The Effect of Wasta on Business Conduct and HRM in Jordan

Despite rapid globalisation, boom in multinational business and increasing interest in international human resource management (IHRM) generally, research on developing countries in the Middle-East is limited. A three year PhD research project seeks to begin to fill this gap by studying the effect of Jordanian culture on the transfer of western recruitment and selection (R&S) frameworks into Jordan. This paper opens up an investigation into a cultural concept at the heart of management and human resource management (HRM) in Jordan: ‘wasta’. Wasta is a concept that springs from tribalism; favouritism based on family and tribal relations. For multinational organisations this presents a challenge in balancing the western idea of fairness, equal opportunities and diversity and the local system based on favouritism. We argue that the perceived benefits of wasta cannot match the moral case for a merit based model.

Increased water charges improve efficiency and equity in an irrigation system

Conventional wisdom in many agricultural systems across the world is that farmers cannot, will not, or should not pay the full costs associated with surface water delivery. Across Organisation for Economic Co-operation and Development (OECD) countries, only a handful can claim complete recovery of operation, maintenance, and capital costs; across Central and South Asia, fees are lower still, with farmers in Nepal, India, and Kazakhstan paying fractions of a U.S. penny for a cubic meter of water. In Pakistan, fees amount to roughly USD 1-2 per acre per season. However, farmers in Pakistan spend orders of magnitude more for diesel fuel to pump groundwater each season, suggesting a latent willingness to spend for water that, under the right conditions, could potentially be directed toward water-use fees for surface water supply. Although overall performance could be expected to improve with greater cost recovery, asymmetric access to water in canal irrigation systems leaves the question open as to whether those benefits would be equitably shared among all farmers in the system. We develop an agent-based model (ABM) of a small irrigation command to examine efficiency and equity outcomes across a range of different cost structures for the maintenance of the system, levels of market development, and assessed water charges. We find that, robust to a range of different cost and structural conditions, increased water charges lead to gains in both efficiency and concomitant improvements in equity as investments in canal infrastructure and system maintenance improve the conveyance of water resources further down watercourses. This suggests that, under conditions in which (1) farmers are currently spending money to pump groundwater to compensate for a failing surface water system, and (2) there is the possibility that through initial investment to provide perceptibly better water supply, genuine win-win solutions can be attained through higher water-use fees to beneficiary farmers.

THE PHYSICS OF IDEAS: INFERRING THE MECHANICS OF OPINION FORMATION FROM MACROSCOPIC STATISTICAL PATTERNS

In a microscopic setting, humans behave in rich and unexpected ways. In a macroscopic setting, however, distinctive patterns of group behavior emerge, leading statistical physicists to search for an underlying mechanism. The aim of this dissertation is to analyze the macroscopic patterns of competing ideas in order to discern the mechanics of how group opinions form at the microscopic level. First, we explore the competition of answers in online Q&A (question and answer) boards. We find that a simple individual-level model can capture important features of user behavior, especially as the number of answers to a question grows. Our model further suggests that the wisdom of crowds may be constrained by information overload, in which users are unable to thoroughly evaluate each answer and therefore tend to use heuristics to pick what they believe is the best answer. Next, we explore models of opinion spread among voters to explain observed universal statistical patterns such as rescaled vote distributions and logarithmic vote correlations. We introduce a simple model that can explain both properties, as well as why it takes so long for large groups to reach consensus. An important feature of the model that facilitates agreement with data is that individuals become more stubborn (unwilling to change their opinion) over time. Finally, we explore potential underlying mechanisms for opinion formation in juries, by comparing data to various types of models. We find that different null hypotheses in which jurors do not interact when reaching a decision are in strong disagreement with data compared to a simple interaction model. These findings provide conceptual and mechanistic support for previous work that has found mutual influence can play a large role in group decisions. In addition, by matching our models to data, we are able to infer the time scales over which individuals change their opinions for different jury contexts. We find that these values increase as a function of the trial time, suggesting that jurors and judicial panels exhibit a kind of stubbornness similar to what we include in our model of voting behavior.

An experimental investigation of air flow and convective heat transfer in enclosures having large ventilative flow rates

The prediction of convective heat transfer in enclosures under high ventilative flow rates is primarily of interest for building design and simulation purposes. Current models are based on experiments performed forty years ago with flat plates under natural convection conditions.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

Contactless Spectral-Dependent Measurement of Bulk Lifetime and Surface Recombination Velocity in Silicon Photovoltaic Materials

Charge carrier lifetime measurements in bulk or unfinished photovoltaic (PV) materials allow for a more accurate estimate of power conversion efficiency in completed solar cells. In this work, carrier lifetimes in PV- grade silicon wafers are obtained by way of quasi-steady state photoconductance measurements. These measurements use a contactless RF system coupled with varying narrow spectrum input LEDs, ranging in wavelength from 460 nm to 1030 nm. Spectral dependent lifetime measurements allow for determination of bulk and surface properties of the material, including the intrinsic bulk lifetime and the surface recombination velocity. The effective lifetimes are fit to an analytical physics-based model to determine the desired parameters. Passivated and non-passivated samples are both studied and are shown to have good agreement with the theoretical model.

Investigation of the hazards of substrate current injection: transient external latchup and substrate noise coupling

Substrate current injection is the origin of external latchup and substrate noise coupling. The trigger current for external latchup depends on the duration of the trigger event. A physics-based model is provided to model the effects of aggressor to victim spacing and orientation on transient triggering of external latchup. The latchup susceptibility of standard cell based designs is also investigated. Guard rings are used to reduce latchup susceptibility and to reduce the substrate noise coupled to sensitive analog circuits. In this work, the effectiveness of different guard ring topologies for the reduction of substrate noise coupling is also investigated.

Towards the systematic simplification of mechanistic models

Mechanistic models used for prediction should be parsimonious, as models which are over-parameterised may have poor predictive performance. Determining whether a model is parsimonious requires comparisons with alternative model formulations with differing levels of complexity. However, creating alternative formulations for large mechanistic models is often problematic, and usually time-consuming. Consequently, few are ever investigated. In this paper, we present an approach which rapidly generates reduced model formulations by replacing a model’s variables with constants. These reduced alternatives can be compared to the original model, using data based model selection criteria, to assist in the identification of potentially unnecessary model complexity, and thereby inform reformulation of the model. To illustrate the approach, we present its application to a published radiocaesium plant-uptake model, which predicts uptake on the basis of soil characteristics (e.g. pH, organic matter content, clay content). A total of 1024 reduced model formulations were generated, and ranked according to five model selection criteria: Residual Sum of Squares (RSS), AICc, BIC, MDL and ICOMP. The lowest scores for RSS and AICc occurred for the same reduced model in which pH dependent model components were replaced. The lowest scores for BIC, MDL and ICOMP occurred for a further reduced model in which model components related to the distinction between adsorption on clay and organic surfaces were replaced. Both these reduced models had a lower RSS for the parameterisation dataset than the original model. As a test of their predictive performance, the original model and the two reduced models outlined above were used to predict an independent dataset. The reduced models have lower prediction sums of squares than the original model, suggesting that the latter may be overfitted. The approach presented has the potential to inform model development by rapidly creating a class of alternative model formulations, which can be compared.

RCOS: Real time context sharing across a fleet of smart mobile devices

Today, biodiversity is endangered by the currently applied intensive farming methods imposed on food producers by intermediate actors (e.g.: retailers). The lack of a direct communication technology between the food producer and the consumer creates dependency on the intermediate actors for both producers and the consumers. A tool allowing producers to directly and efficiently market produce that meets customer demands could greatly reduce the dependency enforced by intermediate actors. To this end, in this thesis, we propose, develop, implement and validate a Real Time Context Sharing (RCOS) system. RCOS takes advantage of the widely used publish/subscribe paradigm to exchange messages between producers and consumers, directly, according to their interest and context. Current systems follow a topic-based model or a content-based model. With RCOS, we propose a context-awareness approach into the matching process of publish/subscribe paradigm. Finally, as a proof of concept, we extend the Apache ActiveMQ Artemis software and create a client prototype. We evaluate our proof of concept for larger scale deployment.

Urban dynamics evaluation of Envigado city

This article presents the methodological conceptualization and the main results of a System Dynamics model, which main objective is to support the housing policies in the city of Envigado -- The used methodology developed a scenario-based model to emulate the approximate evolution of the housing demand and supply for the city, using a scenario of the Gross Domestic Product (GDP) and a housing authorization strategy as input -- Diverse results were obtained, for instance it was found that due to the soil availability, the housing supply reaches the saturation point between 2040 and 2046 -- Finally this article could be considered as an example of how academic tools such as System Dynamics can be used by decisions makers in the government

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

Habitat selection and phenotypic diversity in the anchovy of the Bay of Biscay

Diversity among individuals in a population is an important feature linking vital rates with behaviour and spatial occupation. We measured the growth increments in the otolith of individual fishes collected on the annual fisheries survey PELGAS from 2001 to 2015. Individuals who grew larger at juvenile stage occupied later in life more off-shore habitats. Further, we analysed the allozymes of 13 different loci from 2001 to 2006. Alleles of the enzyme IDH showed different frequencies in inshore and offshore habitats. The population spatially segregates along a coast to off-shore gradient with individuals showing different early growth and allele frequencies. Results show how individuals in a population segregate spatially in different habitats in relation with phenotypic diversity. This implies modelling the population with individual-based and physiological approaches to fully grasp its dynamics. It also implies developing management strategies to conserve infra-population diversity as a means to garantee the occupation of the full range of habitats.

An Analysis of Colombian Power Market Price Behavior from an Industrial Organization Perspective

We analyze the behavior of spot prices in the Colombian wholesale power market, using a series of models derived from industrial organization theory -- We first create a Cournot-based model that simulates the strategic behavior of the market-leader power generators, which we use to estimate two industrial organization variables, the Index of Residual Demand and the Herfindahl-Hirschman Index (HHI) -- We use these variables to create VAR models that estimate spot prices and power market impulse-response relationships -- The results from these models show that hydroelectric generators can use their water storage capability strategically to affect off-peak prices primarily, while the thermal generators can manage their capacity strategically to affect on-peak prices -- In addition, shocks to the Index of Residual Capacity and to the HHI cause spot price fluctuations, which can be interpreted as the generators´ strategic response to these shocks

Modelo de competências no contexto da escola pública portuguesa : análise da função do diretor

Mestrado em Ciências Empresariais

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research.