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Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method's robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.

Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a novel sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE's time domain into smaller time segments to create "easier" PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost.Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100.

Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accurate model requires extensive hyperparameter tuning, network architecture design, and costly and exhaustive training processes. To avoid such obstacles and make the training of physics-informed models less precarious, in this paper, a data-driven multi-fidelity physics-informed framework is proposed based on transfer learning principles. The framework incorporates the knowledge from low-fidelity auxiliary systems and limited labeled data from target actual system to significantly improve the performance of conventional physics-informed models. While minimizing the efforts of designing a complex task-specific network for the problem at hand, the proposed settings guide the physics-informed model towards a fast and efficient convergence to a global optimum. An adaptive weighting method is utilized to further enhance the optimization of the model composite loss function during the training process. A data-driven strategy is also introduced for maintaining high performance in subdomains with significant divergence between low- and high-fidelity behaviours. The heat transfer of composite materials undergoing a cure cycle is investigated as a case study to demonstrate the proposed framework's performance compared to conventional physics-informed models.

Rhizoctonia solani anastomosis group AG2-2 IIIB is a severe sugar beet and maize pathogen. It causes crown and root rot disease which leads to yield losses world-wide. The soil-borne pathogen is difficult to detect and quantify by conventional methods. We developed a real-time PCR (qPCR) assay for the quantification of genomic DNA of Rhizoctonia solani AG2-2 IIIB based on the ITS region of rDNA genes. The limit of quantification of the assay is 1.8 pg genomic DNA. The amplification efficiency was 96.4. The assay will be helpful in the diagnoses of Rhizoctonia solani infection of sugar beet and maize roots and in the quantification of R. solani AG2-2 IIIB inoculum in plant debris and soil.

The Dark Energy problem is forcing us to re-examine our models and our understanding of relativity and space-time. The Standard Model of particle physics and its extensions are already in crisis. Having failed so far to include gravity in a proper unified framework, these are now faced with an additional unwanted fifth force of repulsion. How does one understand this 3+1+1 fundamental force dilemma? Quite clearly this points to a limitation of our present understanding and demands extension of our theoretical framework. To be able to go beyond these limitations, here we introduce a novel idea of the Fundamental Forces. This allows us to perceive the General Theory of Relativity and Einstein's Equation from a different perspective. This will give us an additional and an all-encompassing way of classifying these five fundamental forces in a consistent manner. In addition to providing us with an improved understanding of space and time, it will be shown how it leads to a resolution of the Dark Energy problem., Comment: 10 pages, no figures

Recently, it has been reported that as one goes from oxygen to fluorine, just the addition of one more proton, provides extraordinary stability to fluorine which can bind six more neutrons beyond what oxygen can. It is shown here that this surprising stability can be understood if neutron rich nuclei, $^{24}O$ and $^{27}F$ are treated as bound states of eight and nine-tritons respectively. Also the recently discovered $^{42}Si$ is predicted to have a bound state structure of fourteen tritons., Comment: 7 pages, 1 figure

Traditionally, the difference in binding energy from the experimental value with respect to the theoretical liquid-drop model value, has been seen as indication of independent-particle character along with magicity for particular number of protons and neutrons. We study this carefully to demonstrate that it actually indicates that the liquid-drop and the independent-particle phases of the nucleus have equal fundamental primacy and coexist simultaneously in a nucleus to provide a complete and a consistent description of the same., Comment: 7 pages, 2 figurs

The Liquid Drop Models (LDM) and the Independent Particle Models (IPM) have been known to provide two conflicting pictures of the nucleus. The IPM being quantum mechanical, is believed to provide a fundamental picture of the nucleus and hence has been focus of the still elusive unified theory of the nucleus. It is believed that the LDM at best is an effective and limited model of the nucleus. Here, through a comprehensive study of one nucleon separation energy, we give convincing evidence that actually the LDM is as fundamental and as basic for the description of the nucleus as the IPM is. As such the LDM and the IPM provide simultaneously co-exiting complementary duality of the nuclear phenomena. This fundamental duality also provides solution to the decades old Coester Band problem in the nucleus. Similarity and differences with respect to the well known wave-particle duality, as envisaged in Bohr's Complementarity Principle, is pointed out. Thereafter implications of this new Duality in the nucleus for quantum mechanics is discussed., Comment: 17 pages, 6 figures

An approach is developed to find approximate solutions to the restricted circular three body problem. The solution is useful in approximately describing the position vectors of three spherically symmetric masses, one of which has a much smaller mass than the other two. These masses perform free motion under each others gravitational influence. The set of solutions is found using the lamberts wave function., Comment: 18 pages, 4 figures

Clumped dark matter arises naturally within the framwork of generic cosmological dark matter models. Invoking the existence of dark matter clumps can also solve may unexplained mysteries in astrophysics and geology or geophysics, eg. the galactic gamma-ray halo and the periodic terrestrial flood basalt volcanic episodes. Clumped dark matter is dynamically stable to friction and will not heat the disk. Such clumps may have already been discovered in the form of dwarf spheroidals, and further searches are encouraged by the results of this paper., Comment: Revised Version, includes new relevant references, Latex File, 16 pages, no figures

Dark matter capture and annihilation in planets and their satellites, in addition to producing neutrinos (already the basis of several ongoing experiments), is shown to lead to more significant heat generation in these bodies for a uniform dark matter halo. This thermal output becomes more prominent when clumped dark matter passes through the solar system. The dark matter annihilations should be treated as a new source of heat in the solar system which in some special cases may lead to unique imprints. Such new signatures of the dark matter are found in the generation of the puzzling magnetic field of Ganymede. This new source of heat perhaps cannot explain the formation and segregation of Ganymedean structure (which are probably due to conventional gravitational and radioactive heating), but provides a possible explanation of the origin of the Ganymedean magnetic field., Comment: Revised substantially, 9 pages Latex