Combatant recruitment and the outcome of war

Why do some civil wars terminate soon, with victory of one party over theother? What determines if the winner is the incumbent or the rebel group?Why do other conflicts last longer? We propose a simple model in whichthe power of each armed group depends on the number of combatants itis able to recruit. This is in turn a function of the relative 'distance' between group leaderships and potential recruits. We emphasize the moralhazard problem of recruitment: fighting is costly and risky so combatantshave the incentive to defect from their task. They can also desert alto-gether and join the enemy. This incentive is stronger the farther away thefighter is from the principal, since monitoring becomes increasingly costly.Bigger armies have more power but less monitoring capacity to preventdefection and desertion. This general framework allows a variety of interpretations of what type of proximity matters for building strong cohesivearmies ranging from ethnic distance to geographic dispersion. Di¤erentassumptions about the distribution of potential fighters along the relevantdimension of conflict lead to di¤erent equilibria. We characterize these,discuss the implied outcome in terms of who wins the war, and illustratewith historical and contemporaneous case studies.

Vision-Aided Navigation for Autonomous Vehicles Using Tracked Feature Points

This thesis discusses the evaluation, implementation, and testing of several navigation algorithms and feature extraction algorithms using an inertial measurement unit (IMU) and an image capture device (camera) mounted on a ground robot and a quadrotor UAV. The vision-aided navigation algorithms are implemented on data-collected from sensors on an unmanned ground vehicle and a quadrotor, and the results are validated by comparison with GPS data. The thesis investigates sensor fusion techniques for integrating measured IMU data with information extracted from image processing algorithms in order to provide accurate vehicle state estimation. This image-based information takes the forms of features, such as corners, that are tracked over multiple image frames. An extended Kalman filter (EKF) in implemented to fuse vision and IMU data. The main goal of the work is to provide navigation of mobile robots in GPS-denied environments such as indoor environments, cluttered urban environments, or space environments such as asteroids, other planets or the moon. The experimental results show that combining pose information extracted from IMU readings along with pose information extracted from a vision-based algorithm managed to solve the drift problem that comes from using IMU alone and the scale problem that comes from using a monocular vision-based algorithm alone

Development of an efficient Ad Hoc broadcasting scheme for critical networking environments

Mobile ad hoc network has been widely deployed in support of the communications in hostile environment without conventional networking infrastructure, especially in the environments with critical conditions such as emergency rescue activities in burning building or earth quick evacuation. However, most of the existing ad hoc based broadcasting schemes either rely on GPS location or topology information or angle-of-arrival (AoA) calculation or combination of some or all to achieve high reachability. Therefore, these broadcasting schemes cannot be directly used in critical environments such as battlefield, sensor networks and natural disasters due to lack of node location and topology information in such critical environments. This research work first begins by analyzing the broadcast coverage problem and node displacement form ideal locations problem in ad hoc networks using theoretical analysis. Then, this research work proposes an efficient broadcast relaying scheme, called Random Directional Broadcasting Relay (RDBR), which greatly reduces the number of retransmitting nodes and end-to-end delay while achieving high reachability. This is done by selecting a subset of neighboring nodes to relay the packet using directional antennas without relying on node location, network topology and complex angle-of-arrival (AoA) calculations. To further improve the performance of the RDBR scheme in complex environments with high node density, high node mobility and high traffic rate, an improved RDBR scheme is proposed. The improved RDBR scheme utilizes the concept of gaps between neighboring sectors to minimize the overlap between selected relaying nodes in high density environments. The concept of gaps greatly reduces both contention and collision and at the same time achieves high reachability. The performance of the proposed RDBR schemes has been evaluated by comparing them against flooding and Distance-based schemes. Simulation results show that both proposed RDBR schemes achieve high reachability while reducing the number of retransmitting nodes and end-to-end delay especially in high density environments. Furthermore, the improved RDBR scheme achieves better performance than RDBR in high density and high traffic environment in terms of reachability, end-to-end delay and the number of retransmitting nodes

Leveraging FAERS and Big Data Analytics with Machine Learning for Advanced Healthcare Solutions

This research study explores the potential of leveraging the FDA Adverse Event Reporting System (FAERS), combined with big data analytics and machine learning techniques, to enhance healthcare solutions. FAERS serves as a comprehensive database maintained by the U.S. Food and Drug Administration (FDA), encompassing reports of adverse events, medication errors, and product quality issues associated with diverse drugs and therapeutic interventions.By harnessing the power of big data analytics applied to the vast information within FAERS, healthcare professionals and researchers gain valuable insights into drug safety, discover potential adverse reactions, and uncover patterns that may not have been discernible through traditional methods. Particularly, machine learning plays a pivotal role in processing and analyzing this extensive dataset, enabling the extraction of meaningful patterns and prediction of adverse events.The findings of this study demonstrate various ways in which FAERS, big data analytics, and machine learning can be leveraged to provide advanced healthcare solutions. Machine learning algorithms trained on FAERS data can effectively identify early signals of adverse events associated with specific drugs or treatments, allowing for prompt detection and appropriate actions.Big data analytics applied to FAERS data facilitate pharmacovigilance and drug safety monitoring. Machine learning models automatically classify and analyze adverse event reports, efficiently flagging potential safety concerns and identifying emerging trends.The integration of FAERS data with big data analytics and machine learning enables signal detection and causality assessment. This approach aids in the identification of signals that suggest a causal relationship between drugs and adverse events, thereby enhancing the assessment of drug safety.By analyzing FAERS data in conjunction with patient-specific information, machine learning models can assist in identifying patient subgroups that are more susceptible to adverse events. This information is instrumental in personalizing treatment plans and optimizing medication choices, ultimately leading to improved patient outcomes.The combination of FAERS data with other biomedical information offers insights into potential new uses or indications for existing drugs. Machine learning algorithms analyze the integrated data, identifying patterns and making predictions about the efficacy and safety of repurposing existing drugs for new applications.The implementation of FAERS, big data analytics, and machine learning in advanced healthcare solutions necessitates meticulous consideration of data privacy, security, and ethical implications. Safeguarding patient privacy and ensuring responsible data use through anonymization techniques and appropriate data governance are paramount.The integration of FAERS, big data analytics, and machine learning holds immense potential in advancing healthcare solutions, enhancing patient safety, and optimizing medical interventions. The findings of this study demonstrate the multifaceted benefits that can be derived from leveraging these technologies, paving the way for a more efficient and effective healthcare ecosystem

Optimization of Vehicle-to-Grid Scheduling in Constrained Parking Lots

An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on/off states of power generating units easily. Vehicles are presented by signed integer number instead of 0/1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging/discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots

Economic Load Dispatch Using Bacterial Foraging Technique with Particle Swarm Optimization Biased Evolution

This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods

Quantized enveloping superalgebra of type PP

We introduce a new quantized enveloping superalgebra $\mathfrak{U}_q{\mathfrak{p}}_n$ attached to the Lie superalgebra ${\mathfrak{p}}_n$ of type $P$. The superalgebra $\mathfrak{U}_q{\mathfrak{p}}_n$ is a quantization of a Lie bisuperalgebra structure on ${\mathfrak{p}}_n$ and we study some of its basic properties. We also introduce the periplectic $q$-Brauer algebra and prove that it is the centralizer of the $\mathfrak{U}_q {\mathfrak{p}}_n$-module structure on ${\mathbb C}(n|n)^{\otimes l}$. We end by proposing a definition for a new periplectic $q$-Schur superalgebra.Comment: 14 page

Development of Regional Human Rights Regime in Asia: Defining the Challenges

This study analyses the progress made toward establishing a cohesive regional human rights framework in Asia while identifying key obstacles that hinder its evolution. It also explores the intricate nature of constructing a unified structure for human rights in the region through a comprehensive examination of historical, cultural, political, and economic elements. The examination of current mechanisms such as the ASEAN Intergovernmental Commission on Human Rights (AICHR) and the ASEAN (Association of Southeast Asian Nations) Asian Human Rights Declaration highlights both the constraints and potential avenues for advancement. The study posits that a collaborative approach, involving regional organizations, civil society, and international collaboration, is crucial despite historical legacies, cultural differences, and economic paradoxes. The significance of addressing these challenges is highlighted by the potential advantages of establishing a unified human rights regime, which encompasses regional stability and the safeguarding of individual rights. The imperative of promoting a transformative human rights framework in Asia necessitates the maintenance of an inclusive dialogue among stakeholders moving forward

Spectrophotometric and theoretical studies on the determination of etilefrine hydrochloride in pharmaceutical formulations and biological samples

Purpose: To develop a simple and cost effective spectrophotometric method for the determination of etilefrine hydrochloride (ET) in pharmaceutical formulations and human plasma.Methods: The method is based on extraction of ET into chloroform as ion-pair  complexes with bromocresol green (BCG) and methyl orange (MO) in acidic medium. The interaction of ET with BCG and MO reagents were investigated using  B3LYP/6-31G(d) level of theory. The geometrical parameters of the interacting species and the ion pairs formed were characterized based on their frontier  molecular orbitals, atomic charges, electrostatic potential map, as well as NBO analysis.Results: The colored species exhibited absorption maxima at 410 and 479 nm for the two systems in universal buffer of pH range (3.0 - 3.5), with molar absorptivity of 2.4 × 104 and 1.7 × 104 Lmol-1cm-1, for BCG and MO methods, respectively. The methods demonstrated good linearity with correlation coefficient ranging from  0.9987 – 0.9991 in the concentration ranges 0.5 – 16 and 2.0 – 18 μgmL-1 for BCG and MO methods, respectively. The composition ratio of the ion-association complexes was 1:1 in all cases as established by Job’s method. Sandell,s  sensitivity, correlation coefficient, detection and quantification limits were also calculated. Molecular descriptors were obtained based on optimized structures of the molecules under investigation, by applying the B3LYP/6-31G(d) method, and used to interpret the mode of interaction between these molecules to form the investigated ion pairs.Conclusion: The proposed methods make use of simple reagents, which a basic  analytical laboratory can afford. No interference was observed from common  pharmaceutical excipients and additives. ETMO ion pair has a larger interaction energy (higher stability) than ET-BCG ion pair as inferred from their interaction energies.Keywords: Density functional theory, Etilefrine hydrochloride, Ion pair complex, Spectrophotometry, Bromocresol green, Methyl orange, Geometric analysi

Therapeutic challenges of COVID-19: strategies of empirical treatment

Coronavirus pandemic, is a progressing worldwide pandemic of coronavirus disease 2019 (COVID-19), brought about by sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The episode was first distinguished in Wuhan, China, in December 2019. The World Health Organization announced a Public Health Emergency of International Concern on 30 January 2020, and a pandemic on 11 March. Scientists around the world are working to establish an effective treatment against SARS-CoV-2 to control the spread of this pandemic. In this review, we summarized the potential therapeutic strategies for treatment of COVID-19 and dividing the treatments to several categories including antiviral drugs which act on decreasing the viral load inside the body of patients, immunotherapy and immunomodulatory which relive the inflammatory process of viral infection. DOI: http://dx.doi.org/10.5281/zenodo.434235