55 trillion), while the spatial growth rate was usually less than 20%. The annual forest ESVs were significantly correlated with temperature, precipitation and evapotranspiration but not with sunshine hours and relative humidity across the 20 years. In most areas (>60%), the positive impact of various climatic factors was dominant, and the area positively affected by precipitation was the largest (76%). The mean annual forest water conservation from 2001 to 2020 was RMB 1.46 trillion based on the InVEST model and was RMB 1.77 trillion based on the equivalent factor method. Overall, the results obtained by the two methods are roughly equal and mostly spatially matched. This study has some guiding significance for utilizing resources rationally, strengthening ecological environment protection and improving adaptability to climate change.
Neural-Network-Based Modeling and Analysis for Time Series Prediction of Ship Motion

This paper presents a data-driven model for time series prediction of ship motion. Prediction based on past time series of data is a powerful function in modern ship support systems. For a large amount of ship sensor data, neural network (NN) is considered as a proper tool in modeling the prediction system. Efforts are made to compact the NN structure through sensitive analysis, in which the importance of each input to the output is quantized and lower ranked inputs are eliminated. Further analysis about the impact of three different learning strategies, i.e., offline, online and hybrid learning on the NN is conducted. The hybrid learning combining the advantages of both the offline learning and the online learning exhibits superior prediction performance. According to the long term prediction ability of recurrent NN, multi-step-ahead prediction under the hybrid learning strategy is realized in a multi-stage prediction form. Experiments are carried out using collected ship sensor data on a vessel. The results show the feasibility of generating a data-driven model through modeling and analysis of the NN for ship motion prediction.
Identifying industry-specific components of product liability response system using Delphi-AHP method

Received: 1 July 2016 Abstract Accepted: 28 July 2016 PL (product liability) response system is an enterprise-wide system that prevents company’s financial loss due to PL-related accidents. Existing researches on PL response system are mainly focused on preventive and/or defense strategies for the companies. Also, it is obvious that each industry has their original characteristics related on PL issues. It means industryspecific characteristics should be considered to adopt PL response strategies. Thus, this paper aims to discuss industry-specific PL response system and their components. Based on prior researches, we tried to reveal the possibility of its application to manufacturing companies of existing PL response strategies using Delphi method with PL experts. Based on first round results, we tried to classify existing PL strategies of manufacturing companies into several categories. To validate our suggestion for essential components of PL response system, second round Delphi method are applied. Analytic hierarchy process (AHP) technique will be applied to identify a prioritized list of each components and strategies. Existing PL response strategies could be categorized with six components – strategy, technology, investment, training, awareness, and organization. Among six components, Technology – it represents the technology needed for improving the safety of all products – is the most important components to prepare PL accidents. The limitation of this paper is on the size of survey and variety of examples. However, the future study will enhance the potential of the proposed method. Regardless of rich research efforts to identify PL response strategies, there is no effort to categorize these strategies and prioritized them. Well-coordinated and actionable PL response strategies and their priorities could help small-and-medium sized enterprise (SME) to develop their own PL response system with their limited resources.
A liquid metal-based structurally embedded vascular antenna: I. Concept and multiphysical modeling

This work proposes a new concept for a reconfigurable structurally embedded vascular antenna (SEVA). The work builds on ongoing research of structurally embedded microvascular systems in laminated structures for thermal transport and self-healing and on studies of non-toxic liquid metals for reconfigurable electronics. In the example design, liquid metal-filled channels in a laminated composite act as radiating elements for a high-power planar zig-zag wire log periodic dipole antenna. Flow of liquid metal through the channels is used to limit the temperature of the composite in which the antenna is embedded. A multiphysics engineering model of the transmitting antenna is formulated that couples the electromagnetic, fluid, thermal, and mechanical responses. In part 1 of this two-part work, it is shown that the liquid metal antenna is highly reconfigurable in terms of its electromagnetic response and that dissipated thermal energy generated during high power operation can be offset by the action of circulating or cyclically replacing the liquid metal such that heat is continuously removed from the system. In fact, the SEVA can potentially outperform traditional copper-based antennas in high-power operational configurations. The coupled engineering model is implemented in an automated framework and a design of experiment study is performed to quantify first-order design trade-offs in this multifunctional structure. More rigorous design optimization is addressed in part 2.
MICROSIMULATION MODELING AND ADVANCEMENTS OF TRANSIT PRIORITY OPTIONS AT MAJOR ARTERIALS

The objective of this study is to examine the impacts of several transit priority strategies as well as to develop a conditional signal priority algorithm for a streetcar comdor in the city of Toronto. The 504 King streetcar was chosen for the study, a high fiequency route with headway of 2 minutes. Six scenarios representing different levels of transit priority were considered, and a microsimultion mode1 was developed to examine these scenarios. The mode1 was enhanced by an Application Programming Interface (API) program to enable modeling passenger-related functions. Based on the selected set of MOEs, the simulation results were analyzed to assess the relative effectiveness of the scenarios. The simulation results aiso showed that conditional TSP algorithm was effective in reducing cross m e t s delay and headway variance. However, the tested conditional TSF algorithm failed to irnprove transit headway regularity on the network with near saturated condition.
Sweet potato yield in response to different potassium sources and splitting of fertilization

ABSTRACT Sweet potatoes are an important staple food for human consumption. This study evaluated sweet potato yield in response to potassium fertilization using different sources and splitting. It was conducted at the Universidade Federal da Paraíba, Areia-PB. A complete randomized block experimental design was used in a 7 × 2 factorial arrangement. The fertilization was split into seven treatments (100% after planting; 100% at 30 days after planting (DAP); 100% at 60 DAP; 50% after planting and 50% at 30 DAP; 50% after planting and 50% at 60 DAP; 50% at 30 DAP and 50% 60 at DAP; 33% after planting, 33% at 30 DAP, and 33% at 60 DAP). Two fertilization sources were used, namely potassium chloride and potassium sulfate, with three replicates. Plant fresh mass, mass of marketable roots, production of marketable roots per plant, leaf K concentration, total and marketable root yields were evaluated. Chloride and sulfate potassium efficiently increased the fresh mass of the plant and the mass of marketable roots, respectively. The marketable yield of the roots (25.16 and 22.28 Mg ha-1) was higher than the national average (14.07 Mg ha-1) when K2O was supplied in the sulfate and potassium chloride sources, respectively. The leaf K concentration remained within the standard levels for the crops under chloride and potassium sulfate fertilization. Potassium sulfate application results in higher sweet potato yields. When supplied in a single application, chloride and sulfate potassium increase the total and commercial yield only when the plants are at 60 DAP.
Probing the hydrophilic behaviour of e-beam evaporated silica thin films for PV-soiling application

In this work, we report on the controlled change of the wetting behaviour of silica thin films deposited by a reactive e-beam evaporation. The as-deposited coated surfaces were treated with acetone solvent using an ultra-sonic bath followed by characterising their contact angles. As confirmed by a repeated contact angle measurement, the silica thin films changed their wetting behaviour from superhydrophilic to hydrophilic which we attributed to the creation of OH groups and hydroxylation process. X-ray photoelectron spectroscopy analysis suggested a SiO x stoichiometry of SiO1.85 for the non-treated samples and SiO1.91 for the solvent-treated ones. Such promising results pave the way to develop a cost-effective and efficient anti-soiling and self-cleaning coating for a large-scale application.