Engineering properties, microplastics and emissions assessment of recycled plastic modified asphalt mixtures.

The use of recycled plastic in asphalt is raising interest since contributing to increase the sustainability of roads pavements. The engineering performance of such roads are commonly assessed but scarcely correlated to the environmental impacts of incorporating recycled plastic in asphalt. This research encompasses an evaluation of the mechanical behaviour and environmental impact of introducing low melting point recycled plastics, low density polyethylene and commingled polyethylene/polypropylene, to conventional hot mix asphalt. While this investigation reveals a reduction in moisture resistance between 5 and 22 % contingent on the plastic content, the benefits include a significant 150 % enhancement in fatigue resistance and 85 % improvement in rutting resistance when compared with conventional hot mix asphalt (HMA). From an environmental perspective, high-temperature asphalt production with higher plastic content resulted in decreased gaseous emissions for both types of recycled plastics up to 21 %. Further comparison studies indicate that microplastic generation from recycled plastic-modified asphalt is comparable to that from commercial polymer-modified asphalt products, long employed by the industry. Overall, the use of low melting point recycled plastics as an asphalt modifier is promising since offering both engineering and environmental benefits when compared to conventional asphalt.

Assessment of an Artificial Intelligence Mandibular Osteotomy Design System: A Retrospective Study.

BACKGROUND: In this study, an AI osteotomy software was developed to design the presurgical plan of mandibular angle osteotomy, which is followed by the comparison between the software-designed presurgical plan and the traditional manual presurgical plan, thus assessing the practicability of applying the AI osteotomy software in clinical practices. METHODS: (1) Develop an AI osteotomy software: design an algorithm based on convolutional neural networks capable of learning feature point and processing clustering segmentation; then, select 2296 cases of successful 3D mandibular angle osteotomy presurgical plans, followed by using those 2296 cases to train the deep learning algorithm; (2) compare the osteotomy presurgical plan of AI osteotomy software and that of manual: first step: randomly selecting 80 cases of typical female head 3D CTs, and designing those 80 cases by means of AI osteotomy software designing (group A) and manually designing (group B), respectively; second step: comparing several indexes of group A and those of group B, including the efficiency index (time from input original CT data to osteotomy presurgical plan output), the safety index (the minimum distance from the osteotomy plane to the mandibular canal), the symmetry indexes (bilateral difference of mandibular angle, mandibular ramus height and mandibular valgus angle) and aesthetic indexes (width ratio between middle and lower faces (M/L), mandibular angle and mandibular valgus angle). RESULTS: The efficiency index: the design time of group A is 1.768 ± 0.768 min and that of group B is 26.108 ± 1.137 min, with P = 0.000; the safety index: The minimum distances from the osteotomy plane to the mandibular canal are 3.908 ± 0.361mm and 3.651 ± 0.437mm, p = 0.117 in groups A and B, respectively; The symmetry indexes: Bilateral differences of mandibular angle are 1.824 ± 1.834° and 1.567 ± 1.059° in groups A and B, respectively, with P = 0.278; bilateral differences of mandibular ramus height are 2.083 ± 1.263 and 2.965 ± 1.433, respectively, with P = 0.119 in groups A and B; Aesthetic indexes: M/L in groups A and B is 1.364 ± 0.074 and 1.371 ± 0.067, respectively, with P = 0.793; mandibular angles in groups A and B are 127.724 ± 5.800° and 127.242 ± 5.545°, respectively, with P = 0.681; Valgus angles in groups A and B are 11.474 ± 5.380 and 9.743 ± 4.620, respectively, with P = 0.273. CONCLUSIONS: With high efficiency, as well as safety, symmetry and aesthetics equivalent to those of a manual design, the AI osteotomy software designing can be used as an alternative method for manual osteotomy designing. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Integrating the MCR and DOI models to construct an ecological security network for the urban agglomeration around Poyang Lake, China.

The development of urban agglomeration in aquatic areas is premised on ecological security. The construction of ecological security network is an effective measure to reduce ecological resistance and guarantee economic development. In this study, a comprehensive ecological security network (ESN) evaluation approach is developed by integrating the Minimum Cumulative Resistance (MCR) model and Duranton and Overman Index (DOI). Taking the urban agglomeration around Poyang Lake in 2015 as a case, the MCR model showed the ecological landscape pattern of discrete connection and large overall ecological resistance. The DOI model indicated that the industries of moderately polluted were global localization, while heavily polluted were small-scale localization and large-scale dispersion. Accordingly, the ESN consisting of 35 ecological sources, 34 ecological corridors, 57 artificial corridors, and 39 eco-economic strategic nodes can avoid the threat of industrial layout to the ecological landscape. The results of this study complemented the guidance of industrial agglomeration theory on the basis of "source-sink" theory. The dual evaluation approach of landscape ecology and economic agglomeration proposed in this study explores interdisciplinary data fusion of different scales, and also provides a reference for inter-municipal coordinated ecological management and restraining urban excessive expansion.

Clinical global assessment of nutritional status as predictor of mortality in chronic kidney disease patients.

BACKGROUND: The value of subjective global assessment (SGA) as nutritional assessor of protein-energy wasting (PEWSGA) in chronic kidney disease (CKD) patients depends on its mortality predictive capacity. We investigated associations of PEWSGA with markers of nutritional status and all-cause mortality in CKD patients. METHODS: In 1031 (732 CKD1-5 non-dialysis and 299 dialysis) patients, SGA and body (BMI), lean (LBMI) and fat (FBMI) body mass indices, % handgrip strength (% HGS), serum albumin, and high sensitivity C-reactive protein (hsCRP) were examined at baseline. The five-year all-cause mortality predictive strength of baseline PEWSGA and during follow-up were investigated. RESULTS: PEWSGA was present in 2% of CKD1-2, 16% of CKD3-4, 31% of CKD5 non-dialysis and 44% of dialysis patients. Patients with PEWSGA (n = 320; 31%) had higher hsCRP and lower BMI, LBMI, FBMI, %HGS and serum albumin. But, using receiver operating characteristics-derived cutoffs, these markers could not classify (by kappa statistic) or explain variations of (by multinomial logistic regression analysis) presence of PEWSGA. In generalized linear models, SGA independently predicted mortality after adjustments of multiple confounders (RR: 1.17; 95% CI: 1.11-1.23). Among 323 CKD5 patients who were re-assessed after median 12.6 months, 222 (69%) remained well-nourished, 37 (11%) developed PEWSGA de novo, 40 (12%) improved while 24 (8%) remained with PEWSGA. The latter independently predicted mortality (RR: 1.29; 95% CI: 1.13-1.46). CONCLUSIONS: SGA, a valid assessor of nutritional status, is an independent predictor of all-cause mortality both in CKD non-dialysis and dialysis patients that outperforms non-composite nutritional markers as prognosticator.