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Smooth and curved microstructural topologies found in nature—from soap films to trabecular bone—have inspired several mimetic design spaces for architected metamaterials and bio‐scaffolds. However, the design approaches so far are ad hoc, raising the challenge: how to systematically and efficiently inverse design such artificial microstructures with targeted topological features? Herein, surface curvature is explored as a design modality and a deep learning framework is presented to produce topologies with as‐desired curvature profiles. The inverse design framework can generalize to diverse topological features such as tubular, membranous, and particulate features. Moreover, successful generalization beyond both the design and data space is demonstrated by inverse designing topologies that mimic the curvature profile of trabecular bone, spinodoid topologies, and periodic nodal surfaces for application in bio‐scaffolds and implants. Lastly, curvature and mechanics are bridged by showing how topological curvature can be designed to promote mechanically beneficial stretching‐dominated deformation over bending‐dominated deformation.

Abstract The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials—truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metamaterials has remained highly limited and often heuristic, due to the vast, discrete design space and the lack of a comprehensive parameterization. We here present a graph-based deep learning generative framework, which combines a variational autoencoder and a property predictor, to construct a reduced, continuous latent representation covering an enormous range of trusses. This unified latent space allows for the fast generation of new designs through simple operations (e.g., traversing the latent space or interpolating between structures). We further demonstrate an optimization framework for the inverse design of trusses with customized mechanical properties in both the linear and nonlinear regimes, including designs exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear behaviors. This generative model can predict manufacturable (and counter-intuitive) designs with extreme target properties beyond the training domain.

Poly(L-lactic acid) (PLLA), a biodegradable polymer from plant sources, is widely employed in biomedical applications for its biocompatibility and versatile mechanical properties. However, comprehending its phase behavior in various solvents poses a challenge for broader applications. In this study, the phase behavior of the polymer was investigated in various solvent and cosolvent media and combinations at a wide range of temperatures and pressures to gain insight into the polymer properties. PLLA of varying intrinsic viscosity (IV): [0.15 dl/g, with an estimated weight average molecular weight (Mw) range of approximately: 1600 to 2400 g/mol; 1.0 dl/g (Mw): 100,000 g/mol); and 1.8 dl/g (Mw): 80,000 to 100,000 g/mol] was employed for this study. The phase behavior of the polymer across different compositions of PLLA and solvent (CH2F2, CHF3, CO2, DME, F-22) in a wide range of temperatures from 333 to 425 K and high pressure 1.55 to 172 MPa was investigated. Miscibility of PLLA in DME and F-22 is predicted to be higher with increasing in concentration than the other cosolvents. Furthermore, in the case of the supercritical solvent CHF3 and DME the phase transition pressure curve of PLLA was observed to exhibit a lower critical solution temperature (LCST) type with a positive gradient with increasing temperature. However, lower phase transition pressure was noticed for lower molecular weight PLLA in supercritical solvent CHF3 and DME, i.e., of the order IV 0.15 dl/g

Abstract We propose an approach for data-driven automated discovery of material laws, which we call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we apply it here to the discovery of plasticity models, including arbitrarily shaped yield surfaces and isotropic and/or kinematic hardening laws. The approach is unsupervised, i.e., it requires no stress data but only full-field displacement and global force data; it delivers interpretable models, i.e., models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a potentially large catalog of candidate functions; it is one-shot, i.e., discovery only needs one experiment. The material model library is constructed by expanding the yield function with a Fourier series, whereas isotropic and kinematic hardening is introduced by assuming a yield function dependency on internal history variables that evolve with the plastic deformation. For selecting the most relevant Fourier modes and identifying the hardening behavior, EUCLID employs physics knowledge, i.e., the optimization problem that governs the discovery enforces the equilibrium constraints in the bulk and at the loaded boundary of the domain. Sparsity promoting regularization is deployed to generate a set of solutions out of which a solution with low cost and high parsimony is automatically selected. Through virtual experiments, we demonstrate the ability of EUCLID to accurately discover several plastic yield surfaces and hardening mechanisms of different complexity.

The outbreak of COVID-19 challenged the sustainability of global agri-food markets. Through this research, the authors have made sincere efforts to recognize and remove/improve the potential factors that contribute towards an inefficient agro-based supply chain. COVID-19 has created much urgency to evaluate the existing supply chain and to propose a valid solution. In this particular research, the involvement of intermediaries is the point of focus. In addition, the profit margins for the farmer are hugely affected due to intermediaries. The current supply chain must be optimized, and this will lead to easier flows between each phase of the supply chain. Surveys were conducted and the farmers’ problems and expected solutions are highlighted in this research. Farmers in the northern states of India experienced more significant disruptions than those in other states because of a decreased availability of foods due to lack of diversity in crops. This research examines the existing supply chain for wheat crops and presents an optimization of the respective supply chain to save farmers more revenue. Through this research, the authors have attempted to observe and explore the problems faced by farmers and consumers due to the inefficient supply chain infrastructure prevalent in India because of the onslaught of COVID-19 and find an efficient solution. Due to the lack of adequate infrastructure, storage facilities, and old food-processing and supply chain technology almost 30–35% of the agricultural produce in India is wasted. A scientific solution to the existing problems is also presented after analyzing the existing supply chain and crop selection of farmers.

Abstract The proposed study reports the essential parameters required for the battery charging schemes deployed for Electric Vehicle (EV) applications. Due to efficient power delivery, cost‐effectiveness, and environmental acclimation, EVs have emerged as a suitable alternative to the Internal Combustion (IC)‐based engines. However, prominent challenges for leveraging the EVs are the suitable availability of battery charging infrastructure for high energy/power density battery packs and efficient charging topologies. Despite the challenges, EVs are gradually being implemented across the globe to avoid oil dependency, which currently has a 5%–7% decline rate of post‐peak production. The vast deployment of EVs as private and commercial vehicles has created a major challenge for the grids in maintaining the power quality and peak load demand. This study, therefore, reviews the various battery charging schemes (battery charger) and their impact when used in EV and Hybrid EV applications. The available constituents of the battery chargers such as ac‐dc/dc‐dc converter topologies, modulations, and control techniques are illustrated in detail. The comprehensive study classifies the charging topologies depending upon the power and charging level. Some appropriate battery charging converter topologies that are suitable for domestic, industrial, and commercial applications like EVs are suggested in the study. In addition, a decision‐making inference is developed through a flow chart that decides on the suitable selection of the converter topology based on the required applications. Furthermore, the charging infrastructures along with the converters' design standards are also discussed concisely, which adds a significant contribution to the review article.

Background: Stroke is one of the leading causes of disability with ~80% of post-stroke survivors suffering from gait-related deficits. Conventional gait rehabilitation settings are labor-intensive and need rigorous involvement of clinicians (who use their expertise to decide the dosage of exercise intensity based on patient's capability). This demands a technology-assisted individualized exercise platform that can offer varying dosage of exercise intensity based on one's capability. Here, we have used an individualized physiology-sensitive treadmill-assisted gait exercise platform. We wanted to investigate the implications of this platform on one's lower limb muscle strength and muscle fatigue by analyzing surface Electromyogram (sEMG)-related indices and standard gait-related clinical measures. Methods: We designed a feasibility study involving post-stroke patients (n=9; 44.4(±15)years; post-stroke period=3.1(±3)years) and gave them multiple exposures to this exercise platform. We investigated the Gastrocnemius Lateralis and Tibialis Anterior muscle activation prior to and post multiple exposures to our exercise platform while they walked overground. The feasibility study ended with collecting feedback on the patients' perception on the implications of having gait exercise using such a platform on their ambulation capability. Results: The results showed that repeated exposures to this gait exercise platform can contribute towards gait rehabilitation of post-stroke patients with rehabilitation outcomes measured in terms of group average improvement of (i)~14% in muscle strength and (ii)marginal (~6%) in functional mobility (as in our case). Conclusion: Such a physiology-sensitive treadmill-assisted gait exercise platform can hold promise towards contributing to post-stroke gait rehabilitation in low-resource settings with the rehabilitation outcomes being measured in terms of sEMG-based observations.

Abstract Objective: To understand the current evidence and guidelines behind the appropriate management of cardiac tumours. Methods: A comprehensive electronic literature search has been performed in major databases - PubMed, Embase, Scopus, Ovid, and Google Scholar. All articles that discussed all different forms of cardiac tumours, their clinical presentation, diagnosis, and management methods have been critically appraised in this narrative review. Results: All relevant studies have been summarized in appropriate sections within our review. Cardiac tumours are rare but can be catastrophic and life-threatening if not identified and managed on timely manner. Utilization of all the available imaging methods can be of equivocal importance, relevant to each cardiac tumour. Surgical excision is the ultimate treatment method, however histopathological results can guide the adjunct treatment. Conclusion: Early detection of cardiac tumours has significant effect on planning the method of intervention. Technological advancements and increased availability of imaging modalities have enabled earlier and more accurate detection of these tumours. Novel medical therapies, recommendations for screening, and operative techniques have all contributed to overall improving knowledge of these tumours and ultimately patient outcomes.