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The purpose of this paper is to examine the association between politically connected (POLCON) firms and stock price synchronicity, and whether this association can be attenuated by institutional investors.

This paper uses an ordinary least square regression model to examine the association between POLCON firms and stock price synchronicity; institutional ownership and stock price synchronicity; the moderating role of institutional ownership on the association between POLCON firms and stock price synchronicity; institutional domiciles and stock price synchronicity; and the moderating role of institutional domiciles on the association between POLCON firms and stock price synchronicity.

The result shows that POLCON firms are positively associated with stock price synchronicity. Further, the author also finds that institutional monitoring, through higher ownership by local institutional investors is associated with lower stock price synchronicity. In addition, this study documents evidence that institutional investors, particularly local institutional investors can improve stock price informativeness in POLCON firms.

The results suggest that POLCON firms are plagued by severe agency problems, resulting in limited flow of firm-specific information to the capital markets. However, the author shows that POLCON firm’s agency problems can be attenuated through effective monitoring by institutional investors. Further, institutional domiciles are shown to be significantly associated with stock price synchronocity. However, effective monitoring is largely driven by local institutional investors, in line with the geographical proximity theory.

The results suggest that regulators should increase their surveillance and monitoring effort, particularly on firms with close ties to the government. In particular, POLCON firms should be required to be more transparent in their corporate dealings. Additionally, auditors should intensify their audit efforts on POLCON firm to provide more reliable financial information to minority shareholders, investors and analysts. Finally, institutional investors should be incentivized by the Malaysian Securities Commission, via, the code of governance to play an effective monitoring role in Malaysian firms.

This study reveals that POLCON firms’ severe agency problems can be alleviated by effective institutional monitoring. Further result identifies institutional domiciles as a significant factor in influencing monitoring effectiveness in POLCON firms. This paper provides insights into the dynamic interaction between political connections, institutional monitoring, firm governance and capital markets behavior of an emerging market.

This paper aims to review empirical research on the relationship between institutional ownership (IO) and board governance (85 studies).

Based on agency and upper echelons theory, the heterogeneous monitoring function of specific types and the nature of institutional investors on board composition, compensation and chief executive officer (CEO) characteristics will be focused.

The author found that most studies have referred to archival studies, analyzed the impact of board governance on IO, focused on CEO characteristics, neglected IO heterogeneity and advanced regression models to address endogeneity concerns. In line with the theoretical framework, the relationship between total IO and board governance is heterogeneous. However, specific types such as foreign, dedicated and pressure-resistant institutions represent active monitoring tools and push for increased board governance.

The author provided useful recommendations for future research from a content and methodological perspective, e.g. the need for analyzing the impact of IO on sustainable board governance and other characteristics of top management team members, e.g. the chief financial officer.

As many regulatory bodies implemented regulations to promote shareholder rights and board governance, this literature review highlights the connections of both corporate governance mechanisms. Managers should conduct a careful and timely investor analysis and change the composition and compensation of the board of directors in line with institutional investors’ preferences.

This analysis makes useful contributions to prior research by focusing on IO and board governance, whereas the author structured the heterogeneous variables and results within the structured literature review. The authors guides researchers, regulatory bodies and business practice in this corporate governance topic.

The purpose of this paper is to examine the main and joint effects of politically connected firms (PCFs) and institutional monitoring on the cost of debt.

Based on a panel data set of Malaysian politically connected and non-politically connected listed firms from 2002 till 2015, the author performs regression analysis. To address the issue of self-selection, the PCFs’ equation is estimated, following Lennox et al. (2012) and Heckman (1979).

This paper finds that PCFs are associated with higher cost of debt. However, the positive association between PCFs and the cost of debt is attenuated by higher institutional ownership (IO). Further test reveals that monitoring by institutional investors is heterogeneous from the perspective of domicile. Local institutional investors are associated with lower cost of debt, particularly in PCFs, while foreign institutional investors are associated with higher cost of debt.

The author shows that firm outcome, i.e. cost of debt in emerging markets can differ from advanced markets due to different institutional setting. Additionally, different types of political ties can produce different firm outcomes: GLCs are associated with lower cost of debt as opposed to connected firms based on personal ties. However, agency problems in PCFs can be alleviated through effective institutional monitoring. Consistent with geographical proximity theory, local institutional investors play a more effective monitoring role in Malaysian listed firms, thus lowering cost of debt. Overall, the results contribute to deeper understanding on variation in firm outcomes between emerging and advanced markets.

This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.

Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.

Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.

Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.

Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.

While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Remaining useful life (RUL) prediction for power transformer maintenance is a challenging task on heterogeneous data. Monitoring data of power transformers are not always compatible or in an identical format; therefore, RUL predictions traditionally work separately on different data. Moreover, chemical molecules used in RUL prediction can be transformed into each other under different conditions, thus forming a complete graph with uncertain adjacency matrix (UAM). This study aims to find and evaluate a new model to achieve better results of RUL prediction than the other baselines.

In this work, the authors propose a spatiotemporal complete graph convolutional network (STCGCN) for RUL prediction in two branches, in which daily and hourly features are extracted from correlated heterogeneous data separately. This study provides a thorough evaluation of the proposed model on real-world data and compare the proposed model with state-of-the-art RUL prediction models.

By using the multibranch structure and EucCos similarity aggregation, STCGCN was able to capture the dynamic spatiotemporal patterns on a variety of heterogeneous data and obtain more accurate prediction results, compared to other time series prediction methods.

In this work, the authors propose a novel multibranch structure to compute feature maps from two heterogeneous data sources efficiently and a novel similarity aggregation method to compute the spatial UAM within the complete graph. Compared with traditional time series prediction models, the model pays attention to the spatial relationships in time series data.

This study examines whether and how the legal origin of foreign institutional investors (FIIs) impacts corporate investment efficiency.

The study employs a large panel dataset of firms from 32 non-USA countries from 2005 to 2018. Financial and institutional ownership data are obtained from the COMPUSTAT Global and Public Ownership databases in S&P Capital IQ, respectively. The study employed ordinary least squares (OLS) regression with year and firm fixed effects. In addition, two-stage least squares with instrumental variable regression (2SLS-IV) and propensity score matching (PSM) approaches were employed to address the potential endogeneity.

The findings of this study suggest that common- and civil-law FIIs differ in their monitoring capabilities to promote investment efficiency. The authors find evidence that increased equity ownership by common-law FIIs, not civil-law investors, strengthens the investment-Q sensitivity, resulting in higher investment efficiency. Consistent with the monitoring and information channel, the results further indicate that the positive impact of common-law FIIs on investment efficiency is stronger in host environments susceptible to agency conflicts and information asymmetry.

This study offers novel evidence on the heterogeneous monitoring role of FIIs with regard to their home countries' legal origins and their impact on investment efficiency in an international context.

This paper aims to overcome the defect of single-source temperature measurement method and improve the measurement accuracy of FTR. The friction temperature rise (FTR) of brake affects braking performance seriously. However, it was mainly detected by single-source indirect thermometry, which has obvious deviations.

A three-point temperature measurement system was built based on three kinds of single-resource thermometry. Temperature characteristics of these thermometry were analyzed to achieve a standard FTR curve. Two fusion-monitoring models for FTR based on multi-source information were established by artificial neural network (ANN) and support vector machine (SVM).

Finally, the two models were verified based on the experimental results. The results showed that the fusion-monitoring model of SVM was more accurate than that of ANN in monitoring of FTR.

Then the temperature characteristics of the three single-source thermometry were analyzed, and the fusion-monitoring models based on multi-source information were established by ANN and SVM. Finally, the accuracy of the two models was compared by the experimental results. The more suitable fusion-monitoring model for FTR monitoring was determined which would be of theoretical and practical significance for remedying the monitoring defect of FTR.

DEPLOY is a successful technology demonstration project showing how state of the art technology can be implemented to achieve, continuous, real‐time monitoring of a river catchment.

The DEPLOY system is a wide area network of monitoring stations delivering data in near real‐time. The demonstration sites chosen are based in the River Lee, which flows through Ireland's second largest city, Cork. The sites include monitoring stations in five zones considered typical of significant river systems and demonstrate the versatility of the technology available. Data were collected from stations at pre‐programmed intervals and transmitted to the DEPLOY servers either by short range ISM band radio or directly via the GSM GPRS network. The data were then processed and made available in a controlled manner at www.deploy.ie Findings – The project demonstrates the capability of multi‐sensor systems to remotely monitor temporal and spatial variations in water quality, through the identification of short‐term events. A system like DEPLOY could be used as a decision support tool by regulatory bodies in managing our aquatic environment with the potential to cut overall monitoring costs and provide better coverage representing long‐term trends in fluctuations of pollutant concentrations.

The demonstration of a truly heterogeneous water quality monitoring networked system was one of the first of its kind in Ireland. Based on the collected data DEPLOY can provide recommendations for water quality monitoring systems from various perspectives, technical, operational and strategic.

The purpose of this paper is to examine the investment preference of various types of institutional investors in Malaysia, and its influence on firm valuation, operating performance and capital expenditure.

This study employs ordinary least squares model to examine: investment preference according to different types of institutional investors; the association between various types of institutional investors and firm valuation; the association between various types of institutional investors and firm performance; and the association between various types of institutional investors and capital expenditure.

The result shows that different types of institutional investors exhibit different investment preference. From the domiciles perspective, local institutional investors (LII) are found to be associated with higher Tobin’s Q, ROA and net profit margin. When viewed from business relationship perspective, “pressure-resistant” institutional investors (PRII) are positively associated with Tobin’s Q, ROA and net profit margin. Both LII and PRII are also associated with higher capital expenditure.

This study reveals the investment preferences of various types of institutional investors in an emerging market economy. The results show that institutional monitoring is associated with higher firm valuation, higher firm performance and higher capital expenditure. However, the effect is largely driven by local and PRII, particularly government-controlled institutional funds. These evidence suggest that different firm outcomes between emerging and advanced economy can be explained by variation in institutional setting.