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This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.

The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.

Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.

Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.

The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.

The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.

A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.

The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.

Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.

Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.

The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.

The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.

A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

Based on the socio-emotional wealth and agency theories, this study empirically investigates the impact of family ownership and management on green innovation (GI) in family businesses, as well as the moderating effects of institutional environmental support factors, namely, the technological achievement marketisation index and the market-rule-of law index.

This study empirically tests the hypotheses based on a sample of listed Chinese family companies with A-shares in 14 heavily polluting industries from 2009 to 2019.

There is a U-shaped relationship between the percentage of family ownership and GI, and an inverted U-shaped relationship between the degree of family management and GI. Additionally, different institutional environmental support factors affect these relationships in different ways. As the technological achievement marketisation index increases, the U-shaped relationship between the percentage of family ownership and GI becomes steeper, while the inverted U-shaped relationship between the degree of family management and GI becomes smoother. The market rule-of-law index weakens the U-shaped relationship between family ownership and GI.

First, the authors enrich the research on the driving factors of GI from the perspective of the most essential heterogeneity of family businesses. This study shows nonlinear and opposite effects of family ownership and management on GI in family firms. Second, this study contributes to the literature on family firm innovation. GI, not considered by researchers, is regarded as an important deficiency in research on innovation in family businesses. Therefore, this study fills that gap. Third, the study expands research on moderating effects in the literature on GI from the perspective of institutional environmental support factors.

In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks.

Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network.

This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness.

A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

The purpose of this paper is to present green supply chain, resilient supply chain, agile supply chain, cold supply chain and lean supply chain (GRACL SC) procedures based on a detailed perspective, analyzing subjects in the past 19 years with a systematic literature review (SLR) of the papers reported from 2000 to 2019, and offering information and guidelines for further studies.

This research is based on 17 keywords in the title and topic of the articles and collects data from Web of Science (WOS) databases and objectively chooses 1,190 articles and performs meta-data analyses. Tables and statistical reports are based on the following three filters: publication year, authors and document type. At least, 39 publications from the ISI WOS has been examined for presenting information of categorization of the conducted research with regard to the content analysis, comprising the conceptual development and obstacles, cooperation with the supply chain elements, as well as mathematical and other optimization models.

Finally, this study answered three main questions in the research and demonstrates that the majority studies in the green supply chain (GSC) and a minimum number of studies on the cold supply chain have been conducted and 27 factors are chosen to achieve the 2000 to 2019 GRACL SCM model which robust and fit for Iranian food industries. The model shows that the agile, resilient and lean supply chain have direct effect on GSC and it can be said that all 27 groups which are selected for the final model of this research can be the main groups in the supply chain.

This paper was actually conducted by authors who reported it. To prevent plagiarized, redoubled efforts have been made and actually this paper is based on SLR methodology and the results are real and the researcher discusses the results appropriately. This investigation can have a positive impact within the field of expanding supply chain flexibility and lessening squander within the Iranian generation framework.

This paper aims to identify key sectors in carbon footprint responsibility, an introduced concept depicting CO₂ responsibilities allocated through the supply chain containing sectoral activities and interactions. In detail, various key sectors could be identified according to comparative advantages in trade, sectoral linkage and sectoral synergy within the supply chain.

A semi-closed input–output model is used to make the household income–expenditure relationship endogenous through the supply chain where sectoral CO₂ emissions are calculated, and the production-based responsibility (PR) principle is evaluated. Thus, according to “carbon footprint responsibility”, modified hypothetical extraction method is applied to decompose sectoral CO₂ in terms of comparative advantages in trade, sectoral linkage and synergy. Finally, key sectors are identified via sectoral shares and associated decompositions in carbon footprint responsibility.

Compared to 2005, in 2012, the PR principle failed to track sectoral CO₂ flow, and embodied CO₂ in import and interprovincial export increased, with manufacturing contributing the most; manufacturing should take more carbon responsibilities in the internal linkage, and tertiary sectors in the net forward and backward linkage, with sectors enjoying low carbonization in the mixed linkage; inward net CO₂ flows of manufacturing and service sectors were more complicated than their outward ones in terms of involved sectors and economic drivers; and residential effects on CO₂ emissions of traditional sectors increased, urban effects remained larger than rural ones and manufacturing and tertiary sectors received the largest residential effects.

The value of this paper is as follows: the household income–expenditure relationship got endogenous in intermediate supply and demand, corresponding to the rapid urbanization in megacities; key sectors were observed to change flexibly according to real sectoral activities and interaction; and the evaluation of the PR principle was completed ahead of using a certain CO₂ accounting principle at the city level.

This study research contributes in fulfilling the gap by carrying out a systematic literature review (SLR) of contemporary research studies in closed-loop supply chain (CLSC). To the best of the author’s knowledge, an SLR rooted in bibliometric analysis has not been carried focusing on advent developments in CLSC. SLR employs scientific methodologies to select papers from standard databases. The SLR using advanced bibliometric and network analysis enables unveiling the key features of the contemporary literature.

The author has analyzed over 333 documents published from 2008 and onward. Using the contemporary tools from bibliometric analysis tools, the author presented an exploratory analysis. A network analysis is utilized to visualize literature and create clusters for the cocited research studies, keywords and publication sources. A detailed multivariate analysis of most influential works published based top 100 articles via a cocitation matrix is done. The multivariate analysis used k-means clustering in which optimal number of clusters are estimated. The analysis is further extended by using a factor analysis, which enables determining the most influential clusters in the k-means clustering analysis.

The SLR using a bibliometric and network analysis enables unveiling the key features of the contemporary literature in CLSC. The author examined published research for influential authors, sources, region, among other key aspects. Network analysis enabled visualizing the clusters of cocited research studies, cowords and publication sources. Cluster analysis of cocited research studies is further explored using k-means clustering. Factor analysis extends findings by identifying most contributing grouping of research areas within CLSC research. Each clustering technique disclosed a unique grouping structure.

CLSC has received considerable attention, and its core areas start with focusing on reverse logistics concepts relating reuse, recycling, remanufacturing, among others. Contemporarily, the studies have enhanced reverse logistics core functionalities interfaced with the other interesting avenues related to CO₂ emission reduction, greening and environmental protection, sustainability, product design and governmental policies. Earlier studies have presented a literature review of CLSC; however, these reviews are commonly conducted in the traditional manner where the authors select papers based on their area of expertise, interest and experience. As such these reviews fall short in utilizing the advanced tools from bibliometric analysis.

The purpose of this paper is to explore the effects of total assets, stock performances, CEOs’ tenures, ages, and board sizes on total CEO compensations of 249 publicly listed US companies over a nine-year period from 2004-2012.

Pedroni’s panel cointegration, generalized method of moments, and dynamic ordinary least squares methodologies are applied.

All variables are non-stationary in log-levels. The findings show significant positive effects of total assets and stock performances on total CEO compensations. The effects of CEO’s tenure and age as well as board size on total CEO compensation deem negative. However, short-run net interactive feedback effects are generally positive with some exceptions.

The above variables matter in rewarding the CEOs. They should be carefully weighed in for proper formulation of CEO compensation policy.

This paper applies relatively new econometric tools for a large panel data set. This work considers some new variables for determining CEO compensation in USA. The findings are relatively new with empirical originality.