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In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.

Biological systems are homochiral, raising the question of how a racemic mixture of prebiotically synthesized biomolecules could attain a homochiral state at the network level. Based on our recent results, we aim to address a related question of how chiral information might have flowed in a prebiotic network. Utilizing the crystallization properties of the central RNA precursor known as ribose-aminooxazoline (RAO), we showed that its homochiral crystals can be obtained from its fully racemic solution on a magnetic mineral surface, due to the chiral-induced spin selectivity (CISS) effect. Moreover, we uncovered a mechanism facilitated by the CISS effect through which chiral molecules, like RAO, can uniformly magnetize such surfaces in a variety of planetary environments in a persistent manner. All this is very tantalizing, because recent experiments with tRNA analogs demonstrate high stereoselectivity in the attachment of L-amino acids to D-ribonucleotides, enabling the transfer of homochirality from RNA to peptides. Therefore the biological homochirality problem may be reduced to ensuring that a single common RNA precursor (e.g. RAO) can be made homochiral. The emergence of homochirality at RAO then allows for the chiral information to propagate through RNA, then to peptides, and ultimately, through enantioselective catalysis, to metabolites. This directionality of the chiral information flow parallels that of the central dogma of molecular biology--the unidirectional transfer of genetic information from nucleic acids to proteins., Comment: 8 pages, 4 figures

Homochirality is a signature of life on Earth yet its origins remain an unsolved puzzle. Achieving homochirality is essential for a high-yielding prebiotic network capable of producing functional polymers like ribonucleic acid (RNA) and peptides. However, a prebiotically plausible and robust mechanism to reach homochirality has not been shown to this date. The chiral-induced spin selectivity (CISS) effect has established a strong coupling between electron spin and molecular chirality and this coupling paves the way for breaking the chiral molecular symmetry by spin-selective processes. Magnetic surfaces can act as chiral agents due to the CISS effect and they can be templates for the enantioselective crystallization of chiral molecules. Here we studied the spin-selective crystallization of racemic ribo aminooxazoline (RAO), an RNA precursor, on magnetite ($Fe_3O_4$) surfaces, achieving an unprecedented enantiomeric excess of about 60$\%$. Following the initial enrichment, we then obtained homochiral crystals of RAO after a subsequent crystallization. Our work combines two necessary features for reaching homochirality: chiral symmetry-breaking induced by the magnetic surface and self-amplification by conglomerate crystallization of RAO. Our results demonstrate a prebiotically plausible way of achieving systems level homochirality from completely racemic starting materials., Comment: 12 pages, 5 figures

Smart manufacturing systems are being deployed at a growing rate because of their ability to interpret a wide variety of sensed information and act on the knowledge gleaned from system observations. In many cases, the principal goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. In particular, there are often multiple types of sensors with varying capabilities and costs. The sensor data characteristics change with the operating point of the environment or machines, such as, the RPM of the motor. The anomaly detection process therefore has to be calibrated near an operating point. In this paper, we analyze four datasets from sensors deployed from manufacturing testbeds. We evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. Taken together, we show that predictive failure classification can be achieved, thus paving the way for predictive maintenance., Comment: arXiv admin note: substantial text overlap with arXiv:2102.05814

IoT systems have been facing increasingly sophisticated technical problems due to the growing complexity of these systems and their fast deployment practices. Consequently, IoT managers have to judiciously detect failures (anomalies) in order to reduce their cyber risk and operational cost. While there is a rich literature on anomaly detection in many IoT-based systems, there is no existing work that documents the use of ML models for anomaly detection in digital agriculture and in smart manufacturing systems. These two application domains pose certain salient technical challenges. In agriculture the data is often sparse, due to the vast areas of farms and the requirement to keep the cost of monitoring low. Second, in both domains, there are multiple types of sensors with varying capabilities and costs. The sensor data characteristics change with the operating point of the environment or machines, such as, the RPM of the motor. The inferencing and the anomaly detection processes therefore have to be calibrated for the operating point. In this paper, we analyze data from sensors deployed in an agricultural farm with data from seven different kinds of sensors, and from an advanced manufacturing testbed with vibration sensors. We evaluate the performance of ARIMA and LSTM models for predicting the time series of sensor data. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor. We then perform anomaly detection using the predicted sensor data. Taken together, we show how in these two application domains, predictive failure classification can be achieved, thus paving the way for predictive maintenance.

Given that the macromolecular building blocks of life were likely produced photochemically in the presence of ultraviolet (UV) light, we identify some general constraints on which stars produce sufficient UV for this photochemistry. We estimate how much light is needed for the UV photochemistry by experimentally measuring the rate constant for the UV chemistry (`light chemistry', needed for prebiotic synthesis) versus the rate constants for the bimolecular reactions that happen in the absence of the UV light (`dark chemistry'). We make these measurements for representative photochemical reactions involving SO$_3^{2-}$ and HS$^-$. By balancing the rates for the light and dark chemistry, we delineate the "abiogenesis zones" around stars of different stellar types based on whether their UV fluxes are sufficient for building up this macromolecular prebiotic inventory. We find that the SO$_3^{2-}$ 'light chemistry' is rapid enough to build up the prebiotic inventory for stars hotter than K5 (4400 K). We show how the abiogenesis zone overlaps with the liquid water habitable zone. Stars cooler than K5 may also drive the formation of these building blocks if they are very active. The HS$^-$ 'light chemistry' is too slow to work even for the Early Earth., Comment: 33 pages, 5 figures, preprint