Miracle-dz/UniFAR-FTUs huggingface dataset:
An adaptive anchor navigation algorithm for localization in MANET

Automatic anchor navigation for sensor node localization in mobile networks is a challenging task. Previous work on anchor navigation has mostly concentrated on static node localization. This paper proposes an adaptive anchor navigation algorithm to localize sensor nodes in a mobile ad-hoc networks (MANET). The novelty of the proposed algorithm lies in fact that an anchor chooses an optimal position at every instant. This adaptive position selection of the anchor ensures low energy consumption. On the other hand, an optimal path mechanism proposed herein constrains the anchor to traverse in a region densely populated with nodes. This ensures that the path traversed by the anchors is minimal. In order to localize the nodes, the ratio of grid benefit to distance (GBD) within the probable region of anchor is first computed. Subsequently, an optimality criterion is used to decide the next anchor location. Localization is then performed using geometric methods. Experimental results on node localization in a mobile network scenario indicate a reasonable performance improvement when compared to conventional methods.
The EAR Motif Controls the Early Flowering and Senescence Phenotype Mediated by Over-Expression of SlERF36 and Is Partly Responsible for Changes in Stomatal Density and Photosynthesis

The EAR motif is a small seven amino acid motif associated with active repression of several target genes. We had previously identified SlERF36 as an EAR motif containing gene from tomato and shown that its over-expression results in early flowering and senescence and a 25–35% reduction of stomatal density, photosynthesis and stomatal conductance in transgenic tobacco. In order to understand the role of the EAR motif in governing the phenotypes, we have expressed the full-length SlERF36 and a truncated form, lacking the EAR motif under the CaMV35S promoter, in transgenic Arabidopsis. Plants over-expressing the full-length SlERF36 show prominent early flowering under long day as well as short day conditions. The early flowering leads to an earlier onset of senescence in these transgenic plants which in turn reduces vegetative growth, affecting rosette, flower and silique sizes. Stomatal number is reduced by 38–39% while photosynthesis and stomatal conductance decrease by about 30–40%. Transgenic plants over-expressing the truncated version of SlERF36 (lacking the C-terminal EAR motif), show phenotypes largely matching the control with normal flowering and senescence indicating that the early flowering and senescence is governed by the EAR motif. On the other hand, photosynthetic rates and stomatal number were also reduced in plants expressing SlERF36ΔEAR although to a lesser degree compared to the full- length version indicating that these are partly controlled by the EAR motif. These studies show that the major phenotypic changes in plant growth caused by over-expression of SlERF36 are actually mediated by the EAR motif.
Range expansion of a selfing polyploid plant despite widespread genetic uniformity.

BACKGROUND AND AIMS Ongoing and previous range expansions have a strong influence on population genetic structure of plants. In turn, genetic variation in the new range may affect the population dynamics and the expansion process. The annual Ceratocapnos claviculata (Papaveraceae) has expanded its Atlantic European range in recent decades towards the north and east. Patterns of genetic diversity were investigated across the native range to assess current population structure and phylogeographical patterns. A test was then made as to whether genetic diversity is reduced in the neophytic range and an attempt was made to identify source regions of the expansion. METHODS Samples were taken from 55 populations in the native and 34 populations in the neophytic range (Sweden, north-east Germany). Using amplified fragment length polymorphism markers an analysis was made of genetic variation and population structure (Bayesian statistical modelling) and population differentiation was quantified. Pollen/ovule ratio was analysed as a proxy for the breeding system. KEY RESULTS Genetic diversity at population level was very low (mean H(e) = 0·004) and two multilocus genotypes dominated large parts of the new range. Population differentiation was strong (F(ST) = 0·812). These results and a low pollen/ovule ratio are consistent with an autogamous breeding system. Genetic variation decreased from the native to the neophytic range. Within the native range, H(e) decreased towards the north-east, whereas population size increased. According to the Bayesian cluster analysis, the putative source regions of the neophytic range are situated in north-west Germany and adjacent regions. CONCLUSIONS Ceratocapnos claviculata shows a cline of genetic variation due to postglacial recolonization from putative Pleistocene refugia in south-west Europe. Nevertheless, the species has expanded successfully during the past 40 years to southern Sweden and north-east Germany where it occurs as an opportunistic neophyte. Recent expansion was mainly human-mediated by single long-distance diaspore transport and was facilitated by habitat modification.
A NOVEL MODEL REFERENCE INTELLIGENT ADAPTIVE CONTROL USING NEURAL NETWORK AND FUZZY LOGIC CONTROLLER

In this paper two novel intelligent model reference adaptive controll ers are proposed. In these schemes the intelligent supervisory loop is incorporated into t he conventional model reference adaptive controller framework by utilizing an online growing neural / f uzzy network structure in parallel with it. In the conventional MRAC scheme, the controller is designed to realize plant output converges to reference mo del output based on the plant which is linear with dist urbance free system. This scheme is for controlling linear plant effectively with unknown parameters. However, using MRAC to control the linear system with disturbance and nonlinearities at real time is diff icult. In this paper, it is proposed to incorporate a neural / fuzzy controller in MRAC to overcome the problem. The control input is given by the sum of the output of conventional MRAC and the output of neural / fuzzy controller. The effectiveness of the proposed contr ol schemes is demonstrated by simulations. The proposed schemes can significantly improve the system's behavior and force the system to follow the referen ce model and minimize the error between the model and plant output
SDIoT: Software Defined Internet of Thing to Analyze Big Data in Smart Cities

Software Defined Networks (SDN) has been attracting various researchers from both academia and industry to enhance the current ICT stakes and networking paradigm. The beauty of SDN is the division of Control and Data planes and make it easy for the engineers to modify the networking protocols without visiting onsite devices. Similarly, Smart Cities concept has been coined recently, where a plethora of smart devices will be connected and providing tons of services to the citizens, officials, and governmental departments. The Internet of Things (IoT) will play a vital role in guaranteeing such services. Few efforts have been made to merge SDN and IoT with the sole purpose of efficient Data retrieval and achieve remotely configurable networks. In this paper, we explicitly define the Internet of Software Defined Things architecture and brought it to Smart Cities as a use-case. The proposed system architecture consists of Data Collection, Data Management, and Application levels that are further connected via two intermediate levels working on SDN principles. Followed by the potentials of SDN and IoT for Smart Cities, we evaluated our proposed architecture using Spark and GraphX with Hadoop Ecosystem, and the results show that efficient transfer of Data over SDN for real-time processing is achieved.
Parameter extraction method of band parameters for III–V compound semiconductors

Band parameters of group III-V compound semiconductors GaAs, InAs and InP are fitted. Because of the random distribution of elements from the same group within the alloy lattice, exact calculations of material parameters are hardly possible. That is why we tried to fit parameters using as many experimental data as possible. Emphasizing the importance of band structure calculation, direct energy band gap, Passlerr parameters, Luttinger parameters, deformation potentials, energy parameter and correction parameter are fitted, which are the most controversial in literature.
Insights on Media Streaming Progress Using BitTorrent-Like Protocols for On-Demand Streaming

This paper develops analytical models that characterize the behavior of on-demand stored media content delivery using BitTorrent-like protocols. The models capture the effects of different piece selection policies, including Rarest-First, two variants of In-Order, and two probabilistic policies (Portion and Zipf). Our models provide insight into system behavior and help explain the sluggishness of the system with In-Order streaming. We use the models to compare different retrieval policies across a wide range of system parameters, including peer arrival rate, upload/download bandwidth, and seed residence time. We also provide quantitative results on the startup delays and retrieval times for streaming media delivery. Our results provide insights into the design tradeoffs for on-demand media streaming in peer-to-peer networks. Finally, the models are validated using simulations.
Learning Multi-Modal Word Representation Grounded in Visual Context

Representing the semantics of words is a long-standing problem for the natural language processing community. Most methods compute word semantics given their textual context in large corpora. More recently, researchers attempted to integrate perceptual and visual features. Most of these works consider the visual appearance of objects to enhance word representations but they ignore the visual environment and context in which objects appear. We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embeddings. We explore various choices for what can serve as a visual context and present an end-to-end method to integrate visual context elements in a multimodal skip-gram model. We provide experiments and extensive analysis of the obtained results.
Model predictive control -- a simple and powerful method to control power converters

This paper presents a detailed description of Finite Control Set Model Predictive Control (FCS-MPC) applied to power converters. Several key aspects related to this methodology are in depth presented and compared with traditional power converter control techniques, such as linear controllers with PWM based modulation methods. The basic concepts, operating principles, control diagrams and results are used to provide a comparison between the different control strategies. The analysis is performed on a traditional three-phase voltage source inverter, used as simple and comprehensive reference frame. However, additional topologies and power systems are addressed to highlight differences, potentialities and challenges of FCS-MPC. Among the conclusions are the feasibility and great potential of FCSMPC due to today's signal processing capability, specially for power systems with a reduced number of switching states and more complex operating principles, such as matrix converters. In addition, the possibility to address different or additional control objectives easily in a single cost function, enables a simple, flexible and improved performance controller for power conversion systems.
Interleukin‐1 and tumor necrosis factor stimulate the formation of human osteoclastlike cells in vitro

Interleukin‐1 (IL‐1) α and β and tumor necrosis factor (TNF) α and β are potent stimulators of bone resorption in vitro and in vivo. However, the mechanisms underlying this increased bone resorption have not been clearly defined. Increased bone resorption can result from increased activity of individual osteoclasts, increased numbers of osteoclasts, or both. Therefore, we have used a long‐term human marrow culture system that forms multinucleated cells (MNC) with the characteristics of osteoclasts to examine the effects of IL‐1 and TNF on osteoclast formation. Human recombinant IL‐1α and IL‐1β and human recombinant TNF‐α and TNF‐β stimulated MNC formation from 4‐ to 60‐fold. IL‐1α, IL‐1β, TNF‐α, and TNF‐β significantly increased MNC formation at very low concentrations: 2.5 × 10−13 M for IL‐1α and IL‐1β, 10−11 M for TNF‐α, and 10−10 M for TNF‐β In addition, these cytokines enhanced MNC formation in the presence of 1,25‐dihydroxyvitamin D3 [1,25‐(OH)2D3], a potent osteotropic factor that stimulates MNC formation by stimulating fusion of mononuclear precursor cells. Simultaneous addition of IL‐1 and TNF to the cultures resulted in a synergistic stimulation of MNC formation. These results suggest that: (1) IL‐1 and TNF stimulate bone resorption in part by increasing osteoclast formation and (2) an extremely low concentration of these factors can synergistically increase osteoclast formation in the absence of other factors, such as 1,25‐(OH)2D3. These data suggest that synergistic interactions among cytokines play an important role in maintaining bone cell activity in normal and pathologic states.
High dose and compartmental target volume may improve patient outcome after radiotherapy for pelvic bone metastases from hepatocellular carcinoma

Purpose Pelvic bone metastases are difficult to treat because of complex pelvic bone anatomy and the proximity of normal organs. The adequacy of radiation dose and field coverage was evaluated. Patients and methods We analyzed 146 cases of pelvic bone metastases from HCC treated with radiotherapy (RT). Bone metastases were confirmed using CT/MRI. Subjective pain response was assessed using the visual analogue scale, and treatment-related toxicity with the Common Terminology Criteria for Adverse Events v3.0. Local failure free survival (LFFS) and overall survival were estimated using the Kaplan-Meier method. Results The local control rate was 80.1% and the pain control rate was 68.5%. Compartmental target volume (CTV), encompassing the whole compartment of the involved bone, was found to be a significant factor (1-year LFFS, 78% vs. 50%; p=0.001). Sites of metastasis were categorized as either upper or lower pelvic bone; both categories showed improved local control with CTV. Metastatic lesions that received more than 50 Gy of EQD2 showed more partial response in pain after RT (58% vs. 79%; p=0.007). No patient showed toxicity higher than Grade IV. Conclusion Compartmental RT targeted to the involved bone was associated with improved local control and LFFS. High-dose radiation was associated with an improved treatment response.
Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor Decomposition

The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of the intracortical LFP data. The advantages of the sparse NTF-based feature extraction approach lies in its capability to yield components common across the space, time, and frequency domains yet discriminative across different conditions without prior knowledge of the discriminating frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVMs) classifier based on the features of the NTF components for single-trial decoding the reported perception. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature.
Anomaly detection in university campus WiFi zones

This paper focuses on a set of anomaly detection techniques based on the analysis of WiFi access point utilization patterns. The aim is to explore the possibilities of detecting patterns that diverge from the usual ones in spaces covered by wireless networks. In this way special events or happenings in physical places could be identified and fed to a world model as an additional source of information for context-aware applications to use.
Post clubfoot release skin necrosis: a preventable disaster

Complications of pedal wounds closed primarily after release of neglected cases of club foot are well known in literature. We treated a 12-year-old boy presenting with widespread necrosis involving medial aspect of foot after release of neglected talipes equinovarus. Such widespread necrosis is even known to end up in amputations and permanent disabilities. In our case, the foot was successfully salvaged by resorting to dedicated wound care, multiple debridements, use of topical negative pressure (vacuum-assisted closure – VAC), and skin grafting. Various means to prevent such adverse events are also presented. The aim of publishing this case is to stress the need to: Take proper measures preoperatively in neglected club feet to decrease the impact of deformity. To be aware of various different flaps and methods of closure as mentioned in literature and to reaffirm the fact that primary closures done under tension are bound to fail and end up in disasters. To be patient and use the modern concepts of wound management like topical negative pressure if such unfortunate complications do occur and thereby attempt to salvage the feet.
An Investigation into Tournament Poker Strategy using Evolutionary Algorithms

In this paper we assess the hypothesis that a strategy including information related to game-specific factors in a poker tournament performs better than one founded on hand strength knowledge alone. Specifically, we demonstrate that the use of information pertaining to opponents' prior actions, the stage of the tournament, one's chip stack size and seating position all contribute towards a statistically significant improvement in the number of tournaments won. Additionally, we test the hypothesis that a strategy which combines information from all the aforementioned factors performs better than one which employs only a single factor. We show that an evolutionary algorithm is successfully able to resolve conflicting signals from the specified factors, and that the resulting strategies are statistically stronger
Feature accentuation in phosphenated images

We present and evaluate different approaches to feature accentuation in phosphenated images for different image resolutions. The goal of this study is to find methods to attract an implantee's visual attention to important image content like faces, obstacles or road signs. We do this by defining an important region in the image and accentuating it by either increasing the brightness of outlining phosphenes or by using elliptical phosphenes to circumscribe the feature. While we only see limited benefit of ellipse phosphenes for a high-resolution prosthesis, the use of elliptical phosphenes of different orientations is a promising way to highlight features in a low-resolution phosphene representation of an image.
Review: Domestic hygiene and diarrhoea – pinpointing the problem

Summary Improving domestic hygiene practices is potentially one of the most effective means of reducing the global burden of diarrhoeal diseases in children. However, encouraging behaviour change is a complex and uncertain business. If hygiene promotion is to succeed, it needs to identify and target only those few hygiene practices which are the major source of risk in any setting. Using biological reasoning, we hypothesize that any behaviours which prevent stools from getting into the domestic arena, the child's main habitat, are likely to have a greater impact on health than those practices which prevent pathogens in the environment from being ingested. Hence safe stool disposal, a primary barrier to transmission, may be more important than hand‐washing before eating, which constitutes a secondary barrier, for example. We review the epidemiological evidence for the effect of primary and secondary barrier behaviours and suggest that it supports this conclusion. In the absence of local evidence to the contrary, hygiene promotion programmes should give priority to the safe disposal of faecal material and the adequate washing of hands after contact with adult and child stools.
Wavelet based Multilevel Sub-Band Adaptive Thresholding for Image Denoising using Modified PSO Algorithm

The image de-noising is one in all the foremost studied areas within the field of image processing. There are many ways (like communication channel, imperfect sensors, interference etc.) by which the noise may affect the image. Depending upon the nature of noise and the image many techniques has been already proposed. However for any technique it is difficult to operate on different level of noises over different kind of images (like SAR images, X- ray images, Ultrasound images etc.). The best possible solution for such cases is to use adaptive techniques. In this paper we are presenting a multilevel wavelet decomposition based adaptive thresholding technique which utilizes the modified Particle Swarm Optimization (PSO) algorithm to find out the optimal values for thresholds and level of decompositions for given objective function. The modification of PSO is done through random perturbation in particle velocities which induces small randomness in new particle position estimation. This randomness can effectively increase the particle search space, which ultimately provides a much better solution than the conventional PSO. Finally the proposed algorithm is validated by testing it over different kind of images corrupted by different values of noise.
Automated aerodynamic performance estimates for flapping flight

The aerodynamic performance of freely flying birds is inherently difficult to measure directly without interfering with their natural flight. Many current methods thus combine kinematic parameters derived from high-speed video with theoretical force and power models. However, these methods often require manual tracking of points in each frame of a video to obtain kinematic data. This project utilizes image processing techniques to automatically extract kinematic data (stroke angle and wingbeat timing) from high speed videos of Pacific parrotlets flying from perch to perch. To analyze aerodynamic performance, this data is then combined with actuator disc theory to estimate the induced power during each wingbeat. Keywords—bird flight; image processing; aerodynamic performance; wingbeat; actuator disc
Interactive Task Learning with Discrete and Continuous Features

Learning tasks from demonstration is key to the flexibility of robots and their accessibility to non-programmers. We present a task learning framework that combines the strengths of discrete and continuous representations. The robot learns a set of criteria and expectations to represent the goal of a demonstrated task. The task consists of performing actions that fulfill expectations on objects that meet the criteria. We propose modeling continuous criteria and expectations with Gaussian distributions. To deal with simultaneous demonstration of multiple tasks, we assume that expectations can be multi-modal and model them as mixtures of Gaussians. We present an implementation of this framework on the robot