Analog and Mixed Signal Verification using Satisfiability Solver on Discretized Models

With increasing demand of performance constraints and the ever reducing size of the IC chips, analog and mixed-signal designs have become indispensable and increasingly complex in modern CMOS technologies. This has resulted in the rise of stochastic behavior in circuits, making it important to detect all the corner cases and verify the correct functionality of the design under all circumstances during the earlier stages of the design process. It can be achieved by functional or formal verification methods, which are still widely unexplored for Analog and Mixed-Signal (AMS) designs. Design Verification is a process to validate the performance of the system in accordance with desired specifications. Functional verification relies on simulating different combinations of inputs for maximum state space coverage. With the exponential increase in the complexity of circuits, traditional functional verification techniques are getting more and more inadequate in terms of exhaustiveness of the solution. Formal verification attempts to provide a mathematical proof for the correctness of the design regardless of the circumstances. Thus, it is possible to get 100% coverage using formal verification. However, it requires advanced mathematics knowledge and thus is not feasible for all applications. In this thesis, we present a technique for analog and mixed-signal verification targeting DC verification using Berkeley Short-channel Igfet Models (BSIM) for approximation. The verification problem is first defined using the state space equations for the given circuit and applying Satisfiability Modulo Theories (SMT) solver to determine a region that encloses complete DC equilibrium of the circuit. The technique is applied to an example circuit and the results are analyzed in turns of runtime effectiveness

Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry

Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues

Surface tailored PS/TiO2 composite nanofiber membrane for copper removal from water

none8siPolystyrene (PS)/TiO2 composite nanofiber membranes have been fabricated by electrospinning process for Cu2+ ions removal from water. The surface properties of the polystyrene nanofibers were modulated by introducing TiO2 nanoparticles. The contact angle of the PS nanofiber membrane was found to be decreased with increasing concentration of TiO2, depicted enhanced hydrophilicity. These membranes were highly effective in adsorbing Cu2+ ions from water. The adsorption capacity of these membranes was found to be 522 mg/g, which is significantly higher than the results reported by other researchers.This was attributed to enhanced hydrophilicity of the PS/TiO2 composite nanofiber membranes and effective adsorption property of TiO2 nanoparticles.noneWanjale, Santosh; Birajdar, Mallinath; Jog, Jyoti; Neppalli, Ramesh; Causin, Valerio; Karger-Kocsis, József; Lee, Jonghwi; Panzade, PrasadWanjale, Santosh; Birajdar, Mallinath; Jog, Jyoti; Neppalli, Ramesh; Causin, Valerio; Karger Kocsis, József; Lee, Jonghwi; Panzade, Prasa

Mechanical reinforcement of poly(1-butene) using polypropylene-grafted multiwalled carbon nanotubes

10.1002/app.30056Journal of Applied Polymer Science11321165-1172JAPN

The effect of clay and of electrospinning on the polymorphism, structure and morphology of poly(vinylidene fluoride)

Electrospun poly(vinylidene fluoride) (PVDF) fibers, containing different amounts of montmorillonite clay were produced, in order to study the effect of clay and of the electrospinning process on the polymorphism, structure and morphology of the PVDF matrix. Clay acted as a processing aid agent, avoiding the formation of beads and improving the quality of the fibers. Clay and the electrospinning process acted synergically on the chain mobility, favoring the formation of \u3b2 phase of PVDF, the most valuable for its piezoelectric properties, and shaping its semicrystalline morphology. Electrospinning did not significantly aid the dispersion of clay within the matrix. The interplay of formulation and processing in these composites allowed to obtain PVDF-based materials with varying polymorphism, structure and morphology, offering the possibility to ultimately control their functional properties