Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis, Characterization and Electrospinning of Novel Chitosan Derivative for Tissue Engineering Applications

Synthesis of quaternized tetramethyl urea thiosemicarbazone O-carboxymethyl chitosan (CMC) as a new quaternized chitosan derivation and electrospinning of quaternized tetramethyl urea thiosemicarbazone CMC/poly (vinyl alcohol) blend solution was investigated. The chemical structure and morphology of the nanofibers were observed by Fourier transform infrared spectra analysis and scanning electron microscopy (SEM). The results indicate that prepared nanofiber may be a new candidate for tissue engineering purposes.
Open-Loop Compensation of Hysteresis and Creep Through a Power-Law Circuit Model

The inverse of a recently proposed hysteresis and creep circuit model is proposed and discussed. The model is particularly suitable for piezoelectric actuators and its inverse can be used for open-loop compensation of the undesired nonlinearities in high-precision applications. The inverse model is defined, analyzed in terms of conditions ensuring a correct compensation, and discretized to provide a digital compensation algorithm suitable for implementation in low-cost programmable devices. Quantitative results on experimental data are provided and discussed, including the compensation on an atomic force microscope.
A new Multi Point Clamped bi-directional DC-DC converter for renewable energy integration

This paper presents, a multi-point clamped bidirectional DC-DC converter (MPBDC) is presented for targeted applications like multi-level inverter based drive control and integrating renewable energies to two terminal DC grid applications. Multi-level topologies can offer the superior quality of voltage, current profile and minimize the requirement of magnetic components as well as filter size. However, while going higher number of voltage levels with classical multi point clamped multi-level converters (MPC-MLC), the dc link capacitor voltages imbalance takes place when using for active power transfer applications. To address the voltage deviation, an external balancing circuit need to be incorporated. The proposed converter can perform bi-directional DC-DC converter operation along with balance the DC link capacitor’s voltage of MPC-MLC. This converter is analysed mathematically and results are verified by MATLAB Simulink model and prototype experimental results.
Design considerations for databus charge recovery

The charge recovery databus is a scheme which reduces energy consumption through the application of adiabatic circuit techniques. Previous work gives a solid theoretical analysis of this scheme, including quantitative data assuming random bus values. We extend this earlier work by presenting a quantitative analysis of the charge recovery databus using 15 benchmarks and four high level bus coding schemes. We show that a very simple implementation of the charge recovery databus is capable of reducing average energy consumption by 28% beyond traditional high-level bus encoding techniques. In addition, we examine delay and energy consumption in the added hardware.
Peeling Back the Onion: Personality, Problem Solving, and Career Decision-Making Style Correlates of Career Indecision

A model to predict affective and informational antecedents of career indecision was proposed. The model attempted to explicate paths between personality dispositions, appraisal of problem-solving skills, career decision-making styles, and antecedents of career indecision. Both personality and coping constructs were posited to examine the relative contribution of dispositional and perceived skill variables. Using a college student sample, results from a path analysis indicated that the disposition of neuroticism was a powerful predictor of perceived problem-solving skills, a dependent decision- making style, and both affective and informational antecedents of career indecision. The predicted effects of the coping constructs were of a much smaller magnitude. Directions for future research are discussed and several suggestions for career counseling assessment and intervention selection are made.
Microgrid Economic load Dispatch including Storage and Reserve

In microgrid systems, economic load dispatch (ELD) with dynamic approach is preferred over static approach because it not only minimizes the cost but also helps to coordinate between distribution generators (DGs). In this paper, microgrid system consisting of diesel engines (DE), wind turbine (WT), a fuel cell (FC) and a battery, model has been developed and analyzed with considering the effect of reserve, emissions, fluctuations in renewable power and the price variation of the grid. The main objective is to minimize the operation and pollutant treatment cost. Microgrid system analysis is done for two operating modes that is the grid connected and the island mode under four different scheduling schemes. By using the GAMS software with MATLAB interfacing, power and cost analysis of each scheduling scheme is done in detail. In the grid connected mode, priority is given either to the DGs or the main grid to have power exchange between the grid and the microgrid. In islanded mode, battery is used instead of the grid for the economic load dispatch.
Efficient Computing Budget Allocation for Simulation-Based Policy Improvement

Policy improvement in discrete event dynamic systems is usually based on simulation, which is time-consuming and provides only noisy performance evaluation. For a given system state, it is of great practical interest to understand how to allocate the computing budget among action candidates so that the best action is correctly selected with high probability. Despite the abundant studies on simulation-based policy optimization, few consider this important allocation problem, which is considered in this paper. We develop the method of optimal computing budget allocation for policy improvement (OCBAPI) which is shown to asymptotically maximize a lower bound of the probability of correctly selecting the best action. OCBAPI can also be used when there are multiple base policies available. This allocation procedure is compared with equal allocation and proportional-to-variance on an academic toy example and an engine maintenance policy optimization problem. The numerical results show that even when there are only finite computing budget to allocate, OCBAPI performs well. We hope this work brings insight to computing budget allocation for simulation-based policy improvement in more general situations.
Fuzzy Logic Based Context Aware Recommender for Smart E-learning Content Delivery

There has been a huge growth of data in education due to integration of ICT in educational and the evolution of E-learning, to bring about technology enabled learning. This huge data in education has subjected learners to information overload in education, without a well-known mechanism to enable learners select their relevant learning content from the huge educational data. This has subjected learners, in most of the times, to take a lot of time accessing data which could not be relevant to their particular learning needs. This research therefore creates a model which will recommend relevant learning content to a learner, from the huge available data. In designing the model we employed context awareness recommender approach. The model will collect the learner’s context. It will then apply type-1 fuzzy logic data mining method to recommend relevant learning content to the learner; based on the learner’s assesment score and the time taken to complete the assesment test. The model was tested using a primary designed dataset stored in the database. The model gave recommendations that were commensurate with the assesment test marks and the time taken to complete the exams. Thus the higher the assessment score, the higher the crisp output; while the higher the time taken to complete the exams, the lower the crisp output.
Enhancing Throughput in Wireless Multi-Hop Network with Multiple Packet Reception

Multi-Packet Reception (MPR) enables simultaneous receptions from different transmitters to a single receiver, which has been demonstrated to bring capacity improvement in wireless network. However, MPR does not improve the transmission capability of intermediate relay nodes in a multi-hop routing and thus these nodes may become the bottlenecks for increasing throughput despite of great reception capability. We investigate the scheduling for multi-hop routing with MPR to improve the network throughput under multiple data flows. We formulate the optimization problem under K-MPR model and analyze the performance upper bound with ideal scheduling. We propose a distributed scheduling scheme based on a k-Connected k-Dominating Set backbone to eliminate bottleneck effects on intermediate relay nodes as to enhance the network throughput. We show the effectiveness of our scheme by comparing its performance with the upper bound and node-disjoint routing.
Further evaluation of the RMMAC method with time-varying parameters

We demonstrate, using Monte-Carlo simulations, the superior performance of the "Robust Multiple- Model Adaptive Control (RMMAC)" method for different time-varying uncertain parameter waveforms, performance bandwidths and constant disturbance intensities using the test example designed and studied in Refs. [1] and [2]. We further show that the RMMAC RMS performance is just about 10-16% worse than that obtained from a system that has perfect model identification (which is unrealizable), while remaining vastly superior to the performance associated with the best robust non-adaptive design.
Multi-armed bandit models for 2D grasp planning with uncertainty

For applications such as warehouse order fulfillment, robot grasps must be robust to uncertainty arising from sensing, mechanics, and control. One way to achieve robustness is to evaluate the performance of candidate grasps by sampling perturbations in shape, pose, and gripper approach and to compute the probability of force closure for each candidate to identify a grasp with the highest expected quality. Since evaluating the quality of each grasp is computationally demanding, prior work has turned to cloud computing. To improve computational efficiency and to extend this work, we consider how Multi-Armed Bandit (MAB) models for optimizing decisions can be applied in this context. We formulate robust grasp planning as a MAB problem and evaluate convergence times towards an optimal grasp candidate using 100 object shapes from the Brown Vision 2D Lab Dataset with 1000 grasp candidates per object. We consider the case where shape uncertainty is represented as a Gaussian process implicit surface (GPIS) with Gaussian uncertainty in pose, gripper approach angle, and coefficient of friction. We find that Thompson Sampling and the Gittins index MAB methods converged to within 3% of the optimal grasp up to 10x faster than uniform allocation and 5x faster than iterative pruning.
Sketching a "low-cost" text-classification technique for text topics in English

The aim of this paper is to sketch a potential methodology for automatic text classification which allows text topic discrimination as a prior step to new case assignment to previously established text topics. Such case assignment will be performed by means of Discriminant Function Analysis based on a series of easily-computable linguistic parameters, in order to reduce computational costs.
Multi-view Convolution Neural Network with Swarm Search Based Hyperparameter Optimization for Enhancing Heart Disease and Breast Cancer Detection

The core of deep learning which is convolution neural network (CNN) has been widely adopted in image processing and object recognition areas. In particular, medical imaging requires very precise, accurate and fine recognition power. Numerous works in the literature have reported promising prospects of CNN applied in prognosis and radiology diagnosis. A common goal among those works, largely is to try achieving a most accurate deep learning model in analysing the insights from the finest details of the medical imaging. To this end, a novel machine learning model that is equipped with multi-view data-preprocessing and swarm-based hyperparameter optimization is proposed. The former is for provi ding additional training data in the hope that salient features could discovered; the latter is for finding the most optimal set of model parameters for the CNN. They both serve only one purpose – to enhance the object recognition power to the highest possible. Preliminary experiments over datasets related to heart disease detection and breast cancer classification over CTG and mammograms respectively indicate encouraging results.
Research and Implementation of High Performance Traffic Processing Based on Intel DPDK

With the rapid development of Internet and the continuous rise of network users, the network traffic in various regions is increasing rapidly. In the face of a large number of high speed and high throughput of the network environment, traditional packet capture methods and processing capabilities cannot reach the corresponding speed, which results in severe packet loss. This paper focuses on a high-performance packet acquisition and distribution method to break through the performance bottleneck of universal servers and network cards. This paper studies a packet capture method based on DPDK platform, and uses the processing of hash value in RSS to improve the efficiency of data packet distribution, which realizes the process from performance acquisition to efficiently multi-core parallel processing. This method can effectively reduce packet loss and improve the data packet processing rate. It can also reduce resource waste and network overhead for traffic capture and distribution. Preliminary experiments show that DPDK-based traffic processing has obvious advantages over PF-RING and Netmap in data processing speed.
IMAGEM CORPORAL E DEPENDÊNCIA DE EXERCÍCIOS FÍSICOS EM FREQUENTADORES DE UMA ACADEMIA DE GINÁSTICA DA CIDADE DO RIO DE JANEIRO

O objetivo do presente estudo foi identificar a insatisfacao com a imagem corporal e a dependencia de exercicios fisicos entre frequentadores de uma academia de ginastica. Participaram do estudo 106 praticantes de exercicios fisicos, de ambos os sexos e idades entre 16 e 79 anos. Para verificacao da insatisfacao com a imagem corporal utilizou-se o Body Shape Questionnaire (BSQ). Para deteccao de dependencia de exercicios fisicos foi usado o Negative Addiction Scale (NAS). O escore medio do BSQ foi de 80,8 (leve preocupacao); 29,62% reportaram alguma preocupacao com a imagem corporal; e 19,81% manifestaram dependencia de exercicios fisicos. Conclui-se que os praticantes de exercicios fisicos apresentam taxas de insatisfacao corporal ligeiramente maiores que outros grupos.
Judeo‐Christian theology and the environment: moving beyond scepticism to new sources for environmental education in the United States

In the USA, many environmental educators have paid little attention to Western Christian and Jewish ecotheology, in spite of its being a potentially rich resource for environmental education. In part, this neglect can be attributed to popular misconceptions about the influence of religious beliefs on environmental values. This essay reviews the results of relevant empirical studies within the environmental sociology literature since 1977 to clarify common misconceptions. Conclusions from these empirical studies and other sources may make it easier for environmentalists to reconsider the resources of ecotheology. Reconsidering ecotheology is also practical in that it provides environmental education a means of better connecting with the primary values of many citizens, offers new partnership possibilities for building environmental education infrastructure, and broadens the pluralistic base for environmental ethics. Several basic tenets of ecotheology offer starting points for educators by providing common ground between theology and environmental education.
Intelligent Intrusion Detection System using Decision Tree Classifier and Bootstrap Aggregation

This paper proposes a novel methodology in the area of threat intelligence by combining statistical analysis with Intrusion Detection System. The preferred threat intelligence model identifies potential malicious connections for a single node computer system utilising the IP addresses of the devices that are connected to the system. It can be inferred from the results that the proposed system performs better in terms of accuracy, True Positive Rate and False Positive Rate.
(Generalised) autoregressive models and their trajectories

The set of classical AR-models is not ‘compact’, so that a sequence of AR-models may fail to converge to anything reasonable. A compactification leads to AR-models with arbitrary singularity at infinity. These limit AR-models are represented by pairs of polynomial and proper rational matrices satisfying certain compatibility condition. It is shown that they produce trajectories in a very natural way. Then the classical equivalence theorem is extended to them, stating that two AR-models having the same behaviour are equivalent.
Evidence That Growth Hormone Exerts a Feedback Effect on Stomach Ghrelin Production and Secretion

Ghrelin is a recently discovered stomach hormone that stimulates pituitary growth hormone (GH) secretion potently. The purpose of these experiments was to test the hypothesis that a stomach-ghrelin-pituitary-GH axis exists in which either an elevation or reduction in systemic GH levels will exert a negative or positive feedback action, respectively, on stomach ghrelin homeostasis. In rats, GH administration decreased stomach ghrelin mRNA levels and plasma ghrelin levels significantly. In GH-releasing hormone (GHRH) transgenic mice, GHRH overexpression decreased stomach ghrelin peptide levels when compared with control mice. In aged rats (25 months) stomach ghrelin mRNA and peptide levels and plasma ghrelin levels were decreased when compared with young rats (5 months). Because GH secretion is reduced in aged rats, the elevated stomach ghrelin production and secretion may reflect a decreased GH feedback on stomach ghrelin, homeostasis, and secretion. Together, these findings suggest that endogenous pituitary GH exerts a feedback action on stomach ghrelin homeostasis and support the hypothesis that a stomach-ghrelinpituitary GH axis exists.
THE ROLE OF FAMILIARITY IN THE DEVELOPMENT OF SOCIAL RECOGNITION BY LAMBS

A series of experiments investigated the role of association and familiarity in the development of social recognition among lambs. In each experiment, lambs were tested successively with 2 different social partners. When separated from their mothers, lambs that were paired with a partner with which they had been housed for 17 or 5 days emitted fewer distress bleats than they did during tests with an unfamiliar lamb. However, this effect was only evident when the test with the unfamiliar partner preceded the test with the familiar partner. When lambs were first tested with an unfamiliar partner treated with the same artificial odorant that had previously been associated with members of their own group, they bleated more than they did during a second test with a partner whose odor was novel. This effect was not observed when the familiar- and novel-odor partners were encountered in the reverse order. Bleating frequencies by lambs paired with their twin did not differ reliably from those of lambs paired with a familiar non-twin. Nonetheless, there was a signficant correlation between the number of bleats by twins that were tested together. Overall, the results indicate that lambs become familiar with and recognize individuals (twins and non-twins) as a result of direct association. Lambs also discriminate between novel scents and artificial odorants associated with their familiar agemates, but such odors neither mask nor substitute effectively for lambs' individually recognizable phenotypes. Bleating frequency increases with the novelty of the social partner and of the test situation, and is therefore markedly affected by repeated testing.
Comparison Between Seller and Buyer Pricing Systems for Energy Trading in Microgrids

In this paper, two systems for energy trading among energy prosumers are proposed: a buyer-pricing-system (BPS), in which buyers determine the price of energy and a seller-pricing-system (SPS) in which sellers determine the price of energy. In order to analyze the BPS, the competition among buyers in the BPS is formulated as a strategic form game named energy-competition-game among buyers (ECG-AB). It is shown that the Nash equilibrium of the ECG-AB exists and furthermore is unique. Both systems are formulated by Stackelberg duopoly game model, and it is shown that there exists a unique subgame perfect equilibrium, which implies that each system is stable. Now, in the SPS, it is shown that an equilibrium solution of the Stackelberg duopoly game model does not reduce social welfare. Thus, the analysis shows that the considered systems are stable and efficient. In order to corroborate the analysis, simulation is performed and numerical results validate the analysis; a full simulation code to simulate the proposed systems is available on GitHub.
Examining what we do to improve our schools: 8 steps from analysis to action

Examining what we do to improve our school: 8 steps from analysis to action by Sandra Harris, Stacey Edmonson, and Julie Combs (2010) is an excellent resource for understanding the school improvement process in schools today. As an assistant professor in educational leadership, I am always looking for resources for my students that address challenging issues from a practitioner’s perspective. Even with painstakingly designed lectures on theory to practice, I found students appreciated most hands-on experiences where they could see how theories worked on the job. As one former student put it: “Dr. Z, leading adults is like herding cats when wanting to get [teachers] to make a change! ” This comment reminds me of Hirsh’s Foreword to Hall and Hord’s text (2010) where she says, “Keep pushing until the rest of us get change right! ” (p. xix). I couldn’t agree with her more. Sometimes we need to push a strategy that will help us get change right! Improving schools has been a passion of mine throughout my career as an educator. As a classroom teacher and learning disabilities specialist, I wanted to change how we instructed students with special needs. As an administrator, I wanted to change how teachers addressed campus issues. As a program evaluator and college professor, I wanted to change how school administrators make that educational change happen and sustain it! After almost 30 years in the education field, I am still looking for best practices leaders can use to facilitate school improvement. So much of what we do as school and district administrators is more representative of transactional leadership (reactive leadership) rather than transformational (thoughtful leadership) (Bass & Bass, 2008). If our goal is to develop and sustain a successful school, we probably need to develop leadership skills and practices that will help us reach that goal and sustain it. Like a neverending story, school improvement doesn’t have a start and stop date. Instead it is a continuous process. Knowing that administering the process can be daunting, administrators facing challenging issues will appreciate the authors’ approach. Leveraging data analysis to improve student learning is a primary focus with the administrator portrayed as the essential key to orchestrating how this will happen. A key theme throughout the text is the importance of making data analysis an ongoing part of the school culture. The key theme is illustrated in the stepby-step directions on how administrators can infuse assessment, analysis, and action strategies in their daily practice. According to the authors, the book was written specifically for those school leaders who: