Marketing of Tourism Destination in the Context of Tiger Safari

Tiger tourism plays a significant role in the overall scenario of Indian tourism. The forest destination managers face a major challenge in satisfying their visitors since tigers are elusive by nature and most of the time tourists return dissatisfied without sighting a tiger after a forest safari. This paper is the first scientific study of its kind based on empirical data in the context of tiger tourism and proposed a model to identify the optimum path in the forest with a higher probability of tiger sighting

Integrating Passive UHF RFID Tags with WSN Nodes: Challenges and Opportunities

Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) have received an ever-increasing attention in recent years, mainly because they represent two of the most important technologies enabling the Internet of Things vision. Although designed originally with different objectives, WSN and RFID represent two complementary technologies whose integration might increase their functionalities and extend their range of applications. However, important technological issues must still be solved in order to fully exploit the potentialities offered by such integration. In this work, an innovative RFID-WSN integration approach is presented and validated. It relies on the interconnection of a new-generation, long-range, EPCglobal Class-1 Generation-2 Ultra-High-Frequency (UHF) RFID tag with a commercial WSN node via the I2C interface. Experimental results have demonstrated the effectiveness of the proposed approach compared to existing solution in the literature. Interesting application scenarios enabled by the proposed RFID-WSN integration approach are briefly summarized at the end of the paper

Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

Running Agent-based-models Simulations Synchronized with Reality to Control Transport Systems

Adaptable, flexible and evolvable manufacturing systems and warehouses are so complex to manage that control systems have to be divided into several aspects. One of these is internal transportation, which has to do with all tasks involved in fulfilling a set of so-called transportation orders, i.e. commands to collect and deliver material from origin to destination spots. A common approach to design the controllers for these applications begins by modeling them as multi-agent systems and continues to final deployment through a cascade of transformations. To minimize development costs of internal transportation controllers, we have proposed a model of construction that includes components that synchronize the events from reality simulation and the ones from actual reality. By using these \textit{synchronizers}, further development is required only for those parts of the initial multi-agent controller models with real counterparts. In this paper, we review the model and the architecture of the proposed internal transportation system controllers and we illustrate the whole design process through the development of a controller for an automated laboratory. Indirectly, we prove the validity of the architecture and of its key component, the synchronizer

Sustainable scheduling policies for radio access networks based on LTE technology

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyIn the LTE access networks, the Radio Resource Management (RRM) is one of the most important modules which is responsible for handling the overall management of radio resources. The packet scheduler is a particular sub-module which assigns the existing radio resources to each user in order to deliver the requested services in the most efficient manner. Data packets are scheduled dynamically at every Transmission Time Interval (TTI), a time window used to take the user’s requests and to respond them accordingly. The scheduling procedure is conducted by using scheduling rules which select different users to be scheduled at each TTI based on some priority metrics. Various scheduling rules exist and they behave differently by balancing the scheduler performance in the direction imposed by one of the following objectives: increasing the system throughput, maintaining the user fairness, respecting the Guaranteed Bit Rate (GBR), Head of Line (HoL) packet delay, packet loss rate and queue stability requirements. Most of the static scheduling rules follow the sequential multi-objective optimization in the sense that when the first targeted objective is satisfied, then other objectives can be prioritized. When the targeted scheduling objective(s) can be satisfied at each TTI, the LTE scheduler is considered to be optimal or feasible. So, the scheduling performance depends on the exploited rule being focused on particular objectives. This study aims to increase the percentage of feasible TTIs for a given downlink transmission by applying a mixture of scheduling rules instead of using one discipline adopted across the entire scheduling session. Two types of optimization problems are proposed in this sense: Dynamic Scheduling Rule based Sequential Multi-Objective Optimization (DSR-SMOO) when the applied scheduling rules address the same objective and Dynamic Scheduling Rule based Concurrent Multi-Objective Optimization (DSR-CMOO) if the pool of rules addresses different scheduling objectives. The best way of solving such complex optimization problems is to adapt and to refine scheduling policies which are able to call different rules at each TTI based on the best matching scheduler conditions (states). The idea is to develop a set of non-linear functions which maps the scheduler state at each TTI in optimal distribution probabilities of selecting the best scheduling rule. Due to the multi-dimensional and continuous characteristics of the scheduler state space, the scheduling functions should be approximated. Moreover, the function approximations are learned through the interaction with the RRM environment. The Reinforcement Learning (RL) algorithms are used in this sense in order to evaluate and to refine the scheduling policies for the considered DSR-SMOO/CMOO optimization problems. The neural networks are used to train the non-linear mapping functions based on the interaction among the intelligent controller, the LTE packet scheduler and the RRM environment. In order to enhance the convergence in the feasible state and to reduce the scheduler state space dimension, meta-heuristic approaches are used for the channel statement aggregation. Simulation results show that the proposed aggregation scheme is able to outperform other heuristic methods. When the aggregation scheme of the channel statements is exploited, the proposed DSR-SMOO/CMOO problems focusing on different objectives which are solved by using various RL approaches are able to: increase the mean percentage of feasible TTIs, minimize the number of TTIs when the RL approaches punish the actions taken TTI-by-TTI, and minimize the variation of the performance indicators when different simulations are launched in parallel. This way, the obtained scheduling policies being focused on the multi-objective criteria are sustainable. Keywords: LTE, packet scheduling, scheduling rules, multi-objective optimization, reinforcement learning, channel, aggregation, scheduling policies, sustainable

Methods and Formal Models for Healthcare Systems Management

A healthcare system is an organization of people, institutions, and resources that deliver healthcare services to meet the health needs of target populations. The size of the systems, the huge number of agents involved and their different expectations make the management of healthcare systems a tough task which could be alleviated through the use of technology. In this thesis, new methods and formal models for healthcare system management are presented. Particularly, the thesis is divided in two main parts: the first one has to do with the modeling and analysis in hospitals by the use of clinical pathways while the second one deals with the planning and scheduling of patients in the operation rooms.Regarding the modeling and analysis of healthcare systems, depending on different visions and expectations, the system can be treated from different perspectives called facets. In chapter 2, the formal definition and characterization of two facets are given: (1) facet of resource management and (2) handshake between clinical pathways facet. They are obtained by applying to Stochastic Well-formed Nets (colored Petri Nets) modeling the healthcare system a set of relaxations, abstraction and modifications. In the first facet the subclass of S4PR is obtained which is a characteristic model of the resource allocation systems while in the second facet Deterministically Synchronized Sequential Process (DSSP) are considered. Both nets (S4PR and DSSP) are formal subclasses of Petri Nets where net level techniques can be applied.In chapters 3 and 4, we will focus on the liveness of the DSSP systems resulting from the facet of communication between clinical pathways. These kinds of nets are composed by agents (modeling clinical pathways) cooperating in a distributed way by the asynchronous messaging passing through the buffers (modeling the communication channels). In particular two approaches have been proposed.The idea behind the first approach is to advance the buffer consumption to the first conflict transition in the agents. Considering healthcare systems modeled by a DSSP, this means that before a patient starts a clinical pathway, all required information must be available. Unfortunately, this pre-assignment method only works in some particular DSSP structures which are characterized. A more general approach (than buffer pre-assignment) for liveness enforcing in non-live DSSP is given in Chapter. 4. The approach is formalized on two levels: execution and control. The execution level uses the original DSSP structure while for the control level we compute a new net system called the control PN. This net system is obtained from the original DSSP and has a predefined type of structure. The control PN will evolve synchronously with the non-live DSSP ensuring that the deadlock states will not be reached. The states (marking) of the control PN will enable or disable some transitions in the original DSSP, while some transitions in the control PN should fire synchronously with some transitions of the original DSSP.The second part of the thesis deals with surgery scheduling of patients in a hospital department. The Operating Rooms (ORs) are one of the most expensive material resources in hospitals, being the bottleneck of surgical services. Moreover, the aging population together with the improvement in surgical techniques are producing an increase in the demand for surgeries. So, the optimal use of the ORs time is crucial inhealthcare service management. We focus on the planning and scheduling of patients in Spanish hospital departments considering its organizational structure particularities as well as the concerns and specifications of their doctors.In chapter 5, the scheduling of elective patients under ORs block booking is considered. The first criterion is to optimize the use of the OR, the second criterion is to prevent that the total available time in a block will be exceeded and the third criterion is to respect the preference order of the patient in the waiting list. Three different mathematical programming models for the scheduling of elective patients are proposed. These are combinatorial problems with high computational complexity, so three different heuristic solution methods are proposed and compared. The results show that a Mixed Integer Linear Programming (MILP) problem solved by Receding Horizon Strategy (RHS)obtains better scheduling in lowest time.Doctors using the MILP problem must fix an appropriate occupation rate for optimizing the use of the ORs but without exceeding the available time. This has two main problems: i) inexperienced doctors could find difficult to fix an appropriate occupation rate, and ii) the uncertain in the surgery durations (large standard deviation) could results in scheduling with an over/under utilization. In order to overcome these problems, a New Mixed-Integer Quadratic Constrained Programming (N-MIQCP) model is proposed. Considering some probabilistic concepts, quadratic constraints are included in N-MIQCP model to prevent the scheduling of blocks with a high risk of exceeding the available time. Two heuristic methods for solving the N-MIQCP problem are proposed and compared with other chance-constrained approaches in bibliography. The results conclude that the best schedulings are achieved using our Specific Heuristic Algorithm (SHA) due to similar occupation rates than using other approaches are obtained but our SHA respects much more the order of the patients in the waiting list.In chapter 6, a three steps approach is proposed for the combined scheduling of elective and urgent patients. In the first step, the elective patients are scheduled for a target Elective Surgery Time (EST) in the ORs, trying to respect the order of the patients on the waiting list. In the second one, the urgent patients are scheduled in the remaining time ensuring that an urgent patient does not wait more than 48 hours. Finally, in the third step, the surgeries assigned to each OR (elective and urgent) are sequenced in such a way that the maximum time that an emergency patient should wait is minimized. Considering realistic data, different policies of time reserved in the ORs for elective and urgent patients are evaluated. The results show that all ORs must be used to perform elective and urgent surgeries instead of reserving some ORs exclusively for one type of patient.Finally, in chapter 7 a software solution for surgery service management is given. A Decision Support System for elective surgery scheduling and a software tool called CIPLAN are proposed. The DSS use as core the SHA for the scheduling of elective patients, but it has other features related to the management of a surgery department. A software tool called CIPLAN which is based on the DSS is explained. The software tool has a friendly interface which has been developed in collaboration with doctors in the “Lozano Blesa” Hospital in Zaragoza. A real case study comparing the scheduling using the manual method with the scheduling obtained by using CIPLAN is discussed. The results show that 128.000 euros per year could be saved using CIPLAN in the mentioned hospital. Moreover, the use of the tool allows doctors to reduce the time spent in scheduling to use it medical tasks.<br /

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin