PurposeIn a country like Saudi Arabia, where the construction industry is witnessing an impressive growth in the post-oil era, it is important to examine the occupational health and safety behaviors of construction workers (CWs).Design/methodology/approachThe present study aims to investigate the impact of emotional intelligence (EI) on workers' stress and safety behaviors. Data were collected from CWs (n = 265) at a major construction site in the city of Jeddah. Two questionnaires comprising 25 and 32 questions were used to measure their EI and stress levels, respectively. Furthermore, structured interviews were conducted with the managers and supervisors to inquire about the safety behavior of their respective workers. Descriptive statistics, simple and companion regression were used for data analysis.FindingsThe findings indicate that EI plays an important role to enhance the safety behaviors of the CWs besides reducing their workplace stresses. Furthermore, workers' stress levels are found to negatively impact their safety behaviors, indicating that any reduction in occupational stress can reciprocally enhance their safety compliance. The findings are further discussed with the concerned stakeholders to recommend a seven-point therapeutic role of EI for the safety of CWs.Originality/valueResults of the study can be used by managers and supervisors of the Saudi construction industry to reduce workplace accidents and improve the productivity of their organizations.

Introduction of modern technologies and methods and quality analysis for the gemstone industry are the main strategic initiatives of the Small and Medium Development Authority (SMEDA) of Pakistan. In this regard, four natural gemstones Quartz, Pyrope-Almandine Garnet, Black tourmaline, and Amethyst brought from Hunza valley Pakistan were analyzed by state-of-the-art spectroscopic techniques including EDX, UV-VIS, and FTIR spectroscopy. EDX revealed the traces of Fe, Mg, and Ca in Pyrope-Almandine garnet, Mg and Fe in Black tourmaline, Au and Ca in Amethyst. UV-VIS data revealed the values of Urbach energies 520, 210, 460, and 430 meV, and the values of direct bandgap energies 5.14, 6.12, 5.54, 5.74 eV, respectively. The higher structural disorder due to the presence of Fe and other impurities in stones except Quartz was attributed to the higher values of Urbach energies and decrease in band gaps: FTIR data Fe-O and Si-O stretching vibration in Pyrope-Almandine garnet, Si-O bending vibrations and O-H stretching vibration in Quartz, Si-O-Si bending and stretching vibrations and C=O stretching vibrations in Black tourmaline, Ca-O stretching vibrations and Si-OH weak-vibrations in Amethyst. Photoluminescence results also showed useful information in investigating the properties of gemstones.

Wireless sensor networks (WSNs) based on the Internet of Things (IoT) are now one of the most prominent wireless sensor communication technologies. WSNs are often developed for particular applications such as monitoring or tracking in either indoor or outdoor environments, where battery power is a critical consideration. To overcome this issue, several routing approaches have been presented in recent years. Nonetheless, the extension of the network lifetime in light of the sensor capabilities remains an open subject. In this research, a CUCKOO-ANN based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IoT-WSN. The proposed method uses time constraints to minimize the distance between sources and sink with the objective of a low-cost path. Using the property of CUCKOO method for solving nonlinear problem and utilizing the ANN parallel handling capability, this method is formulated. The resented model holds significant promise since it reduces average execution time, has a high potential for enhancing data centre energy efficiency, and can effectively meet customer service level agreements. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. The MATLAB software is used to simulate the proposed model.

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

(1) Background: This study investigated the miscibility of carbon-based fillers within industrial scale polymers for the preparation of superior quality polymer composites. It focuses on finding the light distribution in gamma irradiated ultra-high molecular weight polyethylene (UHMWPE). (2) Methods: The Kubleka–Munk model (KMM) was used to extract the optical properties, i.e., absorption coefficients (μa) and scattering coefficients (μs). Samples amounting to 30 kGy and 100 kGy of irradiated (in the open air) UHMWPE from 630 nm to 800 nm were used for this purpose. Moreover, theoretical validation of experimental results was performed while using extracted optical properties as inputs for the Monte Carlo model of light transport (MCML) code. (3) Conclusions: The investigations revealed that there was a significant decrease in absorption and scattering coefficient (μa & μs) values with irradiation, and 30 kGy irradiated samples suffered more compared to 100 kGy irradiated samples. Furthermore, the simulation of light transport for 800 nm showed an increase in penetration depth for UHMWPE after gamma irradiation. The decrease in dimensionless transport albedo  μs(μa+μs) from 0.95 to 0.93 was considered responsible for this increase in photon absorption per unit area with irradiation. The report results are of particular importance when considering the light radiation (from 600 nm to 899 nm) for polyethylene modification and/or stabilization via enhancing the polyethylene chain mobility.