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Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.

This study examines how institutional quality influences variability in financial development among economies in Sub-Saharan Africa (SSA).

Empirical estimations verifying various relationships are performed using the limited information maximum likelihood (LIML) estimation technique.

The results suggest that institutional quality enhances the pace of financial development among economies in the sub-region all things being equal. In a further micro-level analysis where components of institutional quality index are examined separately, the study’s results suggest that effective governance, regulatory quality, rule of law and accountability tend to have a significant positive impact on financial sector development.

Findings of the study suggest that policies geared towards improving governance and regulatory institutions can augment development of the financial sector among economies in SSA; governments and policymakers are therefore encouraged to resource noted institutions to play effective roles for the development of the financial sector.

Compared to related studies, this study reorients existing paradigm, which emphasizes the role of governance and institutional variables in the economic growth discourse. The authors’ empirical inquiry rather focuses on how governance and institutional structures influence regional financial development dynamics. Specifically, this study differs from most macro-level studies found in literature because it examines the impact of hitherto unexamined governance and institutional variables on financial development among economies in SSA.

The main purpose of this study is to examine the impact of different dimensions of institutional quality indices on the economic growth of Sub-Saharan African (SSA) countries.

The study uses a panel data set of 31 SSA countries from 1991 to 2015 and employs a two-step system-GMM (Generalized Method of Moments) estimation technique.

The study's empirical results indicate that investment-promoting and democratic and regulatory institutions have a significant positive effect on economic growth; however, once these institutions are taken into account, conflict-preventing institutions do not have a significant impact on growth.

The study's findings suggest that countries in the region should continue their institutional reforms to enhance the region's economic growth. Specifically, institutions promoting investment, democracy and regulatory quality are crucial.

Unlike previous studies that use either composite measures of institutions or a single intuitional indicator in isolation, the present study has employed principal component analysis (PCA) to extract fewer institutional indicators from multivariate institutional indices. Thus, this paper provides important insights into the distinct role of different clusters of institutions in economic growth.

The purpose of this paper is to examine the role external debt and foreign direct investment play in influencing financial development in Africa.

Annual data on external debt, foreign direct investment and financial development were extracted from the World Bank World Development Indicators from 2002 to 2015. The data employed were analysed within causal research design and the dynamic panel using generalized method of moment estimation approach.

The findings revealed that external debt and foreign direct investment have a significant positive relationship with financial development in African economies. Governments of the sampled economies should enact policies that would help attract high level of foreign direct investment as it contributes positively to financial development. Finally, governments of the sampled African economies should ensure foreign direct investment and external funds borrowed are channelled to productive sectors.

The paper analysed the relationship between external debt, FDI inflows and financial sector development. The paper is the first in terms of such analysis within the framework of the dual-gap framework, which is the first time in these kinds of studies. Previous studies have concentrated on the effect of financial sector on FDI and not the other way around.

The study examines the effect of macroeconomic risk, inflation uncertainty and instability associated with key macroeconomic indicators on the efficiency of financial institutions among economies in sub-Saharan Africa (SSA).

Data for the empirical inquiry were compiled from 35 SSA economies from 1996 to 2019. The empirical estimates were carried out using pooled ordinary least squares (POLS) with Driscoll and Kraay’s (1998) standard errors.

Reported empirical estimates show that macroeconomic risk and exchange rate volatility constrain the efficiency of financial institutions. Further results suggest that inflation uncertainty has a significant influence on the efficiency of financial institutions among economies in the subregion. Additionally, reviewed empirical estimates show that institutional quality positively moderates the nexus between inflation uncertainty and financial institution efficiency. At the same time, political instability is found to worsen the adverse effect of macroeconomic risk on the efficiency of financial institutions.

For policymakers and governments, improved institutional structures are recommended to ensure the operational efficiency of financial institutions, especially during an inflationary period. For decision-makers among financial institutions, the study recommends policies that have the potential to make their institutions less vulnerable to macroeconomic risk and exchange rate fluctuations.

The approach adopted in this study differs significantly from related studies in that the study examines and reviews interactions and relationships not readily found in the reviewed literature.

The purpose of this study is to investigate the effect of financial intermediation functions of banks on economic growth in sub-Saharan Africa.

The study employs data from 11 sub-Saharan African countries over the period 1970–2016. Using broad money supply, bank credit to the private sector and bank deposits as financial intermediation measures, the authors apply the random effects (RE) technique based on the recommendation of the Breusch–Pagan test.

The results show that except for bank deposits, broad money supply and bank credit to the private sector significantly influence economic growth. While broad money has a negative relationship with growth, bank credit to the private sector and bank deposits are positively correlated with economic growth.

The relationship between financial intermediation and economic growth remains unsettled, as results vary across countries. Besides, in developing countries' perspective, extant studies are largely focused on individual countries to investigate the financial intermediation-growth nexus. In this study, the authors take a different direction by employing a panel approach and thus adding to the few cross-country studies on the subject matter. Also, unlike other studies that have focused on a single indicator of financial intermediation, this study uses three indicators of financial intermediation which broadly reflect the intermediation functions of banks.

This article provides a snapshot of several innovative and underused methodological approaches employed by scholars from across public management.

A comparative review of methodological approaches to public management research is used.

The authors find evidence of scholars applying numerous novel methodological approaches to study social science phenomena, including agent-based modeling, nonparametic approaches, social network analysis, Granger equations and techniques for correcting selection bias.

The review does not cover all of the innovative methodological approaches used in social science. However, the methodological techniques showcased offer promise for advancing public management research, whether used as primary applications or as one of multiple methods (triangulation) to test the validity of research findings and/or to more comprehensively understand the phenomena being studied.

The techniques reviewed include example applications to aid researchers who may wish to apply them in their own research. The novel tools and methods already in use by public management researchers contradict the perception that public management methods have lagged behind other social science disciplines.