Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity $Q$ -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.
Exploring the usefulness of scenario archetypes in science-policy processes: experience across IPBES assessments

Scenario analyses have been used in multiple science-policy assessments to better understand complex plausible futures. Scenario archetype approaches are based on the fact that many future scenarios have similar underlying storylines, assumptions, and trends in drivers of change, which allows for grouping of scenarios into typologies, or archetypes, facilitating comparisons between a large range of studies. The use of scenario archetypes in environmental assessments foregrounds important policy questions and can be used to codesign interventions tackling future sustainability issues. Recently, scenario archetypes were used in four regional assessments and one ongoing global assessment within the Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (IPBES). The aim of these assessments was to provide decision makers with policy-relevant knowledge about the state of biodiversity, ecosystems, and the contributions they provide to people. This paper reflects on the usefulness of the scenario archetype approach within science-policy processes, drawing on the experience from the IPBES assessments. Using a thematic analysis of (a) survey data collected from experts involved in the archetype analyses across IPBES assessments, (b) notes from IPBES workshops, and (c) regional assessment chapter texts, we synthesize the benefits, challenges, and frontiers of applying the scenario archetype approach in a science-policy process. Scenario archetypes were perceived to allow syntheses of large amounts of information for scientific, practice-, and policy-related purposes, streamline key messages from multiple scenario studies, and facilitate communication of them to end users. In terms of challenges, they were perceived as subjective in their interpretation, oversimplifying information, having a limited applicability across scales, and concealing contextual information and novel narratives. Finally, our results highlight what methodologies, applications, and frontiers in archetype-based research should be explored in the future. These advances can assist the design of future large-scale sustainability-related assessment processes, aiming to better support decisions and interventions for equitable and sustainable futures.
Russian business under economic sanctions: is there evidence of regional heterogeneity?

ABSTRACT Sanctions against Russia, beginning in early 2014, provide us with a unique opportunity to study whether, and how, sanctions affect a territorially-vast global power. This study empirically examines the economic impact of these sanctions, paying particular attention to the existence or nonexistence of its regional heterogeneity. For these purposes, this study utilises survey data that asked the executive managers of Russian regional companies to assess the impact of sanctions on their management activities in late 2015. There are several key findings. First, approximately half of those interviewed perceived the economic sanctions as having a negative impact. Second, no regional variation in the impact of the sanctions could be found. It follows that financial sanctions, aimed at an entire nation, exert a significant and geographically uniform impact. Moreover, even regional businesses near the Asia-Pacific region, holding strong connections with Asian countries, were unable to avoid the impact of sanctions.
Assessing Sustainability in Rural Water Supply Systems in Developing Countries Using a Novel Tool Based on Multi-Criteria Analysis

Rural water supply systems (RWSS) in developing countries typically have deficiencies that threaten their sustainability. This research used Multi-Criteria Analysis and the Analytical Hierarchy Process to identify indicators that can be used to assess the sustainability of RWSS. The assessment tool developed is composed of 17 attributes with 95 quantifiable indicators. The tool enables the assessment of the sustainability of RWSS, using data collected through semi-structured interviews, social cartography, technical inspection, household surveys, and water monitoring. The tool was applied in a case study of a RWSS in the Andean region of Colombia, illustrating a participatory, holistic, and structured assessment that provided a single sustainability measure for the system (3.0/5.0). The tool’s completeness is represented by its extensive attributes and indicators that deliver a robust baseline on the state of a system, help identify improvement strategies, and monitor system performance over time that can assists rural community organizations with RWSS management.
The Climate Spread of Corporate and Sovereign Bonds

Aligning finance to sustainability requires metrics and methods to price forward-looking cli-mate risks and opportunities in financial contracts and in investors' portfolios. Traditionalapproaches to financial pricing cannot incorporate the nature of climate risks, i.e. deep uncertainty, non-linearity, complexity and endogeneity. To fill this gap, we develop a frameworkfor climate-financial risk assessment and management under uncertainty. We consider a riskaverse investor with an information set given by past market valuation, information on futureclimate economic shocks, and utility maximization based on the minimization of the ClimateValue at Risk (VaR) in presence of incomplete markets. We then consider a disorderly policy transition to 2°C scenarios that leads to unanticipated shocks in economic trajectories of fossil fuel and renewable energy sectors, estimated using Integrated Assessment Models. We model the shock transmission from the change in sectors' Gross Value Added to firms' profitability and to sovereign fiscal revenues. We then introduce the forward-looking climatepolicy shocks in sovereign bonds valuation introducing scenario- conditioned financial riskmetrics (Climate VaR, Climate Spread). We provide an application to OECD sovereignbonds of Austrian National Bank's portfolio. We find that investments' climate alignmentcan strengthen the sovereign fiscal and financial position by decreasing the climate spread. In contrast, misalignment can negatively affect countries' economic competitiveness and financial stability, and thus the performance of investors who own such bonds. Our analysissupports investors' portfolios risk management strategies in the low-carbon transition andand financial supervisors in the design of prudential risk measures.
Factors influencing audit findings of the Indonesian district governments’ financial statements

Abstract The Regional Governments’ Financial Statements in Indonesia that have obtained unqualified opinions had increased in recent years. However, the audit findings on the local government’s financial reports have also increased. Those conditions have motivated us to empirically analyze the factors that influence the increasing number of audit findings in district governments in Indonesia for the 2016 and 2017 fiscal years. The independent variables are the Size of the Regional Government, Local Income, Capital Expenditures, and Follow-Up of Audit Recommendations. We measure their influence on the Non-Compliance with Regulations using the Weaknesses of the Internal Control System as the moderating variable. Our results show that Size has a positive impact on the Internal Control System. Otherwise, the Completion of Audit Recommendation negatively affects both the Internal Control System and the Non-Compliance with Regulatory. The other independent variables of Local Income and Capital Expenditures did not show any influence on the number of audit findings. Simultaneously, Size, Local Income, Capital Expenditures, and Follow-Up of Audit Recommendation have an indirect relationship to Non-Compliance with Regulation findings through Internal Control System Weaknesses. Our research emphasizes the importance of developing a comprehensive internal control system for local governments, along with the improvement of asset governance. Moreover, we also encourage the completion of audit recommendations on local governments to avoid the reoccurrence of similar findings.
An Investigation of Audit Expectation Gap in Bangladesh

Massive Global corporate scandals, financial crisis and collapses of large entities have diluted the investors’ confidence in the worlds capital market The auditing profession has also come under the spotlight, following the melodramatic flop of eminent companies without any prior warning signals and highlighted the miracle phenomena, the audit expectation gap (AEG). Based on a questionnaire survey with auditors and investors, this paper discovers significant audit expectation gap (AEG) in the areas of auditor’s general responsibility, auditor’s responsibility for fraud detection, auditor’s responsibility for internal control reporting assessment, meaning of the audit report, and also auditor’s responsibility for going concern reporting in Bangladesh. No expectation gap is emerged regarding the usefulness of audit report. Most of the gap has been refereed as reasonableness gap, but it can lead the standard setters and the practitioners of audit profession to set higher standards of performance.
Mixed-Signal Implementation of Low-Density Parity-Check Decoder

The receiver side of many communication systems incorporates an error-correction decoder to achieve good bit-error rate (BER) performance. While good BER is a metric of reliable communication, high throughput and energy-efficiency are also desired. Low-density parity-check (LDPC) decoders are able to perform well in term of these metrics. In this thesis, the Modified Differential Decoding Binary Message Passing (MDD-BMP) algorithm of LDPC codes has been chosen to implement in mixed-signal domain. The goal of this research is to achieve energy-efficiency in LDPC decoding while maintaining high-throughput in an implemented design of reasonable effective area. The re-design of some digital parts of the LDPC decoder in analog domain is expected to offer energy-efficiency and high throughput. However, these benefits come at a cost of analog impairments, such as, different random mismatch between similar inverters arising from process variation during fabrication. The comparative contribution of these impairments on the BER performance of the decoder has been investigated. During the design of the decoder, an on-chip calibration scheme has been arranged and global routing of the tuning signals has been maintained to address these random mismatches. Furthermore, modulation of the decoding speed by off-chip tuning has been made possible. For the purpose of high-speed testing of the decoding process, enough on-chip memory has been placed to store 10 codewords and feed them to the decoder through a binary-weighted capacitor-based digital to analog converter. Design and placement of analog MUXes enable us to debug sensitive analog nodes inside the decoder from off-chip. Finally, the full process of the physical design of the decoder in TSMC 65nm has been almost fully automated in Cadence SKILL code. Over 100 simulations including parasitic capacitance of long wires in physical design yield an average decoding speed of approximately 2.04 ns in moderate speed mode, therefore, providing a high throughput of 134 Gb/s. Taking into account the average current drawn by the circuits during both the pre-charge phase and the decoding phase, the calculated average energy per bit consumed by the decoder is 1.267 pJ/bit.
Adaptive Discriminative Deep Correlation Filter for Visual Object Tracking

Correlation filter trackers building on deep convolution neural networks (CNNs) contribute efficient visual object trackers but remain challenged with severe target appearance variations. The reason for this is that CNNs trained for image classification tasks are less discriminative to the dynamic variations of targets and backgrounds. In this paper, we propose an adaptive discriminative deep correlation filter (adaDDCF), which, by incorporating discriminative feature fine-tuning with adaptive appearance modeling, pursues stable object tracking in complex backgrounds. In adaDDCF, a convolutional Fisher discriminative analysis (FDA) layer is implemented for positive and negative instance mining and scene-specific feature learning. A correlation layer is then embedded to learn the correlation response of consecutive frames for target appearance modeling. With an online learning procedure using forward–backward propagation, the FDA layer and the correlation layer are effectively coupled, leading to effective and discriminative fine-tuning for the proposed tracker, which consequently alleviates the target drifting problem. Extensive experiments on the challenging benchmarks OTB2013, OTB2015, and OTB50 demonstrate that the proposed adaDDCF tracker outperforms many state-of-the-art trackers.
Projecting Urbanization and Landscape Change at Large Scale Using the FUTURES Model

Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape.
Universality or Differences in Marketing Elasticities in Emerging versus Developed Markets?: The Moderating Role of Brand Equity

Consumption in emerging markets is growing much faster than in developed markets. Accordingly, many global brand manufacturers venture into emerging markets, where they battle vigorously for market share. At the same time, emerging-market brands increasingly expand to more developed markets. While most firms have considerable expertise competing in similar markets, they lack insights on how consumers respond to marketing activities in economically dissimilar markets. Therefore, this paper quantifies the market-share elasticities of (i) three key marketing-mix instruments (price, distribution and line length), for (ii) 1,600+ international and domestic brands, across (iii) 14 durable categories (covering more recent categories like smartphones and mature ones like refrigerators), in (iv) 7 emerging and 7 developed markets from Asia and Australasia, for (v) up to 11 years. The findings suggest that differences in marketing elasticities between emerging and developed markets depend not only on the type of marketing variable (strongest difference for distribution), but also on brand equity (which affects elasticities more favorably in emerging markets than in developed markets). The paper discusses implications for marketing theory and practice.
The Intrametropolitan Geography of Knowledge-Intensive Business Services (KIBS): A Comparative Analysis of Six European and U.S. City-Regions

Analyzing the intrametropolitan locations of knowledge-intensive business services (KIBS) provides insight into the geography of the knowledge economy. This study focuses on the urban spatial structure and implications of KIBS for urban or regional development and planning through a comparison of KIBS’ structures in six city-regions representing different global contexts. This includes varied socioeconomic and political frameworks: three European city-regions (London, Paris, and Madrid) and three U.S. city-regions (New York, Los Angeles, and Chicago). The results show that (a) there is a relationship between urban spatial structure and KIBS location; (b) KIBS locate in a polycentric form in search of urbanization economies; but (c) certain KIBS are highly concentrated in just a few subcenters, looking for localization economies; (d) proximity to the core and agglomeration economies are a factor in the location of KIBS; and (e) the European cases have more KIBS subcenters but closer to their central business districts, while the American cases have fewer and larger KIBS subcenters located farther from their central business districts.
Quality management, innovation capability and firm performance

Many organizations are facing competitive challenges due to the rapid pace of technological changes. Both quality management (QM) and innovation are the competitive factors that are intensely embedded into organizational products, services and processes. In order to achieve higher firm performance, manufacturing firms are needed to adopt QM practices as well as develop innovation capability. Therefore, the purpose of this paper is to examine the relationship among QM, innovation capability (IC) and firm performance under both mediation and moderation models using structural equation modeling. ,The approach of this study is quantitative. The data used to test the hypotheses were gathered from Indian small- and medium-sized enterprises (SMEs) interviewing senior managers with a structured questionnaire. These hypothesized relationships are tested with data collected from 134 Indian manufacturing firms by using SPSS and AMOS statistical software. ,Overall, the findings clearly show that QM through the firm’s IC is indirectly associated with a firm’s business performance. It supported the notion that QM practices encourage the definition of innovation strategies of products and processes within a manufacturing setup, which positively affected different aspects of firm performance. More importantly, this study supports the findings of past studies that questioned the role of QM practices in improving a firm’s IC. ,Some limitations of this study include: although a cross-sectional survey has been applied, the research does not permit us to account for the lag between implementation and performance. It also brings the opinion of a limited number of senior managers of Indian manufacturing SMEs, and hence both the sample size could be increased and the nationality of the respondent/responding firms could be expanded for future research. ,In light of the obtained results, several recommendations were introduced to assist decision makers in manufacturing companies. The paper contains suggestions for improving manufacturing firm’s performance through developing IC and adopting QM practices. ,This paper extends theoretical contribution in production and operations management literature, highlighting how QM practices and firm’s IC have to interact in determining an organization’s success and sustaining its global competitiveness.
A surface modelling approach for attribution and disentanglement of the effects of global warming from urbanization in temperature extremes: application to Lisbon

Attribution and disentanglement of the effects of global greenhouse gas and land-use changes on temperature extremes in urban areas is a complex and critical issue in the context of regional-to-local climate change mitigation and adaptation. Here, an innovative modelling framework based on a large ensemble of urban climate simulations, using SURFEX (a land-surface model) coupled to TEB (an urban canopy model), forced by E20C (a GCM-based reanalysis), is proposed, and applied to the capital of Portugal—Lisbon. This approach allowed to disentangle the main drivers of change of extreme temperatures in Lisbon, while also improving the simulated summer temperature variability compared to E20C, using station observations as reference. The improvements were physically linked to the strong sensitivity of summer mean and extreme temperatures to local land-use properties. The sensitivity was systematically investigated and robustly demonstrated here, with built-fraction (buildings + roads), albedo and emissivity emerging as key surface parameters. The results revealed a very strong summer temperature increase between 1951–1980 and 1981–2010 periods: 0.90 °C for daily maximum temperature (Tmax), and 0.76 °C for daily minimum temperature (Tmax). These changes were sensitive to considering different (but constant throughout the simulation) land-uses, varying by about 10% for Tmax, and around 17% for Tmin. Regarding the temperature extremes (quantified by extreme hot days, EHD, and extreme hot nights, EHN, respectively defined as exceeding the 95th-percentile of Tmax and Tmin) the changes and their dependencies with the land-use are much more drastic. The isolated effect of changing land-use (keeping the climate forcing unchanged) from rural/natural (low built-fraction) towards dense urbanization (high built-fraction) caused a significant increase in EHN (up to ∼+130 d per 30 years, larger than the effect due to climate forcing alone), and in EHD (∼+60 d per 30 years, which is similar to the effect due to climate forcing alone).