This paper investigated the effect of the tenon length on the strength and stiffness of the standard mortise and tenon joints, as well of the double mortise and tenon joints, that were bonded by poly(vinyl acetate) (PVAc) and polyurethane (PU) glues. The strength was analyzed by measuring applied load and by calculating ultimate bending moment and bending moment at the proportional limit. Stiffness was evaluated by measuring displacement and by calculating the ratio of applied force and displacement along the force line. The results were compared with the data obtained by the simplified static expressions and numerical calculation of the orthotropic linear-elastic model. The results indicated that increasing tenon length increased the maximal moment and proportional moment of the both investigated joints types. The analytically calculated moments were increased more than the experimental values for both joint types, and they had generally lower values than the proportional moments for the standard tenon joints, as opposed to the double tenon joints. The Von Mises stress distribution showed characteristic zones of the maximum and increased stress values. These likewise were monitored in analytical calculations. The procedures could be successfully used to achieve approximate data of properties of loaded joints.

Rydberg dressing provides optically controllable interactions between neutral atoms, of interest for generating many-body entanglement. We demonstrate these interactions and emulate transverse-field Ising dynamics in a cold gas of cesium atoms.

The paper introduces a novel control strategy for simultaneous control of position and interaction force for multi-degrees of freedom robotic systems (multi-DOF). The strategy enables both position control in free motion, and interaction force control during contact with an environment. In that sense, it differs from classical control algorithms which are switching between two different controllers, namely, position controller and force controller. The transition between position control mode and force control mode in the newly proposed structure is smooth, removing oscillations often present in the classical algorithms. This improves safety of the interaction between a controlled system and its environment.

Compared to the traditional industrial robots that use rigid actuators, the advanced robotic systems are mobile and physically interact with unknown and dynamic environments. Therefore, they need intrinsically safe and compact actuators. In the last two decades, Series Elastic Actuators (SEAs) have been one of the most popular compliant actuators in advanced robotic applications due to their intrinsically safe and compact mechanical structures. The mobility and functionality of the advanced robotic systems are highly related to the torque-density of their actuators. For example, the amount of assistance an exoskeleton robot can provide is determined by the trade-off between the weight and output-torque, i.e., torque-density, of its actuators. As the torque outputs of the actuators are increased, the exoskeleton can expand its capacity yet it generally becomes heavier and bulkier. This has significant impact on the mobility of the advanced robotic systems. Therefore, it is essential to design light-weight actuators which can provide high-output torque. However, this still remains a big challenge in engineering. To this end, this paper proposes a high-torque density SEA for physical robot environment interaction (p-REI) applications. The continuous (peak) output-torque of the proposed compliant actuator is 147Nm (467 Nm) and its weight is less than 2.5kg. It is shown that the weight can be lessened to 1.74, but it comes at cost. The performance of the proposed compliant actuator is experimentally verified.

Interest in CRISPR technology, an instrumental component of prokaryotic adaptive immunity which enables prokaryotes to detect any foreign DNA and then destroy it, has gained popularity among members of the scientific community. This is due to CRISPR’s remarkable gene editing and cleaving abilities. While the application of CRISPR in human genome editing and diagnosis needs to be researched more fully, and any potential side effects or ambiguities resolved, CRISPR has already shown its capacity in an astonishing variety of applications related to genome editing and genetic engineering. One of its most currently relevant applications is in diagnosis of infectious and non-infectious diseases. Since its initial discovery, 6 types and 22 subtypes of CRISPR systems have been discovered and explored. Diagnostic CRISPR systems are most often derived from types II, V, and VI. Different types of CRISPR-Cas systems which have been identified in different microorganisms can target DNA (e.g. Cas9 and Cas12 enzymes) or RNA (e.g. Cas13 enzyme). Viral, bacterial, and non-infectious diseases such as cancer can all be diagnosed using the cleavage activity of CRISPR enzymes from the aforementioned types. Diagnostic tests using Cas12 and Cas13 enzymes have already been developed for detection of the emerging SARS-CoV-2 virus. Additionally, CRISPR diagnostic tests can be performed using simple reagents and paper-based lateral flow assays, which can potentially reduce laboratory and patient costs significantly. In this review, the classification of CRISPR-Cas systems as well as the basis of the CRISPR/Cas mechanisms of action will be presented. The application of these systems in medical diagnostics with emphasis on the diagnosis of COVID-19 will be discussed.

Interest in CRISPR technology, an instrumental component of prokaryotic adaptive immunity which enables prokaryotes to detect any foreign DNA and then destroy it, has gained popularity among members of the scientific community. This is due to CRISPR’s remarkable gene editing and cleaving abilities. While the application of CRISPR in human genome editing and diagnosis needs to be researched more fully, and any potential side effects or ambiguities resolved, CRISPR has already shown its capacity in an astonishing variety of applications related to genome editing and genetic engineering. One of its most currently relevant applications is in diagnosis of infectious and non-infectious diseases. Since its initial discovery, 6 types and 22 subtypes of CRISPR systems have been discovered and explored. Diagnostic CRISPR systems are most often derived from types II, V, and VI. Different types of CRISPR-Cas systems which have been identified in different microorganisms can target DNA (e.g. Cas9 and Cas12 enzymes) or RNA (e.g. Cas13 enzyme). Viral, bacterial, and non-infectious diseases such as cancer can all be diagnosed using the cleavage activity of CRISPR enzymes from the aforementioned types. Diagnostic tests using Cas12 and Cas13 enzymes have already been developed for detection of the emerging SARS-CoV-2 virus. Additionally, CRISPR diagnostic tests can be performed using simple reagents and paper-based lateral flow assays, which can potentially reduce laboratory and patient costs significantly. In this review, the classification of CRISPR-Cas systems as well as the basis of the CRISPR/Cas mechanisms of action will be presented. The application of these systems in medical diagnostics with emphasis on the diagnosis of COVID-19 will be discussed.

A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal's length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for application to raw data and a complex multi-class multi-label classification problem, even if the training data is from diverse datasets and of differing lengths.