Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance evaluation for OFDM PAPR reduction methods

The ultimate goal of Peak to Average Power Ratio (PAPR) reduction for Orthogonal Frequency Division Multiplexing (OFDM) signals is to reduce the signal's sensitivity to nonlinearity and to improve signal quality and transmitter power efficiency. Performance evaluation has traditionally been based mainly on the PAPR distribution curves. For clipping related methods, however, tradeoffs between PAPR and signal quality must be made, but is often made in a heuristic manner. In this paper, we propose to include amplifier characteristics and use the required backoff to evaluate the performance of PAPR reduction methods. The required backoff OBO* is defined as the minimum required transmit power backoff for the signal to meet quality requirements such as spectrum mask and error vector magnitude constraints. Effective PAPR reduction should lead to smaller OBO*, not just smaller PAPR. Simulation results for IEEE 802.11a wireless local area network signals with clipping related methods are presented.
Coverage Optimization Scheme Based on Artificial Fish Swarm Algorithm for Wireless Sensor Networks in Complicated Environment

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that artificial fish swarm algorithm may easily lead to regional optimization. As for this, the global searching ability of algorithm is improved and the solving efficiency is optimized. Moreover, the algorithm is able to adapt to complicated environment. Shown by related simulation experiment, the improved fish swarm algorithm could effectively optimize sensor network node deployment and improve network coverage rate. Compared with basic artificial fish swarm algorithm, improved fish swarm algorithm increases network coverage rate by 8.9%.
Seismic Behavior of the Low-rise Shear Wall with CFST Frame and Embedded Steel-plate

In this paper, two low-rise shear wall specimens are tested on the shaking table, one is shear wall with CFST frame and embedded steel-plate, the other one is no concrete outside the steel-plate in comparison of the first one. The re- duced scale of the two specimens is 1/12. The seismic wave "Taft" is input in the shaking table test. Based on the experi- ment, the author conducts the time history analysis (including elastic stage and plastic stage) of the specimens. The calcu- lated results and measured results are compared.
PCR detection of Listeria monocytogenes in milk and milk products and differentiation of suspect isolates

Holko I. , J. Urbanova, M. Kantikova, K. Pastorova, V. KmeE: PCR Detection of Listeria monocytogenes in Milk and Milk Products and Differentiation of Suspect Isolates. Acta Vet. Brno 71, 2002: 125-131. The main goal of our work was to solve the problems of normative diagnostic method for the detection of Listeria monocytogenes in foods by STN ISO 10560 (1999), i.e. to shorten the long time of analysis (5-10 days) and differentiate the non-haemolysed strains of Listeria monocytogenes from non-pathogenic Listeria innocua yielding the same results of biochemical and serological typing (serotype 4ab). PCR was employed to confirm the method for testing 60 suspect isolates from other laboratories of the Slovak Republic. We verified the detection of Listeria monocytogenes in milk and dairy products by nested PCR. The objective of this experiment was to analyze 100 various samples by traditional cultivation method and compare the results with those yielded by PCR. The results were in good agreement; all 18 positive and 72 negative samples were detected by both methods. However, the PCR method yields results within 2 days. This makes diagnosis in food control laboratories much more efficient. For PCR detection, two pairs of primers (PRFA 1 and 2, LIP 1 and 2) were used with affinity to prfA gene involved in the regulation of listeriolysin synthesis. The size of the PCR product was 1060 bp fragment in a first step of PCR and 273 bp fragment in nested PCR. The sensitivity and reliability of PCR was comparable with conventional methods. The PCR method solved the problem of interpretation of classical biochemical and serological typing in the only one step without the necessity of using additional examinations. Furthermore, we found that in most of the strains isolated from foods with a biochemical profile of non-haemolytic Listeria monocytogenes on blood agar serotype 4ab were not Listeria monocytogenes but a non-pathogenic Listeria innocua. This finding is very important from the point of view of food evaluation according to the STN ISO 10560 (1999). Diagnostic methods, food, Listeria innocua, non-haemolysed strains of Listeria monocytogenes, prfA gene Bacteria of the genus Listeria are widely distributed in the environment and they frequently contaminate food. Listeria monocytogenes is a pathogenic species causing listeriosis, increasing incidence of which has been reported in the last two decades. Food products most frequently associated with listeriosis are soft cheeses, particularly those made from unpasteurized milk, and ready-to-eat meat-containing food products (Farber and
Inside-Out: Reliable Performance Prediction for Distributed Storage Systems in the Cloud

Many storage systems are undergoing a significant shift from dedicated appliance-based model to software-defined storage (SDS) because the latter is flexible, scalable and cost-effective for modern workloads. However, it is challenging to provide a reliable guarantee of end-to-end performance in SDS due to complex software stack, time-varying workload and performance interference among tenants. Therefore, modeling and monitoring the performance of storage systems is critical for ensuring reliable QoS guarantees. Existing approaches such as performance benchmarking and analytical modeling are inadequate because they are not efficient in exploring large configuration space, and cannot support elastic operations and diverse storage services in SDS. This paper presents Inside-Out, an automatic model building tool that creates accurate performance models for distributed storage services. Inside-Out is a black-box approach. It builds high-level performance models by applying machine learning techniques to low-level system performance metrics collected from individual components of the distributed SDS system. Inside-Out uses a two-level learning method that combines two machine learning models to automatically filter irrelevant features, boost prediction accuracy and yield consistent prediction. Our in-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. Our experiments show that Inside-Out can predict end-to-end performance with 91.1% accuracy on average. Its prediction accuracy is consistent across diverse storage environments.
A drug targeting only p110α can block phosphoinositide 3-kinase signalling and tumour growth in certain cell types

Genetic alterations in PI3K (phosphoinositide 3-kinase) signalling are common in cancer and include deletions in PTEN (phosphatase and tensin homologue deleted on chromosome 10), amplifications of PIK3CA and mutations in two distinct regions of the PIK3CA gene. This suggests drugs targeting PI3K, and p110α in particular, might be useful in treating cancers. Broad-spectrum inhibition of PI3K is effective in preventing growth factor signalling and tumour growth, but suitable inhibitors of p110α have not been available to study the effects of inhibiting this isoform alone. In the present study we characterize a novel small molecule, A66, showing the S-enantiomer to be a highly specific and selective p110α inhibitor. Using molecular modelling and biochemical studies, we explain the basis of this selectivity. Using a panel of isoform-selective inhibitors, we show that insulin signalling to Akt/PKB (protein kinase B) is attenuated by the additive effects of inhibiting p110α/p110β/p110δ in all cell lines tested. However, inhibition of p110α alone was sufficient to block insulin signalling to Akt/PKB in certain cell lines. The responsive cell lines all harboured H1047R mutations in PIK3CA and have high levels of p110α and class-Ia PI3K activity. This may explain the increased sensitivity of these cells to p110α inhibitors. We assessed the activation of Akt/PKB and tumour growth in xenograft models and found that tumours derived from two of the responsive cell lines were also responsive to A66 in vivo. These results show that inhibition of p110α alone has the potential to block growth factor signalling and reduce growth in a subset of tumours.
Performance and Logistical Challenges of Alternative HIV-1 Virological Monitoring Options in a Clinical Setting of Harare, Zimbabwe

We evaluated a low-cost virological failure assay (VFA) on plasma and dried blood spot (DBS) specimens from HIV-1 infected patients attending an HIV clinic in Harare. The results were compared to the performance of the ultrasensitive heat-denatured p24 assay (p24). The COBAS AmpliPrep/COBAS TaqMan HIV-1 test, version 2.0, served as the gold standard. Using a cutoff of 5,000 copies/mL, the plasma VFA had a sensitivity of 94.5% and specificity of 92.7% and was largely superior to the VFA on DBS (sensitivity = 61.9%; specificity = 99.0%) or to the p24 (sensitivity = 54.3%; specificity = 82.3%) when tested on 302 HIV treated and untreated patients. However, among the 202 long-term ART-exposed patients, the sensitivity of the VFA decreased to 72.7% and to 35.7% using a threshold of 5,000 and 1,000 RNA copies/mL, respectively. We show that the VFA (either on plasma or on DBS) and the p24 are not reliable to monitor long-term treated, HIV-1 infected patients. Moreover, achieving acceptable assay sensitivity using DBS proved technically difficult in a less-experienced laboratory. Importantly, the high level of virological suppression (93%) indicated that quality care focused on treatment adherence limits virological failure even when PCR-based viral load monitoring is not available.
A decentralized utility-based grid scheduling algorithm

Grid systems have gained tremendous importance in past years since application requirements increased drastically. The heterogeneity and geographic dispersion of grid resources and applications place some difficult challenges such as job scheduling. A scheduling algorithm tries to find a resource for a job that fulfills the job's requirements while optimizing a given objective function. Utility is a measure of a user's satisfaction that can be seen as an objective function that a scheduler tries to maximize. Many utility functions have been proposed as an objective for scheduling algorithms. However, the proposed algorithms do not consider partial requirement satisfaction by awarding an utility based on the total fulfillment of the requirement. Most of them follow centralized or hierarchical approaches, suffering from scalability and fault tolerance problems. Our solution proposes a decentralized scheduling architecture with utility based scheduling algorithm that considers partial requirements satisfaction to overcome the shortcomings of actual solutions. Performance results show that user utility, submission and execution times are improved and a slightly more balanced system is achieved.
An Accurate Induction Method of Player Ratings from Tournament Results

Competitive co-evolution has been successfully applied to breed artificial players. The fitness of players is evaluated through competition with other players. The estimation of the fitness (rating) of players is the computational bottleneck of this approach. It is therefore desirable to exploit as best as possible the information contained in the outcomes of played games. In this paper, we introduce an efficient induction method of player ratings from tournament results. We demonstrate empirically that the proposed formula gives more accurate results than the estimation formula that is traditionally used.
Frontiers in Computational Neuroscience

for CaN activation is to consider its activity being proportional to the concentration of calcium-loaded calmodulin. Associating the kinase activity to LTP (making it quadratic in calmodulin activation) and phosphatase activity to LTD (making it linear in cal-modulin activation), it is possible to build a very simple model of synaptic plasticity. While the core of the model is very simple, extensive modeling work has shown that it is possible for simulations of the biochemical signals within postsynaptic spines to reproduce these results (Bhalla, 2002; Hayer and Bhalla, 2005; Graupner and Brunel, 2007). In addition to the induction, these models have the property of including a bistable switch at the biochemical level. While experimental evidence of CaMKII autophosphorylation in the presence of protein phosphatase 1 (PP1) failed to observe bist-ability (Bradshaw et al., 2003), it can be included in the model at protein translation level (Aslam et al., 2009). Traditional induction protocols: high and low frequency affer-ent stimulation, as well as more direct manipulations of calcium concentrations in postsynaptic spines (Yang et al., 1999; Cho et al., 2001; Cormier et al., 2001) support the " differential threshold hypothesis. " However, in typical spike-timing-dependent plasticity (STDP) induction (Markram et al., 1997; Bi and Poo, 1998), such a model would predict a second depression window at a long pre–post time interval. The model can be rescued by considering that pre stimulation alone does not produce calcium concentrations lower
Vibrotactile haptic feedback for human-robot interaction in leader-follower tasks

In this paper we explore a vibrotactile feedback paradigm which allows the human to intuitively interact in human-robot applications. In particular we focus on a haptic bracelet which helps the human to move along trajectories that are feasible for the leader-follower formation tasks. The bracelet consists of three vibrating motors circling the forearm and represents a non invasive way to provide essential information to the human. Experiments performed on a public of 15 subjects revealed the effectiveness of the proposed device.
A Stochastic Traffic Assignment Model for Transportation Network

Traffic information in the future is likely to be available to all parts of a transportation network, and there will be various futuristic optimization models for traffic engineering. Assuming that the system can access statistical information of the aggregated traffic demands between source-destination pairs or zones in the transportation network, traffic congestion can be reduced by using the total travel time of the system as a measure in a traffic assignment model. This paper focuses on a traffic or route assignment problem that incorporates stochastic traffic demand, which can be solved with a Genetic Algorithm.
Interactive image filtering with multiple levels-of-control on mobile devices

With the continuous development of mobile graphics hardware, interactive high-quality image stylization based on nonlinear filtering is becoming feasible and increasingly used in casual creativity apps. However, these apps often only serve high-level controls to parameterize image filters and generally lack support for low-level (artistic) control, thus automating art creation rather than assisting it. This work presents a GPU-based framework that enables to parameterize image filters at three levels of control: (1) presets followed by (2) global parameter adjustments can be interactively refined by (3) complementary on-screen painting that operates within the filters' parameter spaces for local adjustments. The framework provides a modular XML-based effect scheme to effectively build complex image processing chains-using these interactive filters as building blocks---that can be efficiently processed on mobile devices. Thereby, global and local parameterizations are directed with higher-level algorithmic support to ease the interactive editing process, which is demonstrated by state-of-the-art stylization effects, such as oil-paint filtering and watercolor rendering.
Unsupervised gesture recognition system for learning manipulative actions in virtual basketball

For natural human-agent interaction, the recognition of important gestures to facilitate communication is necessary. Recent advances in technology have made it possible to use the human body as a means for this interaction. In this paper, we combine techniques to create a process flow which can discover and extract a body gesture, create a low-dimensional model, and then use it to recognize body gestures provided in real-time. We show that this technique can be applied to a virtual basketball game, an environment which is highly dynamic and where the variation of gestures is high but the number of training samples is low. We demonstrate this technique to the extraction and recognition of a passing gesture.
Concussion/mild traumatic brain injury-related chronic pain in males and females: A diagnostic modelling study

Abstract Pain is an unpleasant, complex, and perceived experience that places a significant burden on patients and clinicians. Its severity may be mediated by emotion, attitude, and environmental influences, and pain may be expressed differently in males and females. Traumatic brain injury (TBI) is frequently associated with chronic pain. This diagnostic modeling study examined sex differences in the construct of chronic pain in patients with delayed recovery from concussion/mild traumatic brain injury (mTBI). Data were collected from standardized questionnaires, neuroimaging records, and comprehensive clinical assessments. Bivariate associations were calculated using the Spearman correlation coefficient or analysis of variance. We established sex-specific stepwise multivariate linear regression models of factors associated with pain. Of the 94 participants diagnosed with mTBI (the mean age was 45.20 ± 9.94 years; 61.2% were males; the median time since injury was 197 days [interquartile range 139–416]), head/neck, and bodily pain were reported by 93% and 64%, respectively. No sex differences were identified in pain frequencies or severity. Pain was significantly associated with certain socio-demographic, injury-related, behavioral, and clinical variables. In the multivariable regression analysis, several determinants explained 60% of the pain variance in males and 46% in females. Pain is common in patients with delayed recovery from mTBI and is significantly associated with potentially modifiable clinical and nonclinical variables. Examining the multidimensional construct of pain in concussion/mTBI through a sex lens garners new directions for future longitudinal research on the pain mechanisms involved in postconcussion syndrome.
Spatial learners display enhanced oculomotor performance

ABSTRACT Attention is important during navigation processes that rely on a cognitive map, as spatial relationships between environmental landmarks need to be selected, encoded, and learned. Spatial learners navigate using this process of cognitive map formation, which relies on the hippocampus. Conversely, response learners memorise a series of actions to navigate, which relies on the caudate nucleus. The present study aimed to investigate the relationship between spatial learning and oculomotor performance. We tested 23 response learners and 23 spatial learners, as determined by the 4-on-8 virtual maze, on an antisaccade task with a gap and emotional visual stimulus manipulation. Spatial learners displayed decreased saccadic reaction time latencies compared to response learners. Performance cost from the gap manipulation was significantly higher in response learners. These results could represent an attentional practice effect through the use of spatial strategies during navigation or a more global increase in cognitive function amongst spatial learners.
Web Based Intelligent Decision Support System for Type 2 Diabetes Patients

Intensive treatment of type 2 diabetes can respond as a considerable decrease in the complications related to diabetes. In spite of specialist care there are still many patients that have poor control over their diabetes and the chronic nature of type 2 diabetes makes patients dependent on their care providers. In Type 2 diabetes patients require empowerment and self-management for initial and long term treatment. The required transfer of care from health care providers to patients needs an intervention of computerized decision support systems. In widespread health care practice the interventions that have demonstrated effective results in the management of chronic and/or progressive diseases have often been difficult to develop because of disease nature. The paper describes a web based intelligent decision support system (IDSS) for type 2 diabetes patients, where more emphasis is put on patient empowerment and self-management in treatment. The IDSS is based on well documented decision support system, designed mainly for type 2 diabetes patients. The scope of IDSS is broad from being mainly used by type 2 diabetes patients as a tool for their empowerment, self-management, communication, and education.
Research Online Research Online Teacher Disposition Scale (TDS): construction and psychometric validation Teacher Disposition Scale (TDS): construction and psychometric validation

Abstract Despite the growing prominence and importance of the field of teacher dispositions for addressing selection criteria and assessment in educational research, there is an absence of psychometrically evaluated instruments to facilitate investigations. Furthermore, research thus far has not explored the relationship between teacher dispositions and better student outcomes. To address this paucity, key dispositional factors conducive to successful teaching and learning were identified through interviewing high performing teachers. Based on the interview, data a 24-item Teacher Disposition Scale (TDS) was constructed consisting of five dimensions: Motivation to teach; Teacher efficacy; Willingness to learn; Conscientiousness; and Interpersonal and communication skill. The scale was psychometrically tested on 179 pre-service undergraduate students at an Australian university. Both factor analysis and Rasch measurement modelling indicated evidence of a reliable and valid multi-dimensional scale. The article concludes with a discussion on the implications of the scale for future research. Disposition Scale (TDS): construction and psychometric validation.