Miracle-dz/UniFAR-FTUs huggingface dataset:
Procurement of innovation terminology usage in health care: A scoping review protocol

Background: Although procurement of innovation is an established policy tool used to stimulate collaboration between supply- and demand-side entities during the development of new technologies, there is little scientific literature describing the process as applied in health care settings. Furthermore, what literature exists contains inconsistencies of terms, definitions, and/or concepts related to procurement of innovation. This protocol details our process for a systematic scoping review to describe the current scope of literature and to provide terminology clarification. Methods: A search strategy will be used to search PubMed, EMBASE [OVID], CINAHL [EBSCO], PsycINFO [ProQUEST], ABI/INFORM, ISI Web of Knowledge, EBSCO, JSTOR, the Cochrane Database of Systematic Reviews, and Google Scholar; grey literature, non-scientific reports, policy documents and expert recommendations will also be considered as additional sources for texts. Two researchers will screen titles and abstracts for inclusion/exclusion criteria, followed by full texts. We will extract the following data, if applicable: title, authors, date, author affiliations, country, journal/publication characteristics, setting, aims/purpose, methodology, sample characteristics, assessment/evaluation tools, outcome parameters, key findings, relevance, and terminology usage/definitions. Results will be presented narratively and visually. Discussion: This paper describes the steps of our proposed systematic scoping review to identify and analyse scientific and non-scientific literature related to procurement of innovation and/or innovation of procurement in health care settings, with a particular focus on digital health technologies. Results are intended to demonstrate the current scope of literature, to provide clarity in language and therefore to serve as a first step for further research in this growing field.
A CIRCULAR BUSINESS MODEL FOR CLOUD COMPUTING SERVICES PROVIDERS

Aim/Purpose The main objective of the current study is to develop a business model for service providers of cloud computing which is designed based on circular economy principles and can ensure the sustainable consumption. Background Even though the demand for cloud computing technology is increasing day by day in all over the world, the current the linear economy principles are incapable to ensure society development needs. To consider the benefit of the society and the vendors at the same time, the principles of circular economy can address this issue. Methodology An extensive literature review on consumption, sustainable consumption, circular economic, business model, and cloud computing were conducted. the proposed model of Osterwalder, Pigneur and Tucci (2005) is admitted designing the circular business model. Contribution The proposed model of the study is the contribution of this study where provides the guidelines for the cloud computing service providers to achieve both their economic profits and the society’ needs. Findings Finding reveals that if the cloud computing service providers design their business model based on the “access” principle of circular economy, they can meet their economic profits and the society’ needs at a same time. Recommendations for Practitioners It is recommended to the startup and the existing businesses to utilize the proposed model of this study to reach a sustainable development. Recommendation It proposes a new circular business model and its linkages with community A Circular Business Model for Cloud Computing 106 for Researchers building. Impact on Society The proposed model of the study provides guidelines to the cloud computing service providers to design a business model which is able not only to meet their economic profit, but also to meet the society’s and customers’ benefits. Future Research Future researches can build on this research model which proposed in this study to examine the limitations of this model by using empirical researches.
Guidelines for recording transport event logs

A process is an ordered set of related activities taking place in a given time. Processes are present in all branches of the economy, engineering, science, etc. Due to the huge amount of data produced the rapid development of data mining techniques has been observed. Similar methods are also used in the context of processes and are called process mining. The main task of process mining is to create a process model, which is used to reason about the process and to make decisions inside it. The process model may be used to discuss responsibilities, simulations, predictions, etc. The main data structures in process mining are event logs. It is always very important to have correct data which makes creating a reliable process model possible. In this paper the basic guidelines for recording such event logs have been described and conclusions were drawn. The main focus of this research was transport problems.
PERFORMANCE ANALYSIS OF QPRS THROUGH A MICROCELLULAR RADIO CHANNEL

This paper investigates the performance of quadrature partial response signaling (QPRS) scheme in a multipath fading channels, with emphasis on microcellular radio channel. The channel is assumed to be Rician fading channel subject to cochannel interference. Perfect synchronization has been assumed. The results are compared with that of the quadrature phase shift keying (QPSK) subject to the same environments. For QPSK a raised-cosine pulse of 0.235 roll-off factor has been used with a corresponding spectral efficiency of 1.62 b/s/Hz. The effect of cell splitting and cell sectorization are also considered. Performance is derived and computed analytically in terms of the bit error probability as a function of the number of time division multiplexing (TDM) users in the digital microcellular radio channel. Results show that the QPRS scheme performs better than the QPSK signaling scheme whenever the transmission is subject to multipath fading and cochannel interference, which is the case for microcellular radio channels
An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.
Short-Term Prediction of Air Pollution inMacau Using Support Vector Machines

Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVMmodel is often hinged on the appropriate choice of the kernel.
Designing visual decision support for sociotechnical enterprises

As automation becomes more pervasive in industry, human decision-makers are becoming increasingly dependent on sensor-data to monitor and interpret performance across largescale enterprises. At the same time an exponential growth in data volumes, brought about by widespread automation, can make it di cult to transform low-level data into meaningful information. Information visualisation has been proposed as a means for helping humans to cope with data overload, but research in this area has predominantly focussed on data analytics tools rather than control interfaces. To support control, an interface should highlight important information but it must also present this information within the context of the systems functional goals. Graphic displays that achieve both of these objectives can provide visual decision support for human problem solving but their creation poses a serious design challenge. Cognitive Systems Engineering (CSE) is a discipline that was founded to develop principles, methods and techniques that guide the design of control interfaces for complex work systems. Despite considerable developments in CSE theory over the last two decades, many aspects of design remain poorly de ned. In practice, designers bridge these gaps in design knowledge using past experience and tacit skills, but this ad-hoc approach is unsuitable for dealing with the scale and complexity of enterprise-level work systems. This research aims to de ne a CSE approach that can inform the design of visual decision support systems in large-scale, sociotechnical enterprises. A literature review speci es analytical, representational and procedural gaps in design knowledge and identi es the limitations of current CSE frameworks in relation to enterprise level systems. This review reveals a fundamental problem facing the CSE discipline; given that design is a deeply contextual activity and work systems can have widely disparate characteristics, how can generic principles inform design practice? In
A multiobjective evolutionary algorithm using dynamic weight design method

In most multiobjective evolutionary algorithms (MOEA) based on aggregating objectives, the weight vectors are user-supplied or generated randomly, and they are static in the algorithms. If the Pareto front (PF) shape is not complex, the algorithms can (cid:12)nd a set of uniformly distributed Pareto optimal solutions along the PF; otherwise, they might fail. A dynamic weight design method based on the projection of the current nondominant solutions and equidistant interpolation is proposed in this paper. Even if the PF is complex, we can (cid:12)nd evenly distributed Pareto optimal solutions by this method. Some test instances are constructed to compare the performance of the MOEA/D using dynamic weight design method with that of MOEA/D. The results indicate that the dynamic weight design method can dramatically improve the performance of the algorithms.
Katana and Grand Guru: a Game of the Lost Words (DEMO)

We present here a prototype of a role playing game which allows to both i) crowdsource lexical units (including idioms) for a language and ii) help the player improve their knowledge of the language. Our implementation of the game is focused on non-standardized languages, for which the intergenerational transmission is not as efficient as it used to be. In order to address this, we incentivize the participation of a "Grand Guru", from whom the player needs help to fulfill their mission.
Sella turcica variations in lateral cephalometric radiographs and their association with malocclusions

Objectives: Classification of the skeletal facial types is performed using certain reference points and planes in lateral cephalometric radiographs to plan orthodontic treatments. One of these reference points is sella turcica which is closely associated with craniofacial bone development. The aim of this study was to identify the association between the sella turcica variations and skeletal Class I, II, and III malocclusions. Methods: This study retrospectively evaluated 94 orthodontic patients (48 males and 46 females) between 14–26 years of age. Lateral cephalometric radiographs of the patients with skeletal Class I, II, and III malocclusions were classified into six groups according to sella turcica morphology: normal sella turcica, oblique anterior wall, double contour of the floor, sella turcica bridge, irregularity in the posterior part, and pyramidal shape of sella turcica. The length, depth, and diameter of sella turcica were measured. Sella turcica variations and radiographs of patients with Class I, II, and III malocclusions were compared statistically. Results: The correlation between the sella turcica variations and skeletal sagittal classification was statistically significant (p=0.017). 36.8% of the radiographs, which were classified as normal sella turcica were classified as Class I patients. There were no statistically significant differences between the skeletal Class I, II, and III malocclusions and sella turcica variations in terms of the length, depth, and diameter. Conclusion: For adequate patient referral and management, orthodontists should recognize sella turcica variations in lateral cephalometric radiographs, and these findings should arise an index of suspicion for associated pathologies, especially of the hypophyseal gland.
Systematic Identiflcation of Optimal HIV-1 CTL Epitopes

Characterization of the cellular immune response against HIV has led to the identification of numerous HLA class I restricted CTL epitopes and many laboratories have contributed to the description of CTL epitopes in most of the HIV-1 proteins. Since the last update of this CTL epitope list in 1996, a number of new epitopes have been published and are included in the current update. References have also been updated for epitopes previously listed as personal communications which have since been published. For this update, we maintained the same rigid criteria for inclusion in the list of optimal epitopes as in previous years. First, the peptide has to be involved in the natural immune response against HIV. Thus, only epitopes that have been shown to induce a specific CTL response upon HIV infection in vivo are included. Second, the peptides have to be truncated to the size of the optimal epitope, which is the shortest epitope that is recognized at the lowest peptide concentration; and third, the HLA class I restricting molecule has to be identified. We therefore excluded epitopes from the list of optimal epitopes if only the binding to a certain class I molecule has been shown but not the immunogenicity in vivo, or if no peptide titration assay with truncated peptides has been reported. However, those epitopes are included in the extended CTL epitope list in Part I of this database. Nine newly listed optimal epitopes are included this year, bringing the total number to 110. These epitopes add to the previously identified pattern of CTL epitopes being widely distributed over the expressed HIV proteins. In order to keep this list updated it would be very helpful for us to receive information about newly identified epitopes as they become available. For comments and/or contributions to this CTL epitope list (C.B. or B.W.) or the main database (B.K.) please call, write, or mail us at:
Bayesian Time Series Learning with Gaussian Processes

SUMMARY The analysis of time series data is important in fields as disparate as the social sciences, biology, engineering or econometrics. In this dissertation, we present a number of algorithms designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. First, they aim at making predictions which quantify the uncertainty due to limitations in the quantity and the quality of the data. Second, they are flexible enough to model highly complex data whilst preventing overfitting when the data does not warrant complex models. We begin with a unifying literature review on time series models based on Gaussian processes. Then, we centre our attention on the Gaussian Process State-Space Model (GP-SSM): a Bayesian nonparametric generalisation of discrete-time nonlinear state-space models. We present a novel formulation of the GP-SSM that offers new insights into its properties. We then proceed to exploit those insights by developing new learning algorithms for the GP-SSM based on particle Markov chain Monte Carlo and variational inference. Finally, we present a filtered nonlinear auto-regressive model with a simple, robust and fast learning algorithm that makes it well suited to its application by non-experts on large datasets. Its main advantage is that it avoids the computationally expensive (and potentially difficult to tune) smoothing step that is a key part of learning non-linear state-space models. ACKNOWLEDGEMENTS I would like to thank Carl Rasmussen who took the gamble of accepting a candidate for a PhD in statistical machine learning who barely knew what a probability distribution was. I am also grateful to Zoubin Ghahramani, Rich Turner and Daniel Wolpert for creating such a wonderful academic environment at the Computational and Biological Learning Lab in Cambridge; it has been incredible to interact with so many talented people. I would also like to thank Thomas Schön for hosting my fruitful academic visit at Linköping University (Sweden). I am indebted to my talented collaborators Fredrik Lindsten and Yutian Chen. Their technical skills have been instrumental to develop ideas that were only fuzzy in my head. I would also like to thank my thesis examiners, Rich Turner and James Hensman, and Thang Bui for their helpful feedback that has certainly improved the thesis. Of course, any remaining inaccuracies and errors are entirely my fault! Finally, I can not thank enough all the colleagues, friends and family with whom I have spent so many joyful times throughout my …
Protocol Data Units with Augmented Packet Header

This document describes a machine-readable format for specifying the syntax of protocol data units within a protocol specification. This format is comprised of a consistently formatted packet header diagram, followed by structured explanatory text. It is designed to maintain human readability while enabling support for automated parser generation from the specification document. This document is itself an example of how the format can be used.
Fpga Based Implementation Of Concatenation Matrix

The computer graphics system performance is increasing faster than any other computing application. The Geometric transformations and animation are one of the most important principle of the interactive computer graphics which are essential for modeling and viewing. This paper tends to construct a general form of matrix representation of the geometric transformations and implement it using Field Programmable Gate Array (FPGA). In addition to that the sine and cosine function evaluation is done using two techniques, the lookup table method and CORDIC algorithm.
Brownian bridge

A Brownian bridge is a stochastic process derived from standard Brownian motion by requiring an extra constraint. This gives Brownian bridges unique mathematical properties, fascinating, itself, and useful in statistical and mathematical modeling. Here, we define a Brownian bridge and explain some of its unique mathematical properties. These properties make it useful as a mathematical model for real‐world phenomena and as a tool for statistical analysis. We then give some examples of applications. We will see many mathematical ideas that touch Brownian bridges, such as differential equations, linear spaces, topology, metric spaces of functions, measure theory, stochastic integrals, estimation, and hypothesis testing. Copyright © 2009 Wiley Periodicals, Inc.
Problems of Statistics in the Greek Linguistic Studies: A Computational Approach

This paper aims to explain why we would use statistical methods for Ancient Greek linguistics? The statistics are typically using a large machine-readable corpus, in order to discover general principles of linguistic behavior, genre difference, etc. The paper also sets out to prove some hypotheses, or identify some linguistic phenomena, such as morphological, syntactic, and semantic phenomena. The proposed paper will consist of the following points: What kinds of linguistic data can they handle? What are the advantages and disadvantages of statistical linguistics? What is the nature of the assumptions they require of the analyst? What is the strategy for studying of linguistic phenomena?
Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.
APLIKASI PEMBELAJARAN TEKNIK MESIN OTOMOTIF KENDARAAN RINGAN DENGAN METODE COMPUTER ASSISTED INSTRUCTION (STUDI KASUS : SMK SWASTA KARYA PENDIDIK)

Pembelajaran di dalam sekolah masih menggunakan metode tradisional, khususnya Teknik mesin yaitu pembelajaran yang menggunakan teknik ceramah khususnya dalam mata pelajaran Teknik kendaraan ringan. Hal ini sangat berdampak kepada minat dan motivasi siswa dalam mengikuti dan menyerap pelajaran khususnya pelajaran Teknik kendaraan ringan karena dalam metode ini hanya guru yang terlibat dalam penyampaian materi, siswa hanya sebagai pendengar oleh karena itu siswa akan merasa bosan dalam mengikuti pembelajaran Teknik kendaraan ringan khususnya dengan materi Teknik mesin.Untuk mengatasi hal tersebut di atas, maka diperlukan Aplikasi Pembelajaran Teknik mesin. Dengan adanya Aplikasi Pembelajaran ini diharapakan dapat membantu guru dalam menyampaikan materi kepada siswa serta menambah minat belajar siswa dan menambah motivasi siswa untuk mengikuti pelajaran di sekolah khusunya mata pelajaran Teknik kendaraan ringan. Karena dalam aplikasi ini siswa juga dapat melihat video pembelajaran Teknik mesin.Siswa juga dapat melatih kemampuan dengan materi drill and practice , kemudian siswa juga bisa bermain games interaktif ,yang disebut CAI (computer assisted Instruction) yaitu pengajaran yang berbasis komputer yang merupakan pengembangan dari pada teknologi informasi terpadu yaitu komunikasi (interaktif), audio, video, penampilan citra (image) yang dikemas dengan sebutan teknologi multimedia untuk penyajian materi khususnya materi Teknik mesin.