Purpose: This research paper aims to justify the need for the Quality of Hire (QOH) construct as a value-adding focus for strategic human resource management (SHRM). The traditional focus on efficiency and cost-oriented recruitment metrics overlooks the importance of QOH in providing a competitive advantage and delivering long-term value. The study expands the economic theory of human resource development and develops a profit-building concept relevant to SHRM by exploring the practices that enable QOH in organizations. Design: The study utilizes a case-study method to examine a target firm’s mechanisms to build QOH in its recruitment process. It applies a structuration theory lens to analyze the behavior of various actors, their agencies, and the continuous interplay between structure and action in enabling QOH. Findings: The findings suggest that assessing and building measures for getting QOH is a complex task for organizations due to the inherent reliance on lag measures such as performance and tenure. The study highlights that QOH can be enabled through changes in the firm’s recruitment practices. Originality: This paper contributes to recruitment research in two significant ways. First, it expands on the under-researched construct of QOH, providing clarity on its definition and importance. Second, it identifies lead practices that organizations can incorporate into their recruitment and selection processes to enable QOH. By using a structuration theory lens, the study explores how actors in the recruitment process adapt and align with new structural rules to enable QOH. Research implications: The research builds on the structuration theory in recruitment and selection and exhorts practitioners in organizations to move beyond efficiency-oriented recruitment practices and focus on practices that contribute to QOH. By considering post-hire outcomes, such as job performance and long-term retention, organizations can improve their talent acquisition and retention strategies, creating long-term value for the organizations.

Two-dimensional hexagonal boron nitride nanosheets (h-BNNS) were synthesized on silver (Ag) substrates via a scalable, room-temperature atmospheric pressure plasma (APP) technique, employing borazine as a precursor. This approach overcomes the limitations of conventional chemical vapor deposition (CVD), which requires high temperatures (>800 °C) and low pressures (10−2 Pa). The h-BNNS were characterized using FT-IR spectroscopy, confirming the presence of BN functional groups (805 cm−1 and 1632 cm−1), while FESEM/EDS revealed uniform nanosheet morphology with reduced particle size (80.66 nm at 20 min plasma exposure) and pore size (28.6 nm). XRD analysis demonstrated high crystallinity, with prominent h-BN (002) and h-BN (100) peaks, and Scherrer calculations indicated a crystallite size of ~15 nm. The coatings exhibited minimal disruption to UV-VIS reflectivity, maintaining Ag's optical properties. Crucially, Vickers hardness tests showed a 39% improvement (38.3 HV vs. 27.6 HV for pristine Ag) due to plasma-induced cross-linking and interfacial adhesion. This work establishes APP as a cost-effective, eco-friendly alternative for growing h-BNNS on temperature-sensitive substrates, with applications in optical mirrors, corrosion-resistant coatings, energy devices and gas sensing.

The intermittent flow cold storage heat exchanger is one of the most important components of the pulse tube expansion refrigerator based on the reverse Brayton cycle. In the experimental system, the volume and heat transfer of the helical tube play a decisive role in the stable operation of the whole experimental system. However, there are few studies on heat transfer in a helical tube under helium working medium and intermittent flow conditions. In this paper, a process and method for calculating the volume of a helical tube are proposed based on the gas vessel dynamics model. Subsequently, a three-dimensional simulation model of the helical tube was established to analyze the heat transfer process of cryogenic helium within the tube. The simulations revealed that the temperature of helium in the tube decreases to the wall temperature and does not change when the helical angle exceeds 720°. Moreover, within the mass flow rate range of 1.6 g/s to 3.2 g/s, an increase in the mass flow rate was found to enhance the heat transfer performance of the helical tube. This study provides a reference for the selection and application of a helical tube under intermittent flow conditions and also contributes to the experimental research of inter-wall heat exchanger and pulse tube expansion refrigerators.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

This study aims to determine the extent to which talent identification is implemented in talent management. A Systematic Literature Review (SLR) was conducted to summarize the application of talent identification in the last six years. Researchers use Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to process scientific articles. The literature reveals that while topics related to talent management garner significant attention, research on talent identification within talent management remains relatively scarce despite a gradual increase each year. We compared documents indexed by Scopus Q1 and Q2. The results show that the United States accounted for a significant portion of research on talent identification, representing 16% of the total existing research. Researchers have conducted extensive studies on the medical and pharmaceutical sectors, public services, tourism, and hospitality. The number of citations varied greatly from 1 to 93, with a median value of 20. These studies have also used various research methods with different theoretical bases and produced different analyses. This finding enriches the perspective of talent identification.