Make me an Offer: Forward and Reverse Auctioning Problems in the Tourism Industry

Most tourist destinations are facing regular and consistent seasonality with significant economic and social impacts. This phenomenon is more pronounced in the post-covid era, where demand for travel has increased but unevenly among different geographic areas. To counter these problems that both customers and hoteliers are facing, we have developed two auctioning systems that allow hoteliers of lower popularity tier areas or during low season periods to auction their rooms in what we call a forward auction model, and also allows customers to initiate a bidding process whereby hoteliers in an area may make offers to the customer for their rooms, in what constitutes a reverse auction model initiated by the customer, similar to the bidding concept of priceline.com. We develop mathematical programming models that define explicitly both types of auctions, and show that in each type, there are significant benefits to be gained both on the side of the hotelier as well as on the side of the customer. We discuss algorithmic techniques for the approximate solution of these optimization problems, and present results using exact optimization solvers to solve them to guaranteed optimality. These techniques could be beneficial to both customer and hotelier reducing seasonality during middle and low season and providing the customer with attractive offers.Comment: 21 pages, 7 figure

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics

Timely and accurate detection of anomalies in power electronics is becoming increasingly critical for maintaining complex production systems. Robust and explainable strategies help decrease system downtime and preempt or mitigate infrastructure cyberattacks. This work begins by explaining the types of uncertainty present in current datasets and machine learning algorithm outputs. Three techniques for combating these uncertainties are then introduced and analyzed. We further present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer, which are applied in the context of a power electronic converter dataset. Specifically, the Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. The STUMPY python library implementation of the iterative Matrix Profile is used for the creation of the detector. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy. Our numerical results show that, with simple parameter tuning, the detector provides high accuracy and performance in a variety of fault scenarios

Plasticity in dendroclimatic response across the distribution range of Aleppo pine (Pinus halepensis)

We investigated the variability of the climate-growth relationship of Aleppo pine across its distribution range in the Mediterranean Basin. We constructed a network of tree-ring index chronologies from 63 sites across the region. Correlation function analysis identified the relationships of tree-ring index to climate factors for each site. We also estimated the dominant climatic gradients of the region using principal component analysis of monthly, seasonal, and annual mean temperature and total precipitation from 1,068 climatic gridpoints. Variation in ring width index was primarily related to precipitation and secondarily to temperature. However, we found that the dendroclimatic relationship depended on the position of the site along the climatic gradient. In the southern part of the distribution range, where temperature was generally higher and precipitation lower than the regional average, reduced growth was also associated with warm and dry conditions. In the northern part, where the average temperature was lower and the precipitation more abundant than the regional average, reduced growth was associated with cool conditions. Thus, our study highlights the substantial plasticity of Aleppo pine in response to different climatic conditions. These results do not resolve the source of response variability as being due to either genetic variation in provenance, to phenotypic plasticity, or a combination of factors. However, as current growth responses to inter-annual climate variability vary spatially across existing climate gradients, future climate-growth relationships will also likely be determined by differential adaptation and/or acclimation responses to spatial climatic variation. The contribution of local adaptation and/or phenotypic plasticity across populations to the persistence of species under global warming could be decisive for prediction of climate change impacts across populations. In this sense, a more complex forest dynamics modeling approach that includes the contribution of genetic variation and phenotypic plasticity can improve the reliability of the ecological inferences derived from the climate-growth relationships.This work was partially supported by Spanish Ministry of Education and Science co-funded by FEDER program (CGL2012-31668), the European Union and the National Ministry of Education and Religion of Greece (EPEAEK- Environment – Archimedes), the Slovenian Research Agency (program P4-0015), and the USDA Forest Service. The cooperation among international partners was supported by the COST Action FP1106, STREeSS

A MSFD complementary approach for the assessment of pressures, knowledge and data gaps in Southern European Seas : the PERSEUS experience

PERSEUS project aims to identify the most relevant pressures exerted on the ecosystems of the Southern European Seas (SES), highlighting knowledge and data gaps that endanger the achievement of SES Good Environmental Status (GES) as mandated by the Marine Strategy Framework Directive (MSFD). A complementary approach has been adopted, by a meta-analysis of existing literature on pressure/impact/knowledge gaps summarized in tables related to the MSFD descriptors, discriminating open waters from coastal areas. A comparative assessment of the Initial Assessments (IAs) for five SES countries has been also independently performed. The comparison between meta-analysis results and IAs shows similarities for coastal areas only. Major knowledge gaps have been detected for the biodiversity, marine food web, marine litter and underwater noise descriptors. The meta-analysis also allowed the identification of additional research themes targeting research topics that are requested to the achievement of GES. 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license.peer-reviewe

Grid-Based Interactive Virtual Scientific Experiments for Distributed Virtual Communities

E-learning technologies have matured to a point where distance learning classes are commonly offered from many leading Universities around the world. A major challenge in such distributed classrooms is the formation of virtual communities among the participating students, enhancing the overall learning experience. Shared virtual laboratories offer the possibility of forming such virtual communities as students form lab teams to run the same interactive simulation and in the course of such experiments learn to interact and understand each other better. We have designed and implemented a Virtual Scientific Experiment architectural framework on top of a Grid infrastructure for running interactive virtual laboratory experiments for such distributed student communities with visualization capabilities. The architecture is based on Web Services standard protocols such as WSDL and WS-Notification as implemented in the WSRF specification. For the first concrete instantiation of this architecture, we ported a stand-alone Wireless Sensor Network simulator written in Java in our Grid-based architecture and extended it to allow for initial collaborative parameter setup and on-the-fly visualization of the simulation execution and interaction with it, a capability not present in the original simulator. We report on results from running such simulations on a local Grid infrastructure. System evaluation results from a distributed pool of students show the added value of our system in enhancing distance-learning programs and Virtual Classes with extensible collaborative and interactive Virtual Laboratories sessions

Grid-based virtual laboratory experiments for a graduate course on sensor networks

This paper presents the pedagogical and technical challenges the authors faced in developing a distributed laboratory for the execution of virtual scientific experiments (VSEs) superimposed on a Grid infrastructure, for a course on sensor networks that is part of the Master's in Information Networking (MSIN) program jointly offered by Carnegie Mellon University (CMU), USA and Athens Information Technology (AIT), Athens, Greece. The MSIN program utilizes virtual classroom technologies because of its strong distance learning component. Courses taught by CMU faculty are attended in real-time by students in Athens, Greece, via video-wall teleconferencing sessions. Vice versa, visiting CMU faculty to AIT teach classes that are attended by students at CMU. Students in both institutions enjoy full interactivity with their classmates on the other side of the Atlantic Ocean. A distributed shared virtual laboratory is needed for many of the more empirical courses. This paper describes the challenges and issues the authors faced in developing such a lab

Optimal Equi-Partition of Rectangular Domains for Parallel Computation

We present an efficient method for the partitioning of rectangular domains into equi-area sub domains of minimum total perimeter. For a variety of applications in parallel computation, this corresponds to a load-balanced distribution of tasks that minimize interprocessor communication. Our method is based on utilizing, to the maximum extent possible, a set of optimal shapes for sub domains. We prove that for a large class of these problems, we can construct solutions whose relative distance from a computable lower bound converges to zero as the problem size tends to infinity. PERIX GA, a genetic algorithm employing this approach, has successfully solved to optimality million variable instances of the perimeter minimization problem and for a one billion variable problem has generated a solution within 0.32% of the lower bound. We report on the results of an implementation on a CM5 supercomputer and make comparisons with other existing codes

Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition

We present an efficient method for assigning any number of processors to tasks associated with the cells of a rectangular uniform grid. Load balancing equi-partition constraints are observed while approximately minimizing the total perimeter of the partition, which corresponds to the amount of interprocessor communication. This method is based upon decomposition of the problem size grows large in all parameters, he error bound associated with this feasible solution approaches zero. We also present computational results from a high level parallel Genetic Algorithm that utilizes this method, and make comparisons with other methods. On a network of workstations, our algorithm solves within minutes instances of the problem that would require one billion binary variables in a Quadratic Assignment formulation