A simulation study of service differentiation for the bandwidth request scheme in fixed IEEE 802.16 network

Various flexible mechanisms related to quality of service (QoS) provisioning have been specified for uplink traffic at the medium access control (MAC) layer in the IEEE 802.16 standards. Among the mechanisms, contention based bandwidth request scheme can be used to indicate bandwidth demands to the base station for the non-real-time polling and best-effort services. These two services are used for most applications with unknown traffic characteristics. Due to the diverse QoS requirements of those applications, service differentiation (SD) is anticipated over the contention based bandwidth request scheme. In this paper we investigate the SD with the bandwidth request scheme by means of assigning different channel access parameters and bandwidth allocation priorities at different packets arrival probability. The effectiveness of the differentiation schemes is evaluated by simulations. It is observed that the initial backoff window can be efficient in SD, and if combined with the bandwidth allocation priority, the SD performances will be better.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Irrigation scheduling using ICSWAB model.

The paper presents a simple method of irrigation scheduling using ICSWAB model for dry land crops. The main inputs to this approache are daily precipitation or irrigation amounts and open pan evaporation (US class 'A' pan-mesh covered). The fixed cumulative evapotranspiration procedure is better than fixed days or fixed percentage soil moisture procedures of irrigation scheduling. Fixed days procedures could be reasonably applied during nonrainy season.

Photoelastic Analysis Of Fixed Partial Prosthesis Crown Height And Implant Length On Distribution Of Stress In Two Dental Implant Systems.

The aim of this study was to evaluate by photoelastic analysis stress distribution on short and long implants of two dental implant systems with 2-unit implant-supported fixed partial prostheses of 8 mm and 13 mm heights. Sixteen photoelastic models were divided into 4 groups: I: long implant (5 × 11 mm) (Neodent), II: long implant (5 × 11 mm) (Bicon), III: short implant (5 × 6 mm) (Neodent), and IV: short implants (5 × 6 mm) (Bicon). The models were positioned in a circular polariscope associated with a cell load and static axial (0.5 Kgf) and nonaxial load (15°, 0.5 Kgf) were applied to each group for both prosthetic crown heights. Three-way ANOVA was used to compare the factors implant length, crown height, and implant system (α = 0.05). The results showed that implant length was a statistically significant factor for both axial and nonaxial loading. The 13 mm prosthetic crown did not result in statistically significant differences in stress distribution between the implant systems and implant lengths studied, regardless of load type (P > 0.05). It can be concluded that short implants showed higher stress levels than long implants. Implant system and length was not relevant factors when prosthetic crown height were increased.

Segurança no circo : questão de prioridade = Circus safety: a priority issue

Universidade Estadual de Campinas . Faculdade de Educação Física

Características oclusais de pacientes com Classe II, divisão 1, tratados sem e com extrações de dois pré-molares superiores

OBJETIVOS: o objetivo deste estudo foi identificar características oclusais iniciais de pacientes Classe II, divisão 1, tratados sem e com extrações de dois pré-molares superiores. MÉTODOS: foram selecionados 62 pacientes que apresentavam má oclusão de Classe II, divisão 1, os quais foram divididos em dois grupos, de acordo com a forma de tratamento proposta, sendo o grupo 1 constituído de 42 pacientes (23 do sexo feminino e 19 do sexo masculino), com idade média de 12,7 anos, tratados sem extrações e com aparelho fixo combinado com extrabucal; e o grupo 2, composto de 20 pacientes (6 do sexo feminino e 14 do sexo masculino), com idade média de 13,5 anos, tratados também com aparelho fixo combinado com uso de extrabucal, mas que tiveram indicação de extrações de dois pré-molares superiores em seus planos de tratamento. Para observar as características oclusais iniciais e finais, assim como suas alterações com o tratamento, foi utilizado o Índice de Prioridade de Tratamento (IPT). Os valores dos índices foram submetidos à análise estatística pelo teste t independente, para comparar as variáveis entre os grupos. RESULTADOS E CONCLUSÕES: os resultados demonstraram que o grau de má oclusão inicial foi diferente nos dois grupos quando avaliados pelo IPT, sendo maior no grupo tratado com extrações de dois pré-molares superiores

A Comparison of Bimatoprost 0.03% Versus the Fixed-Combination of Latanotoprost 0.005% and Timolol 0.5% in Adult Patients With Elevated Intraocular Pressure: An Eight-Week, Randomized, Open-Label Trial

Introduction and Purpose: Bimatoprost and the fixed combination of latanoprost with timolol maleate are 2 medications widely used to treat glaucoma and ocular hypertension (OHT). The aim of the study is to compare the efficacy of these 2 drugs in reducing intraocular pressure (IOP) after 8 weeks of treatment in patients with primary open angle glaucoma (POAG) or OHT. Methods: In this randomized, open-label trial, 44 patients with POAG or OHT were allocated to receive either bimatoprost (1 drop QD) or latanoprost/timolol (1 drop QD). Primary outcome was the mean diurnal IOP measurement at the 8th week, calculated as the mean IOP measurements taken at 8:00 AM, 10: 00 AM, and 12: 00 PM Secondary outcomes included the baseline change in IOP measured 3 times a day, after the water-drinking test (performed after the last IOP measurement), and the assessment of side effects of each therapy. Results: The mean IOP levels of latanoprost/timolol (13.83, SD = 2.54) was significantly lower than of bimatoprost (16.16, SD = 3.28; P < 0.0001) at week 8. Also, the change in mean IOP values was significantly higher in the latanoprost/timolol group at 10:00 AM (P = 0.013) and 12:00 PM (P = 0.01), but not at 8: 00 AM (P = ns). During the water-drinking test, there was no signifi cant difference in IOP increase (absolute and percentage) between groups; however, there was a signifi cant decrease in mean heart rate in the latanoprost/timolol group. Finally, no signifi cant changes in blood pressure and lung spirometry were observed in either groups. Conclusions: The fixed combination of latanoprost/timolol was significantly superior to bimatoprost alone in reducing IOP in patients with POAG or OHT. Further studies with large sample sizes should be taken to support the superior efficacy of latanoprost/timolol, as well as to better assess its profile of side effects.

Changes in temporomandibular joint disc position and form following Herbst and fixed orthodontic treatment

Objective: To determine the changes in the position and form of the temporomandibular joint articular disc in adolescents with Class II division 1 malocclusion and mandibular retrognathism treated with the Herbst appliance (phase I) and fixed orthodontic appliance (phase II). Materials and Methods: Thirty-two consecutive adolescents went through phase I of treatment and 23 completed phase II. The temporomandibular joints were evaluated qualitatively by means of magnetic resonance images at the beginning of treatment (T1), during phase I (T2), at the end of phase I (T3), and at the end of phase II (T4). Results: Significant changes in disc position were not observed with the mouth closed between T1 X T3 (P = .317), T3 X T4 (P = .287), or T1 X T4 (P = .261). At T2, on average, the disc was positioned regressively. With the mouth open, no difference was observed between T1 X T3 (P = .223) or T1 X T4 (P = .082). We did observe a significant difference between T3 X T4 (P < .05). Significant changes in the disc form were found with the mouth closed between T1 X T2 (P < .001) and T2 X T3 (P < .001). Conclusions: At the end of the two-phase treatment, in general terms, the position and form of the initial articular discs were maintained; however, in some temporomandibular joints some seemingly adverse effects were observed at T4. (Angle Orthod. 2010;80:843-852.)

Robust STATCOM control for the stabilisation of fixed-speed wind turbines during low voltages

This paper presents a robust voltage control scheme for fixed-speed wind generators using a static synchronous compensator (STATCOM) controller. To enable a linear and robust control framework with structured uncertainty, the overall system is represented by a linear part plus a nonlinear part that covers an operating range of interest required to ensure stability during severe low voltages. The proposed methodology is flexible and readily applicable to larger wind farms of different configurations. The performance of the control strategy is demonstrated on a two area test system. Large disturbance simulations demonstrate that the proposed controller enhances voltage stability as well as transient stability of induction generators during low voltage ride through (LVRT) transients and thus enhances the LVRT capability. (C) 2011 Elsevier Ltd. All rights reserved.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

An upflow fixed-bed anaerobic-aerobic reactor for removal of organic matter and nitrogen from L-lysine plant wastewater

This paper reports on the design of a new reactor configuration - an upflow fixed-bed combined anaerobic-aerobic reactor - can operate as a single treatment unit for the removal of nitrogen (approximate to 150 mg N/L) and organic matter (approximate to 1300 mg COD/L) from Lysine plant wastewater. L-Lysine, an essential amino acid for animal nutrition, is produced by fermentation from natural raw materials of agricultural origin, thus generating wastewater with high contents of organic matter and nitrogen. The best operational condition of the reactor was obtained with a hydraulic retention time of 35 h (21 h in the anaerobic zone and 14 h in the aerobic zone) and a recycling ratio (R) of 3.5. In this condition, the COD, total Kjeldahl nitrogen (TKN), and total nitrogen (TN) removal efficiencies were 97%, 96%, and 77%, respectively, with average effluent concentrations of 10 +/- 36 mg COD/L, 2 +/- 1 mg NH(4)(+)-N/L, 8 +/- 3 mg Org-N/L, 1 +/- 1 mg NH(2)(-)-N/L, and 26 +/- 23 mg NH(3)(-)-N/L.

Simulated annealing applied to the irregular rotational placement of shapes over containers with fixed dimensions

This work deals with the problem of minimizing the waste of space that occurs on a rotational placement of a set of irregular two dimensional polygons inside a two dimensional container. This problem is approached with an heuristic based on simulated annealing. Traditional 14 external penalization"" techniques are avoided through the application of the no-fit polygon, that determinates the collision free area for each polygon before its placement. The simulated annealing controls: the rotation applied, the placement and the sequence of placement of the polygons. For each non placed polygon, a limited depth binary search is performed to find a scale factor that when applied to the polygon, would allow it to be fitted in the container. It is proposed a crystallization heuristic, in order to increase the number of accepted solutions. The bottom left and larger first deterministic heuristics were also studied. The proposed process is suited for non convex polygons and containers, the containers can have holes inside. (C) 2009 Elsevier Ltd. All rights reserved.

Placement over containers with fixed dimensions solved with adaptive neighborhood simulated annealing

This work deals with the problem of minimizing the waste of space that occurs on a rotational placement of a set of irregular bi-dimensional items inside a bi-dimensional container. This problem is approached with a heuristic based on Simulated Annealing (SA) with adaptive neighborhood. The objective function is evaluated in a constructive approach, where the items are placed sequentially. The placement is governed by three different types of parameters: sequence of placement, the rotation angle and the translation. The rotation applied and the translation of the polygon are cyclic continuous parameters, and the sequence of placement defines a combinatorial problem. This way, it is necessary to control cyclic continuous and discrete parameters. The approaches described in the literature deal with only type of parameter (sequence of placement or translation). In the proposed SA algorithm, the sensibility of each continuous parameter is evaluated at each iteration increasing the number of accepted solutions. The sensibility of each parameter is associated to its probability distribution in the definition of the next candidate.

A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints

Pipeline systems play a key role in the petroleum business. These operational systems provide connection between ports and/or oil fields and refineries (upstream), as well as between these and consumer markets (downstream). The purpose of this work is to propose a novel MINLP formulation based on a continuous time representation for the scheduling of multiproduct pipeline systems that must supply multiple consumer markets. Moreover, it also considers that the pipeline operates intermittently and that the pumping costs depend on the booster stations yield rates, which in turn may generate different flow rates. The proposed continuous time representation is compared with a previously developed discrete time representation [Rejowski, R., Jr., & Pinto, J. M. (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers and Chemical Engineering, 28, 1511] in terms of solution quality and computational performance. The influence of the number of time intervals that represents the transfer operation is studied and several configurations for the booster stations are tested. Finally, the proposed formulation is applied to a larger case, in which several booster configurations with different numbers of stages are tested. (C) 2007 Elsevier Ltd. All rights reserved.