Search

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Background The reverse shoulder arthroplasty (RSA) was originally designed for cuff tear arthropathy (CTA). Over time, the indications have expanded to rheumatoid arthritis (RA). This study aimed to compare the outcomes of RSA in patients with RA and CTA to determine if there is any impact on clinical and radiographic outcomes. Methods In this retrospective comparative study (performed from August 2010 to March 2017), 61 shoulders from 59 consecutive patients (RA group: 24 patients [26 shoulders], CTA group: 35 patients [35 shoulders]) who underwent primary RSA, were included. The average follow-up period was 31 months (range, 24-64 months). Patients were assessed with the use of the visual analog scale pain score, the American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form score, the Single Assessment Numeric Evaluation score, range of motion (ROM), and imaging studies included the β angle, glenoid component superior tilt, and scapular notching. Results The clinical results improved significantly in both groups, but there was no statistically significant difference between the RA group and the CTA group. Significant intergroup differences were observed regarding the β angle (73° ± 11° for the RA group vs. 85° ± 8° for the CTA group; P Conclusion Compared with CTA patients, RA patients achieved similar clinical outcomes following RSA. However, surgeons should pay attention to the positioning of the glenoid component during the surgery and the risk of intraoperative fractures in RA patients.

The purpose of this study was to investigate the incidence of symptomatic venous thromboembolism (VTE) after chemoprophylaxis in patients with pelvic and lower-extremity fractures, and to identify risk factors for VTEs in this subgroup of patients. To detect VTE, multi-detector computed tomography (CT) angiography was performed. Of 363 patients assessed, the incidence of symptomatic VTE was 12.4% (45 patients), and the incidence of symptomatic PE was 5.2% (19 patients). For the risk-factor analysis, a higher Charlson comorbidity index (p = 0.037), and a history of external fixator application (p = 0.007) were associated with increased VTE risk. Among patients who had VTE, male sex (p = 0.017), and above-the-knee fractures (p = 0.035) were associated with increased pulmonary embolism (PE) risk. In conclusions, the incidence of VTE in post-traumatic patients is not low after chemoprophylaxis. Risk factors for VTE and PE are different among patients with pelvic and lower-extremity fractures.

We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal–organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m2/g, cm3/g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure–property relationships among MOFs as well as collecting a large set of data for references.

Recently, several research studies on things abstraction framework have been proposed in order to implement the multi-service Internet of Things (IoT) system, where various IoT services share the thing devices. Distributed things abstraction has an IoT service duplication problem, which aggravates power consumption of mobile devices and network traffic. On the other hand, cloud server-centric things abstraction cannot cover real-time interactions due to long network delay. Fog server-centric things abstraction has limits in insufficient IoT interfaces. In this paper, we propose Smart Fog which is a fog server-centric things abstraction framework to resolve the problems of the existing things abstraction frameworks. Smart Fog consists of software modules to operate the Smart Gateway and three interfaces. Smart Fog is implemented based on IoTivity framework and OIC standard. We construct a smart home prototype on an embedded board Odroid-XU3 using Smart Fog. We evaluate the network performance and energy efficiency of Smart Fog. The experimental results indicate that the Smart Fog shows short network latency, which can perform real-time interaction. The results also show that the proposed framework has reduction in the network traffic of 74% and power consumption of 21% in mobile device, compared to distributed things abstraction.