Modeling Distributed Team Resource Allocation within A Geographical Environment

Many of today's large scale systems require dynamic coordination among distributed decisionmakers. A crucial element in the coordination involves the use of resources which have finite setup times and are constrained by their effective durations. The presence of these constraints may induce human cognitive limitations and biases and lead to coordination failure. This paper presents a quantitative methodology for identifying the impact of resource constraints on distributed team coordination in a dynamic and uncertain task processing environment. The approach uses Petri net as the formalism to model team decisionmaking process. By incorporating rational decisionmaking strategies and recognizable human limitations, various resource and task conditions are simulated. Hypotheses on the impact of resource constraints are then generated based on simulation results, and a distributed human team task processing experiment has been designed and conducted to help validate the hypotheses. Experimental data are currently being analyzed, and model-data comparison will help examine the impact of the resource constraints on human team decisionmaking and coordination processes, and identify human cognitive limitations and biases.

Team Relative Operating Characteristic: A Measure of Team Expertise in Distributed Detection Tasks 1

The distributed nature of human decisionmaking tasks within an organization--civilian as well as military--interacts strongly with the information structure inherent to that organization. Very often these tasks are too complex to be performed by only a single decisionmaker, due to the inherent human limitations of memory, restricted planning horizon, information processing capacity, and mental workload demands. Thus, the functions that make-up any organizational structure are characterized by the interconnections of multiple tasks performed by several humans. In various man machine systems, communications are of crucial importance to each local operator's performance, as well as to the overall team performance. A strong relationship exists among 1) the degree of interdependence of the subsystems, 2) the communications, information and control sharing requirements, and 3) performance. In this paper, several different theories that have been proposed for studying human team performance are overviewed and their limitations are identified. On the one hand, the existing distributed decisionmaking and control theories do not take into account the inherent human operator limitations. On the other hand, the state of the art models for human decisionmaking deal mainly with the case of a single operator. Thus, there is a potential to fuse both areas of distributed decisionmaking and man machine systems into a single theory which can serve the purpose of analyzing multihuman decisionmaking processes. An attempt to set the bases of such a theory is made for simple human teams with different information structures. The need for the presence of a coordinator, one level of hierarchy above the other decisionmakers is also examined. These studies, together with the normative-descriptive Dynamic Decision Model (DDM) previously developed and validated for a single operator, will form the framework of future experimental designs involving two or more humans making decisions in a multi-task environment.

VDS Inc., based in California, US, was founded by three Indian entrepreneurs in January 2000. The company provided VoIP (Voice over Internet Protocol) solutions to wireless, wire line and cable service providers in the US, Europe, and recently, Asia. VDS India, based in Noida, India, was established in September 2000 as the offshore product development base of VDS Inc. The product portfolio of VDS Inc. was steadily growing and gaining recognition in the industry. As a result, the product development base in India was becoming more important than ever. A new CEO, David Joseph was appointed in October 2003 to head VDS India. To standardize work processes to cope with growth in demand and to control attrition rates, David undertook initiatives such as the establishment of a human resources team, setting up of a technical documentation team, the tightening of team processes pertaining to induction and job allocation, and the initiation of organizational processes of employee performance appraisals. David noticed that changes were not easy to implement. Outsourcing of the human resources team and problems of coordination between the technical documentation team and the engineers posed major challenges to David. Integration of communication and information with VDS Inc. also was another area of concern. The case is useful for the examination of human elements in the implementation process.

A multi-objective train scheduling model and solution

Many companies are trying to generate product variants through platforming to cater to diverse customer needs in a cost-effective fashion. Although product platforming may provide a cost-effective solution, platforming may also render undesirable performance degradation of individual product variants. Creating product variants based on multiple platforms instead of a single platform can be adopted as a strategy to reduce performance degradation. In the current paper, a methodology is proposed to determine the minimum number of scalable platforms needed for creating known product variants by considering the trade off between cost effectiveness and performance degradation. The solution methodology also provides values of common and scalable design variables for each platform. The objective function is defined as minimization of the weighted demand and total cost of providing each variant. The total cost is a function of the cost of each product variant and the cost associated with performance loss owing to platforming. The cost of performance loss is estimated using the Taguchi loss function. The application of the methodology is demonstrated using the case of a family of axial piston pumps. This case of axial piston pumps described in this study is new to the product platforming literature and can be potentially used as a benchmark problem by other researchers in the field to test the efficacy of existing scalable product platforming approaches.

The reliability of collision avoidance systems for Maritime Autonomous Surface Ships is one of the most critical factors for their safety. In particular, since many ship collisions occur in coastal areas, it is crucial to ensure the reliability of collision avoidance algorithms in geographically limited coastal waters. However, studies on maritime autonomous surface ships collision avoidance algorithms mainly focus on the traffic factor despite the importance of the geographic factor. Therefore, this study presents a methodology for establishing a practical collision avoidance system test bed, considering the geographic environment. The proposed methodology is a data-driven approach that objectively categorizes collision risk situations by extracting these risks using Automatic Identification System (AIS) and Electronic Navigational Chart (ENC) data, followed by clustering algorithms. Consequently, the research results present a direction for establishing test beds from the perspective of geographic and traffic factors.

The resilience of a large-scale water infrastructure system to cascading effects isfundamentally dependent on the interdependencies of its components within theinfrastructure network. These interdependencies—which means that the states oftwo or more infrastructure components are tightly interrelated through mechanismssuch as physical connection, geographical proximity, and information relay—cancause a localized event to spread into a system-wide event. Of these, logicalinterdependencies remain poorly understood. Little is known about how twoinfrastructures affect the state of each other through human decisions and how suchlogical connections can be detected and measured. In this study, we tackled thisgap by conducting an applied case study on the Lake Mendocino Reservoir inCalifornia, USA. Crucially, our approach focuses on reservoir institutions (rules)that structure human decisions around reservoir systems. Reservoir managementrelies heavily on operational rules and regulations, but climate change demandsmore adaptive and discretionary decision-making by operators. This may furtherintroduce logical interdependencies in a reservoir system. We develop a novelframework that integrates Institutional Analysis using Large Language Models toadvance Natural Language Processing (NLP) techniques and Bayesian NetworkModeling to systematically analyze and quantify risk associated with logicalinterdependencies. We aim to improve decision-making and risk management inreservoir operations. This research provides essential insights into enhancing theresilience of water management infrastructures, particularly in the face of climatechange.

Understanding the status of high-performance computing platforms and correlating applications to resource usage provide insight into the interactions among platform components. A lot of efforts have been devoted into developing monitoring solutions; however, a large-scale HPC system usually requires a combination of methods/tools to successfully monitor all metrics, which will lead to a huge effort in configuration and monitoring. Besides, monitoring tools are often left behind in the procurement of large-scale HPC systems. These challenges have motivated the development of a next-generation out-of-the-box monitoring tool that can be easily deployed without losing informative metrics. In this work, we introduce MonSTer, an “out-of-the-box” monitoring tool for high-performance computing platforms. MonSTer uses the evolving specification Redfish to retrieve sensor data from Baseboard Management Controller (BMC), and resource management tools such as Univa Grid Engine (UGE) or Slurm to obtain application information and resource usage data. Additionally, it also uses a time-series database (e.g. InfluxDB) for data storage. MonSTer correlates applications to resource usage and reveals insightful knowledge without having additional overhead on the application and computing nodes. This paper presents the design and implementation of MonSTer, as well as experiences gained through real-world deployment on the 467-node Quanah cluster at Texas Tech University's High Performance Computing Center (HPCC) over the past year. In this work, we introduce MonSTer, an “out-of-the-box” monitoring tool for high-performance computing platforms. MonSTer uses the evolving specification Redfish to retrieve sensor data from Baseboard Management Controller (BMC), and resource management tools such as Univa Grid Engine (UGE) or Slurm to obtain application information and resource usage data. Additionally, it also uses a time-series database (e.g. InfluxDB) for data storage. MonSTer correlates applications to resource usage and reveals insightful knowledge without having additional overhead on the application and computing nodes. This paper presents the design and implementation of MonSTer, as well as experiences gained through real-world deployment on the 467-node Quanah cluster at Texas Tech University's High Performance Computing Center (HPCC) over the past year.

There has been much research investigating team cognition, naturalistic decision making, and collaborative technology as it relates to real world, complex domains of practice. However, there has been limited work in incorporating naturalistic decision making models for supporting distributed team decision making. The aim of this research is to support human decision making teams using cognitive agents empowered by a collaborative Recognition-Primed Decision model. In this paper, we first describe an RPD-enabled agent architecture (R-CAST), in which we have implemented an internal mechanism of decision-making adaptation based on collaborative expectancy monitoring, and an information exchange mechanism driven by relevant cue analysis. We have evaluated R-CAST agents in a real-time simulation environment, feeding teams with frequent decision-making tasks under different tempo situations. While the result conforms to psychological findings that human team members are extremely sensitive to their workload in high-tempo situations, it clearly indicates that human teams, when supported by R-CAST agents, can perform better in the sense that they can maintain team performance at acceptable levels in high time pressure situations.

Under the background of global climate change, the variation in the spatial distribution and ice volume of mountain glaciers have a profound influence on regional economic development and ecological security. The development of glaciers is like biological succession; when climate change approaches or exceeds the threshold of suitable conditions for glacier development, it will lead to changes in potential distribution pattern. Therefore, from the perspective of the "biological" characteristics of glaciers, it is a beneficial exploration and attempt in the field of glaciology to explore its potential distribution law with the help of the niche model. The maximum entropy model (MaxEnt) can explain the environmental conditions suitable for the survival of things by analyzing the mathematical characteristics and distribution laws of samples in space. According to glacier samples and the geographical environment data screened by correlation analysis and iterative calculation, the potential distribution pattern of Tianshan glaciers in China in reference years (1970–2000) was simulated by MaxEnt. This paper describes the contribution of geographical environmental factors to distribution of glaciers in Tianshan Mountains, quantifies the threshold range of factors affecting the suitable habitat of glaciers, and predicts the area variation and distribution pattern of glaciers under different climate scenarios (SSP1-2.6, SSP5-8.5) in the future (2040–2060, 2080–2100). The results show that the MaxEnt model has good adaptability to simulate the distribution of glaciers. The spatial heterogeneity of potential distribution of glaciers is caused by the spatio-temporal differences of hydrothermal combination and topographic conditions. Among the environmental variables, precipitation during the wettest month, altitude, annual mean temperature, and temperature seasonality have more significant effects on the potential distribution of glaciers. There is significant spatial heterogeneity in the potential distribution of glaciers in different watersheds, altitudes, and aspects. From the forecast results of glacier in various climatic scenarios in the future, about 18.16–27.62% of the total reference year glacier area are in an alternating change of melting and accumulation, among which few glaciers are increasing, but this has not changed the overall retreat trend of glaciers in the study area. Under the low emission scenario, the glacier area of the Tianshan Mountains in China decreased by 18.18% and 23.73% respectively in the middle and end of the 21st century compared with the reference years and decreased by 20.04% and 27.63%, respectively, under the high emission scenario, which showed that the extent of glacier retreat is more intense under the high emission scenario. Our study offers momentous theoretical value and practical significance for enriching and expanding the theories and analytical methods of the glacier change.

Landslide susceptibility evaluation and determination of critical influencing factors is a prerequisite for preventing hazardous risks, especially in landslide-prone mountainous areas. However, in densely vegetated Southwest mountainous areas, identifying assessment approach of shallow landslides susceptibility and their major inducing factors is still a huge challenge. To address this challenge, we applied five advanced machine learning models (Logistic Regression Model, Generalized Additive Model, Random Forest Model, Support Vector Machine Model, Artificial Neural Network Model) to assess the spatial distribution of shallow landslide susceptibility, considering several relevant factors that affect landslide occurrence. These factors include geological, topographic and vegetation factors, as well as four new vegetation factors: stock volume, stand density, average tree age, and stand types. Furthermore, we employed SHAP algorithm and Structural Equation Models to quantify the relative importance and explanatory power of these factors on shallow landslide susceptibility and to clarify the interaction mechanisms among various factors in Huaying Mountain. The results shown that Random Forest Model proves to be the most accurate (95.1 %) in assessing the spatial distribution of shallow landslides susceptibility, followed by the Artificial Neural Network model (78.6 %), the Support Vector Machine model (69.8 %), the Generalized additive model (68.1 %) and the Logistic Regression model (67.6 %).The area with high susceptible landslide possibility was 25.3 km2 occupying 14.8 % of the study region, it is mainly distributed in the west of Tianchi Lake, southeast of Huaying City and west of the study area, along with Xiangyu Railway. Geographical environment and vegetation features were found to significantly explain 67.4 % and 32.6 % of the total effects in shallow landslides susceptibility, respectively. Specifically, the spatial distribution of shallow landslides susceptibility were primarily influenced by geological engineering rock group, distance to faults、stand types and distance to river. Geographical environment factors could indirectly affect changes in vegetation features, thereby indirectly affecting the spatial distribution of shallow landslides susceptibility. Findings from this research could be helpful for scientific decision-making and technical assistance for early warning, prevention, and control of rainstorm-induced landslides in highly vegetation covered areas.

A genomic perspective on adaptation to heterogeneous environments for <i>Cycas segmentifida</i> (Cycadaceae), an endemic cycad in southwest China

Projection based model order reduction has become a mature technique for simulation of large classes of parameterized systems. However, several challenges remain for problems where the solution manifold of the parameterized system cannot be well approximated by linear subspaces. While the online efficiency of these model reduction methods is very convincing for problems with a rapid decay of the Kolmogorov n-width, there are still major drawbacks and limitations. Most importantly, the construction of the reduced system in the offline phase is extremely CPU-time and memory consuming for large scale and multi scale systems. For practical applications, it is thus necessary to derive model reduction techniques that do not rely on a classical offline/online splitting but allow for more flexibility in the usage of computational resources. A promising approach with this respect is model reduction with adaptive enrichment. In this contribution we investigate Petrov-Galerkin based model reduction with adaptive basis enrichment within a Trust Region approach for the solution of multi scale and large scale PDE constrained parameter optimization.KeywordsPDE constraint optimizationReduced basis methodTrust region method

Natural languages such as English are exceedingly complicated media for the communication of information, attitudes, beliefs, and feelings. Computer systems that attempt to process natural languages in more than the most trivial ways are correspondingly complex. Not only must they be capable of dealing with elaborate descriptions of how the language is put together (in the form of large dictionaries, grammars, sets of inference strategies, etc.), but they must also be able to coordinate the activities and interactions of the many different components that use these descriptions. For example, speech understanding systems of the sort that are currently being developed under ARPA sponsorship must have procedures for the reception of speech input, phonological segmentation and word recognition, dictionary consultation, and morphological, syntactic, semantic, and pragmatic analyses. The problems of coordination and control are reduced only slightly in less ambitious projects such as question answering, automatic programming, content analysis, and information retrieval. Of course, large-scale software systems in other domains might rival natural language programs in terms of the number and complexity of individual components. The central theme of the present paper, however, is that natural language control problems have a fundamentally different character from those of most other systems and require a somewhat unusual solution: the many natural language procedures should be conceptualized and implemented as a collection of asynchronous communicating parallel processes.