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Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.

Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.

The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.

There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.

The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.

This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs.

The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs.

The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy.

In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.

Cultural differences have been frequently cited as a major source of risks for international joint ventures (IJVs). Cultural differences may cause extensive conflicts in technology, norms and emotion among the international joint venture (IJV) partners. The purpose of this study is to explore the interactive effects of national culture differences (NCDs) and conflict management approaches on the performance of international construction joint ventures (ICJV).

Data were collected using a questionnaire survey method with 143 valid responses. Partial least squares structural equation modeling (PLS-SEM) was used to test the research hypotheses.

It was found that ICJV performance declined with a high degree of NCDs. The negative effect of NCDs on ICJV performance was mitigated by adopting the cooperative conflict management approach; while it was aggravated by adopting the competitive conflict management approach. The findings may provide an alternative way (i.e. adopting the cooperative conflict management approach rather than avoiding or competitive approaches) to address the cultural conflicts in the multicultural project management teams.

Firstly, as NCD negatively impacts performance of ICJVs, project managers should pay attention to cultural issues and learn how to manage them; Secondly, as cooperative and competitive conflict management approaches have different moderating effects on the relationship between NCD and ICJV performance, project managers must choose appropriate conflict management styles in multination teams. Thirdly, as the avoiding approach has no significant moderating effect on the negative relationship between NCD and ICJV performance, it is important for Chinese partners not to employ avoiding approach to deal with conflicts in ICJV.

This study uniquely adds to the literature on cultural issues in managing ICJVs by integrating the moderating effects of conflict management approaches. The interactive effects of conflict management approaches and national cultural differences on ICJV project performance may contribute to the theories regarding conflict management theory in the context of cross-cultural management.

This study aims to shed light on the corrosion behavior of X80 steel when sulfate-reducing bacteria (SRB) and permeating hydrogen interact.

In this study, electrochemical tests were conducted between 25 and 55 °C, and the surface morphology of the specimen was observed using scanning electron microscopy and three-dimensional photos. The composition of the oxide film was characterized by X-ray photoelectron spectroscopy (XPS).

Under the condition of 6 MPa simulated natural gas (15% H₂), the content of S-containing compounds (FeS and FeSO₄) in the corrosion products on the surface of the specimen decreases from 60.8% to 54.4%. This finding indicates that hydrogen permeation inhibits the metabolic processes of SRB in this environment. By comparing the hydrogen-uncharged specimen, it was found that under the condition of 6 MPa simulated natural gas (15% H₂) hydrogen charging, the uniform corrosion on the X80 surface was weakened, and the protection of the oxide film on the specimen surface in this environment was better than that without hydrogen charging.

To the best of the authors’ knowledge, most of these existing studies have focused on the effect of hydrogen on the mechanical properties of materials and very little is known about corrosion behavior in the hydrogen environment. In this study, a self-designed small gas phase hydrogen charging device was used to study the X80 surface corrosion behavior in the environment of the H₂-doped natural gas pipeline.

Corporate philanthropy is increasingly a vital decision-making basis for consumers to purchase and establish relationships with enterprises. However, few studies have examined corporate philanthropy from the perspective of community evolution. To address this gap, this study aims to provide a more in-depth and holistic investigation of corporate philanthropy by examining the evolution of social media brand communities caused by corporate philanthropy and the characteristics of consumer interactive behavior.

Web crawlers developed by Python were employed to collect data of ERKE from Sina Weibo (the Chinese equivalent of Twitter). A total of 2,736 posts and 7,774 comments were collected and investigated using social network and sentiment tendency analyses.

The results showed that the evolution of the social media brand community presented a prominent three-stage characteristic influenced by corporate philanthropy. The findings not only support the benefits of corporate philanthropy but also show the possible disadvantages. Besides, this study further concluded the characteristics of consumer interactive behavior in the social media brand community.

This paper addresses an attractive and practical issue related to the impact of corporate philanthropy. Moreover, this study is one of the first studies to examine the impact of corporate philanthropy in the context of the social media brand community. The findings of this study will provide a valuable reference for community operations and practitioners of brands.

The purpose of this paper is to unpack the “black box” of the relationship between contract and inter-organizational trust, both theoretically and empirically. Two mediators, namely perceived safeguard and restriction, are identified to build up two seemingly contrary possible paths between contract and trust from current literature. Both paths are tested in the context of Chinese construction industry due to our access to sample.

A survey of 295 contractor-subcontractor relationships from Chinese construction industry was conducted. A three-step multiple regression model was employed to test the mediating effect of perceived safeguard and restriction. Then, a hierarchical regression model was used to test the possible moderating effect of bilateral transaction-specific investment.

The empirical results support the mediating effect of perceived safeguard between contract and trust in the construction subcontracting industry. Bilateral transaction-specific investments enhance the positive effect of contract on safeguard perception.

Theoretically, this study contributes to governance literature by opening up the “black box” of the relationship between contract and trust. It provides a better understanding of how and when contract complexity impacts trust, instead of simply focusing on whether contract and trust act as complements or substitutes. Practically, this study provides guidelines for construction firms to decide the degree of contract complexity under various degrees of bilateral transaction-specific investments to enhance the other party’s trust, so as to improve performance outcomes.

The objective of this research is to develop a decision method that can generate appropriate risk response strategies for international construction project managers (PMs) and allow these strategies to reflect their different risk preferences.

The optimal model approach is adopted. A credibility-based fuzzy chance constrained programming (CFCCP) model is developed, which simultaneously minimizes the expected losses of risk events and total costs of risk response. To solve this multi-objective model, a fuzzy interactive solution method is used. Moreover, the model performance is demonstrated by a real international industrial plant project. In addition, a sensitivity analysis of the model is conducted.

The result of the sensitivity analysis indicates that PMs with a greater risk aversion can lead to a higher mitigation ratio of expected losses of risk events and a higher total cost of risk response.

This research provides contractors with an effective decision-making model to develop a project risk response plan, and it will assist contractors to minimize risk losses and enhance the project performance in the international construction market.

Previous studies overlook the risk preference, which is an important behavioral factor influencing decisions in risk response strategy selection. This research proposed a novel risk response strategy selection decision method that considers different attitudes toward risk among decision makers.

This research aims to build a three-tiered driver system that entices contractor rule violations and explores the importance and the relationships among these drivers, hence providing theoretical support for the contractor rule violations governance.

A literature review based on fraud diamond theory identified drivers from Pressure, Opportunity, Rationalization and Capability that drive contractor rule violations. In the Chinese context, through feedback, discussion and analysis of semistructured interviews with ten experts, an improved three-tiered driver system was drafted. Based on this system, a survey was conducted and scored with experts to provide the data for this research. The decision-making trial and evaluation laboratory (DEMATEL) method was used to determine relationships and influences between factors, and the DEMATEL-based analytic network process method was used to weigh these factors.

This paper systematically studied the drivers of contractor rule violations, specifically, the results showed that pressure had an important driving effect across the driver system, and those five factors – poor cultural atmosphere, weak internal control, prior experience, moral disengagement and information asymmetry – had the most influence on contractor rule violations. The results also indicated the strong effect pressure has on enticing rule violations and revealed that culture atmosphere and internal company governance played crucial roles in the occurrence of rule violations.

This study provided construction practitioners with a robust tool to analyze the drivers of contractor rule violations. The rule violation drivers in the construction practice scenes identified in this study can provide more direct and effective violation-related guidance for contractors, regulators and the industry.

Based on the new perspective of fraud diamond, this paper systematically bulit a three-tiered driver system combining theory with practice. This study contributed to understand the driver mechanism of contractor rule violations especially the importance of internal factors of contractors, which provided theory reference for compliance governance of construction industry.