We examine the role of teamwork within the top executive teams in generating management forecasts. Using social connections within the executive team to capture the team’s interaction, cooperation, and teamwork, we find that social connections among team members are associated with higher management forecast accuracy, consistent with economic theories that information is dispersed within a firm and with sociology insights that social connections facilitate information sharing. Further analyses show that the association between social connections and forecast accuracy is stronger when the teams are just beginning to work together, when their firms face more uncertainty or adversity, and when the CEOs are less powerful. Our results hold for a subsample of executive teams that experience pseudo exogenous shocks to their social connectedness. Taken together, our results underscore the importance of teamwork among executives in the forecast generation process. This paper was accepted by Suraj Srinivasan, accounting.

Urban road networks significantly influence traffic noise. However, existing studies have neglected the causal chain between road characteristics and traffic noise; thus, clarity on their influencing mechanisms is lacking. In this study, structural equation models were developed to explore the mediated effect of road characteristics on traffic noise through traffic flow using data from field measurement in Dalian City, China; paired comparisons of scenarios though microscopic and macroscopic traffic simulations were performed for further analysis. The results show that lane number influences traffic noise mainly in terms of the number of vehicles in a group (NVG). More lanes indicate increased traffic demand due to connected urban land, which increases the NVG and, in turn, increases noise intensity but decreases noise amplitude. The influence of road segment length (RSL) on traffic noise mainly depends on the suppression effect. A longer RSL allows for higher vehicle speeds, leading to increased noise intensity and reduced noise amplitude. This also indicates that traffic flows disperse more easily, decreasing the NVG and, in turn, reducing noise intensity and increasing noise amplitude. Road junctions (RJ), which are classified according to the presence or absence of traffic lights, have significant direct effects on both noise intensity and noise amplitude, which are both likely to increase as drivers accelerate or decelerate in the middle of the road segment. These findings provide a reference for local governments and urban planners when working to improve quality of life in urban areas.

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Construction noise is an integral part of urban social noise. Construction workers are more directly and significantly affected by construction noise. Therefore, the construction noise situation within construction sites, the acoustic environment experience of construction workers, and the impact of noise on them are highly worthy of attention. This research conducted a 7-month noise level (LAeq) measurement on a construction site of a reinforced concrete structure high-rise residential building in northern China. The noise conditions within the site in different spatial areas and temporal stages was analyzed. Binaural recording of 10 typical construction noises, including earthwork machinery, concrete machinery, and hand-held machinery, were performed. The physical acoustics and psychoacoustic characteristics were analyzed with the aid of a sound quality analysis software. A total of 133 construction workers performing 12 types of tasks were asked about their subjective evaluation of the typical noises and given a survey on their noise experience on the construction site. This was done to explore the acoustic environment on the construction site, the environmental experience of construction workers, the impact of noise on hearing and on-site communications, and the corresponding influencing factors. This research showed that the noise situation on construction sites is not optimistic, and the construction workers have been affected to varying degrees in terms of psychological experience, hearing ability, and on-site communications. Partial correlation analysis showed that the construction workers’ perception of noise, their hearing, and their on-site communications were affected by the noise environment, which were correlated to varying degrees with the individual’s post-specific noise, demand for on-site communications, and age, respectively. Correlation analysis and cluster analysis both showed that the annoyance caused by typical construction noise was correlated to its physical and psychoacoustic characteristics. To maintain the physical and mental health of construction workers, there is a need to improve on the fronts of site management, noise reduction, equipment and facility optimization, and occupational protection.

Blockchain-like ledger databases emerge in recent years as a more efficient alternative to permissioned blockchains. Conventional ledger databases mostly rely on authenticated structures such as the Merkle tree and transparency logs for supporting auditability, and hence they suffer from the performance problem. As opposed to conventional ledger DBMSes, we design VeDB - a high-performance verifiable software (Ve-S) and hardware (Ve-H) enabled DBMS with rigorous auditability for better user options and broad applications. In Ve-S, we devise a novel verifiable Shrubs array (VSA) with two-layer ordinals (serial numbers) which outperforms conventional Merkle tree-based models due to lower CPU and I/O cost. It enables rigorous auditability through its efficient credible timestamp range authentication method, and fine-grained data verification at the client side, which are lacking in state-of-the-art relational ledger databases. In Ve-H, we devise a non-intrusive trusted affiliation by TEE leveraging digest signing, monotonic counters, and trusted timestamps in VeDB, which supports both data notarization and lineage applications. The experimental results show that VeDB-VSA outperforms Merkle tree-based authenticated data structures (ADS) up to 70× and 3.7× for insertion and verification; and VeDB Ve-H data lineage verification is 8.5× faster than Ve-S.

As a prevailing concept in 5G, virtualization provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers (SPs) to share different RATs’ infrastructure. This paper proposes a generic framework for virtualizing heterogeneous wireless network with different RATs. A novel “VMAC” (virtualized medium access control) concept is introduced to converge different RAT protocols and perform inter-RAT resource allocation. To suit the proposed framework, a virtualization based resource allocation scheme is devised. We formulate the problem as a mixed combinatorial optimization, which jointly considers network access and rate allocation. First, to solve the network access problem, “adaptability ratio” is developed to model the fact that different RATs possess different adaptability to different services. And a Grey Relational Analysis (GRA) method is adopted to calculate the adaptability ratio. Second, services are modeled as players, bargaining for RAT resources in a Nash bargaining game. And a closed-form Nash bargaining solution (NBS) is derived. Combining adaptability ratio with NBS, a novel resource allocation algorithm is devised. Through simulation, the superiority and feasibility of the proposed algorithm are validated.

In this paper, we believed that social tag is a new paradigm of resource organizing and sharing. It is a very suitable technology using in e-learning systems. We discussed with the learning process in the e-learning system based on social tags. We brought forward a new individual learning process model based on social tags. And we compared with the new learning process model and traditional process model. The new individual learning process model could improve the interaction between learners and between human and computers. The social tags could help the e-learning system build the learners preference model and knowledge model.