Miracle-dz/UniFAR-FTUs huggingface dataset:
DAVRANIŞSAL FİNANS AÇISINDAN BİLİŞSEL ÇELİŞKİ, AŞIRI GÜVEN VE TAKLİT VE SÜRÜ DAVRANIŞLARI BOYUTLARINDA BİREYSEL YATIRIMCI KARARLARI ÜZERİNE BİR İNCELEME

The purpose of this study is to investigate behavioral finance in which the effects of finance are relatively new. Behavioral finance explores the cognitive and emotional factors affecting the decision making process of individuals, groups and organizations. It deals with the behavioral and psychological consequences of financial decisions and is a multidisciplinary subdivision of finance. In this research, individual investors are taken for sampling and the main purpose is to examine the irregular behaviors of these investors in their investment decisions and their relations with demographic variables. Snowball sampling was used for non-random sample types and an online questionnaire was applied. 13 participants were excluded from the study due to the answer to the control question and the answers of 115 participants were analyzed. The attitudes of individual investors to the dimensions of “cognitive dissonance”, “overconfidence” and “imitation and the herd behaviors” were evaluated. The frequencies, percentages and mean values of the demographic and scale questions were determined, reliability analysis (Cronbach's Alpha), normal distribution test and chi-square independence test were applied, interpretations and recommendations were made.
Data-Driven Photovoltaic Generation Forecasting Based on a Bayesian Network With Spatial–Temporal Correlation Analysis

Spatiotemporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial–temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst the PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 min). The model utilizes historic PV generation and weather data, and incorporates the abovementioned spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.
An Efficient Approach to Detect and Track Breaking News on Twitter

Twitter is an intriguing stage for the scattering of news. The constant nature and curtness of the tweets are helpful for sharing of data identified with critical occasions as they unfurl. Yet, one of the best difficulties is to discover the tweets that we can portray as news in the sea of tweets. In this paper, we propose a novel technique for recognizing and following breaking news from Twitter in genuine time. We channel the flood of approaching tweets to evacuate garbage tweets utilizing a content arrangement calculation. At long last, we rank the news utilizing a dynamic scoring framework which additionally enables us to follow the news over some undefined time frame.
Challenges and limitations for scaling up to a rewilding project: scientific knowledge, best practice, and risk

Rewilding, understood as the reintroduction and moreor-less passive management of species for ecological restoration (Root-Bernstein et al. 2017a), continues to increase in popularity in Europe and around the world (e.g. Zimov 2005; Louys et al. 2014; Jepson 2016; Zamboni, Di Martino, and Jiménez-Pérez 2017). Technically, it is not necessarily innovative. Conservationists already have decades of experience carrying out successful reintroduction projects, and passive habitat management techniques have also been successfully used for decades and are often based on traditional practices. However, rewilding projects extend the lessons of reintroductions and restoration projects in two primary ways: (1) they involve civil society in profound ways even if not always explicitly, and can question the relation between society and nature in different ways than most restoration and reintroduction projects; (2) they do not seek to merely reinstate known ecological conditions, but also allow for the conditions under which unknown ecological conditions can arise. Common criticisms of rewilding projects are that because they are open-ended and adaptive rather than planned and controlled interventions aiming for a carefully assessed and justified endpoint, they are not science and cannot be learned from, they are unprofessional, or they are dangerous. Here, we demonstrate some of the ways that we have gained ecological and socio-ecological insights, used professional diligence and best practice, and mitigated risks to ecosystems and humans in the planning of a rewilding project in a hotspot of endemicity in central Chile.
Interactions with Gigantic Point Clouds

During the last decade the increased use of laser range-scanners for sampling the environment has led to gigantic point cloud data sets. Due to the size of such data sets, tasks like viewing, editing, or presenting the data have become a challenge per se, as the point data is too large to fit completely into the main memory of a customary computer system. In order to accomplish these tasks and enable the interaction with gigantic point clouds on consumer grade computer systems, this thesis presents novel methods and data structures for efficiently dealing with point cloud data sets consisting of more than 109 point samples. To be able to access point samples fast that are stored on disk or in memory, they have to be spatially ordered, and for this a data structure is proposed which organizes the points samples in a level-of-detail hierarchy. Point samples stored in this hierarchy cannot only be rendered fast, but can also be edited, for example existing points can be deleted from the hierarchy or new points can be inserted. Furthermore, the data structure is memory efficient, as it only uses the point samples from the original data set. Therefore, the memory consumption of the point samples on disk, when stored in this data structure, is comparable to the original data set. A second data structure is proposed for selecting points. This data structure describes a volume inside which point samples are considered to be selected, and this has the advantage that the information about a selection does not have to be stored at the point samples. In addition to these two previously mentioned data structures, which represent novel contributions for point data visualization and manipulation, methods for supporting the presentation of point data sets are proposed. With these methods the user experience can be enhanced when navigating through the data. One possibility to do this is by using regional meshes that employ an out-of-core texturing method to show details in the mesoscopic scale on the surface of sampled objects, and which are displayed together with point clouds. Another possibility to increase the user experience is to use graphs in 3D space, which helps users to orient themselves inside point cloud models of large sites, where otherwise it would be difficult to find the places of interest. Furthermore, the quality of the displayed point cloud models can be increased by using a point size heuristics that can mimic a closed surface in areas that would otherwise appear undersampled, by utilizing the density of the rendered points in the different areas of the point cloud model. Finally, the use of point cloud models as a tool for archaeological work is proposed. Since it becomes increasingly common to document archaeologically interesting monuments with laser scanners, the number application areas of the resulting point clouds is raising as well. These include, but are not limited to, new views of the monument that are impossible when studying the monument on-site, creating cuts and floor plans, or perform virtual anastylosis.
A Low Voltage and High Resolution Comparator for Successive Approximation ADC

A low voltage comparator for successive approximation Analog to digital converter(ADC) is designed.It consists of switched preamplifier and dynamic latch.Amplifying of the input signal can be achieved by preamplifier.High gain improves the accuracy of the comparator and positive feedback increased the speed of it.The design is based on 0.6 μm CMOS the power voltage is the single 2.5 V.The analysis of circuit theory and the simulation based on the SMARTSPICE softw,results indicate that the comparator has the merits of high speed,high resolution and low-power.The circuit is applicable to successive approximation low-voltage ADC.
Biomedical Engineering Fundamentals

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Distributed Real-Time Analysis of High-Velocity Many-Dimensional Data Sets

Fast aggregation of data with many hierarchical dimensions is a key component of many data analysis applications. Recent years have seen increased interest in the ability to do so in real-time, allowing live analysis of rapidly changing data sets. At the same time, data sets of interest have increased in size well beyond the capabilities of a single machine to process efficiently. With the widespread availability of virtualized cloud computing environments, organizations increasingly demand systems that can be deployed on commodity platforms. Though each of these requirements in isolation can be addressed with well-known solutions, their combination introduces several system design challenges. As a result, users are forced to sacrifice certain functionality, or use several systems to handle different uses of the same data set. This dissertation proposes a scalable distributed system, VelocityOLAP (VOLAP), which meets all of these requirements at once. Specifically, the system can ingest data at a very high rate while answering queries which aggregate large fractions of the database. Users are not required to "slice" along particular dimensions or generate separate indices in order to query the database. Data is distributed across many machines, and the system can grow or shrink dynamically to accommodate changing load. The distribution of data is dynamically adjusted to maintain performance and provide fault-tolerance without interrupting real-time processing. The system can provide strong guarantees that query results are fresh, even as new items are inserted at a high rate. These results are achieved by building the system around a spatial index data structure which is specially designed to store hierarchical multi-dimensional data with rapid ingestion. Despite the lack of any user tuning for specific query workloads, extensive benchmarks on diverse data sets confirm that VOLAP scales well and can maintain high performance for a wide range of queries on a large and rapidly growing database.
On The Expansion Problem of Reliable ip Networks with Performance Guarantees

Abstract In this paper, we tackle the problem of how to expand Internet protocol (IP) networks with performance guarantees in a cost-effective way. We first propose an optimization model that deals with selecting the location of the routers, selecting their types, expanding the access and the backbone networks and selecting the transmission link types. The model also considers the routing of the II’ (rallie using a standardized routing protocol. The performance guarantees we refer to are bandwidth guarantees between the clients’ routers. The performance guarantees stand not only for the normal state of the network, but also for all failure scenarios of interest to the network planner. A tabu-based metaheuristic algorithm is proposed to find solutions of the model. Numerical results show that “good” solutions can be found using the pioposed algorithm.
A Porous Media Numerical Approach for the Simulation of Stirling Engine Regenerators

Received: 19 February 2019 Accepted: 4 May 2019 The Stirling engine design process is mostly focused on the study of the engine regenerator, whose thermal efficiency is strictly related to the global efficiency of the engine. The present work aims to setup a numerical model by means of OpenFOAM libraries to simulate the engine regenerator as a porous media, in order to reduce the overall computational cost of the simulations and to obtain a suitable model that can be eventually used for topological optimization. The solid part of the numerical domain, i.e. the stoked wires, is represented as a zone with the highest porosity, whereas intermediate areas are defined in order to simulate the boundary layers in proximity of the walls. The presence of porous media is modelled by introducing a Darcy-type pressure drop in the momentum equation, whereas in the energy equation the thermal properties of the solid, intermediate and fluid materials are modelled by means of a linear function of the porosity value in the domain. After a process of tuning, the results obtained with this model are reported for different flow conditions in terms of both aerodynamic and thermal performances, thus showing a good agreement with the numerical results obtained with a more classical CFD methodology.
Search Personalization in Folksonomy by Exploiting Multiple and Temporal Aspects of User Profiles

Social tagging data, also known as folksonomy, are a valuable indication for the user’s understanding of a resource. The nature of folksonomy data in which a user annotates a resource with their opinions provides immense potential to contribute to search personalization. The challenge lies in extracting interests from the folksonomy data and building accurate user profiles while maintaining their characteristics. Furthermore, the current state-of-the-art technologies that utilize folksonomy for search personalization have not fully exploited both multiple and temporal aspects in user profiles. In this paper, we propose a search personalization framework that constructs a user profile network with identification of the multiple topics of the user and the temporal values of tags. Then, the user profile network is further explored through a link analysis technique for the network to score the tags by their importance. The performance of the proposed framework is evaluated against various state-of-the-art folksonomy-based personalization models and it consistently outperforms all of the compared models under the conditions of the best combination of ranking functions and link analysis techniques.
Modeling a Consortium based Distributed Ledger Network with Applications for Intelligent Transportation Infrastructure

Emerging distributed-ledger networks are changing the landscape for environments of low trust among participating entities. Implementing such technologies in transportation infrastructure communications and operations would enable, in a secure fashion, decentralized collaboration among entities who do not fully trust each other. This work models a transportation records and events data collection system enabled by a Hyperledger Fabric blockchain network and simulated using a transportation environment modeling tool. A distributed vehicle records management use case is shown with the capability to detect and prevent unauthorized vehicle odometer tampering. Another use case studied is that of vehicular data collected during the event of an accident. It relies on broadcast data collected from the Vehicle Ad-hoc Network (VANET) and submitted as witness reports from nearby vehicles or road-side units who observed the event taking place or detected misbehaving activity by vehicles involved in the accident. Mechanisms for the collection, validation, and corroboration of the reported data which may prove crucial for vehicle accident forensics are described and their implementation is discussed. A performance analysis of the network under various loads is conducted with results suggesting that tailored endorsement policies are an effective mechanism to improve overall network throughput for a given channel. The experimental testbed shows that Hyperledger Fabric and other distributed ledger technologies hold promise for the collection of transportation data and the collaboration of applications and services that consume it.
A Random Sampling O(n) Force‐calculation Algorithm for Graph Layouts

This paper proposes a linear‐time repulsive‐force‐calculation algorithm with sub‐linear auxiliary space requirements, achieving an asymptotic improvement over the Barnes‐Hut and Fast Multipole Method force‐calculation algorithms. The algorithm, named random vertex sampling (RVS), achieves its speed by updating a random sample of vertices at each iteration, each with a random sample of repulsive forces. This paper also proposes a combination algorithm that uses RVS to derive an initial layout and then applies Barnes‐Hut to refine the layout. An evaluation of RVS and the combination algorithm compares their speed and quality on 109 graphs against a Barnes‐Hut layout algorithm. The RVS algorithm performs up to 6.1 times faster on the tested graphs while maintaining comparable layout quality. The combination algorithm also performs faster than Barnes‐Hut, but produces layouts that are more symmetric than using RVS alone. Data and code: https://osf.io/nb7m8/
DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.
Edge‐Enriched Ultrathin MoS2 Embedded Yolk‐Shell TiO2 with Boosted Charge Transfer for Superior Photocatalytic H2 Evolution

Exploring TiO2‐photocatalysts for sunlight conversion has high demand in artificial photosynthesis. In this work, edge‐enriched ultrathin molybdenum disulfide (MoS2) flakes are uniformly embedded into the bulk of yolk‐shell TiO2 as a cocatalyst to accelerate photogenerated‐electron transfer from the bulk to the surface of TiO2. The as‐formed MoS2/TiO2 (0.14 wt%) hybrids exhibit a high hydrogen evolution rate (HER) of 2443 µmol g−1 h−1, about 1000% and 470% of that of pristine TiO2 (247 µmol g−1 h−1) and bulk MoS2 decorated TiO2 (513 µmol g−1 h−1). Such a greatly enhanced HER is attributed to the exposed catalytic edges of the ultrathin MoS2 flakes with a robust chemical linkage (TiS bond), providing rapid charge transfer channels between TiO2 and MoS2. The catalytic stability is promoted by the antiaggregation of the highly dispersed MoS2 flakes in the bulk of yolk‐shell TiO2. The exponential fitted decay kinetics of time‐resolved photoluminescence (ns‐PL) spectra illustrates that embedding ultrathin MoS2 flakes in TiO2 effectively decreases the average lifetime of PL in the MoS2/TiO2 hybrids (τave = 4.55 ns), faster than that of pristine TiO2 (≈7.17 ns) and the bulk MoS2/TiO2 (≈6.13 ns), allowing a superior charge separation and charge trapping process for reducing water.
Application of Flipped Classroom on Auxiliary Machinery

Application of flipped classroom on auxiliary machinery achieved a high degree of integration of online learning and traditional classroom learning. The combination of flipped classroom and auxiliary machinery, not only breaks through the limitations of blended learning to achieve a higher degree of mixing levels, but also improves the students' autonomy in learning, which conquers the scarcity of the traditional teaching model in higher education. Flipped classroom promotes the effective teaching method, and it can provide new ideas for academic advising of students. So it can help a lot in the development of students. Keywords—flipped classroom; auxiliary machinery; higher education; teaching model
ADD-Net: Attention Based 3D Dense Network for Action Recognition

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism（ADD-Net）, recognition of human motion behavior in the video.
Limited factor VIIa surface localization requirement of the factor VIIa–induced overall thrombin generation in platelet‐rich hemophilia A plasma

Thrombin generation assay (TGA) and thrombelastography (TEG) are increasingly employed, global, in vitro methods for assessment of the procoagulant potential of plasma/blood and possibly ideally suited tools to monitor, for example, therapy with recombinant factor VIIa (FVIIa). It remains controversial to what extent results obtained with spiked and postinfusion samples reflect the outcome in patients.