How Clay in Group Art Therapy Helps Female Veterans Maintain Well-being: Development of Methods

Using clay in art therapy has emerged as an evidence-based therapeutic approach to enable treatment for people with anxiety. While clay in art therapy has been researched and proven to be highly effective with children, there is limited research on the benefit for the population of female veterans. Therefore, this study examines the possibility of how clay in art therapy helps female veterans to reduce anxiety and maintain their well-being at Veterans Affairs Hospital (VAH). Three female veterans were engaged in weekly intervention for six weeks through both in-person and online sessions. Results indicate participants experienced beneficial changes in anxiety reduction through the intervention. The art therapy with clay intervention was successfully integrated and implemented during the research

Learning for informative path planning

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 104-108).Through the combined use of regression techniques, we will learn models of the uncertainty propagation efficiently and accurately to replace computationally intensive Monte- Carlo simulations in informative path planning. This will enable us to decrease the uncertainty of the weather estimates more than current methods by enabling the evaluation of many more candidate paths given the same amount of resources. The learning method and the path planning method will be validated by the numerical experiments using the Lorenz-2003 model [32], an idealized weather model.by Sooho Park.S.M

Indirect Band Gap in Scrolled MoS₂ Monolayers

MoS2 nanoscrolls that have inner core radii of similar to 250 nm are generated from MoS2 monolayers, and the optical and transport band gaps of the nanoscrolls are investigated. Photoluminescence spectroscopy reveals that a MoS2 monolayer, originally a direct gap semiconductor (similar to 1.85 eV (optical)), changes into an indirect gap semiconductor (similar to 1.6 eV) upon scrolling. The size of the indirect gap for the MoS2 nanoscroll is larger than that of a MoS2 bilayer (similar to 1.54 eV), implying a weaker interlayer interaction between concentric layers of the MoS2 nanoscroll compared to Bernal-stacked MoS2 few-layers. Transport measurements on MoS2 nanoscrolls incorporated into ambipolar ionic-liquid-gated transistors yielded a band gap of similar to 1.9 eV. The difference between the transport and optical gaps indicates an exciton binding energy of 0.3 eV for the MoS2 nanoscrolls. The rolling up of 2D atomic layers into nanoscrolls introduces a new type of quasi-1D nanostructure and provides another way to modify the band gap of 2D materials.11Nsciescopu

Fast shadow detection for urban autonomous driving applications

This paper presents shadow detection methods for vision-based autonomous driving in an urban environment. Shadows misclassified as objects create problems in autonomous driving applications. Real-time efficient algorithms in dynamic background settings are proposed. Without the static background assumption, which was often used in previous work to develop fast algorithms, our scheme estimates the varying background efficiently. A combination of various features classifies each pixel into one of the following categories: road, shadow, dark object, or other objects. In addition to pixel level classification, spatial context is also used to identify the shadows. Our results show that our methods perform well for autonomous driving applications and are fast enough to work in real time

Fast shadow detection for urban autonomous driving applications

Abstract-This paper presents shadow detection methods for vision-based autonomous driving in an urban environment. Shadows misclassified as objects create problems in autonomous driving applications. Real-time efficient algorithms in dynamic background settings are proposed. Without the static background assumption, which was often used in previous work to develop fast algorithms, our scheme estimates the varying background efficiently. A combination of various features classifies each pixel into one of the following categories: road, shadow, dark object, or other objects. In addition to pixel level classification, spatial context is also used to identify the shadows. Our results show that our methods perform well for autonomous driving applications and are fast enough to work in real time

Graphene quantum dots-decorated ZnS nanobelts with highly efficient photocatalytic performances

Hybrid nanostructures combining inorganic materials and graphene have shown great potential for the environmentally friendly treatment of effluents. Herein, graphene quantum dots (GQDs)-decorated ZnS nanobelts have been synthesized via a facile hydrothermal method. The electrostatic attraction of two materials and the thermal reduction of graphene are the main driving forces to fabricate well-defined composite nanostructures. GQDs in GQD/ZnS nanocomposites have been found to exist discretely and uniformly on the surfaces of ZnS nanobelts. The photocatalytic activity of GQD/ZnS nanocomposites has been found to be highest at a GQD/ZnS mass ratio of 8 x 10(-4). The photocatalytic rate constant (0.0046 min(-1)) of GQD/ZnS nanocomposites having the optimized GQD content in the photodegradation reaction of rhodamine B has been found to be 14 times higher than that of commercially available ZnS powder. Decorated GQDs introduce an additional visible-light response and serve as electron collectors and transporters to block electron-hole recombination efficiently, enhancing the photocatalytic performances of ZnS nanobelts immensely

Adaptive Observation Strategies for Forecast Error Minimization

Abstract. Using a scenario of multiple mobile observing platforms (UAVs) mea-suring weather variables in distributed regions of the Pacific, we are develop-ing algorithms that will lead to improved forecasting of high-impact weather events. We combine technologies from the nonlinear weather prediction and plan-ning/control communities to create a close link between model predictions and observed measurements, choosing future measurements that minimize the ex-pected forecast error under time-varying conditions. We have approached the problem on three fronts. We have developed an infor-mation-theoretic algorithm for selecting environment measurements in a compu-tationally effective way. This algorithm determines the best discrete locations and times to take additional measurement for reducing the forecast uncertainty in the region of interest while considering the mobility of the sensor platforms. Our second algorithm learns to use past experience in predicting good routes to travel between measurements. Experiments show that these approaches work well on idealized models of weather patterns.

Competitive Hybridization of a Microarray Identifies CMKLR1 as an Up-Regulated Gene in Human Bone Marrow-Derived Mesenchymal Stem Cells Compared to Human Embryonic Fibroblasts

Mesenchymal stem cells (MSCs) have been widely applied to the regeneration of damaged tissue and the modulation of immune response. The purity of MSC preparation and the delivery of MSCs to a target region are critical factors for success in therapeutic application. In order to define the molecular identity of an MSC, the gene expression pattern of a human bone marrow-derived mesenchymal stem cell (hBMSC) was compared with that of a human embryonic fibroblast (hEF) by competitive hybridization of a microarray. A total of 270 and 173 genes were two-fold up- and down-regulated with FDR < 0.05 in the hBMSC compared to the hEF, respectively. The overexpressed genes in the hBMSC over the hEF, including transcription factors, were enriched for biological processes such as axial pattern formation, face morphogenesis and skeletal system development, which could be expected from the differentiation potential of MSCs. CD70 and CD339 were identified as additional CD markers that were up-regulated in the hBMSC over the hEF. The differential expression of CD70 and CD339 might be exploited to distinguish hEF and hBMSC. CMKLR1, a chemokine receptor, was up-regulated in the hBMSC compared to the hEF. RARRES2, a CMKLR1 ligand, stimulated specific migration of the hBMSC, but not of the hEF. RARRES2 manifested as ~two-fold less effective than SDF-1α in the directional migration of the hBMSC. The expression of CMKLR1 was decreased upon the osteoblastic differentiation of the hBMSC. However, the RARRES2-loaded 10% HA-silk scaffold did not recruit endogenous cells to the scaffold in vivo. The RARRES2–CMKLR1 axis could be employed in recruiting systemically delivered or endogenous MSCs to a specific target lesion

Short-Term Event Prediction in the Operating Room (STEP-OP) of Five-Minute Intraoperative Hypotension Using Hybrid Deep Learning: Retrospective Observational Study and Model Development

BackgroundIntraoperative hypotension has an adverse impact on postoperative outcomes. However, it is difficult to predict and treat intraoperative hypotension in advance according to individual clinical parameters. ObjectiveThe aim of this study was to develop a prediction model to forecast 5-minute intraoperative hypotension based on the weighted average ensemble of individual neural networks, utilizing the biosignals recorded during noncardiac surgery. MethodsIn this retrospective observational study, arterial waveforms were recorded during noncardiac operations performed between August 2016 and December 2019, at Seoul National University Hospital, Seoul, South Korea. We analyzed the arterial waveforms from the big data in the VitalDB repository of electronic health records. We defined 2s hypotension as the moving average of arterial pressure under 65 mmHg for 2 seconds, and intraoperative hypotensive events were defined when the 2s hypotension lasted for at least 60 seconds. We developed an artificial intelligence–enabled process, named short-term event prediction in the operating room (STEP-OP), for predicting short-term intraoperative hypotension. ResultsThe study was performed on 18,813 subjects undergoing noncardiac surgeries. Deep-learning algorithms (convolutional neural network [CNN] and recurrent neural network [RNN]) using raw waveforms as input showed greater area under the precision-recall curve (AUPRC) scores (0.698, 95% CI 0.690-0.705 and 0.706, 95% CI 0.698-0.715, respectively) than that of the logistic regression algorithm (0.673, 95% CI 0.665-0.682). STEP-OP performed better and had greater AUPRC values than those of the RNN and CNN algorithms (0.716, 95% CI 0.708-0.723). ConclusionsWe developed STEP-OP as a weighted average of deep-learning models. STEP-OP predicts intraoperative hypotension more accurately than the CNN, RNN, and logistic regression models. Trial RegistrationClinicalTrials.gov NCT02914444; https://clinicaltrials.gov/ct2/show/NCT02914444