External Dependency Research Articles

Summaries of Articles

Summaries of Articles

Enhancing Behavioral Dependency for Effective Computing in Software

Software plays an important role in effective computing and communication of any services. It become crucial to identify some critical parts of the software that can lead to enhanced computing and increases efficiency of the software. Dependency plays a significant role in finding relationship amongst classes and predicting change prone classes. This paper aims to enhance Behavioral Dependency by defining 6 types of dependencies amongst classes. These are (i) direct behavioral dependency (ii) indirect behavioral dependency (iii) internal behavioral dependency (iv) external behavioral dependency (v) indirect internal behavioral dependency and (vi) Indirect External Behavioral Dependency. Evaluating these dependencies, gives accurate results for the prediction of change prone classes. Further, paper compares proposed approach with existing methods.

Food Import Dependency and National Food Security: A Price Transmission Analysis for the Wheat Sector.

Agricultural trade liberalization and protecting domestic markets encompass conflicting policy goals. Even though after the food crisis in 2008, national governments of food-deficit nations aimed at reducing food supply dependency on external markets, no research has assessed the impacts of food import reliance on price or price volatility transmissions to local markets. We constructed a dynamic conditional correlation (DCC)–generalized autoregressive conditional heteroscedasticity (GARCH) model to examine whether wheat import dependency could make a country vulnerable to overseas shocks by analyzing the inter-relationships between the international wheat price and retail wheat flour prices in 10 net importing countries over the sample period from January 2005 to December 2019. It was found that retail price volatility in each region was positively correlated with international price volatility for most of the period concerned. We also discovered that external dependency could significantly protect the domestic market from the global one, implying that lowering wheat dependency on foreign markets improves “stability” and “availability” of food security without sacrificing “utilization”, but it may aggravate “access”.

Modeling label-wise syntax for fine-grained sentiment analysis of reviews via memory-based neural model

Fine-grained sentiment analysis has shown great benefits to real-word applications, such as for social media texts and product reviews. While the current state-of-the-art methods employ external syntactic dependency knowledge and enhance the task performances, most of them make use of merely the dependency edges, leaving the dependency labels unexploited, which the work presented here shows to be also of great helpfulness to the task. In this study we leverage these syntactic features for improving fine-grained sentiment analysis. Compared to previous studies, our method advances following aspects. First, we are the first to propose a novel label-wise syntax memory (LSM) network for simultaneously encoding both the syntactic dependency edges and labels information in a unified manner. Additionally, we take the advantage of the current state-of-the-art contextualized BERT language models to provide rich contexts towards the targeted aspects. We conduct experiments on five benchmark datasets, and the results demonstrate that our model outperforms current best-performing baselines, and achieves new state-of-the-art performances. Further analysis is conducted, proving the necessity to encode sufficient syntactic dependency knowledge for the task, also illustrating the effectiveness of our LSM encoder on modeling these syntax attributes. By exploiting rich syntactic information, our framework outperforms baselines in identifying multiple aspects of sentiment analysis as well as the long-range dependency issues.

Privacy Leakage and Protection of InputConnection Interface in Android

Leakage of user credentials has been a conventional security threat for mobile users. In this work, we discover a new leakage threat caused by a vulnerability of the input method framework (IMF) on Android. The vulnerability lies in an IMF interface, called InputConnection, which is dynamically built to deliver user inputs from an active input method (e.g., software keyboard) to WebView, which is an essential Android component rendering Web pages. It allows the IMF interface of a WebView component to be hijacked by the app or the third-party library that embeds the WebView. Such hijacking can be exploited to steal user inputs on the Web pages loaded by the WebView. It can also eavesdrop on input fields of all the Web pages loaded by WebView without user awareness; the attack is self-contained and does not require any external dependency. It does not interrupt, delay, or change normal operations. More threateningly, this attack is easy to launch and works for most Android versions (from 4.4 to 11.0). We conduct a field study including more than 1500 tests on our developed IWH attack app. The result shows that the app can successfully steal user inputs in all the tests and identify the input strings with 98.0% accuracy. We further devise two solutions, a Web-based virtual keyboard and an IMF hijacking guardian, for mobile Web services and the Android platform, respectively. We finally prototype them on a Web server and on an Android framework, respectively, to confirm their effectiveness.

Macroeconomic Consequences of Covid-19 in a Small Open Economy: An Empirical Analysis of Nigeria

Nigeria is a small open economy with a high level of external dependency especially on the export of crude oil for foreign earnings and government revenue and import of consumables goods including pharmaceutical products. Currently, China and USA contribute more than 35% of Nigerian total import and in addition with Euro area constitute top export destinations of Nigerian crude oil. Studies in the past have investigated the vulnerability of Nigerian economy to external shocks, however, the emerging shocks from global economy due to COVID-19 seems unprecedented. Thus, it is imperative to preemptively examine the likely spillover effects of COVID-19 pandemic to a small open economy like Nigeria based on shocks to strategic trade partners. Given this background, this study investigates the macroeconomic consequences of COVID-19 in China, the Euro area and United States of America (USA) in Nigeria using Global Vector Autoregressive (GVAR) approach. This modelling approach provides an opportunity to analyze international macroeconomic transmission of shocks and spillovers between different countries. It also provides a framework to offer adequate tools to deal with the curse of dimensionality that may arise during the analysis. Macroeconomic variables such as exchange rate, economic growth, inflation rate, trade flows and consumers’ spending were employed from Nigeria and other COVID-19 infected partner countries to build the GVAR model. Similarly, variable such as oil price and world commodity price index served as global variables. These variables were introduced quarterly to obtain stable behavioural interactions. Subsequently, simulations were performed to capture economic reality of COVID-19 and policy reactions in COVID-19 infected partner countries. The study identified output and inflation shocks in USA and China as important external shocks to the Nigerian economy however, oil price shocks constitute the biggest external threat to the economy during and post COVID-19 era. © 2021 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

The Effect of External and Internal Resource Dependency on Foreign IPO Underpricing

The Effect of External and Internal Resource Dependency on Foreign IPO Underpricing

Renewable Generation Electric System

A stable and sustainable energy industry contributes indubitably to the business development, as well as to guarantee the quality of life of a population. However, the economic and social development of insular regions is very vulnerable, due to the high external energetic dependency. Therefore, it is necessary to change the current paradigm of those regions, to take advantage of their endogenous resources to generate electrical energy. The developed work analyses the potential renewable energies (wind and solar) present on the island of Brava, Cape Verde, and assesses its capacity to combine them with an energy storage system that can meet the energetic requirements of the island. The first step consists in the analysis and profiling of the data (energy consumption, solar radiation, wind speed) observed in the island for one year. Simultaneously, several types of storing technologies are analyzed to find the best fitting solution, given the characteristics of the electrical system of the island. The next step consists in the development of a mathematical model, based on LCOE calculus and on the renewable penetration level that determines the optimal mix of power to install of each technology. The results allow to conclude that, despite the inferior solar power generation when compared to the wind power, the most economical solution to the implementation of a 100% renewable system consists of an electricity production park primarily composed by solar powers.

Vulnerabilities of Brazil's domestic pharmaceutical and biotech industry in the context of the COVID-19 pandemic

During a pandemic, economic, technological, and health systems' vulnerability become even more evident. A key challenge in Brazil is to control the trade deficit and difficulty in access to medicines and health products, even their development. Brazil's heavy external dependency on health inputs and products is one of the negative factors in confronting the global health emergency caused by SARS-CoV-2. The article aimed to discuss the vulnerabilities of the domestic chemical and biotechnological subsystem in the face of the current pandemic, with a focus on the pharmaceutical and biotech industry and the linkage between the fields of health and political economics. A qualitative study was performed with a literature review and analysis of secondary data as the methodological procedures. The results revealed low investment in public health, low innovation capacity, the pharmaceutical industry's profile, external dependency, currency exchange policy, patents, and other factors, demonstrating the major vulnerability in innovation and technology in the domestic pharmaceutical and biotech industry and the essential nature of linkage between various fields, especially health and the economy, for the incorporation of a systemic vision that creates the conditions to reduce vulnerabilities in the response to the pandemic and promote a development project for the country.

Financial forecasting using stochastic models: reference from multi-commodity exchange of India

<abstract> <p>Non-linear forecasting models, including artificial neural networks, are popularly adopted in financial forecasting. These models require high computational infrastructure and resources for practical training and deployment, unlike linear forecasting models. However, this level of infrastructure and resources is not accessible to most market participants. This paper lays down a systematic approach to build a simplistic and effective forecasting model, which empowers investors to make informed decisions using a minimal computational infrastructure. Natural gas futures traded in the Multi-Commodity Exchange of India were identified as an appropriate asset for the study considering the massive, expected movement in energy consumption pattern in India. We have used data analytics and statistical techniques to identify optimal training strategies and features, which resulted in an accurate linear forecasting model. Data analytics also helped to accurately establish the context of the study with the identification of a positive shift in the sentiments of market participants of the asset, which was duly verified using the substantial change in the valuation of the asset. While formulating the forecasting model, several avenues, including identifying weekly and yearly patterns, introducing seasonality and exogenous variables were explored. The paper concludes that such attempts to reduce external dependency and segregate noise data result into better model performance with minimal computational resource and infrastructure.</p> </abstract>

Planning Power System Portfolio of Turkey through Analytic Hierarchy Process and Data Preprocessing Abstract

Bu çalışmada amaç, enerji konusunda dışa bağımlı bir ülke olan Türkiye’nin 2020-2039 yılları arasında ihtiyaç duyacağı elektrik enerjisini karşılayabilecek bir yatırım planı sunmaktır. Elektrik enerjisi üretimi için stratejik yatırım planı sürdürülebilir arz güvenliğinin sağlanması açısından kritik öneme sahiptir. Yatırım planının oluşturulması aşamasında çok kriterli karar verme yöntemlerinden Analitik Hiyerarşi Süreci (AHS) kullanılmıştır. Model oluşturulurken uygulanan bir dizi veri önişleme tekniği sayesinde AHS’nin öznellik yaklaşımı büyük ölçüde ortadan kaldırılarak görece daha nesnel sonuçların elde edilmesi sağlanmıştır. Çalışmaya özgünlük kazandıracak şekilde kullanılan veri önişleme teknikleri ve AHS yöntemiyle Türkiye Cumhuriyeti Enerji ve Tabii Kaynaklar Bakanlığı’nın (ETKB) stratejik hedefleri doğrultusunda bir portföy senaryosu ortaya konmuştur. Araştırma elde edilen portföy senaryosuyla ETKB’nin hedeflerini destekleyecek nitelikte bulgular ortaya koymaktadır. Buna göre, artan ihtiyacı karşılarken kullanılan enerji kaynaklarına göre elektrik üretiminde kömürün %10,6, doğal gazın %3, hidroelektrik kullanımının ise %4,3 azalacağı, rüzgârın %1,68, güneşin %4,34 ve jeotermal kullanımının da %2,98 artacağı öngörülmüştür. Sonuç olarak, yapılan planlama dışa bağımlılık oranını, fosil yakıt kullanım oranını ve emisyon değerlerini düşürürken, yenilenebilir enerji kaynaklarının kullanımını, istihdam potansiyelini ve arz güvenliğini arttırmaktadır. Bu araştırmanın sonuçları ETKB’nin 20 yıllık stratejik planında yer alan hedeflerin tutarlı olduğunu göstermesi açısından büyük önem arz etmektedir.

Lessons learned from the polio eradication initiative in the Democratic Republic of Congo and Ethiopia: analysis of implementation barriers and strategies

BackgroundSince its inception in 1988, the Global Polio Eradication Initiative (GPEI) has partnered with 200 countries to vaccinate over 2.5 billion children against poliomyelitis. The polio eradication approach has adapted to emerging challenges and diverse contexts. Knowledge assets gained from these experiences can inform implementation of future health programs, but only if efforts are made to systematically map barriers, identify strategies to overcome them, identify unintended consequences, and compare experiences across country contexts.MethodsA sequential explanatory mixed methods design, including an online survey followed by key informant interviews (KIIs), was utilized to map tacit knowledge derived from the polio eradication experience from 1988 to 2019. The survey and KIIs were conducted between September 2018 and March 2019. A cross-case comparison was conducted of two study countries, the Democratic Republic of Congo (DRC) and Ethiopia, which fit similar epidemiological profiles for polio. The variables of interest (implementation barriers, strategies, unintended consequences) were compared for consistencies and inconsistencies within and across the two country cases.ResultsSurveys were conducted with 499 and 101 respondents, followed by 23 and 30 KIIs in the DRC and Ethiopia, respectively. Common implementation barriers included accessibility issues caused by political insecurity, population movement, and geography; gaps in human resources, supply chain, finance and governance; and community hesitancy. Strategies for addressing these barriers included adapting service delivery approaches, investing in health systems capacity, establishing mechanisms for planning and accountability, and social mobilization. These investments improved system infrastructure and service delivery; however, resources were often focused on the polio program rather than strengthening routine services, causing community mistrust and limiting sustainability.ConclusionsThe polio program investments in the DRC and Ethiopia facilitated program implementation despite environmental, system, and community-level barriers. There were, however, missed opportunities for integration. Remaining pockets of low immunization coverage and gaps in surveillance must be addressed in order to prevent importation of wild poliovirus and minimize circulating vaccine-derived poliovirus. Studying these implementation processes is critical for informing future health programs, including identifying implementation tools, strategies, and principles which can be adopted from polio eradication to ensure health service delivery among hard-to-reach populations. Future disease control or eradication programs should also consider strategies which reduce parallel structures and define a clear transition strategy to limit long-term external dependency.

Planning for a 100% renewable energy system for the Santiago Island, Cape Verde

Ensuring the supply of affordable energy, improving energy efficiency and reducing greenhouse gas emissions are some of the priorities of the governments of several countries. The pursuit of these energy goals has triggered interest in the exploration and usage of Renewable Energy Sources (RES), which can be particularly appropriate for island systems as is the case of Cape Verde. This work proposes a generation expansion planning model for Cape Verde considering a 20 years’ period. Different scenarios were analysed, each one representing a possible RES contribution for electricity production, reaching a 100% RES share. The results demonstrate that the increase of the RES in the system will lead to an increase of the total cost of energy. However, both CO2 emissions and external energy dependency of the country significantly decrease. The seasonality of the RES resources, and in particular of wind power is shown to be one of the most important challenges for the effective uptake of such a renewable power system. While the proposed model allowed already to present some useful scenarios, it becomes also evident the need to expand the analysis, and the paper concludes with directions for future research.

Environment-friendly and economical scheduling optimization for integrated energy system considering power-to-gas technology and carbon capture power plant

To mitigate global warming, CO2 emission reduction has become an urgent issue in the energy sector. In China, coal-to-gas, carbon capture and renewable energy utilization are important measures available among various low-carbon plans. However, inadequate gas supply, unclear utilization methods of captured CO2, and curtailment of excessive wind and solar power have hampered the effective implementation of the above measures. To solve this issue, this paper presents a coupling model considering power to gas (P2G) technology and carbon capture power plants (CCPP), and extends it into an integrated energy system (IES) including electricity, thermal and gas, then proposes an economic and environment-friendly scheduling model at a high wind power penetration level. Through the coupling of CCPP and P2G in IES, most of CO2 emitted by the system can be captured and sent to the P2G facility to produce synthesized gas, which provides a new idea of utilizing CO2 rather than storage. The synthetic gas can be provided to the system to reduce the external gas dependency. In addition, the simulation results also show that the ability of the system to absorb renewable energy is significantly enhanced with the presence of CCPP and P2G.

Towards a sustainable energy scenario? A worldwide analysis

Abstract This paper studies the worldwide evolution of energy intensity for a large sample of countries during the period 1990–2015, differentiating between non-renewable, non-clean and total energy intensity. This division allows us to establish more precise policy recommendations which, along with the use of the Phillips and Sul (2009) methodology, provides the novelty of the analysis. Our results refute recent evidence favouring the hypothesis of global convergence for all types of energy intensity. Grouping countries either by regions or by income level, the evidence against this hypothesis remains overwhelming, with very few exceptions. Nonetheless, we can observe the presence of several convergence clubs, whose creation strongly depends on energy prices as well as on external energetic dependency. In any event, results relating to the different types of energy intensity are varied, suggesting that previous policy recommendations aimed at tackling climate change based on total energy intensity analyses are questionable.

Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques

Technological advancements coupled with growing world population require the increasing need of energy. Natural gas is one of the most important usable energy resources. Turkey is with high external dependency on energy as it has its own limited natural and underground energy resources. Thus, in order to effectively and productively use of natural gas purchased from foreign countries and to make reliable and robust energy policies for the years ahead, it is crucial to make a reasonable and plausible prediction for natural gas consumption of Turkey. In this paper, we estimate the natural gas consumption using machine learning techniques on the basis of real monthly data representing natural gas consumption of Turkey between the years 2010 and 2018. The performances of machine learning techniques involving Artificial Neural Networks, Random Forest Tree, Regression, Time Series and Multiple Seasonality Time Series are compared in predicting the natural gas consumption of Turkey. Experimental results show that among the five techniques, artificial neural networks produce the best estimation, having the lowest mean square errors, followed by regression method. Time series shows the worst performance among all the techniques.

Efficiency performance of Current Account-BoP flows in advanced world economies considering GHG emissions

Current Account (CA) imbalances reveal the unequal Trade and Foreign Direct Investment (T&FDI) flows between various countries. This paper investigates the performance of the CA component of the Balance of Payments (BoP) of major advanced economies following the recent global financial and economic crisis (2013–2017) using a Data Envelopment Analysis (DEA) second-stage approach. Slack-based inefficiency (SBI) DEA models are proposed, using both a conventional perspective and a sustainability perspective that consider greenhouse gas (GHG) emissions as an undesirable output. The efficiency assessment aims to identify output slacks (i.e. exports and income inflows shortfalls) as well as input slacks (i.e. import and income outflows excesses). In the second stage, explanatory factors of the observed CA inefficiencies have been investigated, using regression models under frequentist and Bayesian frameworks. Although some differences exist between the Conventional and Sustainability scenarios regarding the negative effects of trade diversification and external debt and energy dependency on CA inefficiency, in both scenarios the results indicate links between CA inefficiency and geographical regions, socio-economic development and the burden of customs procedures.

Assessing the conundrums of the Cuban economy under the revolution (1959–2019)

This article evaluates the resilient challenges faced by Cuba’s economy during the six decades of the Revolution (1959–2019), mainly its historical external economic dependency upon a foreign country (the USSR and Venezuela) and the adverse consequences that occur when such relationship ends or deteriorates. Several hypotheses address that issue as well as others such as the hindrance of the US embargo/blockade aggravated by Donald Trump’s punitive sanctions and Cuban continuity of a centrally planned economy and predominance of state enterprises over the market and the non-state sector. The core is an analysis of performance of key economic indicators: GDP growth, gross capital formation, financial stability, mining-industry agriculture output, tourism and exports of professional services. Hypotheses are tested with long-term statistical series elaborated and scrutinized by the author over half a century based on Cuban official sources, as well as legislation, articles by Cuban scholars and from news media. Among the results: external dependency has declined somewhat but demands deeper economic reforms, there are no countries currently capable and willing to fully replace Venezuela’s substantial aid, the ongoing crisis provoked by the deteriorating Venezuelan economy cum Trump’s sanctions should have lesser adverse effects than the 1990s crisis that resulted from the USSR disappearance. The article is a significant contribution to the study of Cuba’s external economic dependence and its consequences, and should be also worthy for other small economies and socialist countries.

The Energy Sector and the Internet of Things – Sustainable Consumption and Enhanced Security through Industrial Revolution 4.0

With integration between machine automation and data being the hallmark of industrial revolution 4.0, the resilience of energy infrastructure in modern economy has taken a new significance. The study aims at discussing the potential and examining the impacts of the Internet of Things (IoT), which are smart devices with embedded sensors and connectivity, enabling data exchange to the energy sector. This technology contributes towards enhancing industry’s sustainable practices through the industrial internet of things. With data from the “edge of the grid,” these sensors assist in efficient energy consumption, providing constant monitoring for the regulatory authority, particularly on pollutant emissions. IoT technology may complement the national electric smart-grid, enhancing its reliability by feeding these raw data into machine learning neural network for the optimal operation. All these technologies shall complement one another, as Malaysia transform from a net energy exporter into an energy importer. Practicing efficient energy consumption can reduce this external dependency, and enhance national energy security. This paper derives statistical data sourced from the Energy Commission and technical data from publications of other scholars. On smaller scale, IoT implementation in manufacturing plants may resulted in 15% operating cost reduction. The benefits on national level implementation however remains unknown.

Development metrics measurement level for component reusability evaluation approach (CREA)

The study of software component reuse is rising in software development field and one of the methods used to reduce the production cost and time. Among the problems faced by software developers in component reuse, is the difficulty to determine which set of components are suitable to use in new software development. Thus, this study was conducted with the purpose; to define the characteristics of software component reusability evaluation approach (CREA) based on experienced software developer’s feedback, and to estimate the measurement level for each of the predefined metric. Three characteristics and sub characteristics, namely understandability (documentation level and observality), adaptability (customizability), and portability (external dependency) were identified that have been used to develop the metrics for CREA. The result for all metrics will be used as an input to the fuzzy inference system (FIS) for measuring the reusability level of the component.