A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

The reality of quality systems software : working for the machine, or smart people around the machine?

This case study-based research examines the outcomes of using quality system software (QSS) in terms of the support it provides to, and the constraints it imposes on, its users. The results indicate that QSS can help facilitate knowledge access and sharing, and to a lesser extent, facilitate communication. It can be an effective mechanism for initiating and managing changes to work processes. Although some writers have opined that information technology (IT) is another way of controlling workers, this was not evidenced in this study. Technical shortcomings did hamper performance of some tasks, and for some managers, extra work was involved.

Internet traffic classification using machine learning : a token-based approach

Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.

Is psychotherapy a vector for isolation and anomie?

A key traditional question the client learns in the conventional psychotherapies is ‘Am I getting what I want?’. But can this question incite a mindset that does not align with the ‘give and take’ essence of sustainable everyday relations? Is it possible that the psychotherapies—if these practices can be bundled together—might teach clients to become more self-centred and relationally illiterate? MARK FURLONG suggests that well-intentioned practitioners can inadvertently de-empathise, ignore or even disrupt their clients’ intimate networks. Findings from his research support the proposition that the action of the mainstream therapies tends to undermine the service users’ prospects for sustainable personal relationships. Exceptions were found in the specialist settings of paediatric and aged care, and in narrative and family therapy practice.

Gaming maching addiction : the role of avoidance, accessibility and social support

Commonality in etiology and clinical expression plus high comorbidity between pathological gambling and substance use disorders suggest common underlying motives. It is important to understand common motivators and differentiating factors. An overarching framework of addiction was used to examine predictors of problem gambling in current electronic gaming machine (EGM) gamblers. Path analysis was used to examine the relationships between antecedent factors (stressors, coping habits, social support), gambling motivations (avoidance, accessibility, social) and gambling behavior. Three hundred and forty seven (229 females: M = 29.20 years, SD = 14.93; 118 males: M = 29.64 years, SD = 12.49) people participated. Consistent with stress, coping and addiction theory, situational life stressors and general avoidance coping were positively related to avoidance-motivated gambling. In turn, avoidance-motivated gambling was positively related to EGM gambling frequency and problems. Consistent with exposure theory, life stressors were positively related to accessibility-motivated gambling, and accessibility-motivated gambling was positively related to EGM gambling frequency and gambling problems. These findings are consistent with other addiction research and suggest avoidance-motivated gambling is part of a more generalized pattern of avoidance coping with relative accessibility to EGM gambling explaining its choice as a method of avoidance. Findings also showed social support acted as a direct protective factor in relation to gambling frequency and problems and indirectly via avoidance and accessibility gambling motivations. Finally, life stressors were positively related to socially motivated gambling but this motivation was not related to either social support or gambling behavior suggesting it has little direct influence on gambling problems.

Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach

This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and opinion. They rarely have access to knowledge that helps predict athletic performances. The omnium presents a unique and complex decision-making challenge as it is not clear what type of athlete is best suited to the omnium (e.g., sprint or endurance specialist) and tactical decisions made by the coach and athlete during the event will have significant effects on the overall performance of the athlete. In the present work, a variety of machine learning techniques were used to analyze omnium competition data from the World Championships since 2007. The analysis indicates that sprint events have slightly more influence in determining the medalists, than endurance-based events. Using a probabilistic analysis, we created a model of performance prediction that provides an unprecedented level of supporting information that assists coaches with strategic and tactical decisions during the omnium.

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Bounded solutions of fermions in the background of mixed vector-scalar inversely linear potentials

The problem of a fermion subject to a general mixing of vector and scalar potentials in a two-dimensional world is mapped into a Sturm-Liouville problem. Isolated bounded solutions are also searched. For the specific case of an inversely linear potential, which gives rise to an effective Kratzer potential in the Sturm-Liouville problem, exact bounded solutions are found in closed form. The case of a pure scalar potential with their isolated zero-energy solutions, already analyzed in a previous work, is obtained as a particular case. The behavior of the upper and lower components of the Dirac spinor is discussed in detail and some unusual results are revealed. The nonrelativistic limit of our results adds a new support to the conclusion that even-parity solutions to the nonrelativistic one-dimensional hydrogen atom do not exist. (c) 2004 Elsevier B.V. All rights reserved.

Klein-Gordon particles in mixed vector-scalar inversely linear potentials

The problem of a spinless particle subject to a general mixing of vector and scalar inversely linear potentials in a two-dimensional world is analyzed. Exact bounded solutions are found in closed form by imposing boundary conditions on the eigenfunctions which ensure that the effective Hamiltonian is Hermitian for all the points of the space. The nonrelativistic limit of our results adds a new support to the conclusion that even-parity solutions to the nonrelativistic one-dimensional hydrogen atom do not exist. (c) 2005 Elsevier B.V. All rights reserved.

TRIPLE-VECTOR-BOSON PROCESSES IN GAMMA-GAMMA COLLIDERS

We study the production of three gauge bosons (W(+)W(-)Z(0) and W(+)W(-)gamma) at the next generation of linear e(+)e(-) colliders operating in the yy mode. We analyze the total cross sections as well as several kinematical distributions of the final state particles. We find out that a linear e(+)e(-) machine operating in the rr mode will produce 5-10 times more three-gauge-boson states compared to the standard e(+)e(-) mode at high energies.

Line based camera calibration in machine vision dynamic applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.

Tri-state Space Vector Modulation for Three-phase integrated inverters

The aim of this work is to present a modified Space Vector Modulation (SVM) suitable for Tri-state Three-phase inverters. A standard SVM algorithm and the Tri-state PWM (Pulse Width Modulation) are presented and their concept are mixed into the novel SVM. The proposed SVM is applied to a three-phase tri-state integrated Boost inverter, intended to Photovoltaic Energy Applications. The main features for this novel SVM are validated through simulations and also by experimental tests. The obtained results prove the feasibility of the proposal. © 2011 IEEE.

Amazonian malaria: Asymptomatic human reservoirs, diagnostic challenges, environmentally driven changes in mosquito vector populations, and the mandate for sustainable control strategies

Across the Americas and the Caribbean, nearly 561,000 slide-confirmed malaria infections were reported officially in 2008. The nine Amazonian countries accounted for 89% of these infections; Brazil and Peru alone contributed 56% and 7% of them, respectively. Local populations of the relatively neglected parasite Plasmodium vivax, which currently accounts for 77% of the regional malaria burden, are extremely diverse genetically and geographically structured. At a time when malaria elimination is placed on the public health agenda of several endemic countries, it remains unclear why malaria proved so difficult to control in areas of relatively low levels of transmission such as the Amazon Basin. We hypothesize that asymptomatic parasite carriage and massive environmental changes that affect vector abundance and behavior are major contributors to malaria transmission in epidemiologically diverse areas across the Amazon Basin. Here we review available data supporting this hypothesis and discuss their implications for current and future malaria intervention policies in the region. Given that locally generated scientific evidence is urgently required to support malaria control interventions in Amazonia, we briefly describe the aims of our current field-oriented malaria research in rural villages and gold-mining enclaves in Peru and a recently opened agricultural settlement in Brazil. (C) 2011 Elsevier B.V. All rights reserved.

Trypanosoma (Megatrypanum) melophagium in the Sheep Ked Melophagus ovinus from Organic Farms in Croatia: Phylogenetic Inferences Support Restriction to Sheep and Sheep Keds and Close Relationship with Trypanosomes from Other Ruminant Species

Trypanosoma (Megatrypanum) melophagium is a parasite of sheep transmitted by sheep keds, the sheep-restricted ectoparasite Melophagus ovinus (Diptera: Hippoboscidae). Sheep keds were 100% prevalent in sheep from five organic farms in Croatia, Southeastern Europe, whereas trypanosomes morphologically compatible with T. melophagium were 86% prevalent in the guts of the sheep keds. Multilocus phylogenetic analyses using sequences of small subunit rRNA, glycosomal glyceraldehyde-3-phosphate dehydrogenase, spliced leader, and internal transcribed spacer 1 of the rDNA distinguished T. melophagium from all allied trypanosomes from other ruminant species and placed the trypanosome in the subgenus Megatrypanum. Trypanosomes from sheep keds from Croatia and Scotland, the only available isolates for comparison, shared identical sequences. All biologic and phylogenetic inferences support the restriction of T. melophagium to sheep and, especially, to the sheep keds. The comparison of trypanosomes from sheep, cattle, and deer from the same country, which was never achieved before this work, strongly supported the host-restricted specificity of trypanosomes of the subgenus Megatrypanum. Our findings indicate that with the expansion of organic farms, both sheep keds and T. melophagium may re-emerge as parasitic infections of sheep.