Correction Mechanism Research Articles

Corruption Risk Maps as a Solution for the Management of Resources in the Context of Environmental Disasters

Corruption in the public sector is an elusive and opaque phenomenon. Government reaction to disasters and its consequent investment in economic resources constitute a favourable opportunity for nepotism, embezzlement and other criminal situations. Detection of these scenarios requires the application of sophisticated methodologies that should be incorporated into the processes of disaster risk management. The use of corruption risk maps has been put forward as a potential solution to this problem. This technique acts as a disincentive to corrupt behaviour in government processes through collective and agreed anticipation of acts of corruption. Corruption may happen at all levels of government, given that economic resources for disaster relief are distributed at municipal, provincial and national levels. This tool helps detect and measure incidents of corruption in government processes and simultaneously designs and executes corrective mechanisms. This paper discusses the advantages derived from the analogue and digital application of this instrument, presenting the requirements for its use, its potential and future challenges. The identification of benefits has been made possible by the incremental application of the tool within the framework of a process of institutional transformation of the national government in Paraguay with the institutional and financial support of UNDOC and USAID.

Association between Financial Inclusion and Economic Growth in India: A Co-Integration Approach

This paper examines the association between financial inclusion and economic growth in the context of the Indian economy. The Auto Regressive Distributive Lag (ARDL), Error Correction Mechanism (ECM), and impulse response function have been used for establishing the association between the two. Using panel data regression over the time period 2001 to 2019, the paper shows that there exists a positive, bidirectional association between financial inclusion and economic growth in the long run. However, there is a unidirectional relationship in the short run, i.e., only economic growth is the precondition for financial inclusion. Therefore, in the long run, economic growth helps to remove demand and supply side obstacles while financial inclusion reduces poverty and inequality, and hence promotes inclusive growth.

Corruption: Governance challenge towards reducing unemployment in Nigeria

The study examines the possibility of having a very low rate of unemployment in Nigeria, if there is a reduced rate of corruption in the long‐term. While using cointegration regressions and Vector Error Correction Mechanism (VECM) over the period 1996–2020, it is affirmed that corruption could increase unemployment rate in the long run. The two corruption indicators employed (control of corruption and corruption index) are found to have a substantial effect on unemployment rate. Further evidence confirms that corruption and unemployment are cyclically interdependent. Findings indeed stress that a high level of corruption is harmful to employment growth. On the other hand, in the absence of sufficient job opportunities, rent‐seeking government officials would be more interested in collecting bribes from job seekers, which results in sustained unlawful practices among the public officials. Thus, adopting effective corruption‐control measures is critical. It is therefore suggested that to effectively tackle corruption incidents, there should be incentives for citizens or public officials to report bribery and the process of reporting corruption incidents should be further simplified. Strengthening anti‐corruption agencies and developing a sound legal framework that promotes a culture of lawfulness and impeccable practices in the public sector are central.

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

Once NEET, always NEET? A synthetic panel approach to analyze the Moroccan labor market

AbstractIn many regions of the world, the persistent, and growing, proportion of young people who are currently Not in Employment, Education or Training—a group increasingly referred to as NEET—is of global concern. This is no less true of Morocco: about 30% of the Moroccan population between age 15 and 24 are currently NEET. Drawing from various rounds of Moroccan Labor Force Surveys, this paper contributes to understanding the complex dynamics of labor markets in developing countries. First, it identifies the socioeconomic determinants of Morocco's NEETs. Second, employing a synthetic panel (SP) methodology in the context of labor market analysis, the paper describes how the condition of NEETs changes over time. One striking, and worrisome, pattern that emerges from the 2010 SP data is that, even after 8 years, a majority of the NEETs remained outside either the labor market or education, with very little chance of moving out of their situation. Their chronic stagnancy confirms the powerful effect that initial conditions have on determining young people's future outcomes. It also highlights the absence of corrective mechanisms and policy interventions within the Moroccan political economy landscape that could help to change NEETs' outcomes along the line.

Assessing Fiscal Sustainability in Indonesia: Error Correction Mechanism Diagnostic

Indonesia's debt is increasing and is not controlled properly, which will result in a fiscal budget deficit. This study aims to determine the condition of fiscal sustainability in Indonesia by looking at the factors that affect the debt-to-GDP ratio in 2012 Quarter I to 2022 Quarter II. Fiscal sustainability can be seen from the debt-to-GDP ratio proxy variable and the independent variables used are the previous quarter's debt ratio, economic growth, inflation, and the exchange rate. This research is a qualitative type with a brief descriptive about the state of the debt ratio and the variables that influence it and quantitatively using the Error Correction Mechanism (ECM) using statistical software called EViews. The results show that in the long term the debt-to-GDP ratio in Indonesia is significantly influenced by the previous quarter's debt ratio, economic growth, inflation, and the exchange rate. Meanwhile, in the short term, changes in the debt to GDP ratio are significantly influenced by changes in the debt ratio in the previous quarter, changes in economic growth, and changes in inflation.

INSTITUTIONALIZATION OF FISCAL RULES: INTERNATIONAL AND RUSSIAN EXPERIENCE

Economic instability forces governments to urgently support businesses and the public at the expense of budgetary imbalances. Such circumstances test flexibility of fiscal rules - restrictions on the main budget parameters. They are often suspended, the limit values of their indicators are adjusted, temporary concessions for their use are introduced. Both the introduction of such instrument and its sustainability over time depend primarily on thegovernment and policymakers. At the same time world experience proves that budget restrictions supported by the authorities need a complementing system of adaptation and controlling conditions allowing for a better adaptation to economic challenges and following their original task - to ensure medium-term budget balance while maintaining a conditionally constant level of budget expenditures. The subject in the paper is the institutional conditions for fiscal rules’ functioning. The purpose of the study is to identify the institutional framework used to ensure proper performance of fiscal rules and assess its practical applicability in Russia. Using the methods of descriptive statistical and comparative analysis of international and Russian practice in applying fiscal rules, the authors identify and analyze the list of such institutional conditions as medium-term expenditure frameworks, fiscal councils, correction mechanisms, escape clauses. The results of the study consist in formulated required and sufficient institutional framework for effective fiscal rules. Due to the use of only medium-term budget planning in Russia the remaining three may serve as a guideline for implementing budget restrictions during the current reform .

Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective

The most recent approaches for clustering single-cell RNA-sequencing data rely on deep auto-encoders. However, three major challenges remain unaddressed. First, current models overlook the impact of the cumulative errors induced by the pseudo-supervised embedding clustering task (Feature Randomness). Second, existing methods neglect the effect of the strong competition between embedding clustering and reconstruction (Feature Drift). Third, the previous deep clustering models regularly fail to consider the topological information of the latent data, even though the local and global latent configurations can bring complementary views to the clustering task. To address these challenges, we propose a novel approach that explores the interaction between local and global latent configurations to progressively adjust the reconstruction and embedding clustering tasks. We elaborate a topological and probabilistic filter to mitigate Feature Randomness and a cell-cell graph structure and content correction mechanism to counteract Feature Drift. The Zero-Inflated Negative Binomial model is also integrated to capture the characteristics of gene expression profiles. We conduct detailed experiments on real-world datasets from multiple representative genome sequencing platforms. Our approach outperforms the state-of-the-art clustering methods in various evaluation metrics.

Different neuromuscular control mechanisms regulate static and dynamic balance: A center-of-pressure analysis in young adults

The analysis of the center of pressure (CoP) trajectory, derived from force platforms, is a widely accepted measure to investigate postural balance control. The CoP trajectory could be analyzed as a physiological time-series through a general stochastic modeling framework (i.e., Stabilogram Diffusion Analysis (SDA)). Critical point divides short-term from long-term regions and diffusion coefficients reflect the level of stochastic activity of the CoP. Sample Entropy (SampEn) allows quantifying the CoP complexity in terms of regularity. Thus, this study aimed to understand whether SDA and SampEn could discriminate the neuromuscular control mechanisms underpinning static and dynamic postural tasks. Static balance control and its relationship with dynamic balance control were investigated through the CoP velocity (Mean Velocity) and the area of the 95th percentile ellipse (Area95). Balance was assessed in 15 subjects (age: 23.13 ± 0.99 years; M = 9) over a force platform under two conditions: static (ST) and dynamic, both in anterior-posterior (DAP) and medio-lateral (DML) directions. During the DAP and DML, subjects stood on an unstable board positioned over a force platform. Short-term SDA diffusion coefficients and critical points were lower in ST than in DAP and DML (p < 0.05). SampEn values resulted greater in ST than in DAP and DML (p < 0.001). As expected, lower values of Area95 (p < 0.001) and Mean Velocity (p < 0.001) were detected in the easiest condition, the ST, compared to DAP and DML. No significant correlations between static and dynamic balance performances were detected. Moreover, differences in the diffusion coefficients were detected comparing DAP and DML (p < 0.05). In the anterior-posterior direction, the critical point occurred at relatively small intervals in DML compared to DAP (p < 0.001) and ST (p < 0.001). In the medio-lateral direction, the critical point differed only between DAP and DML (p < 0.05). Overall, SDA analysis pointed out a less tightly regulated neuromuscular control system in the dynamic tasks, with closed-loop corrective feedback mechanisms called into play at different time intervals in the three conditions. SampEn results reflected more attention and, thus, less automatic control mechanisms in the dynamic conditions, particularly in the medio-lateral task. The different neuromuscular control mechanisms that emerged in the static and dynamic balance tasks encourage using both static and dynamic tests for a more comprehensive balance performance assessment.

Drift Resilient Frequency-Based Sensor Interface Architectures with Adaptive Clock Frequency

Maintaining the accuracy of a sensor system across various operating conditions has always been a challenge, especially for those operating in harsh surroundings such as a radiation environment. Concerning frequency-based sensor interfaces, supply voltage drifts and gain shift of the voltage-to-frequency converter (VFC) are critical design issues. These manifest as gain, offset, and linearity errors at the system level and therefore require continuous correction mechanisms. In this paper, dynamic gain and offset error-compensated open-loop frequency-based sensor interface architectures with adaptive clock frequency are proposed, which result in a ratiometric digital output. To address the mismatch issue, two architectures, one with periodic swapping of the VFCs’ inputs and outputs, and the other with the use of a single analog-to-digital converter (ADC) as an analog front end, are developed. The concepts were demonstrated with implementations on a Zynq board (ZYBO). The results of the first architecture showed that for a 25% gain mismatch between the VFCs, the output gain error was reduced from around 7.4% to 0.79% and the offset error was reduced from around 11.8% to 0.01%. Additionally, for the second architecture, a maximum of 0.11% gain error and 0.1% offset error were recorded for an emulated ±25% supply drift.

Determinants of Private Investment in Nigeria

This study investigates the determinants of private investment in Nigeria. Employing time series data for the period 1990 to 2020 gotten from the Central Bank of Nigeria Statistical Bulletin and World Development Indicators, the Auto-Regressive Distributed Lag Model (ARDL) and the Error Correction Mechanism estimation techniques were used to test private investment as the dependent variable and interest rate, money supply, credit to the private sector, inflation rate, and regulatory quality index as the independent variables. The results showed that all variables were correctly signed, and had statistical significance in explaining private investment in Nigeria during the period of study. The study, therefore, encouraged low interest rates to support and enhance lending to the private sector needed for investments, a control of rising inflation rate, monetary authorities developing and implementing policies to increase credit allocated to the private sector, creating and implementing sound policies and laws to support and encourage private sector development as recommendations.

Peran Kepolisian Dalam Menanggulangi Tindak Pidana Pengedaran Obat Secara Illegal (Studi Kasus Polres Gorontalo Kota)

This study aims to find out and analyze the role of the police in tackling illegal drug trafficking and find out what obstacles the police face in dealing with illegal drug trafficking at the Gorontalo City Police, based on the title raised in this study, researchers see the weakness of Indonesia's defense From the invasion of things that endanger society, thousands of illegal drugs have been circulating throughout Indonesia. This study uses empirical methods. The data collection process examined by researchers in this study were primary data and secondary data. Data collection techniques that support and relate to this research are interviews, observation and literature studies..The results of this study indicate that the cause of people becoming perpetrators of illegal drug distribution is because they want to get rich quickly, perpetrators take advantage of the lack of public knowledge about illegal drugs and the correct distribution mechanism according to the rules. There are several factors that support perpetrators involved in illegal drug trafficking. , namely: Economic factors and environmental and social factors. Illegal drug distribution in Indonesia is regulated in Law no. 36 of 2009 concerning health. The free circulation of illegal drugs in Indonesia has turned out to be used by many elements to gain large profits. Some transactions also occur online. The opportunity to buy cheap drugs and sell them at high/profitable prices is the motive of several individuals who have been caught in illegal drug trafficking. This will not happen if people are educated about economics and law. This is a joint responsibility of the government, the community, BPOM and the Indonesian National Police.

Network Coded Constrained Application Protocol With Improved Energy Efficiency for IIoT Networks

Constrained Application Protocol (CoAP) for low-power low-rate data transport in Industrial Internet of Things (IIoT) networks is typically running with two modes, namely, confirmable mode and non-confirmable mode, respectively. Confirmable mode relies on retransmission to ensure a guaranteed Quality of Service (QoS) in terms of packet loss rate at the cost of increased latency and power consumption. Whereas non-confirmable mode consumes less power, it is known to be packet loss prone. To enrich the CoAP transport for IIoT networks, we propose a packet-level Forward Error Correction (FEC) mechanism based on systematic coding with adaptive code rate to provide energy-efficient and reliable packet delivery. We mathematically analyze packet loss and cost of energy consumption of the proposed mechanism and compare it with confirmable CoAP transport scheme in the Gilbert-Elliott channel model. We demonstrate that the proposed mechanism can enhance the performance of non-confirmable CoAP to be comparable to confirmable CoAP in terms of packet loss rate, while outperforming it in energy consumption. The analytical and simulation results verify that the proposed mechanism is suitable for IIoT networks especially in high erasure burstiness scenarios.

Trade, Foreign Direct Investment and Economic Growth in Albania. Evidence from Time Series

Trade and Foreign Direct Investment has been treated as crucial factors underlying the relative growth rates experienced by the Albanian Economy, especially during the late years, thus, boosting economic growth in the country and improving the degree of integration of the Albanian economy into the World markets. The paper aims to provide an empirical assessment of the relationship between Trade, Foreign Direct Investment, and Economic Growth in Albania, by examining Trade and FDI nexus growth interactions using yearly time series data for a time span of 1993-2018. For this purpose, we employed cointegration analysis and Granger causality analysis. The co-integration tests, based on Vector Error Correction Mechanism (VECM), confirm the presence of a long-run relationship between the variables. VECM results support a negative relationship between trade and GDP in the long-run and a positive relationship between trade and FDI. Granger Causality tests support the causality evidence of one-directional reinforcement of GDP on trade in Albania and changes in GDP and trade are causing changes in FDI. The VAR analysis confirms that changes in GDP and FDI are encouraging changes in trade. The paper outlines policy implications with respect to promoting relevant institutional policies for the enhancement of trade and FDI activities in the country, which potentially could enhance economic growth in the country.

Spatial Heterogeneity in the Distribution of European Research and Development Funds and its Effects on Territorial Cohesion

The latest research and development (R&amp;D) framework programmes of the European Union (EU), “Horizon 2020” and “Horizon Europe”, have significantly increased the resources available to promote science and innovation in Europe. However, the strong competitiveness of the research teams and their search for excellence may cause inequality in the spatial distribution of investment effort in R&amp;D. The aim of this paper is to analyse the geographic distribution of R&amp;D spending in the EU. A greater concentration of funds is observed in the most advanced and dynamic economies, capable of promoting more competitive research teams and projects. Through an empirical analysis, estimated by a spatial convergence model, it is found that EU R&amp;D funds are preventing cross-regional convergence in Europe by driving growth mainly in wealthier regions. Based on these results, it seems relevant to consider spatial correction mechanisms for the distribution of R&amp;D resources so that they achieve greater territorial cohesion in Europe.

Linkages between different types of globalization and socio-economic variables: panel data analysis for 129 countries

Present study explores the linkages between three categories of globalization to socio-economic indicators, which include poverty, inequality, unemployment and Human Development Index (HDI) using the sample of 129 countries over the period 1990 to 2019. The results show presence of co-integration among socio-economic variables and three categories of globalization. The findings indicate that economic, political and social globalization is positively related to each other. It is worth mentioning that all the three types of globalization decreases poverty but the role of economic dimension is appeared to be insignificant. Some negative effect of globalization on inequality and unemployment is also reported by this study. The traditional link between inequality, poverty and unemployment in the presence of globalization show that these three variables are closely related to one another, deterioration in any one variable has adverse effect on other two variables. Furthermore, results of Vector Error Correction Mechanism (VECM) show that socio-economic variables not only cause each other but they also improve human welfare that is measured by HDI. Long run causality indicates that all variables cause each other as error correction term is significant.

Rationalising the retro-Diels-Alder fragmentation pattern of viscutins using electrospray interface-tandem mass spectrometry coupled to theoretical modelling.

Although mass spectrometry (MS) is a powerful tool in structural elucidation of unknown flavonoids based on their unique fragmentation patterns, proposing the correct fragmentation mechanism is still a challenge from tandem mass spectrometry data only. In recent years, computational tools such as molecular networking and MS2LDA have played a major role in the identification of structurally related compounds through an in-depth survey of their fragmentation patterns. Therefore, in this study, three viscutin molecules in Viscum combreticola Engl. crude extracts were characterised using ultra-high-performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry and MS2LDA, a computational tool. Ion-trap mass spectrometry and density functional theoretical modelling were used as confirmatory tools to rationalise the unique fragmentation patterns observed for these molecules. Here, MS2LDA revealed the presence of a unique Mass2Motif in all the three viscutin molecules at m/z 137, which was confirmed to be a 1,3 A- RDA (retro-Diels-Alder) fragmentation product using liquid chromatography-ion-trap mass spectrometry and density functional theoretical modelling. Moreover, MS2LDA proved to be useful in differentiating this spectral feature that was specific to viscutin molecules in the presence of other isobaric ions at m/z 137 occurring in compounds in other molecular families. Therefore, the results of the current study showed that computational tools such as MS2LDA are essential in uncovering some gas-phase fragmentation reactions of molecules in MS and that theoretical modelling is a powerful tool in rationalising these reactions in metabolite identification.

Learning about unprecedented events: Agent-based modelling and the stock market impact of COVID-19

We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioural heterogeneous agents’ model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event.

The Digital Markets Act: Regulating the Wild West

This contribution critically assesses the European Union’s Digital Markets Act (DMA). The DMA is the first comprehensive legal regime to regulate digital gatekeepers with the aim of making platform markets fairer and more contestable. To this end, the DMA establishes 22 per se conduct rules for designated platforms. It also precludes national gatekeeper regulation by EU Member States, thereby calling into question the legality of the pioneering German sec. 19a GWB. The analysis shows that the DMA’s rules are not as rigid as they may appear at first sight. While it is more accepting of false positives than of false negatives, the DMA contains several corrective mechanisms that could allow the Commission to finetune the rules to address both the danger of over- and under-inclusiveness. A further positive is that the new regulation incorporates key concepts of the GDPR, and requires coordination between the Commission and key EU data protection bodies. On the downside, the DMA does not contain any substantive principles for the assessment of gatekeeper acquisitions, leaving a worrying gap. While the DMA’s conduct rules outlaw specific leveraging strategies in digital ecosystems and may thereby indirectly address certain non-horizontal concerns arising from gatekeeper acquisitions, it remains that the European Union’s existing guidance on merger control is seriously out of date. The merger guidelines therefore urgently need updating to include (workable) theories of harm for concentrations in the digital economy. Digital Markets Act (DMA), platform markets, gatekeepers, fairness, ne bis in idem, digital economy, abuse of dominance, merger control

Economic Growth and CO2 Emission in ASEAN: Panel-ARDL Approach

This paper investigates the relationship between economic growth and CO2 emissions from 1994 to 2018 using a panel approach from eight ASEAN countries. We found an established result using the Panel ARDL Pooled Mean Group method. First, the panel Cointegration analysis shows a significant long-term relationship between GDP and CO2 emissions. Second, the error correction mechanism shows a stable and consistent value. Third, we found that GDP has a significant long-term effect on CO2 emissions in ASEAN countries. Fourth, our results also show that GDP significantly impacts CO2 emissions in the short term for four countries: Indonesia, Malaysia, Thailand, and Cambodia. Based on these empirical results, implications and policy recommendations are presented. ASEAN countries should implement green growth policies by encouraging economic development which does not suppress the environment.