Cold Positrons from Decaying Dark Matter

Many models of dark matter contain more than one new particle beyond those in the Standard Model. Often heavier particles decay into the lightest dark matter particle as the Universe evolves. Here we explore the possibilities that arise if one of the products in a (Heavy Particle) $\rightarrow$ (Dark Matter) decay is a positron, and the lifetime is shorter than the age of the Universe. The positrons cool down by scattering off the cosmic microwave background and eventually annihilate when they fall into Galactic potential wells. The resulting 511 keV flux not only places constraints on this class of models but might even be consistent with that observed by the INTEGRAL satellite.Comment: 20 pages, 7 figure

Enhanced fuzzy finite state machine for human activity modelling and recognition

A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved 95.7% and 97.6% for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were 94.2% and 99.3%, respectively

Fuzzy logic as-a-service for Ambient Intelligence Environments

Fuzzy Logic Systems (FLSs) are normally associated with dedicated hardware/software systems. However, the distributed and pervasive architecture of many modern hardware/software systems is driving increasing interest in pervasive, distributed FLSs. Achieving this vision will require the design of FLS implementations which support client-server models and more specifically, cloud-computing and service-oriented solutions. Here, FLSs become a globally accessible service that enables openness, device independence, load balancing, resource sharing and ultimately cost effectiveness. In this paper, the recently standardised fuzzy mark-up language (IEEE-1855) and proposed extensions are used for designing Web Services for FLS computations. The novelty of this approach is in integrating different FLS components (input collection, processing and output) into a single web service platform which uses a well specified language for communication over the Web via HTTP request/responses. The utility of this approach is shown in the context of implementing FLSs in Ambient Intelligent Environments

Asymmetric Information in Iranian's Health Insurance Market: Testing of Adverse Selection and Moral Hazard

BACKGROUND: Asymmetric information is one of the most important issues in insurance market which occurred due to inherent characteristics of one of the agents involved in insurance contracts; hence its management requires designing appropriate policies. This phenomenon can lead to the failure of insurance market via its two consequences, namely, adverse selection and moral hazard. OBJECTIVE: This study was aimed to evaluate the status of asymmetric information in Iran's health insurance market with respect to the demand for outpatient services. MATERIALS/PATIENTS AND METHODS: This research is a cross sectional study conducted on households living in Iran. The data of the research was extracted from the information on household's budget survey collected by the Statistical Center of Iran in 2012. In this study, the Generalized Method of Moment model was used and the status of adverse selection and moral hazard was evaluated through calculating the latent health status of individuals in each insurance category. To analyze the data, Excel, Eviews and stata11 software were used. RESULTS: The estimation of parameters of the utility function of the demand for outpatient services (visit, medicine, and Para-clinical services) showed that households were more risk averse in the use of outpatient care than other goods and services. After estimating the health status of households based on their health insurance categories, the results showed that rural-insured people had the best health status and people with supplementary insurance had the worst health status. In addition, the comparison of the conditional distribution of latent health status approved the phenomenon of adverse selection in all insurance groups, with the exception of rural insurance. Moreover, calculation of the elasticity of medical expenses to reimbursement rate confirmed the existence of moral hazard phenomenon. CONCLUSIONS: Due to the existence of the phenomena of adverse selection and moral hazard in most of health insurances categories, policymakers need to adjust contracts so that to reduce these phenomena. Given the importance of financing, the presence of such problems can lead to less coverage of health insurance provided by insurers, loss of contracts with health care institutions and service providers, and lower quality of health services

On the Wang-Landau Method for Off-Lattice Simulations in the "Uniform" Ensemble

We present a rigorous derivation for off-lattice implementations of the so-called "random-walk" algorithm recently introduced by Wang and Landau [PRL 86, 2050 (2001)]. Originally developed for discrete systems, the algorithm samples configurations according to their inverse density of states using Monte-Carlo moves; the estimate for the density of states is refined at each simulation step and is ultimately used to calculate thermodynamic properties. We present an implementation for atomic systems based on a rigorous separation of kinetic and configurational contributions to the density of states. By constructing a "uniform" ensemble for configurational degrees of freedom--in which all potential energies, volumes, and numbers of particles are equally probable--we establish a framework for the correct implementation of simulation acceptance criteria and calculation of thermodynamic averages in the continuum case. To demonstrate the generality of our approach, we perform sample calculations for the Lennard-Jones fluid using two implementation variants and in both cases find good agreement with established literature values for the vapor-liquid coexistence locus.Comment: 21 pages, 4 figure

Serum level of interleukin-6 in patients with oral tongue squamous cell carcinoma

Introduction: The clinical outcome of patients with squamous cell carcinoma (SCC) located in the head and neck has remained poor despite ongoing advances in diagnosis and management. Interleukin-6(IL-6) is a multi-functional cytokine that plays an important role in the process of cell differentiation and is increased in several malignancies. The aim of this study was to investigate the serum levels of interleukin-6 in patients with oral tongue SCC. Materials and Methods: In a cross-sectional study, 17 patients with oral tongue SCC were compared with the same number of age- and gender-matched healthy subjects. Serum IL-6 level fluctuation was determined using an immunological technique, before detecting its possible association with the subjects' age, gender, drinking and smoking history, cancer site, and disease severity. Results: The intensity of serum IL-6 in patients with oral tongue SCC was statistically significantly higher than that in healthy subjects (P<0.001). Serum IL-6 level was independent of the patients' age, gender, smoking and drinking history as well as cancer stage. Conclusion: IL-6 is a valuable biomarker in the diagnosis of oral tongue SCC. Its high sensitivity makes prediction of this condition possible, while this biomarker can also be used to screen high-risk patients

Serum level of interleukin-6 in patients with oral tongue squamous cell carcinoma

Introduction: The clinical outcome of patients with squamous cell carcinoma (SCC) located in the head and neck has remained poor despite ongoing advances in diagnosis and management. Interleukin-6(IL-6) is a multi-functional cytokine that plays an important role in the process of cell differentiation and is increased in several malignancies. The aim of this study was to investigate the serum levels of interleukin-6 in patients with oral tongue SCC. Materials and Methods: In a cross-sectional study, 17 patients with oral tongue SCC were compared with the same number of age- and gender-matched healthy subjects. Serum IL-6 level fluctuation was determined using an immunological technique, before detecting its possible association with the subjects' age, gender, drinking and smoking history, cancer site, and disease severity. Results: The intensity of serum IL-6 in patients with oral tongue SCC was statistically significantly higher than that in healthy subjects (P<0.001). Serum IL-6 level was independent of the patients' age, gender, smoking and drinking history as well as cancer stage. Conclusion: IL-6 is a valuable biomarker in the diagnosis of oral tongue SCC. Its high sensitivity makes prediction of this condition possible, while this biomarker can also be used to screen high-risk patients

Task-oriented intelligent solution to measure Parkinson’s disease tremor severity

Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing

Developing a cloud-based service-oriented architecture for fuzzy logic systems

Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system