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Leveraging multiple data sources to enhance tourism resource management and visitor behavior analysis has become a key challenge in the context of the booming smart tourism industry. In this study, we explore how to integrate and optimize multiple data sources including social media activities, user reviews, tourism statistics, and geographic information to build a comprehensive information management platform for smart tourism resources. Given the limitations inherent in isolated and decentralized data processing approaches in the smart tourism domain, we propose a new approach using deep learning autoencoders for efficient extraction and fusion of meaningful features from heterogeneous datasets. Our methodology encompasses a rigorous data collection and preprocessing phase, ensuring data quality and consistency, followed by the application of autoencoders to learn high-level feature representations conducive to data integration. The fused data facilitate the development of strategies for the optimal allocation of tourism resources and nuanced analysis of visitor behavior patterns. Experimental evaluations demonstrate the model’s proficiency in capturing intricate data relationships, significantly enhancing the predictive accuracy for tourism demand forecasting, and enabling personalized visitor recommendations. The results underscore the potential of our approach to revolutionize smart tourism management practices by providing actionable insights into resource optimization and visitor engagement strategies, thereby contributing to the sustainable growth of the tourism sector.

Gang scheduling has been widely used as a practical solution to the dynamic parallel job scheduling problem. To overcome some of the limitations of traditional Gang scheduling algorithms, Concurrent Gang is proposed as a class of scheduling policies which allows the flexible and simultaneous scheduling of multiple parallel jobs. It hence improves the space sharing characteristics of Gang scheduling while preserving all other advantages. To provide a sound analysis of Concurrent Gang performance, a novel methodology based on the traditional concept of competitive ratio is also introduced. Dubbed dynamic competitive ratio, the new method is used to compare dynamic bin packing algorithms used in this paper. These packing algorithms apply to the Concurrent Gang scheduling of a workload generated by a statistical model. Moreover, dynamic competitive ratio is the figure of merit used to evaluate and compare packing strategies for job scheduling under multiple constraints. It will be shown that for the unidimensional case there is a small difference between the performance of best fit and first fit; first fit can hence be used without significant system degradation. For the multidimensional case, when memory is also considered, we concluded that the packing algorithm must try to balance the resource utilization in all dimensions simulataneously, instead of given priority to only one dimension of the problem.

This paper presents a new approach to resource management and scheduling in computational grids, in order to simplify the vision users have from grid resources and their management. Scheduling decisions are moved to the site where resources are hosted, allowing quick response to changes in load and resource availability. Users do not need to be aware of the resources they use and are instead supplied with "virtual resources" representing the amount of computational power available to them in the site. This approach results in new challenges, like the management of two-level scheduling schema and the need to define a site capacity measure, but simplifies and optimizes scheduling in grids. In this paper we present details of this approach – called Site Resource Scheduler (SRS) – as well as some issues regarding its simulated performance and its deployment in a site. We show that the main advantage of this approach is an overall reduction in the execution time of tasks in most scenarios.

Cloud/ fog computing resource pricing is a new paradigm in the blockchain mining scheme, as the participants would like to purchase the cloud/ fog computing resource to speed up their mining processes. In this paper, we propose a novel two-stage game to study the optimal price-based cloud/ fog computing resource management, in which the cloud/fog computing resource provider (CFP) is the leader, setting the resource price in Stage I, and the mining pools act as the followers to decide their demands of the resource in Stage II. Since mining pools are bounded rational in practice, we model the dynamic interactions among them by an evolutionary game in Stage II, in which each pool pursues its evolutionary stable demand based on the observed price, through continuous learning and adjustments. Backward induction method is applied to analyze the sub-game equilibrium in each stage. Specifically in Stage II, we first build a general study framework for the evolutionary game model, and then provide a detailed theoretical analysis for a two-pool case to characterize the conditions for the existence of different evolutionary stable solutions. Referring to the real world, we conduct a series of numerical experiments, whose results validate our theoretical findings for the case of two mining pools. Additionally, the impacts from the size of mining block, the unit transaction fee and the price of token on the decision makings of participants are also discussed.

This paper has a research on resource management and scheduling policy based on grid technology for Agricultural Internet of Things (AIoT). Facing the situation of a variety of complex and heterogeneous agricultural resources in AIoT, it is difficult to represent them in a unified way. But from an abstract perspective, there are some common models which can express their characteristics and features. Based on this, we proposed a high-level model called Agricultural Resource Hierarchy Model (ARHM), which can be used for modeling various resources. It introduces the agricultural resource modeling method based on this model. Compared with traditional application-oriented three-layer model, ARHM can hide the differences of different applications and make all applications have a unified interface layer and be implemented without distinction. Furthermore, it proposes a Web Service Resource Framework (WSRF)-based resource management method and the encapsulation structure for it. Finally, it focuses on the discussion of multi-agent-based AG resource scheduler, which is a collaborative service provider pattern in multiple agricultural production domains.

As the demand for mobile services has increased, the need for an efficient allocation of channels is essential to ensure good performance, given the limited spectrum available. Techniques for increasing flexibility in radio resource acquisition are needed to handle the heterogeneity of services and bit rates to be supported in the forthcoming generations of mobile communications. To improve the performance and efficiency of the channel allocation, we propose the use of a particular agent architecture that allows base stations to be more flexible and intelligent, including planning to attempt to balance the load in advance of reactive requests. The simulation results prove that the use of intelligent agents controlling the allocation of channels is feasible and the agent negotiation is an important feature of the system in order to improve perceived quality of service and to improve the load balancing of the traffic.

Delivering the right amount of information to the right person is vital on the tactical battlefield. With the increasing use of mobile devices by the military, delivering relevant information instantaneously to the warfighter becomes possible. However, large quantities of data are being generated constantly while the human processing power and communication channels are limited. Therefore, data must be processed so it can be evaluated against operational needs. This data is collected in multiple modalities include images, videos and field reports with multi-sensor data. Providing automated processing of unstructured information promises to effectively connect information processing with operational decision making, dramatically reducing the time needed to identify relevant information for mission planning and execution. We describe a multi-view learning technique that augments the feature set used by a classifier in one modality with entity relationships discovered in other modalities. To accommodate the limited computation power of field devices, mostly handhelds, the multi-view learning algorithm is low complexity. It applies to multiple modalities, leveraging many-to-many correspondences among different modalities. Experiments on image and text are presented in the paper which show more than 20% improvement over categorizing text or images independently. The categorized information is matched to the mission and task needs. Finally, relevant information needs to be transmitted via limited bandwidth negotiated from limited resources.

Optimizing area and timing have long been considered to be the main design challenges in high-level synthesis. A lot of research has been conducted in this area and many techniques to improve performance have been suggested. However, as design applications become more power-sensitive, and with the emergence of portable devices that operate under stringent power constraints, power consumption surfaced as a major issue to be considered in the design and optimization processes. This work studies the effects of binding and scheduling on power consumption in high-level synthesis by analyzing unnecessary switching. The major contribution of this work is to reduce the unnecessary switching at the inputs of a circuit's functional units, referred to as spurious switching activities. For this purpose, all spurious and nonspurious switching inputs in a circuit were identified, and many techniques were studied to find the optimal register bindings without inducing any increase in the number of storage elements. Power reduction was attained through altering register bindings using a cool-down simulated annealing approach. To test these techniques, a high-level synthesis environment, "Eridanus", was developed and several benchmarks, consisting of various complexities, have been tested. Using the approach suggested in this work, spurious switching activity was reduced by 40% on average.

One of the major enhancements that can be made to the high-level synthesis (HLS) process is reducing the overall area of a design in order to either decrease the manufacturing costs or to introduce more functionality to the circuit. Optimizing the area of the datapath is considered a primary field of research in HLS. This work proposes an approach to reduce the area in field programmable gate array (FPGA) by simultaneously tackling the three central tasks of HLS. Scheduling, allocation, and binding are performed and the optimal solution based on area reduction is obtained by using simulated annealing with a priority function. The aim of the priority function is to guide the simulated annealing process into finding the best solution while at the same time incurring the least possible execution time. In order to achieve better results than the initial solution, rescheduling, swapping operations between functional units, swapping variables between registers, and swapping inputs to functional units are considered in the annealing process. A cost function is devised to evaluate a potential move's success or failure. The simulation environment "Eridanus" has been developed in order to support implementation and testing. Several benchmarks were tested and the numerical results consisting of the execution time along with the best solution were recorded to illustrate the performance of the proposed technique. Area reduction was obtained compared to the conventional HLS flow; furthermore, an average substantial reduction in design space exploration time was obtained compared to non-priority based area optimization techniques.

In this paper, we apply market mechanism and agent to build grid resource management, where grid resource consumers and providers can buy and sell computing resource based on an underlying economic architecture. All market participants in the grid environment including computing resources and services can be represented as agents. Market participant is registered with a Grid Market Manager. A grid market participant can be a service agent that provides the actual grid service to the other market participants. Grid market participants communicate with each other by communication space that is an implementation of tuple space. In this paper, Grid agent model description is given. Then, the structure of Grid Market is described in detail. The design and implementation of agent oriented and market oriented grid resource management are presented in this paper.

System resource management for high-assurance applications such as the command and control of a battle group is a complex problem. These applications often require guaranteed computing services that must satisfy both hard and soft deadlines. Over time, their resource demands can also vary significantly with bursts of high activity amidst periods of inactivity. A traditional solution has been to dedicate resources to critical application tasks and to share resources among non-critical tasks. With the increasing complexity of high-assurance applications and the need to reduce system costs, dedicating resources is not a satisfactory solution. The Amaranth Project at Carnegie Mellon is researching and developing a framework for allocating shared resources to support multiple quality of service (QoS) dimensions and to provide probabilistic assurances of service. This paper is an overview of the Amaranth framework for policy-based QoS management, the current results from applying the framework, and the future research directions for the Amaranth project.

The paper deals with the analysis of the resources and energy consumption at different levels of economic management. The most effective management tools are characterized. The quantitative and qualitative analysis of the use of different types of resources by agri-food enterprises is carried out. Using the results of the analysis, a model that takes into account the features of general and specific for agri-food enterprises’ types of resources is proposed. The practical significance of this model lies in accumulating the most effective resource-saving and energy-saving technologies of renewable and non-renewable resources of agro-sphere (according to experience in application by different countries).

Ecosystem management requires understanding society's goals for an ecosystem and managing for some optimal solution. Unlike terrestrial ecosystem managers, coastal and marine ecosystem management seldom integrates across sectors or scientific disciplines to achieve desired social benefits. An Integrated Ecosystem Assessment (IEA) considers the ecosystem (including humans) as a unit and can assist in setting goals, determining an ecosystem's ability to support ecological processes and society's desires, and predicting the outcome of alternatives. The use of Coupled Ecological-Societal Systems Models utilised within the Integrated Assessment and Ecosystem Management Protocol (IAEMP) allows managers to extend a graphical picture of risk hypotheses to forecast scenarios that can be analysed relative to management goals. Scenarios predicted to meet management goals are evaluated against management constraints to select the "optimal" option for a management action in an adaptive management process. The IAEMP thus helps characterise potential causes of environmental problems, select appropriate response options, and implement and evaluate the selected option, thereby either addressing the concern or improving the ecosystem model for future decisions.

One may expect the Internet to evolve from being information centric to knowledge centric. This paper introduces the concept of a Knowledge Society Operating System (KSOS) that allows users to form knowledge societies in which members can search, create, manipulate and connect geographically distributed knowledge resources (including data, documents, tools, people, devices, etc.) based on semantics (“meaning”, “intention”) in order to solve problems of mutual interest. Built on top of the current Internet infrastructure, a KSOS can take advantage of existing resources to enable the use of applications or services through a web browser. This paper discusses some crucial aspects of a KSOS.

The Colorado River is a river system spanning seven states in the United States (US) and two in Mexico. Water in the river has been over-allocated, which has led the Colorado River Delta in Mexico to dry up, thus endangering the indigenous species. The two nations made several temporary, costly allocation agreements to transfer water to the Delta for ecological restoration. However, there is still no long-term economic solution for the Delta, which is what this study aims to address. In this work, I investigate solutions for rerouting water to the Delta that will minimize costs without causing excessive damage to the agrarian economy in the US. The cost of conserving water for the Delta was analyzed using numerical simulations with crop data from the Imperial Irrigation District in California. The objective is to find a policy that would help allocate 100,000 acre-feet per year to the Colorado River Delta at a minimum lifetime cost. Two scenarios are studied that would yield enough water for a sustainable restoration of the ecosystem: fallowing croplands and changing the irrigation system to be more water-efficient. Results indicate that fallowing 20,000 ac of alfalfa would be the least costly way of accumulating this resource at a cost ranging from $5.5 million to $13 million per year for a 31-year time horizon. This paper provides new insight into ways in which the US and Mexico can secure the future of ecosystems like the Colorado River Delta.

This chapter describes the role that cordyceps sinensis resources¹ plays in the socioeconomic development in Tibetan-inhabited areas of Qinghai Province, emphasizing the importance of protecting the eco-environment as the basis for developing the resources. Reform of the pasture contract system must be intensified in order to grant Tibetan herders resource ownership in their contract pastures as an incentive for them to protect cordyceps sinensis and the eco-environment.

In today’s business world, many companies and government agencies depend on the infrastructure of cloud services to host and process their information. Load processing of many cloud services is distributed in a dynamic manner that allows service providers to share cloud resources between different customers and this approach demands an efficient resource allocation to achieve an efficient resource allocation. In this paper, we introduce a predictive approach that uses data from an incoming network connection to predicting incoming load of a cloud. The predicted data will be used in a cloud simulator called CloudSim to simulate a cloud load in infrastructure and to achieve intelligent load balancing, Pairwise comparison approach will be used to find an optimal amount of resources for an incoming workload. This paper is an exploratory study on the predictive approach for the dynamic resource distribution of cloud services.

As the Sustainable Development Goals (SDGs) are a call to action by countries to promote prosperity while protecting the planet, the 17 goals aim to monitor 169 targets that collectively describe the progress towards achieving a sustainable future. This was necessitated by global challenges in resources degradation and depletion due to population growth, urbanization, migration, and improved living standards, demographic shifts, changing lifestyles, a burgeoning middle class, and the growing influence of climate change on the demand and supply chains of mainly water, energy, and food. As the SDGs were formulated in the context of challenges related to resource insecurity, climate change, and human wellbeing, and are designed to recognize the inter-linkages between human wellbeing, economic prosperity, and a healthy environment, this chapter establishes the linkages between resources. It provides pathways to assess progress towards sustainability. The principal question addressed in the chapter is whether the water–energy–food (WEF) nexus is an appropriate approach for linking and monitoring progress towards related SDGs, particularly Goals 2, 6, and 7, as reflected in the pursuit of water, energy, and food security.

The connection to the Internet by full-mobile users, is expected to be of crucial importance for the success of radio mobile systems as UMTS. Such systems must support services like Web Browsing, the most popular Internet application today, which is characterized by high bit rate bursty asymmetric traffic and like Video Call, evolution of traditional speech service, which are characterized by high sensitivity to delays and more symmetric traffic.

While the resulting traffic is highly asymmetric, mobile air interfaces like UTRAN FDD have been initially designed as systems supporting asymmetric user traffic in an overall system symmetric traffic scenario.

This paper, outcome of the ongoing EU founded project OverDRiVE, focuses on downlink enhancements of the UTRAN FDD air interface and proposes specific resource management of additional radio spectrum in order to handle efficiently asymmetric traffic.

A dynamic gird computing environment is characterized by entity autonomy, and distribution. Within this paper, we describe a grid resource management model KRMM, Knowledge-based Resource Management Model, which manage the resource in a decentralized way while query and match resource based on global grid knowledge. With supporting of RDF and RDQL, KRMM can easily support grid resource set-matching and gang-matching. Further more, by extending ontology and inference rule, KRMM can support network centric resource correlated query by derived knowledge reasoning. This model is flexible and extensible, and is suitable for large scale grid resources matchmaking.