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The study aims at providing a reliable system of real-time monitoring for underground mine and tunnels which detects any structural change in the network and reconfigures it for resuming the data delivery process. In high stress environments, e.g. underground mines and tunnels, real-time activity monitoring is an emerging issue. Wireless sensor networks (WSNs) play a key role in ensuring the safety of people working in underground mines and tunnels. WSN not only provide real-time monitoring of underground environment but also detects any structural change in the network itself.

In this paper, results of empirical implementation of a re-configurable WSN, capable of self-healing approach, reconfigure the network connectivity upon failure or addition of nodes in the system. An open-source radio-frequency identification standard for WSN, named as DASH7, is used for practical implementation. The proposed system is capable of determining cluster breakage by sudden disruptions caused by roof falls, explosions and node failures, sensor coverage hole, node re-addition to the network and distress priority signal generation by the miner.

The proposed platform contributes to re-attain network state for establishing a communication link with fusion center in terms of: instant and accurate detection of collapse holes, acceptable error rate, time to re-attain network state, rapid distress signal propagation and low deployment cost. This platform is deployed in four different environments of anechoic chamber, hallway, outdoor and underground mine environment, to test the aforementioned scenarios using DASH7-compatible Bitsense Sensor Motes operating at 433 MHz. The effectiveness of the proposed approach has been experimentally validated for the single and multiple adjacent and disjoint node failures in all the four environments.

The number of monitoring systems was implemented for safety assurance in high stress environments before, but the novelty of our platform is long range, cost effectiveness, quick response to any structural change in the network, rapid and accurate data delivery using WSN operated on DASH7 protocol stack.

This paper aims to examine the moderating effect of ownership concentration on the relationship between corporate social responsibility performance (CSRP) and information asymmetry using a sample of Chinese firms.

The authors use a sample of 208 listed firms from nine different sectors in China over the period of 2008–2018. They use the generalized method of moment approach to examine the dynamic relationship between CSRP, information asymmetry and ownership concentration. CSRP index is constructed using environmental performance, social performance and corporate governance performance measures.

The results indicate that CSRP positively affects the information asymmetry. Moreover, by taking ownership concentration as a moderating variable, the results indicate that ownership concentration negatively moderates the association between CSRP and information asymmetry.

The findings of the study advance the understanding of CSR practices in China. The findings have important implications for the regulators and managers in China for adopting socially responsible activities for the improvement of firm performance and protecting shareholder rights.

The study extends the existing research on the association between CSRP and information asymmetry by including the ownership concentration as a moderating variable. The research showed that CSR plays an important role in reducing the informational gap between managers and outside stakeholders. However, the relationship between CSR and information asymmetry is not studied yet with the moderating role of ownership concentration.

The purpose of this study was to identify key areas for benchmarking (BM) towards the improvement of small and medium scale enterprises (SMEs) construction firms in developing countries.

The study employed the use of the quantitative research method in the collection and analysis of primary data collected from field surveys using a piloted close-ended questionnaire created following a review of available literature on BM. Based on 63 solicited views of professionals with SMEs (quantity surveyors, project managers and architects) data collected were statistically analysed using a one-sample t-test.

The findings of the study indicate that the key areas for BM towards the improvement of SME construction firms in developing countries in order of relevance are “Financial Performance”, “Competitiveness”, “Customer Satisfaction”, “Technology Advancement”, “Communication Skills”, “Collaboration”, “Employee Satisfaction” and “Product orientation”.

The study has given more insight into the areas that need more attention for SMEs BM to achieve improvement. It can therefore be suggested firms that adapted identified areas will derive the benefits of BM. It is further opined that more BM education should be provided to various SMEs construction firms to facilitate an effective and efficient BM regime aiming at performance enhancement in project delivery.

This forging research attempts to identify the key areas for BM towards the improvement of SMEs construction firms in developing countries. Although there have been several efforts to create BM tools for the construction industry.

The paper proposes a conceptual integration between two variables, both considered as capable of affecting public firm performance: technology and intellectual capital.

The analysis is performed by testing a structural equation model (SEM) which allows to measure simultaneously a plurality of variables, highlighting all the possible connections. Data is collected by administering more than 500 paper questionnaires to accountants working within Local Health Firms of Naples and Salerno.

The study seems to align with the considerations according to which intellectual capital expressed through its three dimensions – relational capital, human capital and organizational capital – exert a positive influence on perceived performance of healthcare firms, ultimately impacting on the Employees' Satisfaction.

The study acts as a useful guide from a managerial point of view, because it may support firm decision-making. In fact, public sector managers can leverage an instrument capable of activating functional mechanisms to improve firm performance.

The work allows overcoming the literature gap due to the fact that, although there is a wide recognition of the potential of technology and intellectual capital, there are no studies that synergistically integrate both the aspects in the attempt to understand their value in terms of influence on the performance of public firms, on the one hand, and on employees' satisfaction, on the other. In this vein, the work, in an attempt to provide further scientific support to the link between technology and intellectual capital, is a tool capable of highlighting how this link positively impacts on company performance and employee satisfaction.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.