Miracle-dz/UniFAR-FTUs huggingface dataset:
Ieee Computer Graphics and Applications Visualizing Spatial Multivalue Data

W e introduce multivalue data as a new data type in the context of scientific visualization. While this data type has existed in other fields, the visualization community has largely ignored it. Formally, a multivalue datum is a collection of values about a single variable. This collection can be denoted as M = (v1, v2, … ,vn) where each vi is a value of variable v. The collection might arise from a measurement process or a modeled process. In the latter case, it is useful to consider probabilistic models where the collection of values describes the set of possible outcomes of the modeled process. Multivalue data sets can be defined for multiple dimensions. A spatial multivalue data set consists of a multi-value datum at each physical location in the domain. The time dimension is equally valid. This leads to spatio-temporal multivalue data sets where there is time varying , multidimensional data with a multivalue datum at each location and time. To illustrate the challenge of visualizing spatial mul-tivalue data sets, consider having to visualize a time varying volumetric temperature field. We could possibly generate direct volume renderings of each temperature volume and create an animation, or extract temperature isosurfaces and track how they change over time. Either method can help reveal how the temperature volume changes over time. Now, imagine that instead of this time varying volumetric temperature field, we now have n versions of them, each one slightly different from the other. Using the traditional approach, we would have produced n volume rendered animations, or n isosurface animations. The variations, consensus, or other group properties of such a multi-value data set would be difficult to comprehend by sequentially watching these animations. Compounding matters, the previous example was just a simple scalar multivalue data set. We also need to consider vector, or more generally, multivariate multivalue data sets. Specifically, another data descriptor is whether a data set is univariate or multivariate. A mul-tivariate multivalue data set has a multivalue datum for each variable. Multivariate data are often represented as a vector of values—one for each variable. While the term multidimensional data is often used interchangeably with multivariate data in literature from different disciplines , we want to make a clear distinction between their use in this article. Multidimensionality refers to the spatial and temporal dimensions of a data set, while multivariateness refers to the number of variables a data set …
Recurrent neural network with Extended Kalman Filter for prediction of the number of tourist arrival in Lombok

Tourism has become a major sector for economic development on Lombok. Tourist expenditure in Lombok give a good implications on public revenue. Tourist expenditures are not only distributed to the tourism sector, but also to other sectors. Prediction of tourists visit is very important as an information and planning for the future. Prediction is one of very important element in decision, because the effectiveness of the decision generally depends on several factors in present and past. This research tries to predict tourist arrival by examining time-series data on tourist arrival in Lombok by using Recurrent Neural Network with a training algorithm Extended Kalman Filter. Based on the accuracy of prediction in the data testing, this method are good for predicting time series data. The best result of the prediction in the data testing showed MSE value is 0.052232 with the accuracy of the prediction is 86.059 %.
Airway management of patients with traumatic brain injury/C-spine injury

Traumatic brain injury (TBI) is usually combined with cervical spine (C-spine) injury. The possibility of C-spine injury is always considered when performing endotracheal intubation in these patients. Rapid sequence intubation is recommended with adequate sedative or analgesics and a muscle relaxant to prevent an increase in intracranial pressure during intubation in TBI patients. Normocapnia and mild hyperoxemia should be maintained to prevent secondary brain injury. The manual-in-line-stabilization (MILS) technique effectively lessens C-spine movement during intubation. However, the MILS technique can reduce mouth opening and lead to a poor laryngoscopic view. The newly introduced video laryngoscope can manage these problems. The AirWay Scope® (AWS) and AirTraq laryngoscope decreased the extension movement of C-spines at the occiput-C1 and C2-C4 levels, improving intubation conditions and shortening the time to complete tracheal intubation compared with a direct laryngoscope. The Glidescope® also decreased cervical movement in the C2-C5 levels during intubation and improved vocal cord visualization, but a longer duration was required to complete intubation compared with other devices. A lightwand also reduced cervical motion across all segments. A fiberoptic bronchoscope-guided nasal intubation is the best method to reduce cervical movement, but a skilled operator is required. In conclusion, a video laryngoscope assists airway management in TBI patients with C-spine injury.
Engineering the Expression and Characterization of Two Novel Laccase Isoenzymes from Coprinus comatus in Pichia pastoris by Fusing an Additional Ten Amino Acids Tag at N-Terminus

The detail understanding of physiological/biochemical characteristics of individual laccase isoenzymes in fungi is necessary for fundamental and application purposes, but our knowledge is still limited for most of fungi due to difficult to express laccases heterologously. In this study, two novel laccase genes, named lac3 and lac4, encoding proteins of 547 and 532-amino acids preceded by 28 and 16-residue signal peptides, respectively, were cloned from the edible basidiomycete Coprinus comatus. They showed 70% identity but much lower homology with other fungal laccases at protein level (less than 58%). Two novel laccase isoenzymes were successfully expressed in Pichia pastoris by fusing an additional 10 amino acids (Thr-Pro-Phe-Pro-Pro-Phe-Asn-Thr-Asn-Ser) tag at N-terminus, and the volumetric activities could be dramatically enhanced from undetectable level to 689 and 1465 IU/l for Lac3 and Lac4, respectively. Both laccases possessed the lowest K m and highest k cat/K m value towards syringaldazine, followed by ABTS, guaiacol and 2,6-dimethylphenol similar as the low redox potential laccases from other microorganisms. Lac3 and Lac4 showed resistant to SDS, and retained 31.86% and 43.08% activity in the presence of 100 mM SDS, respectively. Lac3 exhibited higher decolorization efficiency than Lac4 for eleven out of thirteen different dyes, which may attribute to the relatively higher catalytic efficiency of Lac3 than Lac4 (in terms of k cat/K m) towards syringaldazine and ABTS. The mild synergistic decolorization by two laccases was observed for triphenylmethane dyes but not for anthraquinone and azo dyes.
Pooled Testing for Effective Estimation of the Prevalence of Schistosoma mansoni

Rapid and accurate identification of the prevalence of schistosomiasis is key for control and eradication of this devastating disease. The current screening standard for intestinal schistosomiasis is the Katz-Kato method, which look for eggs on slides of fecal matter. Although work has been done to estimate prevalence using the number of eggs on a slide, the procedure is much faster if the laboratory only reports the presence or absence of eggs on each slide. To further help reduce screening costs while maintaining accuracy, we propose a pooled method for estimating prevalence. We compare it to the standard individualed method, investigating differences in efficiency, measured by the number of slides read, and accuracy, measured by mean square error of estimation. Complication is introduced by the unknown and varying sensitivity of the procedure with population prevalence. The DeVlas model for the worm and egg distributions in the population describes how test sensitivity increases with age of the epidemic, as prevalence and intensity of infection increase, making the problem fundamentally different from earlier work in pooling. Previous literature discusses varying sensitivity with the number of positive samples within a pool, known as the “dilution effect. ” We model both the dilution effect and varying sensitivity with population prevalence. For model parameter values suited to younger age groups, the pooled method has less than half the mean square error of the individualed method. Thus, we can use half as many slides while maintaining accuracy. Such savings might encourage more frequent measurements in regions where schistosomiasis is a serious but neglected problem.
An implementable lossy version of the Lempel-Ziv algorithm - Part I: Optimality for memoryless sources

A new lossy variant of the fixed-database Lempel-Ziv coding algorithm for encoding at a fixed distortion level is proposed, and its asymptotic optimality and universality for memoryless sources (with respect to bounded single-letter distortion measures) is demonstrated: as the database size m increases to infinity, the expected compression ratio approaches the rate-distortion function. The complexity and redundancy characteristics of the algorithm are comparable to those of its lossless counterpart. A heuristic argument suggests that the redundancy is of order (log log m)/log m, and this is also confirmed experimentally; simulation results are presented that agree well with this rate. Also, the complexity of the algorithm is seen to be comparable to that of the corresponding lossless scheme. We show that there is a tradeoff between compression performance and encoding complexity, and we discuss how the relevant parameters can be chosen to balance this tradeoff in practice. We also discuss the performance of the algorithm when applied to sources with memory, and extensions to the cases of unbounded distortion measures and infinite reproduction alphabets.
Neurocognitive enhancement: mindfulness practices applied to management

Managerial world is usually characterized by environmental and performance distress. A longlasting distress often brings to maladaptive organism’s coping, which may in turns influence mental and cognitive abilities. Mindfulness practices could help managers to enhance peak performance and to manage emotional, behavioral and interpersonal aspects of their daily living by training awareness of bodily and automatic affective reactions (Balconi, Fronda, Venturella, & Crivelli, 2017; Tang et al., 2007). The aim of the present research was to study the potential impact of an intensive empowerment intervention, based on awareness practices and supported by a wearable brain sensing device, on psychological, neuropsychological, cognitive profiles and on stress levels in a professional management context. A sample of 16 managers took part to the experiment and it was assessed at the start, at the middle and at the end of the training. Neuropsychological and cognitive performance measures, psychometric measures of stress management, and restingstate/task-related electrophysiological indices (electroencephalography and biofeedback) were collected. Managers carried out a 14-days training, which consisted of daily practices based on focusing on breathing and related bodily sensations. During the training, duration of practices was gradually increased starting from 10 minutes to 20 minutes per day. Electrophysiological measures highlighted an increased alpha/beta index, suggesting a balance between relaxation and activation. In addition, increased alpha blocking measures allowed to infer heightened responsiveness and reactivity of the system. These findings of the training effect were supported by greater focus and control of arousal gained by managers also in a stressful condition. This could be seen in the increase of the heart rate variability index. The intervention seemed then to be useful in enhancing fundamental managerial skills with higher executive control and inhibition processes, decrease of clear perceived stress levels and slight decrease of anxiety levels. These encouraging results could find interesting further implications for stress prevention to others applied fields.
Lysis: A Platform for IoT Distributed Applications Over Socially Connected Objects

This paper presents Lysis, which is a cloud-based platform for the deployment of Internet of Things (IoT) applications. The major features that have been followed in its design are the following: each object is an autonomous social agent; the platform as a service (PaaS) model is fully exploited; reusability at different layers is considered; the data is under control of the users. The first feature has been introduced by adopting the social IoT concept, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability and information discovery efficiency. The major components of PaaS services are used for an easy management and development of applications by both users and programmers. The reusability allows the programmers to generate templates of objects and services available to the whole Lysis community. The data generated by the devices is stored at the object owners cloud spaces. This paper also presents a use-case that illustrates the implementation choices and the use of the Lysis features.
A Hierarchical Latent Variable Model for Ordinal Data with \No Answer" Responses

An item response theory model for ordinal responses proposes that the probability of a particular response from a person on an speci c item is a function of latent person and question parameters and of cuto s for the ordinal response categories. This structure was incorporated into a Bayesian hierarchical model by Albert and Chib (1993). We extend their formulation by modeling \No Answer" responses as due to either lack of a strong opinion or indi erence to the entire question. In our hierarchical Bayesian framework, prior means for the person and item e ects are related to observed covariates. An application of the model to the DuPont Corporation 1992 Engineering Polymers Division Customer Satisfaction Survey is described in detail. The non-conjugate likelihood and prior prevent closed form posterior inference. Three different iterative solutions, using the Griddy Gibbs Sampler (Ritter and Tanner 1992), Metropolis-Hastings Algorithm (Roberts and Smith 1993), and Data Augmentation (Tanner and Wong 1987), are compared. The models are checked using posterior predictive checks (Rubin 1984) and case in uence is diagnosed by importance reweighting (Bradlow and Zaslavsky 1997). The methods illustrated in this research have potential application in other situations in which categorical observations are determined by several latent variables.
The Impact of ODI Coaching on Transformational Leadership and Employees' Perception of Supply Chain Integration and Organizational Performance

Abstract Organization Development Intervention (ODI) is the key to make changes in the organization. Leaders are the backbone of the organization. Transformational leadership style enables a leader to bring a change in the organization. ODI on Transformational Leadership coaching is expected to improve the effectiveness of transformational leadership styles that bring positive change towards employees' perception on Supply Chain Integration and Organizational Performance. ODI on transformational leadership coaching has been conducted at Konco Bakery in Surabaya that involved eleven (11) leaders and thirty (30) employees. The OD researcher has taken four (4) roles in conducting the coaching, which are as an Expert Advisor, a Mentor, a Counselor, and a Facilitator, in an interactive seminar, observation, and discussion. The coaching was conducted by explaining the characteristics of Transformational Leadership in the eight (8) leaders’ roles, e.g. Trend Watcher and Role Model, Mentor, Parent Figure, Change Agent, Innovator, Achiever, and Motivator. Coaching also used The Whole Brain Literacy which synergizes the three thinking skills required in running the eight (8) leaders' role. Leaders who only use Precision Thinking will give the result of becoming administrator which dominate the leadership style before intervention. Three abilities to think which are Open Ended Thinking, Feeling Power Thinking, and Aim Thinking are indispensable in applying the eight (8) the role of transformational leadership. The result of Paired Sample t- Test showed that coaching on Transformational Leadership is significant in enhancing and improving Transformational Leadership and results in a significant increase on employees' perception of Supply Chain Integration and its effect on Organizational Performance.
MOBILE ROBOT REINFORCEMENT LEARNING OF FUZZY OBSTACLES IN UNKNOWN ENVIRONMENT

Mobile Robots are considered as the most widely used class of robots and in this research area, obstacle avoidance and environment learning are among the most important fields. In this paper, a method for mobile robot learning and avoiding fuzzy obstacles in an unknown environment is proposed, assuming that the movement of the robot in the environment is a Markov Decision Process. In this method, which is based on a slightly modified reinforcement learning technique, the robot learns the static fuzzy obstacles in the unknown environment through experience, avoids them based on their fuzzy degree of passing difficulty and reaches its predefined destination while following the best learned path; i.e. the robot decides on the direction it moves based on how hard it is to move through each one of the obstacles in its way. No input is assumed to be available to the robot, except its coordination in the environment as its current state. The more the robot interacts with the environment the better becomes its performance. To be more specific, the robot improves the path it chooses in terms of obstacle fuzzy difficulties and its length. The path is selected according to a cost function based on the time which robot spends to reach its destination as its final state. Finally the simulation results show satisfactory performance of the proposed method.
Physical-Informatical Essence-Duality-Aware Generic Modeling of Threat Handling Processes

Systems interact with real world entities, and must hold internal representations of these entities in order to handle them appropriately. Physical-informatical essence duality (PIED) is the parallel existence of the entity as both the original, usually physical source, and its informatical representation, as held by each agent interacting with the entity. The distinction between the original external entity and its representation is critical for correct modeling and realization of complex interactions of cyber-physical systems with the real world. The implications of this distinction must be recognized and accounted for. Conceptual modeling semantics for the PIED problem make this distinction possible, structured, and well-defined in the system model. We review a formalism based on Epistemic Logic semantics, and a model- based framework based on Object Process Methodology, and demonstrate the applicability of our framework for generic modeling of threat handling processes, common to various cyber-physical systems and various types of threats, such as safety hazards, terror attacks, and cyber-attacks.
Greenhouse Gas Mitigation Opportunities in California Agriculture Review of the Economics February 2014

Although about three-quarters of California farm revenue derives from crop production, crops—mainly tree, vine, and vegetable crops—account for only about one-quarter of GHG emissions. Some studies indicate minimal yield loss from reducing nitrogen fertilizer use, and simulation results show significant percentage reductions in GHG emissions for payments of $20/MTCO 2 e. The economics of reducing emissions from enteric fermentation has been little studied. Manure management to reduce GHG emissions (mainly methane) can be as simple as covering manure lagoons and flaring methane. The more complex option of using manure-generated methane gas to replace fossil fuels has been investigated often. Most case studies and simulations suggest this option is costly. Its economic feasibility depends on specific local conditions, but there is no evidence of large-scale feasibility in California without large subsidies.
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This paper presents technical details, hardware and software of a complete imaging system which uses a fast CCD sensor and a 41 Msample/s A/D converter to acquire full-frame 12 bit/pixel digitised images with a time resolution of 1.25 ms/image. This apparatus permits to resolve intracellular Ca, gradients in individual cells as well as the spatio-temporal pattern of neural activity of cell assemblies in neural tissue.
The Design and Development of 3D Auditory Environment for Computer-Based Aural Rehabilitation Programs

Sound localization testing is a common activity in aural rehabilitation therapy conducted in clinics. In order to computerize this therapy, a 3D sound manipulation should be employed to aid such tests. In this paper, we present an implementation of 3D auditory environment that helps to produce sounds from different directions and intensities for the purpose of aural therapy. A User-Centered Design approach informed the selection and design of system features. The multimedia rehabilitation software takes into account the characteristics of human auditory systems in the evaluation of patient performance in this test and progress in therapy. The proposed sound localization test was evaluated by King Abdul-Aziz University Hospital and a Subject Matter Expert (SME) in Aural Rehabilitation that was independent from the project. The evaluation shows satisfying results in terms of the test usability and effectiveness.
Pair-Wise Separable Quadratic Programming for Constrained Time-Varying Regression Estimation

Estimation of time-varying regression model constrained at each time moment by linear inequalities is a natural statistical formulation of a wide class of nonstationary signal processing problems. The presence of linear constraints turns the originally quadratic three-diagonal problem of minimizing the residual squares sum, which is solvable by the linear Kalman-Bucy filtration-smoothing procedure, into that of quadratic programming, which inevitably leads to the necessity of applying much more complicated nonlinear signal processing techniques. However, the threediagonal kind of the quadratic objective function, on one hand, and the specificity of inequality constraints imposed individually upon each vector variable in the sequence of unknown regression coefficients, on the other, essentially simplify the resulting quadratic programming problem in comparison with its standard form. We call problems of such a kind pair-wise separable quadratic programming problem. Two algorithms of nonstationary regression estimation considered in this paper are built as those of pairwise separable quadratic programming and have linear computational complexity in contrast to polynomial complexity of the quadratic programming problem of general kind. The asymptotically strict iterative algorithm is based on the traditional steepest descent method of quadratic programming, whereas the fast approximate algorithm consists in a single run of a special version of the Kalman-Bucy filter-smoother.