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In this paper, the risk disclosure text information from the dual perspectives of bank managers and credit rating agencies is innovatively incorporated into the bank risk aggregation, which to some extent avoids the deviation of bank risk assessment caused by the risk perception from a single perspective, and measures the overall bank risk more comprehensively. In the empirical analysis part, this paper extracted 342 risk profit and loss data, 511 text risk information in 10-K forms and 356 available bank rating reports from 134 listed commercial banks in the United States. The Sent-LDA model was used to identify and compare risk factors from different perspectives. In addition, by constructing a total risk-adjusted index, this paper studies the effectiveness of textual risk disclosure from the dual perspectives of bank management and credit rating agencies in the overall risk measurement of commercial banks. The empirical results show that the text contains the incremental information of risk assessment, and the inclusion of double-perspective text risk information into the risk aggregation will amplify or reduce the overall risk of banks calculated only based on financial numerical data. In practice, it will be more effective to measure the overall risk of banks by considering the textual risk disclosure from dual perspectives. [ABSTRACT FROM AUTHOR]

Emergency Electronic Brake Light (EEBL) is a VANET safety application designed to combat rear end crashes, which represent 398,000 incidents per year in the United States. Given the ubiquitous presence of smartphones in the population, the smartphone as a VANET platform has the potential for mass adoption. An EEBL application on a smartphone VANET could improve road safety significantly by reducing rear end crashes. However, there are many difficulties inherent in developing safety applications on the VANET, such as lack of sensors, noisy data, inaccurate GPS readings and poor networking capabilities. In this work, we have outlined the primary challenges of developing a smartphone Emergency Electronic Brake Light application, as well as potential solutions to these challenges found in the literature. This paper proposes an architecture for a novel smartphone VANET EEBL. [ABSTRACT FROM AUTHOR]

The deep sea has a large strategic depth, in which target detection is a crucial link, that is, how to detect the target submarine. For this reason, since this century, countries represented by the United States have innovated ideas, innovated underwater detection technology, and enhanced new underwater detection capabilities. In this paper, by consulting more than 100 literatures, the data acquisition technology in underwater acoustic detection in recent years has been investigated in detail, passive detection and active detection have been comprehensively reviewed, and the collaborative detection technology has also been summarized. In order to provide reference for related research. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 in 2020 has a severe impact on all countries. This paper first studies the impact of the epidemic on the macro-economy in China, which is one of the countries with early epidemic situation, in the rapid development stage of the epidemic situation, and the specific performance in macro-economy aspects. On this basis, study the development trend and macroeconomic performance of the United States as one of the most serious countries, and the relationship between the epidemic development and macro-economy in the United States. As for the differences in epidemic control measures between China and the US, China's indicators are significant in terms of sentiment factors, but the impact of COVID-19 on the sentiment factors in the US is different. The Chinese government has not adopted unlimited easing and maintained consistent policies. At present, the economic impact of COVID-19 on the US is far greater than that of China. [ABSTRACT FROM AUTHOR]

The current and future Operational Environment (OE) for the United States (U.S.) military is becoming increasingly complex. This complexity requires Systems Engineers and Architects to develop new approaches for evaluating the variability inherent in the OEs of today and tomorrow. In response to this growing capability gap, the U.S. Department of Defense (DoD) established the Engineered Resilient Systems (ERS) program. A core property of ERS is Broad Utility—the ability of a system to, " perform effectively in a wide range of operations across multiple futures despite experiencing disruptions. " [1] This paper discusses on-going research intended to provide system designers with an approach to architecting systems for Broad Utility. Specifically, this research seeks to accomplish three objectives: (1) Identify gaps in current U.S. DoD doctrine impacting the ability to architect for Broad Utility; (2) Develop an integrated network model of the Operational Environment that highlights how OE variables can impact system effectiveness; and (3) Propose an approach for ensuring that system architectures exhibit Broad Utility, through a process of mapping system Flexibility and Robustness to the variables of the OE. By employing this approach early in the Systems Engineering process, system designers can increase the likelihood that the resulting system responds appropriately to a changing environment. [ABSTRACT FROM AUTHOR]

Image retrieval is a well discussed field in digital image processing. Images can be sort out from a big collection and database of images on the basis of text, color and shape of objects in images. In CBIR systems, using combined features get many most similar and relevant images. In a typical CBIR system, the ocular content of the images from the large collection is extracted and displayed by a storehouse like m-dimensional feature vectors framed out from images. This vector of the images in the collection of images in database is named as a database of features. Most sought out systems represent images with color feature most related to user and shape feature to relate image to exact object from collection and test dataset. In this paper we present introduction, research work on color scheme with HSV colour space and its combination with shape using coiflet wavelet methods. States of art design of overall system is delineated with results evaluated for mean average precision and mean average recall for overall system. [ABSTRACT FROM AUTHOR]

The increasing coverage of Internet has created opportunities and advantages for different aspects of society. However, there come new threats and challenges to information security. One of the typical types of attacks that has increasingly occurred is the APT attack (Advanced Persistent Threat). APT is dangerous with clear purposes. APT attacks employ different sophisticated methods and techniques attacking targets in order to steal confidential and sensitive information. In the past, hackers attacked information systems with personal and financial motives. However, there are nowadays other motives such as political ones and they are potentially backed by governments or nations. Nations that own advanced technologies such as United States, India, Russia, UK are also suffering from special purpose attacks. APT is an advanced type of attacks that consists of many stages and concrete strategies. Besides, techniques and technologies employed in APT attack are usually new and developed by hackers in order to break through the monitoring of security software. However, APT is normally implemented through concrete steps and stages. If one of the steps or stages fails, the entire APT attack will fail. This paper presents a method of detecting APT attacks based on monitoring accesses to unknown domains. This detection method results into high effectiveness in the initial stage of APT attacks. [ABSTRACT FROM AUTHOR]

The paper studies how to improve the trading decision of Taiwan stocks with the information of US stock market. Our method first aligns the trading days between Taiwan and US stock markets. Next, the similarity between the portfolio index (PI, constructed from 100 Taiwan stocks) and one of the US stock indices, the Dow Jones Industrial Average (DJIA), NASDAQ composite index (NASDAQ), or Standard & Poor’s 500 (S&P 500), is computed, respectively. The trading signals of PI or each US stock index are generated by the method of Lee et al. Finally, the consensus signals of PI are determined by the majority vote scheme with the weighted functions, calculated from the similarity. The testing period of PI starts from 2000/1/4 to 2017/12/29, totally 4480 days. As the experimental results show, the index combination (PI, DJIA, NASDAQ) with the weighted function W (4) is considered to be the best combination for trading PI. Its average annualized return (cumulative return) achieves 15.03% (1170.42%), which is better than the method of Lee et al. 13.88% (947.65%), and the buy-and-hold strategy 9.85% (442.90%). [ABSTRACT FROM AUTHOR]

This study explores how high frequency data can be turned to be an opportunity to generate possible trading strategies by examining the information flow direction between Indian and US Crude oil Future Markets. The profit opportunities in a market is special indicator of the market efficiency. Thus this paper simulates the trading strategies to check the efficiency of the Indian & US Crude Oil future Market. First, the informational relationship is examined on short term as well as long term basis. The results indicate that there is bidirectional information flow in long term, but in short term only US market is information provider. Next, VECM suggests that this long term and short term relationship between these two markets can be useful for formulating some trading strategies. And trading simulation confirm that very little profit opportunities exists on regular basis for hedgers. Both U.S. and Indian Future market are found to be efficient on daily basis, because there exist very little profit opportunity. But US Future market is found to be more efficient than the Indian Future market. [ABSTRACT FROM AUTHOR]

This paper proposed a multi-scale correlations perspective to explore the relationship between economic policy uncertainty and sovereign bond yield in the United States, using the Ensemble EMD decomposition algorithm and Pearson correlation coefficient. Results show that these two variables exhibit a completely opposite linear relationship in the long-term trend before and after 27 April 2009, indicating that there is obvious conversion of stage characteristics and the relationship between these two variables is complex. Therefore, further study to investigate this complex relationship using nonlinear correlation measurement is necessary, and all these research will contribute to the policy making of regulators and trading behaviors of investors in the sovereign bond market. [ABSTRACT FROM AUTHOR]

The idea that education in America is deteriorating is emotionally charged and controversial. While there is no disputing that education levels in the United States continue to rise, there is also a pervasive notion that this was accomplished by gradually reducing the readability level and general difficulty of textbooks. One tool often employed in the defense of education is the employment of readability indices in the evaluation of textbooks. There are a variety of these readability indices that serve the purpose of indicating a grade level for a particular piece of writing (Kinkaid, et. al., 1975). It’s relatively easy to find dozens of sites where a teacher or interested person can submit text or a URL with the purpose of finding out the reading level expressed as a grade level for a particular piece of text. Most sites report on five different indices: Automated Readability Index, Flesch Reading Ease, Flesch-Kinkaid Score, Gunning-Fogg Index, and SMOG Index (Simplified Measure of Gobbledygook). This paper addresses these indices, their applications, and the drawbacks of their use.. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe refinements to the recently developed GA-ACO method and to show its application to a real world engineering design problem. The GA-ACO method is a genetic algorithm with a new operator, called an ACO operator. ACO stands for Ant Colony Optimization. The ACO operator uses pheromone trails, a method from Ant Colony Optimization to influence the genetic algorithm. The GA-ACO method is used to optimize an engineering design. Engineers produce a preliminary design for a system using a CAD tool. The output of the CAD tool is then translated into a design graph. Many additional characteristics of the design can be represented by labels on the design graph. The GA-ACO method is then used to optimize these labels. This technique can be applied widely to many design optimization problems. The application considered in this paper concerns optimization of designs for efficient assembly. It uses problems in engineering design encountered at Newport News Shipbuilding, the largest shipyard in the United States. We present a comparison of variations of the GA-ACO method with a standard genetic algorithm for this type of problem. [Copyright &y& Elsevier]

Abstract: During the entire period of the 2007-2009 global financial crisis, different types of countries showed different characteristics on their economic development process. Comparing the economic development process between different types of countries contributes a lot to get an in-depth understanding of the different impacts of the crisis on national economy. In this paper, the method of Functional Analysis of Variance (FANOVA) is applied to make a comparative study on the economic development process of different types of countries, including the differences on the economic growth rate, the time of the economy recession, the extent of the recession and the recovery situation of the economy. Moreover, the paper performs a dynamic test on the significance of the difference on the economic growth rate during the whole stage. [Copyright &y& Elsevier]

Abstract: This paper applies Agent-Based Modeling to the auto industry. The auto industry might be considered as a complicated rather than a complex system, because it is characterized by adaptation and emergence, two of the three major characteristics of complex systems . Yet some characteristics residing in human could lead us to consider the auto industry as a quasi-complex system because individual agents diffuse information and act based on adaptation. The roles of differentiated agents, including dealers, are significant: diverse agents function as autonomous decision makers who rely on localized information, thereby influencing the evolution of the current industry as it looks forward to a new phase of power propulsion development, such as hybrid, electric, and hydrogen fuel cell vehicles. The impact of rising oil prices on potential customers’ decisions, as well as what this paper identifies as the emotion factor between buyers and dealers, are incorporated to determine dynamics in terms of sales volumes. We are also interested in the extent and speed with which the new technology is adopted. Because of the consumer preferences and the long life cycles of cars, the adoption of new technology will be delayed given the current environment and policies in the United States. [Copyright &y& Elsevier]

Abstract: This paper addresses the challenging problem of selecting target country for future Sovereign Wealth Funds’ (SWFs) asset allocation to hedge the industry risk, which is rarely studied in the field. The target country selection includes which country and how much to invest to obtain the return objective and minimize the risk of these funds. In terms of the industrial perspective, the home country as the investor should consider SWF as part of its budget to make decision in long term. In order to control the risk, this paper measures the similarity between the home and the recipient country of SWF investment. The industrial risk of SWFs’ recipient country is also taken into consideration which is measured by concentration ratio. Based on an analytical process of target country selection, the paper finds that Kazakhstan, India, Australia, Greece, Spain, United States, Austria, Portugal, Peru, Netherlands are the top 10 countries that China should consider as its investment priorities. [ABSTRACT FROM AUTHOR]

The war on terrorism, radicalism and violent extremism is no longer confined to the battlefield; it has become omnipresent in the recent years with militant, terrorists and insurgent groups actively recruiting new technologies as platform to impel their ideologies worldwide. Nonetheless, one thing remains constant in the fight against radicalism equation, that is, the information about radical individuals, whether personal, demographical, social or economic information. The ability to identify, classify and profile potential radicals based on such information has an appealing trait for security agencies around the world. The Intelligence and Security Informatics research field emphasizes the importance of engaging multidiscipline technologies to provide security-related applications that serve as aiding tools in combating terrorism and other forms of violent extremism. In this paper, the authors focus on radical Islamism and hypothesize that Islamist radicals have identifiable information and behavioral traits that could be utilized to identify their ideological motive uniquely amongst other radicals. Four different supervised machine learning algorithms are applied to validate this hypothesis using the “profiles of individual radicalization in the United States” dataset and their performance is compared and discussed. The evaluation results support the authors’ hypothesis and show that profiling religious extremists can be achieved with high recall and precision using machine learning models. [ABSTRACT FROM AUTHOR]

Recently, with the advances of information and electrical technologies, Internet of Thing (IoT) has moved from being a far vision to an increasing market reality. IoT promises to connect all physical “things” via a dynamic global network infrastructure and create intelligent behaviors in different contexts without human manipulations. In line with this development, the number of IoT solutions also have augmented exponentially covering many domains from health care, urban management to life quality improvement and so forth. However, separate appearances of these solutions today has led to barriers in enlarging smart environments because they lack a mechanism for integration. It is well-known that in the very near future, IoT requires the capabilities of high scale and dynamic adaptation with linking many new and existing smart contexts together. Hence, the need of having a comprehensive management method for the large-scale IoT environments is urgent at the time. In this paper, we focus on designing an overlay management architecture based on Chord algorithm to manage things in heterogeneous space. The architecture is a collection of multiple peer Chord rings, and thus each of these rings is considered as a smart context. We also bring forward operation mechanisms for the architecture such as thing identification, key insert/remove, key lookup and node joining/leave. Our proposed architecture is simulated and evaluated with diverse properties. The gained results showed the feasibility of our approach in reality meeting scalability, robustness and maintainability requirements applying to manage and control cyber-physical systems. [ABSTRACT FROM AUTHOR]

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent's education status, and time since high school graduation. These inputs and the multi-layer neural network model are used to classify students as: graduates, late graduates, or non-graduates. Several neural network architectures are examined and compared by run time, minimum mean square error achieved (MSE), mean correct classification rate, precision, recall, and specificity. A multi-layer neural network with 50 hidden neurons, momentum value of 0.8, and learning rate of 0.1, with hyperbolic tangent hidden neuron activation functions was able to accurately predict graduation success and achieved the best performance with classification accuracy exceeding 95%, high recall, high precision, and high specificity. This prediction model may be used to inform admission decisions and identify opportunities for required remediation with the potential to improve graduation rates, increase student retention, reduce late graduation, and reduce first-term course failures. [ABSTRACT FROM AUTHOR]

Users of International Land Ports of Entries (LPOEs) at the US – Mexico border experience excessive wait times on a daily basis. This contributes to the increased transportation costs, and also generates health and environmental problems in border regions. Traffic simulation models are effective tools for evaluating passenger vehicle, pedestrian and commercial traffic operations. These tools can be used to assess various scenarios and visualize current traffic conditions. Results obtained from these tools should be taken into account in congestion relief decision making processes. The research team developed, calibrated and validated three traffic models of the Ysleta-Zaragoza LPOE using three different software packages (i.e., TransModeler, VISSIM, and Aimsun). This paper presents advantages and disadvantages of each tool when modeling international LPOEs. Results presented herein can be used by practitioners to decide which tool should to be used for modeling LPOEs at the U.S. – Mexico border. [ABSTRACT FROM AUTHOR]

Nowadays hospitals face the problem of increasing quality and at the same time reducing costs of their services. Clinical pathways approach has established itself as an effective method of reorganization of medical practice in a process-oriented way. Since more than a decade, clinical pathways are being created and applied in hospitals in the USA, Australia, and European countries. Traditional text-based approach for documenting clinical pathways does not allow automatic analysis and makes the maintenance of the models inefficient. Recently, researchers started to apply generic modeling languages, such as UML activity diagrams, EPC or BPMN, as well as domain specific process modeling languages, in order to formalize the representation of clinical pathways. However, none of these languages sufficiently covers the requirements of clinical pathway models, and the choice of a suitable modeling technique remains a problem. In this paper, we propose a modeling methodology and a modeling tool for creating graphical semantically annotated models of clinical pathways. We take into account the characteristics and usage scenarios of clinical pathways and show, how the proposed approach addresses these requirements. [ABSTRACT FROM AUTHOR]

Today, the over $2 trillion US healthcare system is ravaged by fraud, waste, and abuse, with an estimated one-third of all these costs frivolously spent in such ways. Sun Tzu wrote, “Every battle is won or lost before it's ever fought.” To combat healthcare fraud, we must understand it and the forms it takes. In this paper, we systematically evaluate published literature using Webster and Watson's concept matrix technique. From the applicable published literature, we provide a categorization and description of the documented types of fraud in healthcare. [ABSTRACT FROM AUTHOR]

This paper analyzes out-of-sample forecasts of real total business sales. We study monthly data from January 1 970 to June 2012. The predictor variable, 3-month Treasury bill interest rate, was used with both the regression (used as a benchmark) and neural network models. The neural network models’, trained in supervised learning with the Levenberg-Marquardt backpropagation through time algorithm, prediction accuracy was confirmed with correlation coefficient and root mean square tests. The activation function used for the focused gamma models of the time-lag recurrent networks in both the hidden and output layers was tanh. The forecast period ranged from January 2006 to June 2012 thus encompassing the past recession. The real business sales variable is one of the indicators used as a coincident index of the U.S. business cycle, and is included among the variables studied by the Federal Reserve to formulate monetary policy. It is thus an important indicator surrogating for real GDP, which is reported quarterly and with a longer time delay. Our analysis shows that recent recessions have increased in duration, so that using a 36-month change to approximate an average cycle in estimating and forecasting is more relevant and accurate than past usage of a 24-month change. [ABSTRACT FROM AUTHOR]

Abstract: The complexity of the National Airspace System (NAS) in the United States presents a number of novel and unique challenges for the integration of Unmanned Aircraft Systems (UAS). In particular, one challenging aspect is the modeling of UAS safety risk for civil applications given the scarcity of actual operational data. With the creation of a probabilistic model, inferences about changes to the states of the accident shaping or causal factors can be drawn quantitatively. These predictive safety inferences derive from qualitative reasoning to plausible conclusions based on data, assumptions, and/or premises and enable an analyst to identify the most prominent causal factors leading to a risk factor prioritization. Such an approach also facilitates the study of possible mitigation effects. This paper illustrates the development of an Object-Oriented Bayesian Network (OOBN) to integrate the safety risks contributing to a notional “lost link” scenario for a small UAS (sUAS) with the mission of aerial surveying for a bridge infrastructure inspection. As a System of Systems (SoS) approach, an OOBN facilitates decomposition at the sub-system level yet enables synthesis at a higher-order systems level. In essence, the methodology serves as a predictive safety analytics platform to support reasoning to plausible conclusions from assumptions or premises. [Copyright &y& Elsevier]

Abstract: The Customs and Border Protection (CBP) organization must secure the homeland by preventing illegal entry into the United States. In order to successfully accomplish this task, a System of Systems (SoS) of airborne, ground, fixed, mobile, manned, and unmanned capabilities must act together in order to successfully accomplish the objective. However, it may be difficult to identify a specific SoS configuration that will meet the objective and quantify the requirements allocated for each specific system. This paper offers a methodology to evaluate the SoS phases to detect, track, identify, decide, act, and assess the actions of CBP forces. This methodology is exercised to quantify the system contributions to the CBP SoS and the interfaces needed for each system. [Copyright &y& Elsevier]

Abstract: The Community Earth System Model (CESM) is one of US''s leading earth system modeling frameworks, which has decades of development history and was embraced by a large, active user community. In this paper, we first review the software development history of CESM and we explain the general objectives of performance analysis. Then we present an offline global community land model simulation within the CESM framework to demonstrate the procedure of runtime tracing of CESM using the Vampir toolset. Finally, we explain the benefits of runtime tracing to the general earth system modeling community. We hope those considerations can be beneficial to many other modeling research programs involving legacy high-performance computing applications. [Copyright &y& Elsevier]

Abstract: We are living in a global service economy in which services now make up more than 78% of the US Gross Domestic Product (GDP). This growth in Service has also become a global phenomenon and this has brought much needed attention to Service Science and Service Systems Engineering (SSE). As part of these efforts, this paper proposes the enhancement of existing architectural frameworks for Service Systems. We readily concede that IT is at the heart of Service Systems but we argue existing frameworks need to be expanded to address service systems governance and insertion of technology breakthroughs. A modified NIST framework is illustrated, that includes an Enterprise/Service Management plane to analyze energy Smart Grids as an end-to-end service provider. We also realized the need for integration of tools for the real-time dynamic analysis of ever-changing requirements within a Service System. This concept is illustrated in the form of an internet manufacturing laboratory where design, fabrication, IT, and other supporting tools are integrated to rapidly fabricate complex 3D prototype models. Finally, we discuss issues which need to be addressed for the creation of curricula and professional degree programs in Service Systems Engineering at the graduate level. As a result, we propose a plan for including a Service Systems Engineering specialization track within traditional Systems Engineering programs. [Copyright &y& Elsevier]

Abstract: As the United States Department of Defense continues to increase the number of Remotely Piloted Aircraft (RPA) operations overseas, improved Human Systems Integration becomes increasingly important. Manpower limitations have motivated the investigation of Multiple Aircraft Control (MAC) configurations where a single pilot controls multiple RPAs simultaneously. Previous research has indicated that frequent, unpredictable, and oftentimes overwhelming, volumes of communication events can produce unmanageable levels of system induced workload for MAC pilots. Existing human computer interface design includes both visual information with typed responses, which conflict with numerous other visual tasks the pilot performs, and auditory information that is provided through multiple audio devices with speech response. This paper extends previous discrete event workload models of pilot activities flying multiple aircraft. Specifically, we examine statically reallocating communication modality with the goal to reduce and minimize the overall pilot cognitive workload. The analysis investigates the impact of various communication reallocations on predicted pilot workload, measured by the percent of time workload is over a saturation threshold. [Copyright &y& Elsevier]

A discrete event simulation was developed combining the Optical Tracking Network (TeleTrak) and Satellite Position Attained by RF-Keyed Tracking (SPARK) system into one system to track, image, and identify space objects as they pass through the space fence to increase space situational awareness for the United States Air Force. The objectives for this study are threefold: model a “to-be” architecture for a combined TeleTrak and SPARK system to develop system requirements, determine if an optimal position for the telescope exists to return to while waiting for the next collect, and demonstrate knowledge and understanding of DOE and simulation principles and techniques. The paper explores the uses and pitfalls of modeling and simulation with this case study. While a working model remained elusive, several important observations emerged. Six recommendations are given to help others become more successful in a modeling and simulation environment along with ideas to pursue in additional research. [ABSTRACT FROM AUTHOR]