This experimental investigation evaluates the structural behavior of reinforced concrete slab-column connections incorporating columns with square and circular cross-sectional geometries under gravity and lateral cyclic loading. Four interior slab-column specimens were designed, fabricated, and tested, with identical slab dimensions of 1050×1050×80 mm and a column height of 500 mm. The circular columns were intended to have an equivalent second moment of inertia to the square column, ensuring a fair comparison of geometric effects. One specimen identified as SC-G, incorporating a square column, was tested under a progressively increasing vertical load to determine its maximum gravity load-bearing capacity. The remaining three specimens SC-2 with a square tied column, SC-9 with a circular spirally-reinforced column, and SC-10 with a circular column incorporating a column capital were tested under a constant gravity load equivalent to 60% of SC-G’s ultimate capacity (75 kN), combined with a lateral displacement protocol conforming to ACI 374 guidelines. Results indicated that the shape of the column (circular) and reinforcement (spiral) in SC-9 produces a comparatively higher ultimate load, stiffness, and ductility than SC-2 of the square column and tied reinforcement. The ultimate lateral load increase was +13.3%, -26.1%. Circular spiral columns with column capital in slab-column connections led to a noticeable rise in ultimate lateral load by about +62%, -80%. It was also shown that incorporating a column capital in SC-10 significantly enhanced the punching shear resistance and energy dissipation capacity. Circular columns demonstrated more stable hysteretic behavior and improved ductility compared to the square column specimen.
Plastic pollution represents a significant global environmental challenge, with millions of tons of plastic waste accumulated annually in landfills and natural ecosystems. The COVID-19 pandemic intensified this issue due to the widespread use of single-use personal protective equipment. As a response to this problem, this study examines the use of recycled polyethylene terephthalate and single-use face masks as additives in porous asphalt mixtures to improve the mixtures’ properties and promote sustainability in pavement engineering. The research aimed to evaluate the combined effect of incorporating recycled polymers into a hybrid system, rather than separately modified mixtures of polyethylene terephthalate and single-use face mask fibers. Laboratory tests indicated that the hybrid mixture exhibited optimal performance, achieving a 34% increase in Marshall stability, a 19% decrease in flow, and a 20% reduction in air-void content. Additionally, permeability decreased by 36%, while remaining within the accepted limit, improving moisture susceptibility, and cantabro abrasion losses decreased by up to 11%, suggesting improvements in cohesion, structure, and durability. The findings demonstrate that hybrid polymer modification offers an environmentally sustainable and high-performance approach for future porous asphalt pavements.
Photovoltaic (PV) systems have gained importance as one of the major renewable energy technologies because of its clean and sustainable characteristics; however, PV nonlinear current-voltage and power-voltage characteristics are a significant factor in limiting the maximum available power determined by the amount of available power under different environmental and load conditions. Maximum Power Point Tracking (MPPT) algorithms are one of the most important performance enhancements to help make PV energy conversion systems more efficient and reliable. Conventional MPPT techniques such as Perturb and Observe (P&O) have been widely used due to their simple and easy implementation in terms of control strategies, however, they exhibit steady-state oscillations, slow convergence, and poor dynamic performance under fast changing conditions of irradiation and load. To overcome these limitations, validation of intelligent and bio-inspired optimization and MPPT algorithms have attracted a great deal of interest. This paper includes a comprehensive comparative analysis of three MPPT techniques that are: conventional P&O algorithm, Grey Wolf Optimization algorithm (GWO), and Teaching-Learning-Based Optimization algorithm (TLBO). A detailed model of PV system coupled with dc-dc boost converter is built in Matlab/Simulink and the algorithms are tested with a constant irradiation and variable load and with simultaneous irradiation and load variation. Performance criteria like tracking performance, speed of convergence, steady state oscillations and robustness under dynamic conditions are analyzed. Based on the simulation results it is proven that both GWO and TLBO clearly outperforms the conventional P&O algorithm. Amongst all the optimization-based approaches, TLBO provides good performance with near-instantaneous convergence, minimum oscillations and consistent tracking efficiency is close to 100% for all the tested scenarios. The results prove that TLBO-based MPPT offers solid and computationally efficient solution for real world applications of PV systems especially in those with frequent and random operating condition variation environment.
Battery life is the hard constraint for massive Internet-of-Things (IoT) at 6G scale. Wake-up radios (WuRs)—ultra-low-power auxiliary receivers that “listen” for short wake-up signals while the main transceiver sleeps—offer orders-of-magnitude energy savings, but suffer from false wake-ups, long tail latency under bursty traffic, and sensitivity/coverage limits. This paper proposes an end-to-end AI-optimized WuR stack that combines (i) a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and (ii) a reinforcement-learning (RL) scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers. The proposed method, tested using a trace-driven simulator calibrated with published WuR power/latency figures, the approach reduces node-average energy by 41–72% versus strong baselines, while meeting 99% latency targets and cutting false wake-ups by >80%.
This research investigates the influence of single and hybrid mineral nanofillers on the morphological, thermal, and electrical properties of epoxy-based glass fiber composites. Nanocomposites were fabricated using hand lay-up technique, incorporating nano-calcium carbonate (CaCO₃) and nano-talc with particle size of 50 to 100 nm into an epoxy matrix with 5-ply E-glass/S-glass fiber. The study evaluates composites with 2-8 wt% of nano-CaCO₃, and hybrid systems containing nano-CaCO₃ (2-8 wt%) and nano-talc (1-4 wt%). Morphological analysis via Scanning Electron Microscopy (SEM) assessed nanofiller dispersion, while thermal properties were analyzed using Differential Scanning Calorimetry (DSC) for glass transition temperature (Tg). Electrical performance was evaluated through dielectric breakdown voltage (ASTM D149) and surface resistivity (ASTM D257). SEM analysis showed filler dispersion depended on concentration, with optimal distribution at 4-6 wt% before significant agglomeration at higher concentrations. DSC results revealed increased Tg with nanofillers, peaking for the composite with 6 wt% CaCO₃ and 3 wt% talc, indicating enhanced thermal stability. The dielectric breakdown voltage peaked at 17.6 kV for 2 wt% CaCO₃ and 1 wt% talc composite, while surface resistivity was highest for 4 wt% CaCO₃ and 2 wt% talc (73.74×1012Ω). All formulations maintained UL 94 V-1 flammability rating. The study concludes that the synergistic interaction between nano-CaCO₃ and nano-talc enables significant tailoring of thermal and electrical properties for advanced applications.
