scholarly journals ARKEOFAUNA KAWASAN KARST BONTOCANI KABUPATEN BONE SULAWESI SELATAN

2018 ◽  
Vol 16 (1) ◽  
pp. 21
Author(s):  
Nfn Fakhri
Keyword(s):  
Data Collection ◽  
Environmental Change ◽  
Comparative Studies ◽  
Rain Forest ◽  
Data Availability ◽  
Karst Area ◽  
Forest Environment ◽  
Faunal Composition ◽  
Wide Range ◽  
Bone Fragments

This study aims to provide a description of the fauna that once interacted with a human in the Bontocani karst Area in Bone District. Of the few excavated sites providing data availability of bone fragments that can be analyzed by conducting comparative studies with existing faunal composition of the fauna. The method of data collection is by excavation at some sites in this Karst Area. The results of this study document a wide range of vertebrates in the Balang Metti fauna including fish, frogs/toads, lizards, snakes, birds, Strigocuscus, Ailurops ursinus, insectivorous bats, Sulawesi monkeys, rats, Sulawesi pigs, babirusa and Anoa. In some layers of culture, the absence of anoa, indicates the environmental change from the environment of the fields and the weeds to the wet rain forest environment around the site, along with the extinction of this fauna. Based on the identified fauna bone analysis, it is illustrated that past habitats and environments in Bontocani Karst area have not changed much. Penelitian ini bertujuan memberikan gambaran secara lebih jelas tentang fauna-fauna yang pernah berinteraksi dengan manusia pendukung kebudayaan yang ada di Kawasan Karst Bontocani di Kabupaten Bone. Beberapa situs yang telah diekskavasi memberikan ketersediaan data berupa fragmen tulang yang dapat dianalisis dengan melakukan studi komparasi dengan komposisi tulang fauna yang ada saat ini. Metode pengumpulan data yang dilakukan adalah dengan ekskavasi di beberapa situs yang ada di Kawasan Karst ini. Hasil penelitian menunjukkan bahwa sebagian besar jenis fauna yang ditemukan di situs ini adalah fauna bertulang belakang antara lain: ikan, kodok/katak, kadal, ular, burung, strigocuscus, Ailurops ursinus, kelelawar pemakan serangga, monyet sulawesi, tikus, babi sulawesi, babi russa dan anoa. Pada beberapa lapisan budaya, tidak adanya temuan fauna anoa, menunjukkan perubahan lingkungan dari lingkungan padang dan ilalang menjadi lingkungan hutan hujan basah di sekitar situs, seiring dengan punahnya fauna ini. Berdasarkan analisis tulang fauna yang berhasil diidentifikasi digambarkan bahwa habitat dan lingkungan masa lampau di Kawasan Karst Bontocani tidak banyak mengalami perubahan.

Evaluation of CORSIM Car-Following Model by Using Global Positioning System Field Data

2000 ◽  
Vol 1710 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Sastry Chundury ◽  
Brian Wolshon
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Field Data ◽  
Goodness Of Fit ◽  
Positioning System ◽  
Goodness Of Fit Test ◽  
Distance Information ◽  
Car Following ◽  
Wide Range ◽  
Global Positioning

It has been recognized that CORSIM (and its constituent program, NETSIM) is one of the most widely used and effective computer programs for the simulation of traffic behavior on urban transportation networks. Its popularity is due in large part to the high level of detail incorporated into its modeling routines. However, the car-following models, used for the simulation of driver behavior in the program, have not been formally calibrated or validated. Since the model has performed well in a wide range of applications for so many years, it has always been assumed to have an implied validity. This study evaluated the NETSIM car-following models by comparing their results with field data. Car-following field data were collected using a new data collection system that incorporates new Global Positioning System and geographic information system technologies to improve the accuracy, ease, speed, and cost-effectiveness of car-following data collection activities. First, vehicle position and speed characteristics were collected under field conditions. Then simulated speeds and distances were based on identical lead vehicle actions using NETSIM car-following equations. Comparisons of simulated and field data were completed using both graphical and statistical methods. Although some differences were evident in the graphical comparisons, the graphs overall indicated a reasonable match between the field and simulated vehicle movements. Three statistical tests, including a goodness-of-fit test, appear to support these subjective conclusions. However, it was also found that definitive statistical conclusions were difficult to draw since no single test was able to compare the sets of speed and distance information on a truly impartial basis.

scholarly journals Curvature-Based Equation of State for Microemulsion-Phase Behavior

SPE Journal ◽  
2018 ◽  
Vol 24 (02) ◽  
pp. 647-659 ◽  
Author(s):  
V. A. Torrealba ◽  
R. T. Johns ◽  
H.. Hoteit
Keyword(s):  
Equation Of State ◽  
Phase Behavior ◽  
Oil Recovery ◽  
Industrial Applications ◽  
Data Availability ◽  
Behavior Modeling ◽  
Average Curvature ◽  
Wide Range ◽  
Definition Of ◽  
Spheroidal Geometry

Summary An accurate description of the microemulsion-phase behavior is critical for many industrial applications, including surfactant flooding in enhanced oil recovery (EOR). Recent phase-behavior models have assumed constant-shaped micelles, typically spherical, using net-average curvature (NAC), which is not consistent with scattering and microscopy experiments that suggest changes in shapes of the continuous and discontinuous domains. On the basis of the strong evidence of varying micellar shape, principal micellar curves were used recently to model interfacial tensions (IFTs). Huh's scaling equation (Huh 1979) also was coupled to this IFT model to generate phase-behavior estimates, but without accounting for the micellar shape. In this paper, we present a novel microemulsion-phase-behavior equation of state (EoS) that accounts for changing micellar curvatures under the assumption of a general-prolate spheroidal geometry, instead of through Huh's equation. This new EoS improves phase-behavior-modeling capabilities and eliminates the use of NAC in favor of a more-physical definition of characteristic length. Our new EoS can be used to fit and predict microemulsion-phase behavior irrespective of IFT-data availability. For the cases considered, the new EoS agrees well with experimental data for scans in both salinity and composition. The model also predicts phase-behavior data for a wide range of temperature and pressure, and it is validated against dynamic scattering experiments to show the physical significance of the approach.

scholarly journals Distribution-preserving data augmentation

2021 ◽  
Vol 7 ◽  
pp. e571
Author(s):  
Nurdan Ayse Saran ◽  
Murat Saran ◽  
Fatih Nar
Keyword(s):  
Data Augmentation ◽  
Image Data ◽  
Large Data ◽  
Data Availability ◽  
Superior Performance ◽  
Color Distribution ◽  
Spatial Transformations ◽  
Wide Range ◽  
Dataset Size ◽  
Existing Data

In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it possible to develop a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, and sometimes not likely in some domains if not challenging. Therefore, researchers proposed data augmentation methods to increase dataset size and variety by creating variations of the existing data. For image data, variations can be obtained by applying color or spatial transformations, only one or a combination. Such color transformations perform some linear or nonlinear operations in the entire image or in the patches to create variations of the original image. The current color-based augmentation methods are usually based on image processing methods that apply color transformations such as equalizing, solarizing, and posterizing. Nevertheless, these color-based data augmentation methods do not guarantee to create plausible variations of the image. This paper proposes a novel distribution-preserving data augmentation method that creates plausible image variations by shifting pixel colors to another point in the image color distribution. We achieved this by defining a regularized density decreasing direction to create paths from the original pixels’ color to the distribution tails. The proposed method provides superior performance compared to existing data augmentation methods which is shown using a transfer learning scenario on the UC Merced Land-use, Intel Image Classification, and Oxford-IIIT Pet datasets for classification and segmentation tasks.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals The Fully Conscious Ape

2018 ◽  
Vol 31 ◽  
Author(s):  
Sue Savage-Rumbaugh ◽  
Itai Roffman ◽  
Sabatien Lingomo ◽  
Elizabeth Pugh
Keyword(s):  
Comparative Studies ◽  
Cognitive Skills ◽  
Nonhuman Primates ◽  
The Self ◽  
The Other ◽  
Human Language ◽  
Wide Range ◽  
Transfer Index ◽  
Modern Era ◽  
Current Experience

Duane Rumbaugh was one of the first primatologists of the modern era (which began after WWII), to engage in comparative studies of the cognitive capacities of nonhuman primates. In fact, it was Rumbaugh who drew the world's attention to the Order Primates and who helped initiate the International Primatological Society, IPS, the first academic society to be organized around an Order rather than a discipline. His work eventually led in two directions, first the development of the Transfer Index, a was completely new way of looking at learning. The TI seperated monkeys from apes as completely as did Gallup's mirror task. From this arose the Primate Test Battery, a video based system to test cognitive skills across a wide range of tasks from memory to numerical skils in primates. The other direction was to look at language and its effect on cognition. Only Apes succeeded in the laguage tasks. With Lana's success arose a raft of critiques that - in the light of more recent findings about the structure of human language, are now rendered invalid. Rumbaugh's initial findings in all domains has remained sound. This includes fundamental differences between monkeys and apes in their capacity to spontaneously begin control their attention, to consciously monitor their own behavior, and then to alter it deliberately, or by their own choice. It is the ape's conscious capacity to control its attention and to conciously monitor outcomes in a cause/effect manner, that allows for the acquisition of langauge. This also allows for the creation of "personal self", as a being that exists apart from the current experience of the self. Language greatly assists the emergence of this ability in apes, as does early rearing in which the ape is carried but not seperated from its mother. This allows pointing and joint reference to appear far ahead of schedule and for the spontaneouls development of human language in cross-species co-reared apes. The presence of a wild-reared mother (not present in other captive environments)also allows for the emergence of a nonhuman form of vocal language. The implications of this work for future investigations of apes are discussed.

Biological Diversity and Ecosystem Functions

2016 ◽  
Author(s):  
Oswald J. Schmitz
Keyword(s):  
Ecosystem Services ◽  
Environmental Change ◽  
Biological Diversity ◽  
Ecosystem Functions ◽  
Wide Range ◽  
The Many ◽  
The Relationship

This chapter reflects on the relationship between biodiversity and ecosystem functions. Drawing connections between ecosystem functions and ecosystem services can make the concept of sustainability less nebulous. It offers tangible ways to translate science into practice by revealing the intricacies of nature and the many threads that link humans to nature through such intricacies. Establishing such connections illustrates why it is important to ensure that ecosystem functions endure. The chapter shows how the New Ecology is helping us appreciate how and why the complex ways that species that have evolved and forged interdependencies with each other matter to sustainability. It argues that maintaining diversity within ecosystems ensures that a wide range of options is available for adapting to environmental change.

Addressing Data Collection Challenges in ICT for Development Projects

2015 ◽  
Vol 7 (3) ◽  
pp. 36-55 ◽  
Author(s):  
Devendra Dilip Potnis
Keyword(s):  
Developing Countries ◽  
Data Collection ◽  
Lessons Learned ◽  
Time Cost ◽  
Marginalized Communities ◽  
The Past ◽  
Success Stories ◽  
Wide Range ◽  
Collection Data ◽  
Ict For Development

This paper equips researchers for addressing a wide range of data collection challenges experienced when interacting with marginalized communities as part of ICT4D projects in developing countries. This secondary research categorizes data collection challenges reported in multiple disciplines, and summarizes the guidance from the past literature to deal with the challenges. The open, axial, and selective coding of data collection challenges reported by the past literature suggests that it is necessary to manage scope, time, cost, quality, human resources, communication, and risks for addressing the data collection challenges. This paper illustrates the ways to manage these seven dimensions using (a) the success stories of data collection in the past, (b) the lessons learned by researchers during data collection as documented by the past literature, and (c) the advice they offer for collection data from marginalized communities in developing countries.

scholarly journals Wireless Middleware Solutions for Smart Water Metering

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1853 ◽  
Author(s):  
Stefano Alvisi ◽  
Francesco Casellato ◽  
Marco Franchini ◽  
Marco Govoni ◽  
Chiara Luciani ◽  
...  
Keyword(s):  
Data Collection ◽  
Field Testing ◽  
Water Utility ◽  
Smart Water ◽  
Computing Paradigm ◽  
Wide Range ◽  
Utility Companies ◽  
Lock In ◽  
Water Meters ◽  
Water Metering

While smart metering applications have initially focused on energy and gas utility markets, water consumption has recently become the subject of increasing attention. Unfortunately, despite the large number of solutions available on the market, the lack of an open and widely accepted communication standard means that vendors typically propose proprietary data collection solutions whose adoption causes non-trivial problems to water utility companies in term of costs, vendor lock-in, and lack of control on the data collection infrastructure. There is the need for open and interoperable smart water metering solutions, capable of collecting data from the wide range of water meters on the market. This paper reports our experience in the development and field testing of a highly interoperable smart water metering solution, which we designed in collaboration with several water utility companies and which we deployed in Gorino Ferrarese, Italy, in collaboration with CADF (Consorzio Acque Delta Ferrarese), the water utility serving the city. At the core of our solution is SWaMM (Smart Water Metering Middleware), an interoperable wireless IoT middleware based on the Edge computing paradigm, which proved extremely effective in interfacing with several types of smart water meters operating with different protocols.

Cognitive Aspects on the Representation of Dynamic Environmental Phenomena Using Animations

2001 ◽  
pp. 13-24 ◽  
Author(s):  
Patrick J. Ogao ◽  
Connie A. Blok
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Problem Solving ◽  
Spatial Data ◽  
Scientific Data ◽  
Data Availability ◽  
Data Exploration ◽  
Spatial Data Analysis ◽  
Wide Range ◽  
Interdisciplinary Nature

Measurements from dynamic environmental phenomena have resulted in the acquisition and generation of an enormous amount of data. This upsurge in data availability can be attributed to the interdisciplinary nature of environmental problem solving and the wide range of acquisition technology involved. In essence, users are dealing with data that is complex in nature, multidimensional and probably of a temporal nature. Also, the frequency by which this data is acquired far exceeds the rate at which it is being explored, a factor that has accelerated the search for innovative approaches and tools in spatial data analysis. These attempts have seen both analytical and visual techniques being used as aids in presentation and scientific data exploration. Examples are seen in techniques as in: data mining, data exploration and visualization.

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