An improved multi-agent simulation methodology for modelling and evaluating wireless communication systems resource allocation algorithms

Multi-Agent Systems (MAS) constitute a well known approach in modelling dynamical real world systems. Recently, this technology has been applied to Wireless Communication Systems (WCS), where efficient resource allocation is a primary goal, for modelling the physical entities involved, like Base Stations (BS), service providers and network operators. This paper presents a novel approach in applying MAS methodology to WCS resource allocation by modelling more abstract entities involved in WCS operation, and especially the concurrent network procedures (services). Due to the concurrent nature of a WCS, MAS technology presents a suitable modelling solution. Services such as new call admission, handoff, user movement and call termination are independent to one another and may occur at the same time for many different users in the network. Thus, the required network procedures for supporting the above services act autonomously, interact with the network environment (gather information such as interference conditions), take decisions (e.g. call establishment), etc, and can be modelled as agents. Based on this novel simulation approach, the agent cooperation in terms of negotiation and agreement becomes a critical issue. To this end, two negotiation strategies are presented and evaluated in this research effort and among them the distributed negotiation and communication scheme between network agents is presented to be highly efficient in terms of network performance. The multi-agent concept adapted to the concurrent nature of large scale WCS is, also, discussed in this paper

Bounding the search space for global optimization of neural networks learning error: an interval analysis approach

Training a multilayer perceptron (MLP) with algorithms employing global search strategies has been an important research direction in the field of neural networks. Despite a number of significant results, an important matter concerning the bounds of the search region---typically defined as a box---where a global optimization method has to search for a potential global minimizer seems to be unresolved. The approach presented in this paper builds on interval analysis and attempts to define guaranteed bounds in the search space prior to applying a global search algorithm for training an MLP. These bounds depend on the machine precision and the term guaranteed denotes that the region defined surely encloses weight sets that are global minimizers of the neural network's error function. Although the solution set to the bounding problem for an MLP is in general non-convex, the paper presents the theoretical results that help deriving a box which is a convex set. This box is an outer approximation of the algebraic solutions to the interval equations resulting from the function implemented by the network nodes. An experimental study using well known benchmarks is presented in accordance with the theoretical results

Solving the linear interval tolerance problem for weight initialization of neural networks

Determining good initial conditions for an algorithm used to train a neural network is considered a parameter estimation problem dealing with uncertainty about the initial weights. Interval Analysis approaches model uncertainty in parameter estimation problems using intervals and formulating tolerance problems. Solving a tolerance problem is defining lower and upper bounds of the intervals so that the system functionality is guaranteed within predefined limits. The aim of this paper is to show how the problem of determining the initial weight intervals of a neural network can be defined in terms of solving a linear interval tolerance problem. The proposed Linear Interval Tolerance Approach copes with uncertainty about the initial weights without any previous knowledge or specific assumptions on the input data as required by approaches such as fuzzy sets or rough sets. The proposed method is tested on a number of well known benchmarks for neural networks trained with the back-propagation family of algorithms. Its efficiency is evaluated with regards to standard performance measures and the results obtained are compared against results of a number of well known and established initialization methods. These results provide credible evidence that the proposed method outperforms classical weight initialization methods

Reliable estimation of a neural network’s domain of validity through interval analysis based inversion

Reliable estimation of a neural network’s domain of validity is important for a number of reasons such as assessing its ability to cope with a given problem, evaluating the consistency of its generalization etc. In this paper we introduce a new approach to estimate the domain of validity of a neural network based on Set Inversion Via Interval Analysis (SIVIA), the methodology established by Jaulin andWalter [1]. This approach was originally introduced in order to solve nonlinear parameter estimation problems in a bounded error context and proved to be effective in tackling several types of problems dealing with nonlinear systems analysis. The dependence of a neural network output on the pattern data is a nonlinear function and hence derivation of the impact of the input data to the neural network function can be addressed as a nonlinear parameter estimation problem that can be tackled by SIVIA. We present concrete application examples and show how the proposed method allows to delimit the domain of validity of a trained neural network. We discuss advantages, pitfalls and potential improvements offered to neural networks

Fracture Incidence, Quality of Life, and Back Pain during 18-Months Treatment with Teriparatide in Greek Postmenopausal Women with Osteoporosis: Results from the European Forsteo Observational Study

Objective. To evaluate fracture incidence, effects on health-related quality of life(QoL), back pain (BP) occurrence and treatment compliance in Greek post-menopausal osteoporotic women treated with teriparatide (TPTD) for up to 18 months, in a naturalistic setting. Methods. 301 patients provided baseline information on demographic characteristics, fracture history, osteoporosis-related medication and risk factors. During treatment, QoL and BP severity were evaluated. Results. Mean (SD) age was 69.5 (±8.5) years. Fracture history was reported by 92.5% of patients. Incidence of fractures (per 10,000 patients/years) ranged from 402 during 0–6 months of treatment, to 346 during 12–18 months. All 5 dimensions of QoL showed improvement. At baseline and 18 months, BP was reported by 93.2% and 64.2% of patients, respectively. BP and limitation of activities were quantified as moderate or severe by 89.9% and 62.3% of patients at baseline versus 32.4% and 14.8% at 18 months. Patients on treatment at 6, 12, 17, and 18 months were 92.6%, 88.3%, 79.6%, and 36.5%, respectively. Conclusions. In the Greek EFOS study cohort, patients prescribed TPTD were severely osteoporotic, with considerable health-related problems. Significant improvements in QoL and BP together with low fracture rates and high compliance have been recorded during treatment

Bioluminescent imaging in induced mouse models of endometriosis reveals differences in four model variations

Our understanding of the etiology and pathophysiology of endometriosis remains limited. Disease modelling in the field is problematic as many versions of induced mouse models of endometriosis exist. We integrated bioluminescent imaging of ‘lesions’ generated using luciferase-expressing donor mice. We compared longitudinal bioluminescence and histology of lesions, sensory behavior of mice with induced endometriosis and the impact of the GnRH antagonist Cetrorelix on lesion regression and sensory behavior. Four models of endometriosis were tested. We found that the nature of the donor uterine material was a key determinant of how chronic the lesions were as well as their cellular composition. The severity of pain-like behavior also varied across models. Whilst Cetrorelix significantly reduced lesion bioluminescence in all models, it had varying impacts on pain-like behavior. Collectively, our results demonstrate key differences in the progression of the ‘disease’ across different mouse models of endometriosis. We propose that validation and testing in multiple models, each of which may be representative of the different subtypes / heterogeneity observed in women should become a standard approach to discovery science in the field of endometriosis

Vitamin D and Delirium in Older Adults: A Case-Control Study in Geriatric Acute Care Unit

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The datasets generated for this study are available on request from the corresponding author after notification and authorization of the competent authorities.Objective: Vitamin D is involved in brain health and function. Our objective was to determine whether the serum 25-hydroxyvitamin D (25OHD) concentration was associated with delirium in a case-control study of geriatric inpatients. Methods: Sixty cases with delirium (mean ± SD, 84.8 ± 5.7years; 58.3% female) and 180 age- and gender-matched controls were enrolled in a geriatric acute care unit between 2012 and 2014. The diagnosis of delirium was made using the Confusion Assessment Method. Hypovitaminosis D was defined using consecutively the consensual threshold value of 50 nmol/L and a threshold value calculated from a sensitivity-specificity analysis. Age, gender, number of acute diseases, use of psychoactive drugs, season of testing, and serum concentrations of calcium, parathyroid hormone, creatinine, albumin, TSH, vitamin B9 and vitamin B12 were used as potential confounders. Results: The 60 cases with delirium exhibited lower 25OHD concentration than 180 matched controls (35.4 ± 30.0 nmol/L vs. 45.9 ± 34.5 nmol/L, p = 0.035). Increased 25OHD concentration was associated with a decrease in delirium prevalence (OR = 0.99 [95CI: 0.98–0.99] per nmol/L of 25OHD, p = 0.038). The concentration distinguishing between cases and controls with the best sensitivity-specificity was found between 29.5 and 30.5 nmol/L. The regression models showed that delirium was associated with hypovitaminosis D defined either as 25OHD ≤ 50 nmol/L (OR = 2.37 [95CI: 1.07–5.25], p = 0.034) or as 25OHD ≤ 30 nmol/L (OR = 2.66 [95 CI: 1.30–5.45], p = 0.008). Conclusions: Decreased serum 25OHD concentrations were associated with delirium among acute geriatric inpatients. The threshold concentration to differentiate between cases and controls was around 30 nmol/L