Interconnect limits on gigascale integration (GSI) in the 21stcentury

Twenty-first century opportunities for GSI will be governed in part by a hierarchy of physical limits on interconnects whose levels are codified as fundamental, material, device, circuit, and system. Fundamental limits are derived from the basic axioms of electromagnetic, communication, and thermodynamic theories, which immutably restrict interconnect performance, energy dissipation, and noise reduction. At the material level, the conductor resistivity increases substantially in sub-50-nm technology due to scattering mechanisms that are controlled by quantum mechanical phenomena and structural/morphological effects. At the device and circuit level, interconnect scaling significantly increases interconnect crosstalk and latency. Reverse scaling of global interconnects causes inductance to influence on-chip interconnect transients such that even with ideal return paths, mutual inductance increases crosstalk by up to 60% over that predicted by conventional RC models. At the system level, the number of metal levels explodes for highly connected 2-D logic megacells that double in size every two years such that by 2014 the number is significantly larger than ITRS projections. This result emphasizes that changes in design, technology, and architecture are needed to cope with the onslaught of wiring demands. One potential solution is 3-D integration of transistors, which is expected to significantly improve interconnect performance. Increasing the number of active layers, including the use of separate layers for repeaters, and optimizing the wiring network, yields an improvement in interconnect performance of up to 145% at the 50-nm node     

Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches

In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m² on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R² values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment.     

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

Effect of annealing on the morphology and properties of poly(lactic acid)/polyhedral oligomeric silsesquioxane asymmetric porous membranes prepared through non-solvent induced phase separation and its application

Non-solvent induced phase separation (NIPS) method was employed to fabricate biodegradable poly(lactic acid) (PLA) nanocomposite membranes. Morphological studies using scanning electron microscopy revealed that all the membranes prepared display asymmetric structures comprising finger-like macropores. The incorporation of modified polyhedral oligomeric silsesquioxane (POSS) particles into the PLA matrix resulted in enhanced crystallinity, mechanical, and thermal properties. Annealing of the membranes was performed to explore the influence of temperature on the morphology and properties. After annealing, membranes become more thin and compact, and drastic enhancement in crystallinity is also observed. Consequently, Young's modulus experiences a significant improvement. The reduction in oil absorption capacity after annealing can be attributed to the higher level of crystallinity, reduced porosity, and smaller pore diameter observed in the annealed membranes. Additionally, the unannealed PLA nanocomposite membranes demonstrated exceptional oil absorption capacity, reaching approximately 88%. It is foreseeable that these PLA/POSS nanocomposite membranes possess the potential to be utilized as effective tools for oil–water separation, offering the advantage of mitigating secondary pollution.     

An Improved Efficient Dynamic Load Balancing Scheme Under Heterogeneous Networks in Hybrid Cloud Environment

Wireless Personal Communications - In the rapid development of computer network technology. The cloud computing is a novel technology had become a highly demanded service due to several new...