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Aims Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. Methods and results We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667–0.830); specificity, 0.652 (95% CI, 0.614–0.710); AUROC, 0.761 (95% CI, 0.742–0.780); and accuracy, 70.8% (95% CI, 68.8–72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762–0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712–0.761). Conclusion Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes. [ABSTRACT FROM AUTHOR]

In this study, we introduce two ortho‐carboranyl compounds, namely ICzCB and mTPCB, which exhibit different planarity in their appended π‐conjugated aromatic groups, to investigate the influence of geometric effects on the regulation of the radiative efficiency. Both the compounds are fully characterized, revealing distinct geometric differences (planarity) in their single‐crystal structures. While ICzCB presents strong ICT‐based emission in the solid (film) state, mTPCB displays dual emission in the high‐ and low‐energy regions that originates from locally excited (LE)‐ and ICT‐based processes, respectively. Quantitative analysis of the photophysical characteristics of the compounds shows that the ICT‐based radiative efficiency in ICzCB is higher than that in mTPCB. Consequently, these findings demonstrate that the planarity of the π‐conjugated aryl group appended on o‐carborane, which is represented by planar ICzCB and distorted mTPCB, is an important key to control the ICT‐based radiative decay efficiency. [ABSTRACT FROM AUTHOR]

Complementary metal oxide semiconductor‐compatible Ni/SiNx/SiO2/Si devices present good synaptic characteristics for neuromorphic systems on the hardware level. The SiO2 layer detectable by X‐ray photoelectron spectroscopy can improve stability and low‐power resistive switching. The properties of nonvolatile and volatile memory are determined by the compliance current (strength of the conducting path) during DC voltage sweep. For more practical operation, repeated pulse inputs are applied to the memristor to implement both long‐term plasticity and short‐term plasticity, determined by pulse interval time. A shorter interval time between two pulses leads to larger paired‐pulse facilitation related to short‐term plasticity. Finally, ten cycles of potentiation and depression are demonstrated by well‐designed pulse schemes. The coexistence of long‐term and short‐term memory in a Ni/SiNx/SiO2/Si device can provide more flexibility in device design in future neuromorphic systems on the hardware level. [ABSTRACT FROM AUTHOR]

This paper proposes a control method for improving the dynamic characteristic of battery chargers for personal mobility devices (PMDs) using sliding mode control (SMC). Various types of PMDs have batteries with distinct voltages. Therefore, research on battery chargers for PMDs that can be applied to various types of PMD with distinct voltages has been actively pursued. In battery chargers for PMDs, the output voltage can usually be controlled by a proportional-integral (PI) controller. However, increasing the PI controller gain is necessary to enhance the dynamic characteristic of the output voltage. It results in an overshoot that adversely affects the stability of the battery charger. Therefore, in this paper, a control method using sliding mode control (SMC) is presented for improving the dynamic characteristic without overshoot in the output voltage of battery chargers for PMDs. The validity of the proposed control method is verified by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Biclustering is a process of finding groups of genes that behave similarly under a subset of conditions. In this article, we propose an efficient biclustering algorithm, namely RN+, to identify biologically meaningful biclusters in gene expression data. The RN+ algorithm finds biologically meaningful biclusters through a novel gene filtering using protein–protein interaction network, gene searching, gene grouping, and queuing process. It also efficiently removes duplicate biclusters. We tested the proposed RN+ on five real microarray datasets, and compared its performance with seven competitive biclustering algorithms. The experimental results show that RN+ efficiently finds functionally enriched and biologically meaningful biclusters for large gene expression datasets, and outperforms the other tested biclustering algorithms on real datasets. [ABSTRACT FROM AUTHOR]

In this work, the synaptic plasticity from complementary resistive switching in a HfAlOx-based resistive memory device was emulated by a direct current (DC) voltage sweep, current sweep, and pulse transient. The alloyed HfAlOx dielectric was confirmed by X-ray photoelectron spectroscopy analysis. The negative differential resistance observed before the forming and set processes can be used for interface resistive switching with a low current level. Complementary resistive switching is obtained after the forming process at a negative bias. This unique resistive switching is also suitable for synaptic device applications in which the reset process occurs after an additional set process. The current sweep mode provides more clear information on the complementary resistive switching. Multiple current states are achieved by controlling the amplitude of the set and reset voltages under DC sweep mode. The potentiation and depression characteristics are mimicked by varying the pulse voltage amplitude for synaptic device application in a neuromorphic system. Finally, we demonstrate spike-timing-dependent plasticity by tuning the timing differences between pre-spikes and post-spikes. [ABSTRACT FROM AUTHOR]