Trade Studies Research Articles

Methods to Detect Impact-Induced Orbit Perturbations Using Spacecraft Navigation Data

Debris strikes on operational spacecraft are becoming more common due to increasing numbers of space objects. Sample return missions indicate hundreds of minor strikes, but rigorous analysis is often only performed when a strike causes an anomaly in spacecraft performance. Developing techniques to identify and assess minor strikes that do not immediately cause anomalous behavior can help to validate models for debris populations and aid in the attribution of future anomalies. This study develops methods to detect subtle abrupt orbit perturbations indicative of minor debris strikes. An extended Kalman filter with dynamic model compensation is used to estimate a spacecraft’s orbit state based on simulated full-state (i.e., GPS) measurements. The filter is applied to the data forward and backward in time, and then a modified Fraser–Potter smoother is used to produce a fused state estimate. Various test statistics are developed and compared to identify abrupt unexpected changes in spacecraft velocity; techniques include McReynold’s filter-smoother consistency test and the Mahalanobis distance between forward and backward filter states. A trade study is performed to investigate the performance of test statistics as a function of filter parameters, and a Monte Carlo analysis illustrates the filter’s ability to detect and estimate strikes.

Impact of US-China trade war on Asian economies: neural network multilayer perceptron approach

Purpose This study aims to find the impact of the trade war between the USA and China on Asian economies. Apart from macroeconomic variables associated with trade, this study explicitly creates a trade war scenario and trade war participant dummies. Using the neural network multilayer perceptron, this study checks for the causal linkages between the predictors and target output for the panel of Asian economies and the USA. Design/methodology/approach A conceptual model of the after effects of trade war in a quadrant is developed. Variables related to trade and tariffs are included in the study for a panel of 19 Asian economies. The feedforward structure of neural network analysis is used to identify strong and weak predictors of trade war. Findings The hidden layers of the multilayer perceptron reveal the inconsistency in linkages for the predictors’ services exports, tariff measures, anti-dumping measures, trade war scenario dummy with gross domestic product. The findings suggest that to curtail the impact of the trade war on Asian economies, predictors with neural evidence must be paid due weightage in policy determination and trade agreements. Originality/value The study applies a novel and little explored AI/ML technique of Neural Network analysis with training of 70% observations. The paper will provide opportunity for other researchers to explore techniques of AI/ML in trade studies.

The Russian-Iranian Silk Trade during the Reign of Shāh Ṣafī I (1629–1642)

Abstract This article is a case study of the Russian-Iranian silk trade, particularly during the period of the Safavid Shāh Ṣafī I (1629–1642). During his reign, substantial changes occurred in the state silk trade, which also affected the Russian-Iranian trade. This study mainly focuses on the amount of Iranian silk exported to Russia by royal merchants, the form the Russian-Iranian silk trade took and the mode of transport as well as the main trade routes. Our research is based on archival historical sources from the Russian state archives of ancient documents. The study thus aims to shine new facts on the Russian-Iranian trade relations in the early modern period.

The illegal trade in European eels: outsourcing, funding, and complex symbiotic-antithetical relationships

Whilst traditionally, the study of the illegal wildlife trade has been focused on species that consider the role of the Western world as a consumer or transit hub, the study of the illegal trade in European eels is a landmark opportunity to deem the role of Europe as a source area for wildlife trafficking. Based on a qualitative methods research design, this case study delves into the nature of the illegal trade in European eels. In the context of globalization, a global eel market developed at the same time as stocks decreased sharply. The process of criminalization of the trade and fishing of eels, in combination with criminogenic asymmetries, has been facilitating opportunities for crime groups. In the legal-illegal continuum, upper- and underworld actors interact along the chain from rivers to worldwide consumers. This constant interaction fuels the illegal trade by providing the underworld with vital elements for its continuity, such as protection, finance, or laundering. In particular, this research focusses on the different legal-illegal interactions by looking at outsourcing, funding and complex symbiotic-antithetical relationships.

Examples of performance requirement derivation through modelling and simulation in complex projects

ABSTRACT As complexity continues to increase in modern systems development, performance requirement derivation in Defence materiel acquisition projects continues to present challenges. Overspecification introduces technical risk that can lead to project cost and schedule blow-outs. Insights into precise thresholds for system requirement performances can be difficult to ascertain through expert judgement. Operational analysis involves the modelling and simulation of the Defence materiel platform’s operational scenarios. The most challenging scenarios drive system performances, understood in the context of the combinations of systems working together to achieve operational effects. Such modelling and simulation provides insights into the capabilities of interest to Defence Service customers, and is useful for decision-making, trade studies and design negotiations. This work describes two such examples of operational analysis modelling and simulation used in a recent large and complex Defence materiel acquisition project for the derivation of system performance requirements and the facilitation of trade studies and design negotiations.

Global Impacts of a Bilateral Trade Policy on Ballast Water-Mediated Species Spread Risk: A Case Study of Sino-US Trade.

A trade policy could generate both economic and environmental impacts. This work is focused on the impacts of a bilateral trade policy on ballast water-mediated nonindigenous species (NIS) spread risk. Taking the hypothetical Sino-US trade restriction as an example, we integrate a computable general equilibrium model and a higher-order NIS spread risk assessment model to examine the impacts of bilateral trade policy on both the economy and NIS spread risks. We have two important findings. First, the Sino-US trade restriction would cause decreases in NIS spread risks to China and the US, as well as to three quarters of worldwide countries/regions. However, the rest one fourth would experience increased NIS spread risks. Second, the relationship between changes in exports and changes in NIS spread risks might not be directly proportional. This is observed with 46% of countries and regions that would see their exports increase but their NIS spread risks drop, with positive impacts on both their economies and environment under the Sino-US trade restriction. These results reveal both broader global impacts as well as the decoupled economic and ecological impacts of a bilateral trade policy. These broader impacts demonstrate the necessity for national governments, which are parties to bilateral agreements to give due consideration to the economic and environmental impacts on countries and regions outside of the agreement.

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

AbstractWhen formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML) model in the prediction of solid propellant burning rate parameters. This ML model operates over a large parameter space including ammonium perchlorate composite, plastic bonded high explosive, and double‐base propellants. The model's accuracy, adaptability, and prediction capabilities are presented and discussed. The effects of different materials on a propellant's expected burning rate are examined.

Trade, taste and ecology: honey in late medieval Europe

ABSTRACT Often considered a ubiquitous and widely available sweetener, this article represents the first study of the honey trade across Europe in the later Middle Ages. Demand for honey, fuelled by diverse cultural and social factors, encouraged an international trade that by the late medieval period spanned the Mediterranean, western Atlantic, and the North and Baltic Seas, connecting peoples, traders and landscapes from Beirut to Novgorod. As a natural product whose make up and taste was influenced by the environments and ecology in which it was produced, the honeys available to European contemporaries could vary significantly in taste, colour and viscosity, influencing reputation, price and societal value. A study of the honey trade in late medieval Europe sheds new light on how cultural developments, social trends, economic practicalities and political events influenced the consumption of a widely available but diverse commodity.

Wildlife trade study based on the ANOVA method

Today, 78% of new human infectious diseases originate from wild animals. A complete ban on wildlife trade would be effective in stopping the occurrence and spread of infectious diseases, but it is estimated that a ban on wild meat consumption could cost China's economy 50 billion RMB ($7.1 billion) and put millions of people out of work. Therefore, it is worth considering whether the wildlife trade should be banned in the long term. In this paper, we first collected data on wildlife trade for 20 years and performed steps such as integrating and classifying the data, testing statistics, data pre-processing, and eliminating outliers. The most traded species was the macaque. Then, a principal component analysis was conducted to analyze the contribution of each purpose of wildlife trade, and a multiple regression analysis was used to obtain the most important purposes of commercial, zoo, and circus or traveling exhibitions. Finally, through one-way ANOVA, it was found that wildlife trade import and export data did not vary much in different years, but in 2019, the number of imports decreased sharply and the number of exports surged.

Introduction to the concept of modular blocks for lunar infrastructure

With the major progress of the Artemis program, and the development of NASA's largest rocket since the Apollo era capable of interplanetary travel, Americans are closer than ever to reaching back for the Moon, but this time with the intent of establishing a constant human presence. This vision is further strengthened by the rise of the private rocket sector with SpaceX, RocketLab, Relativity Space, Blue Origin, etc. The establishment of the lunar infrastructure will be a stepping-stone towards reaching for a farther destination, Mars. This paper presents a novel idea of lunar infrastructure construction, with a focus on habitation modules, based on the recently published work by the authors, focusing on the preliminary trade study of various concepts of lunar habitats. The modular blocks are introduced, representing the main building components for any lunar infrastructure. The modular blocks for habitat structures are designed as hollow bricks, optimized in a way that requires the least amount of construction material while implementing 3D printing technologies and minimizing the on-site human construction and assembly. Furthermore, different constructability techniques are presented, together with the initial stages of their development and materials which can be used for their manufacturing. Future work will encompass material properties, a more detailed explanation of manufacturing, global design, and structural analyses.

Measuring and Architecting System Resilience Through Trade Study Analysis

Resilience is a dimension of trade study analysis that must be accounted for in early conceptual system design because it can influence cost, reliability, performance, and overall lifecycle cost. Traditional trade studies, which focus on risk, safety, and costs aspects analysis are no longer sufficient because they only account for predicted and known disruptions. This article proposes a practical and implementable methodology for measuring, predicting, and architecting system resilience during conceptual design that considers both known and unknown disruptions. This article extends the current literature by motivating the resilience curve using a Markov process, verifying resilience with empirical data, and incorporating the interaction between subsystems using common cause failure with the binomial failure rate. A verified simulation model is used as a predictive analysis tool and applied to an unmanned surface vehicle case study.

Built On-orbit Robotically assembled Gigatruss (BORG): A mixed assembly architecture trade study.

This paper explores a mixed assembly architecture trade study for a Built On-orbit Robotically assembled Gigatruss (BORG). Robotic in-space assembly (ISA) and servicing is a crucial field to expand endeavors in space. Currently, large structures in space are commonly only deployable and must be efficiently folded and packed into a launch vehicle (LV) and then deployed perfectly for operational status to be achieved. To actualize being able to build increasingly large structures in space, this scheme becomes less feasible, being constrained by LV volume and mass requirements. ISA allows the use of multiple launches to create even larger structures. The common ISA proposals consist of either strut-by-strut or multiple deployable module construction methodologies. In this paper, a mixed assembly scheme is explored and a trade study is conducted on its possible advantages with respect to many phases of a mission: 1) manufacturing, 2) stowage and transport, 3) ISA, and 4) servicing. Finally, a weighted decision matrix was created to help compare the various advantages and disadvantages of different architectural schemes.

Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China.

Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.

Embodied carbon transfers and employment-economic spillover effects in China’s inter-provincial trade

Understanding the embodied carbon transfer in inter-provincial trade and its employment-economic spillover effects is of crucial value in achieving carbon equity management. Surprisingly, few studies have focused on the intrinsic relationship between embodied carbon, embodied GDP, and embodied employment in iinter-provincial trade and its equity implications. Based on the 2012 and 2017 multi-regional input-output tables, our study of inter-provincial trade in 30 Chinese provinces shows that: 1) net outflows of embodied carbon were concentrated in the Beijing-Tianjin region and the eastern and southern coastal regions, while net inflows were in the central and northwestern regions; 2) embodied carbon, GDP, and employment were characterized by nearby transfer, complementary energy economy, and asymmetric transfers in and out; and 3) western provinces, which relied heavily on traditional energy and heavy chemical industries, gained a competitive disadvantage implying by the internal relationship between net transfers of embodied carbon, GDP, and employment. To mitigate the inequity of inter-provincial carbon trade, top-down climate goals must be aligned with bottom-up socio-economic incentives to achieve balanced regional development and improved public welfare.

Enhancing aircraft control surface effectiveness by co-flow jet flap at low energy expenditure

This paper conducts Improved Delayed Detached Eddy Simulation (IDDES) to compare applying co-flow jet (CFJ) active flow control on the flap and on the front main part of an aircraft control surface. Trade studies with varying CFJ momentum coefficients (Cμ) and flap deflection angles (δ) indicate that using CFJ on the flap is much more effective and efficient than applying CFJ on the front main part of the control surface. It is attributed to the feature that CFJ is more advantageous to work in severe adverse pressure gradient (APG). With a small Cμ of 0.025, the configuration with CFJ on the flap achieves a 46.1% increase of lift coefficient (CL) with an 80.8% lower power consumption compared with the one using CFJ on the front part. Moreover, the control surface with CFJ on the flap can attach the flow very well at a very high deflection angle of 70∘ and achieves a CL of 2.5 times larger than that of the baseline control surface.

A Framework for Estimating Effects of Combustor Turbulence on Turbine Profile Loss Generation

Abstract This paper describes a framework to quantify the effect of freestream turbulence, generated by mixing processes in a combustor, on turbulent boundary layer loss generation in the high-pressure turbine downstream of the combustor. The regime of freestream turbulence common to gas turbine aero engines is identified and it is shown that the dissipation loss coefficient in this regime can be determined using existing measurements of the effect of freestream turbulence on skin friction. The paper shows that combustor-generated freestream turbulence can increase the profile loss coefficient of a typical high-pressure turbine blade by as much as 28%. A relation has been derived between a non-dimensional turbulence parameter, which characterizes the freestream turbulence, and the increase in turbine boundary layer dissipation, which quantifies the decrease in turbine efficiency. The relation provides guidelines for combustor turbulence modifications that lead to turbine performance benefits. The framework has been applied in example trade studies which show that increasing the size of dilution ports and increasing the length of the combustor can decrease high-pressure turbine profile loss generation to potentially increase stage efficiency up to 0.5%.

Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions

Methane is a prevalent greenhouse gas with potent heat trapping capabilities, but methane emissions can be difficult to detect. Hyperspectral imagery is an effective method of detection which can be used to locate methane emission sources, as well as provide accountability for reaching emissions reduction goals. Because of methane's absorption features, both shortwave infrared (SWIR) and longwave infrared (LWIR) hyperspectral sensors have been used to accurately detect methane plumes. However, surface, environmental, and atmospheric background conditions can cause methane detectability to vary, and there have not been previous studies which evaluate this variability over a wide range of conditions. To assess this variation, this trade study compared methane detectability for two airborne hyperspectral sensors: AVIRIS-NG in the SWIR and HyTES in the LWIR. We modeled methane plume detection under a wide range of precisely known conditions by making use of synthetic images which were comprised of MODTRAN-generated radiance curves. We applied a spectral matched filter to these images to assess detection accuracy, and used these results to identify the conditions which have the most significant impact on detectability in the SWIR and LWIR. We then computed the specific boundaries on these conditions which make methane most detectable for each instrument; these novel results explore methane detectability over a broader range of conditions and sensors than previous studies. This trade study and methodology can aid decision making about which sensors are most useful for various types of methane emission analysis, such as leak detection and emission rate quantification.

Modeling the Effects of Oscillator Phase Noise and Synchronization on Multistatic SAR Tomography

Recent results have highlighted the potential ability of bi- and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and a low frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multi-platform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the point target response may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in SIMO mode are the most affected by oscillator phase noise error, followed by MIMO, with negligible effects on the SAR-SISO mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

The Digital Revolution in Trade Finance: Exploring The Impact of Smart Blockchain-Based Letters of Credit On E-business Transactions

This paper aims to explore the impact of Smart Blockchain-based Letters of Credit (BTLOC) on business transactions in the realm of trade finance. The involvement of a third party in business transactions often leads to complications such as process heterogeneity, increased complexity, information security risks, and higher costs. To address these challenges, this research proposes an innovative solution for activities dependent on third-party participation, specifically in the context of global trading. To provide a comprehensive understanding of this solution, the study employs business process modeling in a transaction scenario, offering a deeper insight into its mechanics. The implementation of platforms built upon blockchain technology (BT), and smart contracts has the potential to significantly reshape and streamline business procedures, thereby benefiting participants engaged in global trade. This research primarily focuses on investigating the theoretical aspects and feasibility of incorporating BT into global trade, considering a paradigm shift in the field. A novel BTLOC is introduced as a key element of the research, enabling the examination of its practicality. Additionally, we explore the applications of BTLOC in real case study of international Trading and explore its potential integration into trade finance processes. Through a multi-case analysis, this research contributes to the understanding of the paradigm shift facilitated by BT. The findings shed light on the future potential applications of blockchain in finance and serve as an illustrative example of the extended capabilities associated with financial processes.

Marketing in Globalization Prospective: A Case Study of Global Trade in Contemporary Era

Marketing in Globalization Prospective: A Case Study of Global Trade in Contemporary Era