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Purpose of Review: As society continues to advance in technology, it is important to address how this advancement can impact and enhance patient care. The purpose of this review is to identify patient-centered technology currently available for adult and pediatric patients with and those having survived hematologic malignancies. Given that patients with hematologic malignancies often have to adhere to strenuous medication regimens, coordinate care with many different providers, manage symptoms associated with treatment, and manage late effects associated with survivorship, they would benefit greatly from patient-centered technology aimed at decreasing these burdens. Recent Findings: This review found various available digital interventions for this patient population and focuses on an overview of commercially available smartphone applications, patient portals, and technology for remote monitoring. Summary: In summary, many digital interventions exist for use in the medical care of oncology patients. The incorporation of these interventions can allow for more personalized medical care, better organization of treatment plans by caregivers at home, and easy delivery of accurate medical information. [ABSTRACT FROM AUTHOR]

This study provides a holistic list of factors that affect highway sustainability (HWS) and determines the most significant factors throughout the triple-bottom-line of sustainability. Previous studies analyzed many factors related to sustainability performance in highway projects during their life cycle, whereas the social sustainability aspects were commonly ignored. In addition, there is imperative to consider the Sustainable Development Goals (SDGs) of highway projects in local and global future visions. On the other hand, the infrastructure rating systems specified different thresholds to achieve sustainable highways. Thus, this study analyzed published articles, SDGs, and infrastructure rating systems to extract the most important factors affecting HWS. A content analysis was conducted to specify the most relevant publications in HWS, which are strongly related to the research keywords. According to the content analysis, 150 articles out of 6,422 were selected for further investigation. Social Network Analysis (SNA) analyzed and ranked 224 factors that were extracted from the aforementioned sources to determine the factors of utmost importance. We found that 78 factors out of 224 are of paramount influence on HWS. Finally, the evaluation of the most widely used infrastructure rating systems revealed a shortage in the common factors affecting the HWS. [ABSTRACT FROM AUTHOR]

Purpose: To examine the relations between patient-reported outcomes (PROs) within a conceptual model for adults with sickle cell disease (SCD) ages 18 – 45 years enrolled in the multi-site Sickle Cell Disease Implementation Consortium (SCDIC) registry. We hypothesized that patient and SCD-related factors, particularly pain, and barriers to care would independently contribute to functioning as measured using PRO domains. Methods: Participants (N = 2054) completed a 48-item survey including socio-demographics and PRO measures, e.g., social functioning, pain impact, emotional distress, and cognitive functioning. Participants reported on lifetime SCD complications, pain episode frequency and severity, and barriers to healthcare. Results: Higher pain frequency was associated with higher odds of worse outcomes in all PRO domains, controlling for age, gender and site (OR range 1.02–1.10, 95% CI range [1.004–1.12]). Reported history of treatment for depression was associated with 5 of 7 PRO measures (OR range 1.58–3.28 95% CI range [1.18–4.32]). Fewer individual barriers to care and fewer SCD complications were associated with better outcomes in the emotion domain (OR range 0.46–0.64, 95% CI range [0.34–0.86]). Conclusions: Study results highlight the importance of the biopsychosocial model to enhance understanding of the needs of this complex population, and to design multi-dimensional approaches for providing more effective interventions to improve outcomes. [ABSTRACT FROM AUTHOR]

To date, very few researchers employed the Least Square Support Vector Machine (LSSVM) in predicting the resilient modulus (Mr) of Unbound Granular Materials (UGMs). This paper focused on the development of a LSSVM model to predict the Mr of recycled materials for pavement applications and comparison with other different models such as Regression, and Artificial Neural Network (ANN). Blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM) with proportions of 100/0, 90/10, 80/20, 70/30, 55/45, 40/60, 20/80, and 0/100 by the total aggregate mass were evaluated for use as UGMs. RCA/RCM materials were collected from dumps on the sides of roads around Mansoura city, Egypt. The investigated blends were evaluated experimentally by routine and advanced tests and the Mr values were determined by Repeated Load Triaxial Test (RLTT). Regression, ANN, and LSSVM models were utilized and compared in predicting the Mr of the investigated blends optimizing the best design model. Results showed that the Mr values of the investigated RCA/RCM blends were generally increased with the decrease in RCM proportion. Statistical analyses were utilized for evaluating the performance of the developed models and the inputs sensitivity parameters. Eventually, the results approved that the LSSVM model can be used as a novel tool to estimate the Mr of the investigated RCA/RCM blends. [ABSTRACT FROM AUTHOR]

This study addresses the development of traffic characteristics to facilitate the implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG) in Idaho. Classification and weight data collected at 25 weigh-in-motion (WIM) sites were procured from Idaho Transportation Department (ITD). Among these 25 sites, only 12 sites were found to have complete and accurate data. Site-specific axle load spectra (ALS), monthly adjustment factors (MAF), vehicle class distribution factors (VCD), and number of axles per truck type were developed. Predicted distresses and International Roughness Index (IRI) based on a typical pavement section and traffic data obtained at the investigated WIM sites (level 1) were compared to predicted distresses/IRI using statewide/national (level 3) default traffic inputs. This comparison revealed that ALS, VCD, and MAF input level have significant impact on longitudinal cracking. Statewide ALS yielded high differences in alligator cracking predictions while MAF and VCD yielded only moderate differences compared to site-specific ALS. Very low prediction differences occurred in rutting when statewide/national default ALS, MAF, and VCD were used as opposed to site-specific data. The level of input of the investigated traffic parameters did not affect IRI. Finally, it was found that statewide/national number of axles per truck can be used instead of site-specific values without sacrificing accuracy of pavement performance predictions. [ABSTRACT FROM AUTHOR]

The newly Mechanistic-Empirical Pavement Design Guide (MEPDG) allows engineers to design pavement structural layers to sustain predefined limits of distress levels due to traffic loads and environmental conditions based on nationally/locally calibrated performance models. Users are required to input variables related to traffic, environment, and materials. In addition, reliability and limiting values of the distresses are required inputs. MEPDG in its current version is an analysis procedure rather than a design tool. However, it is intended to be also used as a design tool. Thus, the design process of the pavement structure in MEPDG is a trial process. It starts with assuming a structure and material properties and then performing the analysis. If the proposed trail did not meet the criteria, then the material properties and/or layer(s) thicknesses are changed until the design meets the criteria. This paper presents a reference document to assist pavement designers in identifying the most important MEPDG key inputs to modify such that the predicted distresses meet predefined design criteria. Results showed that, pavement structural design should be coupled with the mix design and selection of material properties in order to achieve an economic design complying with the criteria. [ABSTRACT FROM AUTHOR]

The main goal of this study is to assess the technical viability of using cement dust as part of the fines percentage in the unbound granular base/subbase pavement layers' and subgrade soil as a viable sustainable solution. This study investigates the effect of adding cement dust to five types of pavement road materials which are collected from different ongoing roadway construction projects (1) Reclaimed Asphalt Pavement (RAP), (2) crushed stone base aggregates, (3) blend of crushed stone base aggregates with RAP, (4) crushed stone subbase aggregates, and (5) sandy subgrade soil. The resilient modulus (Mr) is selected as the main characteristic for evaluating the investigated materials' stiffness. The regression parameters (k1, k2, and k3) of the universal Mr prediction model are found by fitting the experimental results of Mr testing for two replicates per each material type. The r esults show that using 3-5% of cement dust improves the estimated Mr of the investigated base/subbase materials and subgrades at the anticipated field stresses b y 8-21%.,As a result the structural layer coefficients are enhanced by 11-17%. KENLAYER nonlinear damage analysis confirms that using 3-5% of cement dust improves the predicted rutting life by up to 12% and the fatigue life up to 27%. Based on a typical pavement structure, enhancing pavement base/subbase layers and subgrade soils with 3-5% of cement dust reduces asphalt layer thickness from 11.25 to 12.50% and increases base/subbase layer thickness from 8.75 to 12.50% to maintain the same predicted rutting and fatigue lives of the typical structure. This positively affects the total construction cost in addition to the ecological benefits. [ABSTRACT FROM AUTHOR]