KLE Technological University

Three-dimensional (3D) parts are widely gaining popularity in industries like automotive, aerospace, medical healthcare, consumer goods, and electronics. The properties and performance of these parts vary depending on the parameters chosen at the design and manufacturing stages. This study experimentally evaluates the impact of topology on energy absorption and structural stability of eight strut-based lattice topologies fabricated using Fused Deposition Modeling (FDM) with Polylactic Acid (PLA). Quasi-static compression tests were conducted in accordance with ASTM D695. Specific Energy Absorption (SEA), crush force efficiency (CFE), and newly defined metrics—Peak-to-Plateau Load Ratio (PPLR) and Structural Efficiency (SE)—were used to determine collapse behavior and load stability. All lattices were manufactured within a narrow relative density range (0.29–0.30) to ensure topology-driven assessment. The Double Octagon lattice showed superior performance, achieving a maximum SEA of 6653.18 J kg⁻¹ , total absorbed energy of 57.55 J, and the highest structural efficiency among the investigated configurations. Substantial energy dissipation and trustworthy collapse mechanisms were shown by the Hex Truss and Kelvin cell; however, the Face Centered and Face Diagonal structures showed comparatively lower energy absorption. The proposed PPLR metric quantifies initial peak load severity relative to sustained collapse response, complementing conventional SEA and CFE measures. The findings highlight the critical role of topology optimization in balancing stiffness, weight, and energy absorption in guiding lightweight crashworthy lattice design.

The post-pandemic era's hybrid workforce culture is its defining trait and needs a strategic workforce management practice rebalancing. The section addresses shifting remote team management dynamics and examines AI (Artificial Intelligence) facilitating agility, inclusivity and data-driven decision-making in hybrid work arrangements. Based on a workforce expectation diagnosis, technological reliance and organizational redesign, it offers a nuanced understanding of the transformational role of AI. The section contributes to academic analysis by placing AI integration in the context of greater socio-technical change and highlighting its significance for future-proof resilience, collaboration and inclusive employee involvement.

Low-light image enhancement is an essential step for sustaining computer vision, which can be employed in various fields like surveillance, robotics, and IoT-based applications. Poor illumination drastically lowers the visual perception and decision-making capabilities of these systems. This work is about a smart real-time low-light vision system, which uses a Retinex-based enhancement framework to carry out the function in Raspberry Pi 4. Raspberry Pi 4 was chosen for the work because of its quad-core processor, enough RAM, low power consumption, small size, and good compatibility with Python and OpenCV, all of which make real-time embedded vision processing efficient and feasible. The device utilized for image capture is the Raspberry Pi Camera Module Rev 1.3 (5 MP OV5647 sensor), which supports <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2592 \times 1944$</tex> resolution for images and real-time video capture up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1 0 8 0 p}$</tex> at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{3 0 ~ f p s}$</tex>. The proposed system utilizes a Retinex-based algorithm that is optimized for embedded deployment to enhance the illumination and also keep the reflectance details unchanged. The results of the experiments show that the enhancement is effective with PSNR <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 24.15 ~\text{dB}$</tex>, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SSIM}=0.81, \text{CII}=1.40$</tex>, and entropy increase of 1.40 bits (7.50 - 6.10), thus enabling real-time processing at 8 frames per second. Such results are indicative of the potential of the system to address low-light vision challenges in real-world scenarios. Plans for future work hardware acceleration, higher frame-rate optimization, outdoor deployment, and deep learning-based enhancement techniques integration for increased robustness.