Miracle-dz/UniFAR-FTUs huggingface dataset:
CHAPTER 25 Mobile IP Protocols

The Internet currently offers access to a variety of information worldwide in an efficient and, through the use of web technologies, user-friendly manner. It is based on the Transport Control/Internet Protocol (TCP/IP) protocol stack [6] which has been developed with data communications and fixed access location points in mind. The wide use of wireless technologies for voice communications and the proliferation of handheld and other devices that can provide access to the Internet call for a new paradigm for connecting mobile users to the Internet. Such an endeavor needs to take into account the existing Internet protocols, compatibility issues, and the requirements of mobile users. Mobile IP, as proposed by the Internet Engineering Task Force (IETF) in RFC 2002 [1] and subsequent RFCs [2], provides an efficient, scalable mechanism for node mobility within the Internet. Nodes may move and change their point of attachment to the Internet without changing their IP address. This allows them to maintain transport and higher-layer connections while moving. Node mobility is realized without the need to propagate hostspecific routes throughout the Internet routing fabric. The mobile node uses two IP addresses: a fixed home address and a care-of address that changes at each new point of attachment. Mobile IP is intended to solve node mobility issues over the IP layer. It is just as suitable for mobility across homogeneous media as it is for mobility across heterogeneous media. Mobile IP facilitates node movement from one Ethernet segment to another as well as handling node movement from an Ethernet segment to a wireless local area network (LAN). One can think of mobile IP as solving the “macro” mobility management problem. It is less well suited for more “micro” mobility management applications, for example, handoff amongst wireless transceivers, each of which covers only a very small geographic area. In this situation, link layer mechanisms for link maintenance (i.e., link layer handoff) might offer faster convergence and fewer overheads than mobile IP. Finally, it is noted that mobile nodes are assigned (home) IP addresses largely the same way in which stationary hosts are assigned long-term IP addresses; namely, by the authority that owns them. Properly applied, mobile IP allows mobile nodes to communicate using only their home address, regardless of their current location. Mobile IP, therefore,
International Conference on Grammatical Inference Learning Graph Weighted Models on Pictures

Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrary families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its edges. In this paper, we consider the problem of learning GWMs defined over the graph family of pictures (or 2dimensional words). As a proof of concept, we consider regression and classification tasks over the simple Bars & Stripes and Shifting Bits picture languages and provide an experimental study investigating whether these languages can be learned in the form of a GWM from positive and negative examples using gradient-based methods. Our results suggest that this is indeed possible and that investigating the use of gradient-based methods to learn picture series and functions computed by GWMs over other families of graphs could be a fruitful direction.
The Effect of Green Logistics on Economic growth, Social and Environmental Sustainability: An Empirical Study of Developing Countries in Asia

This panel study investigates the relationship between green logistics indices, economic, environmental, and social factors in the perspective of Asian emerging economies. This study adopted FMOLS and DOLS methods to test research hypothesis, catering the problem of endogenity and serial correlation. The results suggest that logistics operations, particularly LPI2 (efficiency of customs clearance processes), LPI4 (quality of logistics services) and LPI5 (trade and transport-related infrastructure), are positively and significantly correlated with per capita income, manufacturing value added and trade openness. While, greater logistics operations are negatively associated with social and environmental problems including, climate change, global warming, carbon emissions, and poisoning atmosphere. In addition, human health is badly affected by heavy smog, acid rainfall, and water pollution. The findings further extend and reveal that political instability, natural disaster and terrorism are also a primary cause of poor economic growth and environmental sustainability with poor trade and logistics infrastructure. Further, the application of renewable energy resources and green practices can mitigate negative effects on social and environmental sustainability without compromising the performance of economic growth. There is very limited empirical work presented in literature using renewable energy and green ideology to solve macro-level social and environmental problems, while this study will assist the policymakers and researchers to understand the importance of green concept in improving countries’ social, economic and environmental performance.
Selection for Novel , Acid-Tolerant Desulfovibrio spp . 1 from a Closed Transbaikal Mine Site in a Temporal 2 pH-Gradient Bioreactor 3 4

28 Almost all the known isolates of acidophilic or acid-tolerant sulphate-reducing bacteria (SRB) 29 belong to the spore-forming genus Desulfosporosinus. The objective of this study was to isolate 30 acidophilic/acid-tolerant members of the genus Desulfovibrio belonging to deltaproteobacterial 31 SRB. The sample material originated from microbial mat biomass submerged in mine water 32 was enriched for sulphate reducers by cultivation in anaerobic medium with lactate as electron 33 donor. A stirred tank bioreactor with the same medium composition was inoculated with the 34 sulphidogenic enrichment. The bioreactor was operated with a temporal pH gradient, changing 35 daily, of initial pH 7.3 and final pH 3.7. Among the bacteria in the bioreactor culture, 36 Desulfovibrio was the only SRB group retrieved from the bioreactor consortium as observed 37 by 16S rRNA-targeted denaturing gradient gel electrophoresis. Moderately acidophilic/acid38 tolerant isolates belonged to D. aerotolerans-D. carbinophilus-D. magneticus and D. 39 idahonensis-D. mexicanus clades within the genus Desulfovibrio. A moderately acidophilic 40 strain, Desulfovibrio sp. VK (pH optimum 5.7) and acid-tolerant Desulfovibrio sp. ED (pH 41 optimum 6.6) dominated in the bioreactor consortium at different time points and were isolated 42 in pure culture. 43 44
Qualitative Feedback From a Text Messaging Intervention for Depression: Benefits, Drawbacks, and Cultural Differences

Background Mobile health interventions are often standardized and assumed to work the same for all users; however, we may be missing cultural differences in the experiences of interventions that may impact how and if an intervention is effective. Objective The objective of the study was to assess qualitative feedback from participants to determine if there were differences between Spanish speakers and English speakers. Daily text messages were sent to patients as an adjunct to group Cognitive Behavioral Therapy (CBT) for depression. Methods Messages inquired about mood and about specific themes (thoughts, activities, social interactions) of a manualized group CBT intervention. There were thirty-nine patients who participated in the text messaging pilot study. The average age of the participants was 53 years (SD 10.4; range of 23-72). Results Qualitative feedback from Spanish speakers highlighted feelings of social support, whereas English speakers noted increased introspection and self-awareness of their mood state. Conclusions These cultural differences should be explored further, as they may impact the effect of supportive mobile health interventions. Trial Registration Trial Registration: Clinicaltrials.gov NCT01083628; http://clinicaltrials.gov/ct2/show/study/NCT01083628 (Archived by WebCite at http://www.webcitation.org/6StpbdHuq).
Changes in the impact of stroke between 3 and 12 months post-stroke, assessed with the Stroke Impact Scale.

OBJECTIVE To examine data collected using the Stroke Impact Scale 3.0 (SIS) at 3 and 12 months post-stroke, and to explore any clinically meaningful changes in everyday life in relation to age, gender and stroke severity. DESIGN Prospective longitudinal study. METHODS A total of 204 persons were assessed using the SIS at 3 and 12 months after onset of stroke. Changes in domain scores were calculated over time and in relation to age, gender and stroke severity. RESULTS The Strength, Hand Function and Participation domains had the highest perceived impact at 3 and 12 months, indicating problems in everyday life. Stroke recovery was perceived to be significantly higher at 12 than at 3 months irrespective of stroke severity, age or gender. The impact on the Strength and Emotion domains was significantly lower at 12 months than at 3 months. Most clinically meaningful changes, both positive and negative (± 15 points), were seen in the Participation domain and in Stroke recovery. Few changes were associated with age, gender or stroke severity. CONCLUSION Both positive and negative clinically meaningful changes related to impact of stroke were found between 3 and 12 months post-stroke. Therefore it is important to pay close attention to patients' perceptions of their everyday life situation during rehabilitation and at discharge.
Significant Machine Learning and Statistical Concepts and their Applications in Social Computing

The field of Social Computing has tremendous potential to explore the value within the data generated from various social media platforms like Facebook, Instagram, Twitter, Snapchat, LinkedIn etc. Due to the growing volume of this data, it has become difficult to gain insights with the help of traditional techniques. So machine learning techniques have become the bread and butter of researchers working in this area as they can be used to analyze large volumes of data. The purpose of this paper is to get introduced to some important research problems in the area of social computing, the machine learning and statistical methods adopted by the researchers worldwide to address those problems and the research gaps that still need to be addressed. It also emphasizes on an important problem on Twitter platform: The classification of a tweet as fake or real.
Hyperoxia speeds pulmonary oxygen uptake kinetics and increases critical power during supine cycling

What is the central question of this study? Critical power is a fundamental parameter defining high‐intensity exercise tolerance and is related to the phase II time constant of pulmonary oxygen uptake kinetics ( τV̇O2 ). To test whether this relationship is causal, we assessed the impact of hyperoxia on τV̇O2 and critical power during supine cycle exercise. What is the main finding and its importance? The results demonstrate that hyperoxia increased muscle oxygenation, reduced τV̇O2 (i.e. sped up the oxygen uptake kinetics) and, subsequently, increased critical power when compared with normoxia. These results therefore suggest that τV̇O2 is a determinant of the upper limit for steady‐state exercise, i.e. critical power.
Grayscale digital light processing 3D printing for highly functionally graded materials

We present a single-vat grayscale digital light processing 3D printing method for highly functionally graded materials. Three-dimensional (3D) printing or additive manufacturing, as a revolutionary technology for future advanced manufacturing, usually prints parts with poor control of complex gradients for functional applications. We present a single-vat grayscale digital light processing (g-DLP) 3D printing method using grayscale light patterns and a two-stage curing ink to obtain functionally graded materials with the mechanical gradient up to three orders of magnitude and high resolution. To demonstrate the g-DLP, we show the direct fabrication of complex 2D/3D lattices with controlled buckling and deformation sequence, negative Poisson’s ratio metamaterial, presurgical models with stiffness variations, composites for 4D printing, and anti-counterfeiting 3D printing.
A Dynamic Passcode System for Mobile Purchasing without Bank Card

Credit cards use a security system where the personal identification number (PIN) is a constant. This presents a security vulnerability as this gives the attacker more time to find the PIN and defraud the bank. We will propose a scheme that is based on a security code that is dynamic, that is, the code will change every time the mobile device is used for a purchase. This system will not require more hardware, but rather, a few more lines of software. We believe that both consumers and banks will welcome the new system because it will eliminate the expense of a bank card by becoming a smart phone application. We also think that the increase in security from the software will greatly outweigh the extra cost of the software required for the system.
A New Deliberation of Embedded based Assistive System for Yoga

This paper presents a design of Embedded based Assistive System for Yoga (EASY) and its implementation for the yoga postures analysis. Yoga is a proven solution for the human physical and mind activities. The human posture analysis or gesture analysis in noninvasive methods are highly appreciable solutions. In this regard, the proposed newly designed EASY is integrated with image processing techniques. The node points are marked on human anatomy as per skeleton joints virtual method. In the work, an image based skeleton approach algorithm developed to analyze the Yoga postures like Parsva Tadasana. The Embedded based Assistive System for Yoga is embodied using Kinect sensor and LabVIEW. The experimental results of EASY published for real time subjects (human) through this paper.
Three-Stage Subspace Clustering Framework with Graph-Based Transformation and Optimization

Subspace clustering (SC) refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Based on spectral clustering, state-of-the-art approaches solve SC problem within a two-stage framework. In the first stage, data representation techniques are applied to draw an affinity matrix from the original data. In the second stage, spectral clustering is directly applied to the affinity matrix so that data can be grouped into different subspaces. However, the affinity matrix obtained in the first stage usually fails to reveal the authentic relationship between data points, which leads to inaccurate clustering results. In this paper, we propose a universal Three-Stage Subspace Clustering framework (3S-SC). Graph-Based Transformation and Optimization (GBTO) is added between data representation and spectral clustering. The affinity matrix is obtained in the first stage, then it goes through the second stage, where the proposed GBTO is applied to generate a reconstructed affinity matrix with more authentic similarity between data points. Spectral clustering is applied after GBTO, which is the third stage. We verify our 3S-SC framework with GBTO through theoretical analysis. Experiments on both synthetic data and the real-world data sets of handwritten digits and human faces demonstrate the universality of the proposed 3S-SC framework in improving the connectivity and accuracy of SC methods based on $\ell_0$, $\ell_1$, $\ell_2$ or nuclear norm regularization.
An Energy Efficient TDMA based MAC Protocol for Wireless Body Area Networks

Wireless Sensor Networks (WSN) and Wireless Body Area Network (WBAN) combined together with modern health care systems are the future of healthcare practices. WBAN witness an upward interest in healthcare sector and is more advanced than any existing telemedicine system. WBAN is an upgraded version of WSN in which number of sensors used are very less in number as compared to WSN and used for monitoring health care. Energy utilization and network lifetime are the main two major challenges in WBAN as recharging and replace batteries of sensors attached to a human body may lead to one’s uneasiness. In recent years, research has been focused on channel modelling, energy consumption, design of efficient medium access control (MAC) protocols for body area networks. For handling emergency data of networks, in this paper, we deployed in numerous practical WBAN scenarios using MAC protocol and tested in more generic Time Division Multiple access (TDMA) approach for avoid packet collision which leads to higher packet loss rate. It is tested in more generic topologies, contrasting the model’s efficiency to the highly esteemed and most remarkable methodologies in the literature. Simulation results show improved performance of the proposed protocols in comparison to the selected existing ones in terms of the chosen performance metrics.
A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing

This paper introduces the hardware implementation of one of the most popular spiking neuron models which is Izhikevich model. The main target of this implementation is to reduce area and power consumed by the Spiking Neural Network (SNN) neurons as the SNN consists of a large number of neurons to mimic the human brain. Therefore, stochastic computing techniques are used to perform the squaring term that consumes much of the power in the Izhikevich neuron model equations. A hardware implementation of the model is proposed to show the area and power consumption to help the SNN designers to choose between stochastic-based multipliers and the approximate multipliers considering their power, area, and accuracy constraints.
Knowledge Management System in Industries

This paper presents the need for a Knowledge Management System in industries, as more and more industries are realizing the importance of knowledge in an organization. A general methodology that is used in developing a KMS is also presented. A few literature reviews on recent KMS development and implementation researches have also been summarized in this paper. This paper serves as an introduction to a research that is currently being conducted at a case study company. A proposed methodology for this research has also been presented. The methodology proposed is adapted from a previous research, on a collaborative methodology to capture tacit knowledge in an organization.
Quantifying Myocardial Oxygenation with Cardiac Magnetic Resonance Imaging

Myocardial ischemia, resulting from imbalance in myocardial oxygen supply and demand, can be quantitatively assessed by positron emission tomography (PET) with absolute measures of myocardial blood flow and oxygen consumption rate (MVO 2 ). Cardiac Magnetic Resonance (CMR) has notable advantages over PET, with no radiation, high spatial resolution, faster scan times, and excellent soft tissue contrast. We have developed and validated new quantitative CMR oximetry techniques, including measurements of hyperemic myocardial oxygen extraction fraction and MVO 2 through Fick's Law. These may lead to a new understanding of roles of myocardial microcirculation in myocardial ischemia. Other cardiac oximetry methods for directly quantifying MVO 2 with 17 O-labelled water are also under investigation. Quantitative CMR oximetry is a promising, non-invasive, non-radiation approach for exploring the myocardial metabolism's role in cardiac patients.
Dynamic Vector Quantization for Reinforcement Learning (DVQRL)

Vector Quantization is one of the methods used for state space generalization in reinforcement learning. However, all the clusters that will be used in learning are usually created at the beginning of the learning process. This can be disadvantageous if states are encountered in later learning phases that do not fit in any of the clusters created at the beginning of learning. This paper describes a new algorithm that creates the initial set of clusters at the beginning of the learning process, but also creates additional clusters for new states encountered in later learning phases that do not fit any of the existing clusters. The algorithm is based on the standard deviation distance measure. This algorithm is compared with an artificial neural network based solution in learning an obstacle avoidance task. The algorithm performs better than the neural network based approach achieving higher average asymptotic performances in most cases.
Protein Binding Protects Sites on Stable Episomes and in the Chromosome from De Novo Methylation

ABSTRACT We have utilized the Escherichia coli lacrepressor-operator system to test whether protein binding can interfere with de novo DNA methylation in mammalian cells. We find that a DNA binding protein can protect sites on the episome as well as in the genome from the de novo methylation activity of Dnmt3a. Transcriptional machinery moving through the binding sites does not affect the de novo methylation of these sites, and it does not affect the binding protein protection of these sites from de novo methylation. This study and previous studies provide a possible mechanism for the observation that an Sp1 site can serve as a cis-acting signal for demethylation and for preventing de novo methylation of the CpG island upstream of the mouse adenine phosphoribosyltransferase (Aprt) gene. These findings also support the hypothesis that protein binding may play a crucial role in changes of CpG methylation pattern in mammalian cells.
Analyzing the Structures of Clusters in Multi-layer Biological Networks

Nowadays many biological problems are structured in the form of networks called biological networks. Formation of such large networks helps us in analyzing biological systems extensively and effectively. Although the size and structure of biological networks are very complex and it is difficult to interpret such networks without further processing. In this paper, we discuss the analysis of such systems by forming clusters and analyze the whole arrangement using different types of clusters formed in the network. A cluster is a highly connected group of nodes of the net and cluster analysis is one of the prominent features used for the analysis of the biological systems. Here, we present our study by applying some popular clustering algorithms on multi-layer biological networks to see the changes in the shape, size, and structure of the clusters when multiple interactions among biological entities are included and the behavior of objects in such networks.