Miracle-dz/UniFAR-FTUs huggingface dataset:
CoMP Handover Probability Analysis with Different Handover Schemes in Ultra-Dense Networks

This paper conducts handover probability analysis in Coordinated multipoint (CoMP) based ultra-dense networks (UDNs) where $n$ access points (APs) jointly serve the user. To thoroughly investigate the effect of mobility on handover probability, we compare several handover schemes of CoMP coordinating set (CCS) handover and serving cell handover. From the perspective of CCS handover, three schemes are considered including ``best $n$', ``half best $n$'' and ``none of the best $n$''. Meanwhile, from the perspective of the serving cell handover, this paper discusses two schemes: ``not the best one'' and ``not any of the best $n$''. With stochastic geometry methods, theoretical expressions of CoMP handover probability with different schemes are derived. Further, simulation results illustrate that scheme ``best $n$'' provides an upper bound while scheme ``none of the best $n$'' exhibits as a lower bound of CoMP handover probability and other schemes lie between them. To sum up, such analysis may provide a good performance reference for the design of CoMP handover.
Gastrointestinal targets of appetite regulation in humans

The aim of this paper is to describe and discuss relevant aspects of the assessment of physiological functions – and related biomarkers – implicated in the regulation of appetite in humans. A short introduction provides the background and the present state of biomarker research as related to satiety and appetite. The main focus of the paper is on the gastrointestinal tract and its functions and biomarkers related to appetite for which sufficient data are available in human studies. The first section describes how gastric emptying, stomach distension and gut motility influence appetite; the second part describes how selected gastrointestinal peptides are involved in the control of satiety and appetite (ghrelin, cholecystokinin, glucagon‐like peptide, peptide tyrosin‐tyrosin) and can be used as potential biomarkers. For both sections, methodological aspects (adequacy, accuracy and limitation of the methods) are described. The last section focuses on new developments in techniques and methods for the assessment of physiological targets involved in appetite regulation (including brain imaging, interesting new experimental approaches, targets and markers). The conclusion estimates the relevance of selected biomarkers as representative markers of appetite regulation, in view of the current state of the art.
On integrating power and signal routing for shield count minimization in congested regions

With worsening crosstalk in nanometer designs, it is increasingly important to control the switching cross-coupling experienced by critical wires. This is commonly done by inserting shields adjacent to these wires. However, the number of shielded wires can become extremely large, resulting in a large area impact. We address this problem at both the methodological and algorithmic levels, integrating the traditionally separate steps of power and signal routing in a safe manner to minimize the number of shields required to satisfy all shielding constraints. We propose a new abstraction for the block-level global and detailed routing hierarchy that allows accurate early estimation of crosstalk. Furthermore, we postpone the power routing in middle metal layers to after critical signal nets and their shields have been laid out (with maximal shield sharing), and then try to construct a fine-grained power grid out of the already routed shields. Given a routing on a metal layer, our adaptive power routing algorithm adds provably fewest new power lines to complete the power grid on that layer while guaranteeing adequate power delivery. Our approach has proven effective while designing some high-frequency blocks of a commercial gigahertz range microprocessor using a 0.18-/spl mu/m process technology.
A healthy diet indicator: quantifying compliance with the dietary guidelines using the BRFSS.

BACKGROUND The Behavioral Risk Factor Surveillance System (BRFSS) monitors population prevalence of risk factors related to chronic disease through annual telephone surveys. The purpose of this study was to investigate the feasibility of using the BRFSS to estimate an indicator of overall diet quality among adults. METHODS The authors developed a brief set of dietary questions that were included in the 1997 Michigan BRFSS. Responses to these questions were analyzed to calculate the Michigan healthy diet indicator (MI-HDI), which was modeled after the U.S. Department of Agriculture's Healthy Eating Index. RESULTS Among 2,532 respondents, the mean MI-HDI was 55.7 points (maximum score = 100). The mean MI-HDI decreased consistently with declining self-rated eating habits, from 61.5 among those with excellent eating habits to 45.2 among those with poor habits. Diet quality, as measured by the MI-HDI, was higher among women than men and improved with age, education, and factors related to certain interactions with health professionals. CONCLUSIONS A BRFSS-based indicator can be a feasible and valuable tool for evaluating diet quality among adults using an established state-level surveillance system.
A Multi-Constraint Combined Method for Ground Surface Point Filtering from Mobile LiDAR Point Clouds

Point cloud filtering is an essential preprocessing step in 3D (three-dimensional) LiDAR (light detection and ranging) point cloud processing. The filtering of mobile LiDAR scanning point clouds is much more challenging due to their non-uniform distribution, the large-scale of missing data areas and the existence of both large size objects and small land features. This paper proposes a new filtering method that combines range constraint, slope constraint and angular position constraint to filter ground surface points from mobile LiDAR point clouds. Firstly, a cylindrical coordinate system (CCS) is established for each block of mobile LiDAR point clouds and three attributes of mobile LiDAR points, i.e., the angular position attribute (AA), longitudinal distance attribute (LA) and range attribute (RA), are computed. Then, the mobile LiDAR point clouds are structured into a grid according to the AA and LA. Finally, the point clouds are filtered by a single cross-section filter (SCSF) using range constraint and slope constraint, followed by a multiple cross-section filter (MCSF) using range constraint and angular position constraint. Five datasets are used to validate the proposed method. The experimental results show that the proposed new filtering method achieves an average type I error, type II error, and total error of 1.426%, 1.885%, and 1.622%, respectively.
Web-Based Arabidopsis Functional and Structural Genomics Resources

Abstract As plant research moves to a “post-genomic” era, many diverse internet resources become available to the international research community. Arabidopsis thaliana, because of its small size, rapid life cycle and simple genome, has been a model system for decades, with much research funding and many projects devoted to creation of functional and structural genomics resources. Different types of data, including genome, transcriptome, proteome, phenome, metabolome and ionome are stored in these resources. In this chapter, a variety of genomics resources are introduced, with simple descriptions of how some can be accessed by laboratory researchers via the internet.
Mining high utility itemsets using TKO and TKU to find top-k high utility web access patterns

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, but they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. An emerging topic in the field of data mining is utility mining which not only considers the frequency of the itemsets but also considers the utility associated with the itemsets. The main objective of High Utility Itemset Mining is to identify itemsets that have utility values above a given utility threshold. Thus Utility mining plays an important role in many real-time applications and is an important research topic in data mining system to find the itemsets with high profit. In this paper we present the implementation of first module where pre-processing of dataset is done to remove unpromising data from web usage and product base dataset by using TopKRules and also we are proposing a new framework for Top-k high utility web access patterns, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU and TKO are proposed for mining such itemsets. In this paper we present a literature review of the present state of research and the various algorithms for high utility itemset mining.
Automatic Recognition of Mycobacterium Tuberculosis Based on Active Shape Model

SUMMARY This paper realized the automatic recognition of Mycobacterium tuberculosis in Ziehl-Neelsen stained images by the conventional light microscopy, which can be used in the computer-aided diagnosis of the tuberculosis. We proposed a novel recognition method based on active shape model. First, the candidate bacillus objects are segmented by a method of marker-based watershed transform. Next, a point distribution model of the object shape is proposed to label the landmarks on the object automatically. Then the active shape model is performed after aligning the training set with a weight matrix. The deformation regulation of the object shape is discovered and successfully applied in recognition without using geometric and other commonly used features. During this process, a width consistency constraint is combined with the shape parameter to improve the accuracy of the recognition. Experimental results demonstrate that the proposed method yields high accuracy in the images with di ﬀ erent background colors. The recognition accuracy in object level and image level are 92.37% and 97.91% respectively.
Response of Correlated Double Sampling CMOS Imager Circuit to Random Telegraph Signal Noise

This paper presents analytical and experimental noise of a correlated double sampling (CDS) readout circuit used in CMOS active pixel image sensors. The work takes into account low frequency noise, mainly random telegraph signal (RTS) noise, in modern MOS transistors with very small geometries. The impact of the source-follower transistor noise power spectral density through CDS is studied. The results allow the determination of the output rms noise versus random telegraph signal noise characteristics. We show that R.T. S. noise of the source-follower is a major factor influencing the circuit output rms noise. Theoretical results are compared with experimental data
Effects of species invasion on population dynamics, vital rates and life histories of the native species

Funding information MAVA Foundation Abstract Invasions occurring in natural environments provide the opportunity to study how vital rates change and life histories evolve in the presence of a competing species. In this work, we estimate differences in reproductive traits, individual growth trajectories, survival, life histories and population dynamics between a native species living in allopatry and in sympatry with an invasive species of the same taxonomic Family. We used as a model system marble trout Salmo marmoratus (native species) and rainbow trout Oncorhynchus mykiss (non-native) living in the Idrijca River (Slovenia). An impassable waterfall separates the stream into two sectors only a few 100 meters apart: a downstream sector in which marble trout live in sympatry with rainbow trout and an upstream sector in which marble trout live in allopatry. We used an overarching modelling approach that uses tag-recapture and genetic data (>2,500 unique marble and rainbow trout were sampled and genotyped) to reconstruct pedigrees, test for synchrony of population dynamics and model survival and growth, while accounting for individual heterogeneity. The population dynamics of the two marble trout populations and of rainbow trout were synchronous. We found higher prevalence of younger parents, higher mortality and lower population density in marble trout living in sympatry with rainbow trout than in marble trout living in allopatry. There were no differences in the average individual growth trajectories between the two marble trout populations. Faster life histories of marble trout living in sympatry with rainbow trout are consistent with predictions of life history theory.
Telomere length and cardiovascular risk factors in a middle‐aged population free of overt cardiovascular disease

Evidence assembled over the last decade shows that average telomere length (TL) acts as a biomarker for biological aging and cardiovascular disease (CVD) in particular. Although essential for a more profound understanding of the underlying mechanisms, little reference information is available on TL. We therefore sought to provide baseline TL information and assess the association of prevalent CVD risk factors with TL in subjects free of overt CVD within a small age range. We measured mean telomere restriction fragment length of peripheral blood leukocytes in a large, representative Asklepios study cohort of 2509 community‐dwelling, Caucasian female and male volunteers aged approximately 35–55 years and free of overt CVD. We found a manifest age‐dependent telomere attrition, at a significantly faster rate in men as compared to women. No significant associations were established with classical CVD risk factors such as cholesterol status and blood pressure, yet shorter TL was associated with increased levels of several inflammation and oxidative stress markers. Importantly, shorter telomere length was associated with an increasingly unhealthy lifestyle, particularly in men. All findings were age and gender adjusted where appropriate. With these cross‐sectional results we show that TL of peripheral blood leukocytes primarily reflects the burden of increased oxidative stress and inflammation, whether or not determined by an increasingly unhealthy lifestyle, while the association with classical CVD risk factors is limited. This further clarifies the added value of TL as a biomarker for biological aging and might improve our understanding of how TL is associated with CVD.
Pain Management Modalities after Total Knee Arthroplasty: A Network Meta-analysis of 170 Randomized Controlled Trials

Background: Optimal analgesia for total knee arthroplasty remains challenging. Many modalities have been used, including peripheral nerve block, periarticular infiltration, and epidural analgesia. However, the relative efficacy of various modalities remains unknown. The authors aimed to quantify and rank order the efficacy of available analgesic modalities for various clinically important outcomes. Methods: The authors searched multiple databases, each from inception until July 15, 2016. The authors used random-effects network meta-analysis. For measurements repeated over time, such as pain, the authors considered all time points to enhance reliability of the overall effect estimate. Outcomes considered included pain scores, opioid consumption, rehabilitation profile, quality of recovery, and complications. The authors defined the optimal modality as the one that best balanced pain scores, opioid consumption, and range of motion in the initial 72 postoperative hours. Results: The authors identified 170 trials (12,530 patients) assessing 17 treatment modalities. Overall inconsistency and heterogeneity were acceptable. Based on the surface under the cumulative ranking curve, the best five for pain at rest were femoral/obturator, femoral/sciatic/obturator, lumbar plexus/sciatic, femoral/sciatic, and fascia iliaca compartment blocks. For reducing opioid consumption, the best five were femoral/sciatic/obturator, femoral/obturator, lumbar plexus/sciatic, lumbar plexus, and femoral/sciatic blocks. The best modality for range of motion was femoral/sciatic blocks. Femoral/sciatic and femoral/obturator blocks best met our criteria for optimal performance. Considering only high-quality studies, femoral/sciatic seemed best. Conclusions: Blocking multiple nerves was preferable to blocking any single nerve, periarticular infiltration, or epidural analgesia. The combination of femoral and sciatic nerve block appears to be the overall best approach. Rehabilitation parameters remain markedly understudied.
Spatial and temporal distribution of thermotolerant coliform in Canal de Passagem (Vitória - ES) - related to physico-chemical parameters and tide

O Canal de Passagem, Vitoria, ES, e um sistema estuarino em processo de degradacao ambiental devido a descarga de esgoto in natura e semitratado. Os usos da comunidade do entorno dos recursos locais estuarinos sao conflitantes com a qualidade ambiental, o que pode oferecer riscos para a saude publica. Os objetivos do presente trabalho foram: 1. avaliar a distribuicao de coliformes termotolerantes dentro do HCP (metodo dos tubos multiplos), relacionando-a com a distribuicao das fontes de poluicao fecal, a dinâmica de mares e parâmetros fisico-quimicos, e 2. avaliar os resultados dentro das leis brasileiras de limites sanitarios para os variados usos da agua e balneabilidade. Amostras foram feitas espacialmente e temporalmente, visando cobrir o ciclo de mares de sizigia no inverno, outono e primavera, e tambem cobrindo uma quadratura e uma sizigia na amostragem no verao. Foram considerados os parâmetros sanitarios - coliformes termotolerantes, fisicos e quimicos e a acao das mares. Correlacoes significativas foram encontradas com o regime das mares e distribuicao de coliformes. A conclusao retirada do trabalho foi que a distribuicao de coliformes termotolerantes esteve relacionada com as fontes pontuais de poluicao, e as concentracoes foram diferentes entre as mares de quadratura e sizigia. Variacao entre tais mares nao e observada ate o momento pela Legislacao Brasileira para as aguas balneaveis.
Respiratory motion compensation in emission tomography

This thesis work deals with the problem of respiratory motion correction in emission tomography imaging. It has been proven that respiratory motion renders blurred reconstructed images, affecting lesions detection, diagnosis, treatment planning and following of lung cancer. While current motion correction methodologies are based on external breathing tracking devices or specific data acquisition modes. The proposed approach was designed to work without any external tracking devices, which occur on institutions not having access to such material or in cases where the data was already acquired and no tracking device was present at the moment of its capture. The proposed method presents a retrospective scheme of motion correction based on a motion model plugged to the image reconstruction step. The model takes into account displacements and elastic deformations of emission elements (voxels), which allows to consider the non-rigid deformations produced in the thorax during respiration. Furthermore, the chosen voxel modeling improves computations, outperforming classical methods of voxel/detector-tube. The lack of specific patient respiratory information, two estimation models were investigated and developed. A first simplified model consists in adapting a known respiratory motion model, obtained from a single subject, to the patient anatomy. The initial known model describes by means of a displacement vector field, the lungs deformations produced between extremal respiratory states. This displacement vector field is further adapted by means of an affine transformation to the patient's anatomy, yielding a displacement vector field that matches the thoracic cavity of the patient. The second method deals with the possible lack of robustness caused by the fact of using a single subject when constructing the known displacement vector field of the simplified method. Incorporation of subject variability into a statistical respiratory motion model was developed. The statistical study served as well to highlight the main deformation modes of the breathing lungs. The whole methodology was developed under a 3-D image reconstruction framework. The algorithm was parallelized and acceleration schemes are presented as well. Simulations and phantom experiences were carried out. For the first, the SimSET library (Simulation System for Emission Tomography) was used along with the NCAT phantom, upon which a real respiratory motion was incorporated. For phantom experiences, the methodology was tested against translational movements applied within the data acquisition. For both, simulations and phantom experiences, the results obtained show the ability of the proposed method to correct and compensate the effects of motion during data acquisition. For patient data, the methodology was tested against a dataset composed by five patients with lung cancer. Although no ground truth was available, preliminary results on patient data are encouraging since improvements in contrast recovery and signal to noise ratios were found on each case.
Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective

It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to upfront identify software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on twelve real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available.