International Islamic University, Chittagong

In the age of the digital economy, technological advancements are reshaping labor markets, creating both new opportunities and challenges. However, the impact of digitalization, particularly the roles of artificial intelligence (AI) and technological innovation (TI), on employment remains insufficiently examined. This study examines the effect of the digital economy on employment in 36 OECD countries from 2005 to 2022, focusing on the moderating roles of AI and TI, using the Driscoll-Kraay Standard Errors (DKSE) model and various robustness tests. The findings show that: (1) the digital economy enhances employment, with a stronger effect at lower employment quantiles; (2) AI and TI strengthen the digital economy's impact, although AI's direct effect is negative, while innovation's effect is positive; (3) the digital economy increases employment through political, economic, and social globalization; (4) sectoral analysis reveals that the digital economy reduces jobs in agriculture while boosting those in the service sector, and has a positively insignificant effect on the industrial sector; and (5) heterogeneity analysis indicates that the digital economy's benefits are more pronounced in highly developed, industrialized, and less urbanized countries. The study emphasizes the need for tailored policies to maximize the benefits of digitalization, address the challenges posed by AI, and ensure sustainable and equitable labor market growth.

This study reports the synthesis of Dy3+, Sm3+ singly-doped and co-doped Ca2Y1-x-yDyxSmySbO6 phosphors via a high-temperature solid-state reaction method. Photoluminescence spectroscopy determined that the optimal doping concentration, sintering temperature and time are x = 0.03, 1300 degrees C, and 8 h for Ca2Y1-xDyxSbO6, which yields intense yellow emission centered at 576 nm (4F9/2 -> 6H13/2 transition) under 349 nm excitation. For Ca2Y1ySmySbO6, y = 0.025 and sintering at 1500 degrees C produces intense red emission centered at 601 nm (4G5/2 -> 6H7/2 transition) under 407 nm excitation. Co-doping both Dy3+ and Sm3+ into the Ca2YSbO6 host enabled tunable multi-colour luminescence under specific excitation wavelengths. However, temperature-dependent studies (300-500 K) revealed that the Ca2Y0.945Dy0.03Sm0.025SbO6 phosphors co-doped with their respective optimized concentration (x = 0.03 and y = 0.025) exhibits a degradation in both emission intensity and color purity against temperature, attributed to excessive total rare-earth ion doping concentration. Despite this thermal quenching, integrating the co-doped Ca2Y0.945Dy0.03Sm0.025SbO6 phosphor with a 365-nm LED chip successfully generated warm white light, demonstrating potential application in warm white-light-emitting diodes.

In this paper, we utilize the AK-iteration procedure for hyperbolic metric spaces, ensuring the symmetry condition is met and establish the weak w^2-stability and data dependence results for contraction mappings. Additionally, we establish several Δ-convergence and strong convergence theorems for the generalized (α ,β )-nonexpansive (GABNE) mappings. To demonstrate it, we provide a numerical example of the GABNE-mappings, emphasizing the enhanced efficiency of the AK-iteration method relative to other iterative approaches. Additionally, the applications of the main results are demonstrated by solving 2D and 3D Volterra integral equations. These findings extend and improve numerous related results found in the current literature.

Energy storage devices in thermal solar plants play a crucial role in controlling the energy and power demand. Their performance is significantly influenced by the thermal capacity of the materials used. Motivated by the growing need for enhanced thermal energy efficiency, a Williamson ternary hybrid nanofluid is used to examine the non-steady magnetohydrodynamic (MHD) flow through a porous stretching cylinder containing gyrotactic microorganisms. Physics-informed neural network (PINN) with GaussSwish hybrid activation function is utilized in this study. The network minimizes the residuals of the governing equations together with boundary constraints using automatic differentiation and the NADAM optimizer until it converges to the optimal loss. The effects of different flow parameters on temperature, momentum, concentration, and motile density are analyzed. Magnetic and electric field parameters show a drop in the momentum profile, whereas an inverse trend is noticed in the temperature profile. Weissenberg number, curvature, and heat sink parameters contribute to elevate the temperature. Schmidt number lowers the concentration profile; on the other hand, the curvature parameter exhibits an opposite relation. Peclet and bioconvection Lewis number cause the motile microorganism density to decline. Ternary hybrid nanofluid achieves up to 24.3% greater heat transfer, 29.7% mass transfer, and 34.1% higher motile microorganisms density than the hybrid nanofluid, confirming its potential for advanced thermal energy storage systems. The results further show the effectiveness of physics-informed neural networks in handling complex fluid problems.

Chalcogenide perovskites have recently emerged as a promising family of functional materials owing to their tunable electronic structures, chemical stability, and potential in thermoelectric, optoelectronic, and protective-coating applications. In this work, we present a comprehensive first-principles investigation of the structural, mechanical, thermal, electronic and optical properties of the chalcogenide perovskites BaTaS3 and BaNbS3 using density functional theory (DFT). The optimized lattice parameters for BaTaS3 (a = 6.948 Å, c = 5.709 Å) and BaNbS3 (a = 6.932 Å, c = 5.749 Å) agree well with available literature. Both compounds crystallize in the orthorhombic phase and exhibit metallic electronic characteristics. The calculated elastic constants satisfy the Born stability criteria, confirming their mechanical stability, while moderate elastic anisotropy is observed from the directional dependence of Young’s modulus. The evaluated bulk, shear, and Young’s moduli reveal that BaNbS3 is mechanically stiffer than BaTaS3. The Debye temperatures were estimated to be 309.2 K (BaTaS3) and 330.8 K (BaNbS3), while the minimum lattice thermal conductivities were calculated as 2.7 and 2.88 W m⁻¹ K⁻¹, respectively. Optical analysis reveals high reflectivity (> 65