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Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

The purpose of this study is to examine the tangible and intangible benefits of two enterprises resource planning (ERP) packages namely SAP and Oracle. In particular, the operational benefits (OP), managerial benefits (MB), strategic benefits (SB), information technology (IT) benefits and organizational benefits (OR) of SAP and Oracle packages are scrutinized.

A survey method was employed to elicit opinions from SAP and Oracle ERP professionals. The empirical results are analyzed using smartPLS (Version 2.0) software. The results are further compared and contrasted with that of Deloitte and O'Leary's studies.

Through this study, it seems that the tangible benefits such as procurement cost reduction (32 percent for SAP; 30 percent for Oracle), financial cycle reduction (46 percent for SAP; 48 percent for Oracle), productivity improvements (50 percent for SAP; 41 percent for Oracle), personnel reduction (43 percent for both SAP and Oracle) and IT cost reduction (35 percent for SAP; 21 percent for Oracle) percentages indicate Indian companies have achieved more ERP benefits as compared to Deloitte and O'Leary's studies.

Three limitations were identified in this study such as adoption of the survey instrument, potential for response bias associated with targeting packages and the research instrument targeted only for manufacturing organizations. In the future, one can analyze the ERP benefits based on the ERP life cycle stages such as initiation, adoption, adaptation, acceptance, routinization and infusion. Moreover, one can investigate all the benefits mentioned in the literature of Shang and Seddon's study as this study reveals only few tangible and intangible benefits. There is also a possibility of extending the study to the benefits in terms of pre‐ and post‐ERP implementation in manufacturing systems. Finally, introducing some moderators to see the effect on ERP benefits.

This paper provides new insights into the tangible and intangible benefits of SAP and Oracle ERP packages.

No prior study to the best of one's knowledge has been sought to explain in such benefits of SAP and Oracle ERP packages.

The purpose of this paper on the one hand is to reduce the sintering temperature, shorten the sintering time and improve the electrical properties of the sample through the two-step flash sintering method and on the other hand is to study the effect of electric field on the phase structure, microstructure and electrical properties of the flash sintering sample.

In this paper, (Mg_1/3Ta_2/3)_0.01Ti_0.99O₂ giant dielectric ceramics were prepared by conventional sintering and two-step flash sintering, respectively. Further, the effect of electric field (600–750 V/cm) on the electrical properties of (Mg_1/3Ta_2/3)_0.01Ti_0.99O₂ giant dielectric ceramics was studied.

The results show that compared with the conventional sintering, the sintering temperature of the two-step flash sintering can be reduced by 200°C and the sintering time can be shortened by 12 times. All sintered samples were single rutile TiO₂ structure. Compared with conventional sintering, two-step flash sintering samples have finer grain size. The two-step flash sintered sample has similar dielectric properties to the conventional sintered sample. The dielectric constant of flash sintered samples decreases with the increase of electric field. When the electric field is 700 V/cm, the ceramic sample has the optimal dielectric properties, where the dielectric constant is approximately 5.5 × 10³ and the dielectric loss is about 0.18 at 1 kHz. Impedance spectroscopy analysis shows that the excellent dielectric properties are attributed to the internal barrier layer capacitance model.

This paper not only provides a new method for the preparation of co-doped TiO₂ giant dielectric ceramics but also has great potential in greatly improving efficiency and saving energy.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.