A heuristic algorithm for the Capacitated Vehicle Routing Problem with Synchronized Pick-ups and Drop-offs : a case study for medications delivery and supervision in DR Congo

Dans des contextes de post-urgence tels que le vit la partie occidentale de la République Démocratique du Congo (RDC), l’un des défis cruciaux auxquels font face les hôpitaux ruraux est de maintenir un niveau de médicaments essentiels dans la pharmacie. Sans ces médicaments pour traiter les maladies graves, l’impact sur la santé de la population est significatif. Les hôpitaux encourent également des pertes financières dues à la péremption lorsque trop de médicaments sont commandés. De plus, les coûts du transport des médicaments ainsi que du superviseur sont très élevés pour les hôpitaux isolés ; les coûts du transport peuvent à eux seuls dépasser ceux des médicaments. En utilisant la province du Bandundu, RDC pour une étude de cas, notre recherche tente de déterminer la faisabilité (en termes et de la complexité du problème et des économies potentielles) d’un problème de routage synchronisé pour la livraison de médicaments et pour les visites de supervision. Nous proposons une formulation du problème de tournées de véhicules avec capacité limitée qui gère plusieurs exigences nouvelles, soit la synchronisation des activités, la préséance et deux fréquences d’activités. Nous mettons en œuvre une heuristique « cluster first, route second » avec une base de données géospatiales qui permet de résoudre le problème. Nous présentons également un outil Internet qui permet de visualiser les solutions sur des cartes. Les résultats préliminaires de notre étude suggèrent qu’une solution synchronisée pourrait offrir la possibilité aux hôpitaux ruraux d’augmenter l’accessibilité des services médicaux aux populations rurales avec une augmentation modique du coût de transport actuel.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

"Right to Personal Liberty Under the Indian Constitution - With Special Reference to Judicial Process"

The nature and extent of protection secured to personal liberty has been a subject matter of great controversy and debate. The expression "procedure established by law" as a standard of protection for personal liberty has been looked upon as highly unsatisfactory and inadequate. For, unlike the specific attributes of liberty that are separately guaranteed under Art.19, ‘personal liberty‘ as guaranteed by Art.21 does not obligate the .Legislature to comply with the requirements of justice and reasonableness as and when it enchroaches upon that right. Though the concept of ‘personal liberty‘ has received an evolutive and expansive meaning through judicial process, the standard of protection which the judicial process could secure to personal liberty through the interpretation of Art.21 has been far from satisfactory Even after four decades of judicial process in the interpretation of Art.21 the problem of evolving a just and adequate standard of protection for personal liberty in that Article continues to be 21 crucial constitutional issue, craving for a. satisfactory solution. And the present study is a humble attempt to unravel this problem and to Search for a reasonable solution.

Behavioural Competency Management with special reference to Commercial Banks headquartered in Kerala

This study aims to analyze, compare and contrast the behavioral competency of officials in commercial banks headquartered in Kerala. This is done by analyzing the soft skills/behavioral skills possessed by an individual employee in both clerical and managerial levels and the means adopted to enhance their said skills in near future. The study was conducted with the objective of analyzing the behavioral competency of the managers and clerical staff in the commercial banks headquartered in Kerala. The researcher has gone through the available literature with respect to employee competency, job satisfaction and employee performance evaluation to formulate the problem and conceptualize the framework of the study. The study concluded that the competency of the employees differs from one bank to the other but strengthening the employees’ competency is the only possible solution by which the banks can determine their future growth prospects. Only through competency, banks can achieve high level of performance especially under the globalised situation.

The problem of the graphic artist

'The problem of the graphic artist' is a small example of applying elementary mathematics (divisibility of natural numbers) to a real problem which we ourselves have actually experienced. It deals with the possibilities for partitioning a sheet of paper into strips. In this contribution we report on a teaching unit in grade 6 as well as on informal tests with students in school and university. Finally we analyse this example methodologically, summarise our observations with pupils and students, and draw some didactical conclusions.

The status and barriers to the implementation and continuation of the school gardening projects in Germany, Nigeria and the United States of America

In drawing a conclusion for this study, care must be taken in generalizing findings since the population of students and teachers investigated were limited to certain levels in the different schools and countries. This study recognized some complexity of the factors underlying the status of school gardening instruction and activities in Germany, Nigeria and the U.S. as inadequate time for decision-making in the process of gardening, motivation of teachers and students. This was seen as the major impediments that influenced the status of gardening in the three countries. However, these factors were considered to have affected students’ mode of participation in the school gardening projects. This research finding suggests that the promotion and encouragement of students in gardening activities will promote vegetable production and increasing the numbers of practical farmers. Gardening has the potential to create opportunities for learning in an environment where children are able to experience nature first hand and to use the shared experience for communication (Bowker & Tearle, 2007). Therefore, the need for students to be encouraged to participate in gardening programs as the benefit will not only reduce the rate of obesity currently spreading among youths, but will contribute to the improve knowledge on science subjects. To build a network between community, parents and schools, a parent’s community approach should be used as the curriculum. The community approach will tighten the link between schools; community members, parents, teachers and students. This will help facilitate a better gardening projects implementation. Through a close collaboration, teachers and students will be able to identify issues affecting communities and undertake action learning in collaboration with community organizations to assess community needs and plan the implementation strategies as parents are part of the community. The sense of efficacy is a central factor in motivational and learning processes that govern educational improvement, standard and performance on complex tasks of both teachers and students. Dedication and willingness are the major stimulator and achievement of a project. Through a stimulator and provision of incentives and facilities, schools can achieve the best in project development. Teachers and principals should be aware that students are the lever for achieving the set goals in schools. Failure to understand what students need will result in achieving zero result. Therefore, it is advised that schools focus more on how to lure students to work through proper collaboration with the parents and community members. Principals and teachers should identify areas where students need to be corrected, helping them to correct the problem will enable them be committed in the schools’ programs.

A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

How to import files from your J drive into Teamsite

An update to Java meant that you can only import files into Teamsite from your 'My Documents' folder, which isn't much use if they are all stored on your J drive. This video shows you how to fix the problem.

Application of a support system to the design of wastewater treatment plants

This paper presents a case study that explores the advantages that can be derived from the use of a design support system during the design of wastewater treatment plants (WWTP). With this objective in mind a simplified but plausible WWTP design case study has been generated with KBDS, a computer-based support system that maintains a historical record of the design process. The study shows how, by employing such a historical record, it is possible to: (1) rank different design proposals responding to a design problem; (2) study the influence of changing the weight of the arguments used in the selection of the most adequate proposal; (3) take advantage of keywords to assist the designer in the search of specific items within the historical records; (4) evaluate automatically the compliance of alternative design proposals with respect to the design objectives; (5) verify the validity of previous decisions after the modification of the current constraints or specifications; (6) re-use the design records when upgrading an existing WWTP or when designing similar facilities; (7) generate documentation of the decision making process; and (8) associate a variety of documents as annotations to any component in the design history. The paper also shows one possible future role of design support systems as they outgrow their current reactive role as repositories of historical information and start to proactively support the generation of new knowledge during the design process

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem‐based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors’ role is to provide both guidance to facilitate student learning and content knowledge on a just‐in‐time basis

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.

Neurobehavioral Development in Children With Potential Exposure to Pesticides

Children may be at higher risk than adults from pesticide exposure, due to their rapidly developing physiology, unique behavioral patterns, and interactions with the physical environment. This preliminary study conducted in Ecuador examines the association between household and environmental risk factors for pesticide exposure and neurobehavioral development. We collected data over 6 months in the rural highland region of Cayambe, Ecuador (2003–2004). Children age 24–61 months residing in 3 communities were assessed with the Ages and Stages Questionnaire and the Visual Motor Integration Test. We gathered information on maternal health and work characteristics, the home and community environment, and child characteristics. Growth measurements and a hemoglobin finger-prick blood test were obtained. Multiple linear regression analyses were conducted. Current maternal employment in the flower industry was associated with better developmental scores. Longer hours playing outdoors were associated with lower gross and fine motor and problem solving skills. Children who played with irrigation water scored lower on fine motor skills (8% decrease; 95% confidence interval 9.31 to 0.53), problem-solving skills (7% decrease; 8.40 to 0.39), and Visual Motor Integration test scores (3% decrease; 12.00 to 1.08). These results suggest that certain environmental risk factors for exposure to pesticides may affect child development, with contact with irrigation water of particular concern. However, the relationships between these risk factors and social characteristics are complex, as corporate agriculture may increase risk through pesticide exposure and environmental contamination, while indirectly promoting healthy development by providing health care, relatively higher salaries, and daycare options.

A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

The role of schizotypy and creativity in a group problem-solving task

The well-studied link between psychotic traits and creativity is a subject of much debate. The present study investigated the extent to which schizotypic personality traits - as measured by O-LIFE (Oxford-Liverpool Inventory of Feelings and Experiences) - equip healthy individuals to engage as groups in everyday tasks. From a sample of 69 students, eight groups of four participants - comprised of high, medium, or low-schizotypy individuals - were assembled to work as a team to complete a creative problem-solving task. Predictably, high scorers on the O-LIFE formulated a greater number of strategies to solve the task, indicative of creative divergent thinking. However, for task success (as measured by time taken to complete the problem) an inverted U shaped pattern emerged, whereby high and low-schizotypy groups were consistently faster than medium schizotypy groups. Intriguing data emerged concerning leadership within the groups, and other tangential findings relating to anxiety, competition and motivation were explored. These findings challenge the traditional cliche that psychotic personality traits are linearly related to creative performance, and suggest that the nature of the problem determines which thinking styles are optimally equipped to solve it. (C) 2009 Elsevier Ltd. All rights reserved.