Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method for 3D Nanoscale Tracking of 100 nm Polystyrene Particles in Multi-Wavelength Evanescent Fields Microscopy - Absolute Difference Height Verification -

Total internal reflection is an optical imaging technique for nanoparticle tracking and observation employing the scattered light from an evanescent field near the interface or reference surface. Generally, the nanoparticle behavior is the three-dimensional Brownian motion in an aqueous medium. The motion can be traced by an optical microscopy, but it cannot be traced by an electron microscopy technique. In the three-dimensional nanoparticle moving position, the X and Y positions are parallel to the surface, which can be traced by the general microscopy techniques. However, the height position Z of a nanoparticle perpendicular to the surface could not be traced without the longitudinal scanning method. Here, a novel method is proposed to investigate the 3D position of nanoparticles by applying multi-wavelength evanescent fields microscopy, which has a high spatial resolution in the Z-direction without longitudinal scanning. This paper focuses on the verification of measurement in the Z-direction. A piezoelectric actuator was employed to control the nanoparticle displacement in height Z. Standard polystyrene 100 nm particles were randomly adhered on a spherical tip that connected with the piezoelectric actuator. The spherical tip was essentially made from an optical adhesive (n = 1.348) with a refractive index close to the water for decreasing the unnecessary signal from the tip-self during nanoparticle observation in the water. The proposed method could obtain the multi-wavelength scattering lights from the observed nanoparticles by an 8-bit color camera with higher than 50 frames per second recording to investigate the 3D nanoscale tracking. The X and Y positions of nanoparticles were determined by the centroid of the scattering light intensities. The height Z was determined from the logarithm ratios between the detected scattering light intensities of both wavelengths. The measurement repeatability of the absolute difference in height between nanoparticles could be measured less than ±16 nm by using the proposed method. The penetration height measurability range was approximated at 250 nm from the reference surface.
Algoritma Aliran Daya untuk Sistem Distribusi Radial dengan Beban Sensitif Tegangan

Analisa dan evaluasi aliran daya sangat penting untuk sistem distribusi.Begitupun juga untuk sistem distribusi radial.Banyak faktor yang mempengaruhi sistem distribusi radial, salah satunya adalah naik turunnya beban.Pada tugas akhir ini beban berubah–ubah dikarenakan perubahan dari tegangan dan ini dinamakan dengan beban sensitif tegangan. Sudah banyak algoritma daya yang berbasis komputasi digital seperti metode fast decoupled, newton raphson dan gauiss seidel. Metode – metode tersebut tidak dapat selalu digunakan untuk perhitungan pada sistem distribusi radial yang memiliki nilai rasio R/X yang tinggi [1]. Oleh sebab itu pada tugas akhir ini digunakan metode forward-backward sweep. Metode forward backward sweep ini komputasinya efektif disetiap iterasi, serta solusi dan perhitungan numeriknya tepat [2]. Dengan menggunakan metode forward backward sweepdengan beban sensitif tegangan maka simulasi akan medekati kondisi nyata seperti dilapangan. Tugas akhir ini dapat digunakan untuk mengevaluasi aliran daya pada sistem jaringan distribusi radial dengan beban sensitif.Hasil akhir adalah tegangan serta rugi-rugi daya aktif dan daya reaktif.
Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

Simple Summary Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice. Abstract Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.
DeepBacs: Bacterial image analysis using open-source deep learning approaches

Deep Learning (DL) is rapidly changing the field of microscopy, allowing for efficient analysis of complex data while often out-performing classical algorithms. This revolution has led to a significant effort to create user-friendly tools allowing biomedical researchers with little background in computer sciences to use this technology effectively. Thus far, these approaches have mainly focused on analysing microscopy images from eukaryotic samples and are still underused in microbiology. In this work, we demonstrate how to use a range of state-of-the-art artificial neural-networks particularly suited for the analysis of bacterial microscopy images, using our recently developed ZeroCostDL4Mic platform. We showcase different DL approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the DL capacity to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. To aid in the training of novice users, we provide a purposefully-built database of training and testing data, enabling bacteriologists to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of novel tools for bacterial cell biology and antibiotic research.
Rotorcraft Fuselage Drag Reduction using Combustion Powered Actuators

Separation control of the 3-D flow over the aft body of a scale model of the NASA ROBIN mod7 rotorcraft fuselage is investigated in wind tunnel experiments. Pulsed actuation is effected by arrays of momentary, combustion-based actuator jets having a characteristic time scale O[1 ms] that is an order of magnitude shorter than the convective time scale of the flow. The actuators are placed upstream of the transition region between the fuselage and the tail boom and their interactions with the massively separated cross flow in this domain and effects on the global aerodynamic forces and moments are measured using an onboard six-axis load cell and high resolution PIV that is acquired phase-locked to the actuation waveform. The present investigations have demonstrated that the actuation can significantly mitigate separation, and lead to a reduction in drag (although flow attachment is accompanied by some lift penalty). It also is shown that transitory aft flow attachment using burst-modulated actuation can be exploited for effecting significant steering aerodynamic side forces for improved flight maneuverability.
Effects of power/ground via distribution on the power/ground performance of C4/BGA packages

The effects of the distribution of power and ground vias in C4/BGA type packages are studied. Two types of via distributions are evaluated. One type has vias concentrated in the core area. The other type has vias uniformly distributed across the package. The performances of package power and ground supplies are evaluated through their resonance characteristics, impedances and effective inductances. It is found that the lowest package resonant frequency is increased by adding power and ground vias to the package. It is also found that there are no significant differences in the lowest resonant frequencies between the two types of via distributions, but the centered via distribution results in substantially lower effective package inductance.
Personalization and Localization in Human-Robot Interaction: A Review of Technical Methods

Personalization and localization are important when developing social robots for different sectors, including education, industry, healthcare or restaurants. This allows for an adjustment of robot behaviors according to the needs, preferences or personality of an individual when referring to personalization or to the social conventions or the culture of a country when referring to localization. However, there are different models that enable personalization and localization presented in the current literature, each with their advantages and drawbacks. This work aims to help researchers in the field of social robotics by reviewing and analyzing different papers in this domain. We specifically focus our review by exploring different robots that employ distinct models for the adaptation of the robot to its environment. Additionally, we study an array of methods used to adapt the nonverbal and verbal skills of social robots, including state-of-the-art techniques in artificial intelligence.
Modeling and simulation performance of sucker rod beam pump

Artificial lift is a mechanism to lift hydrocarbon, generally petroleum, from a well to surface. This is used in the case that the natural pressure from the reservoir has significantly decreased. Sucker rod beam pumping is a method of artificial lift. Sucker rod beam pump is modeled in this research as a function of geometry of the surface part, the size of sucker rod string, and fluid properties. Besides its length, sucker rod string also classified into tapered and un-tapered. At the beginning of this research, for easy modeling, the sucker rod string was assumed as un-tapered. The assumption proved non-realistic to use. Therefore, the tapered sucker rod string modeling needs building. The numerical solution of this sucker rod beam pump model is computed using finite difference method. The numerical result shows that the peak of polished rod load for sucker rod beam pump unit C-456-D-256-120, for non-tapered sucker rod string is 38504.2 lb, while for tapered rod string is 25723.3 lb. For that reason, to avoid the sucker rod string breaks due to the overload, the use of tapered sucker rod beam string is suggested in this research.
The transition from rest to full activity in muscle: the velocity of shortening

The active state of muscle is characterized by two mechanical properties, the capacity to exert or resist a force and the ability to shorten. The former is known to reach its full extent and very quickly after a stimulus; the development of the latter has been examined experimentally. Isotonic twitches of a frog's sartorius were recorded at 0ºC with equipment of high sensitivity and very small inertia. Following the latent period, about 8 msec, was required for the full velocity of shortening to be reached, after which it was maintained for some time. The delay was not due to inertia, but a substantial part of it is probably to be attributed to a statistical distribution of speed of response in individual fibres. In the ultimate contractile elements, therefore, the transition from rest to full activity is extremely rapid.
BRINGING DISCOVERY OF LOW-PROFILE SIGHTSEEING SPOTS FOR INBOUND TOURISTS - ACQUIRING TOURING SERENDIPITY USING A JOINT TOPIC MODEL -

This study proposes to provide foreign tourists with various information concerning sightseeing spots, including unvisited ones, in an interpretable manner according to relevance to their preferences. Recently, enormous amounts of travel reviews posted on world travel websites have become an important resource for travelers. Nevertheless, several sightseeing spots potentially attractive to foreigners are left undiscovered because of the information gap between reviews written by foreigners and those written by native tourists. Although the reviews written by native tourists include a wide variety of information that are unknown to foreigners, the information is not conveyed to foreigners efficiently, resulting in inbound tourism within a limited range of standard spots. In this study, a topic model, latent Dirichlet allocation (LDA), is applied to specify relevant reviews for foreigners from miscellaneous topics in the native reviews. Subsequently, the joint topic model, an extended LDA model, is applied to augmented reviews, including reviews written by foreigners and the native reviews extracted by LDA, and acquires categories of recommendable sightseeing spots as a form of topic. Each topic consists of sightseeing spots and their interpretable information, reflecting the interests of the travelers. The proposal was applied to reviews related to sightseeing spots in Japan, and it successfully acquired topics including a broader range of sightseeing spots than using information based only on foreign reviews. Moreover, several spots rarely known to foreigners were found to comply with their preferences, further supporting the performance of the proposal.
An Instructional Design Model for TPACK Object-Based Mathematics Card Games

Purpose: This study examined TPACK model as it relates to teacher’s knowledge categories such as methods of teaching subject matter (content knowledge) curriculum knowledge, knowledge about technology and pedagogical know-how etc. Approach/Methodology/Design: Conceptual analysis was discussed to establish content selection, performance procedure and problem-solving while designing an object-based game. Among the templates identified and used for Object-Based Game model are analog game model, managing learning procedure etc. The study adopted formative research in order to elucidate functional concepts and variables within the study. Findings: Games are repertoire of teaching aids and research paradigm which revolves philosophical learning theories and gaming processes. The quality of game developed depends on the qualifications; i.e. proficiency in mathematical theories and their interrelations to suit instructional concepts of game development and creative thinking abilities, pedagogical skills are required to identify learning pattern. There is a need to incorporate self-motivated experience scenes such as gaming, which characterize play and activity as being the young child’s most powerful tool in all areas of learning particularly Mathematics. Practical Implications: The study presents practical implications for teachers of mathematics. Contextualization helps learners to link new ideas to prior knowledge, and the proposed model in this study could be validated and applied in teaching mathematic concepts. Originality/value: The study adopted formative research in order to elucidate functional concepts and variables within the study. Technological Pedagogical and Content Knowledge (TPACK) object-based Mathematic card games model is designed as powerful and potential learning tools.
SPICE-Based Circuit Analysis of Power Flow Coupling Effect in Double-Input Cuk--Buck DC–DC Converters

An equivalent simulation program with integrated circuit emphasis (SPICE) based circuit is derived from the proposed small-signal averaged model of double-input Cuk--Buck dc–dc converter so that the power flow coupling effect amongst input/output ports is easy to be analyzed in terms of circuit theory rather than pure mathematics. Based on the equivalent SPICE-based circuit, common mode and differential mode power flow decomposition is presented to unveil the power flow coupling between three ports, and furthermore benefits frequency response analysis. It is shown that common mode perturbation analysis reveals the power flow coupling amongst input and output ports, whereas differential mode perturbation analysis does the power flow coupling between input ports. Meanwhile, the explanation of circuit knowledge is provided for power flow coupling effect. Particularly, it is found that the occurrence of the pseudo resonant phenomenon is the result of counteract effect of the common mode and differential mode components in power mismatched condition. Besides, the application of frequency-selective decoupling is proposed to weaken the power flow coupling between ports. Finally, theoretical analysis is verified by experimental ones. These results are useful to understand coupling effects in multiple-input converters and provide a guidance to the controller optimization and engineering application.
A Two-Level Semi-supervised Clustering Technique for News Articles

The web and social media are overcrowded with news pieces in terms of amount and diversity. Document clustering is a useful technique that is widely used in organizing and managing data into smaller groups. One of the factors influencing the quality of clustering is the way documents are represented. Some traditional methods of document representation depend on word frequencies and create sparse and large-sized document vectors. These methods cannot preserve proximity information between documents. In addition, neural network-based methods that preserve proximity information suffer from poor interpretability. Conceptual text representation methods have overcome the shortcomings of previous methods, but semi-supervised text clustering does not currently use concept-based document representation. This paper presents a two-level semi-supervised text clustering method that uses labeled and unlabeled data simultaneously to achieve higher clustering quality. In the first level, documents are represented based on the concepts extracted from the raw corpus. Second, the semi-supervised clustering process applies unlabeled data to capture the overall structure of the clusters and a small amount of labeled data to adjust the center of the clusters. Experiments on the Reuters-21578 and BBC News data collections show that the proposed model is superior to other semi-supervised approaches in both text classification and text clustering.
Developing intelligent agent systems - a practical guide

Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents? ' and 'why are they useful? ' but also the crucial question: 'how do I design and build intelligent agent systems? ' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.
Rotor UAV’s Micro-Doppler Signal Detection and Parameter Estimation Based on FRFT-FSST

The micro-Doppler signal generated by the rotors of an Unmanned Aerial Vehicle (UAV) contains the structural features and motion information of the target, which can be used for detection and classification of the target, however, the standard STFT has the problems such as the lower time-frequency resolution and larger error in rotor parameter estimation, an FRFT (Fractional Fourier Transform)-FSST (STFT based synchrosqueezing)-based method for micro-Doppler signal detection and parameter estimation is proposed in this paper. Firstly, the FRFT is used in the proposed method to eliminate the influence of the velocity and acceleration of the target on the time-frequency features of the echo signal from the rotors. Secondly, the higher time-frequency resolution of FSST is used to extract the time-frequency features of micro-Doppler signals. Moreover, the specific solution methodologies for the selection of window length in STFT and the estimation of rotor parameters are given in the proposed method. Finally, the effectiveness and accuracy of the proposed method for target detection and rotor parameter estimation are verified through simulation and measured data.
Intelligent Learning Analytics Dashboards: Automated Drill-Down Recommendations to Support Teacher Data Exploration

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.