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The surface texture of urbanized regions is altered by the replacement of natural vegetated surfaces with hardened pavement surfaces, which have been described as a heat source for the formation of urban heat islands. Grasslands may store rainfall in their roots and leaves for later cooling, but this has received little attention. This study investigated the radiant flux and temperature of a tropical grassland throughout the summer in order to understand the albedo, long-wave radiation, short-wave radiation, and surface temperature of the grassland over 10 days. The grassland had an albedo of 0.13, which did not fluctuate during the day compared to the albedo of other surfaces in metropolitan areas. Even if the local weather changes considerably, this albedo does not alter significantly. The surface temperature and the air temperature above the grassland increase linearly with the upwelling reflectance, incident solar radiation, and upwelling long-wave radiation. These two temperatures do not correspond with downwelling long-wave radiation, which is influenced by cloud cover in the sky. However, the peaks of these temperatures lag behind the incident shortwave radiation and net radiation that reaches the grassland surface. The finding that the thermal properties of grasslands could be harnessed to reduce the heat absorbed by urban surfaces provides valuable insights into the grasslands’ potential to mitigate the impacts of urbanization on temperature.

Diabetes mellitus is a complicated metabolic disease that has become one of the fastest-growing health crises in modern society. Diabetic patients may suffer from various complications, and diabetic foot is one of them. It can lead to increased rates of lower-extremity amputation and mortality, even seriously threatening the life and health of patients. Because its healing process is affected by various factors, its management and treatment are very challenging. To address these problems, smart biomaterials have been developed to expedite diabetic wound closure and improve treatment outcomes. This review begins with a discussion of the basic mechanisms of wound recovery and the limitations of current dressings used for diabetic wound healing. Then, the categories and characteristics of the smart biomaterial scaffolds, which can be utilized as a delivery system for drugs with anti-inflammatory activity, bioactive agency, and antibacterial nanoparticles for diabetic wound treatment were described. In addition, it can act as a responsive system to the stimulus of the pH, reactive oxygen species, and glucose concentration from the wound microenvironment. These results show that smart biomaterials have an enormous perspective for the treatment of diabetic wounds in all stages of healing. Finally, the advantages of the construction of smart biomaterials are summarized, and possible new strategies for the clinical management of diabetic wounds are proposed.

Sensitivity is an important performance index for evaluating surface plasmon resonance (SPR) biosensors. Sensitivity enhancement has always been a hot topic. It is found that the different refractive indices of samples require different combinations of prism and metal film for better sensitivity. Furthermore, the sensitivity can be enhanced by coating two-dimensional (2D) materials with appropriate layers on the metal film. At this time, it is necessary to choose the best film configuration to enhance sensitivity. With the emergence of more and more 2D materials, selecting the best configuration manually is becoming more complicated. Compared with the traditional manual method of selecting materials and layers, this paper proposes an optimization method based on a genetic algorithm to quickly and effectively find the optimal film configuration that enhances sensitivity. By using this method, not only can the optimal number of layers of 2D materials be determined quickly, but also the optimal configuration can be conveniently found when many materials are available. The maximum sensitivity can reach 400°/RIU after optimization. The method provided application value for the relevant researchers seeking to enhance sensitivity.

Purpose: To characterize pediatricians’ perceived barriers and areas of confidence in assessing patient-reported outcomes (PROs) in the U.S., and to test associations of these factors with implementing PRO assessment. Methods: Using a random sample from the members of American Medical Association, we recruited general pediatricians and pediatric subspecialists to complete a survey (July 2011 to December 2013). Perceived barriers and confidence in PRO assessment were compared by age, pediatric specialty (general pediatrics, seven subspecialties), practice settings (academic, private), and region of practice. Multivariable logistic regressions tested associations of demographic factors, barriers, and confidence factors with the implementation of PRO assessment. Findings: The survey was completed by 458 participants (response rate 48.5%); of these, 40.4%, 15.9%, 15.5%, and 8.1% were general pediatricians, cardiology, hematology/oncology, and pulmonary specialists, respectively. PRO assessment was implemented by 29.0% of the pediatricians. The top five barriers for PRO assessment included limited time/manpower (79.0%), limited training (77.4%), lengthy PRO instruments (76%), lack of meaningful cut-offs on PRO scores (75.5%), and unavailable PRO instruments (75.0%). Limited knowledge of PROs (OR 4.10; 95% CI 2.21, 7.60) and unavailability of PRO instruments (OR 1.87; 95% CI 1.01, 3.49) increased the odds of not implementing the assessment, whereas confidence in PRO assessments compatible with norms (OR 0.41; 95% CI 0.23, 0.72) and perceived benefit over clinical judgment alone (OR 0.53; 95% CI 0.31, 0.93) decreased the odds of not implementing the assessment. Interpretation: significant barriers to PRO assessment in pediatric settings suggest the need for providing training, resources, and practical guidance toward implementation. Patient or Public Contribution: healthcare service users contributed to this study by completing a survey and providing feedback about the barriers and areas of confidence in assessing PROs for pediatric populations.

Cloud computing is an emerging technology for rapidly provisioning and releasing resources on-demand from a shared resource pool. When big data is analyzed/mined on the cloud platform, the efficiency of resource provisioning would affect the system performance. This work proposes a framework for proactive resource provisioning in IaaS (Infrastructure as a Service) clouds to improve system performance. The proposed framework consists of the virtual cluster computing system, the profiling system, the resource management system, and the monitoring system. In this framework, the over-commit mechanism is applied to improve resource utilization. Furthermore, a proactive task scheduling approach is also present to prevent the postponement of tasks in critical stages, especially when the amount of aggregated resources requested by virtual machines exceeds that of available resources on the over-committed physical machines. Experimental results show that the over-commit approach indeed improves the resource utilization. However, when the degree of applying the over-commit approach increases, the burden of this proposed approach also conceivably increases. Therefore, the proposed framework further applies the proactive task scheduling approach to execute the time-critical tasks earlier to shorten the processing time. A small-scale cloud system including 3 servers is built for experiments. Preliminary experimental results show the performance improvement of our proposed framework in IaaS clouds.

Thickness and shape are two significant indices in the tandem hot strip rolling process. Excessive thickness correction can cause rolling force imbalance, leading to deteriorated shape conditions. Aiming at this problem, we propose a new monitor automatic gauge control (M-AGC) with consideration of the rolling force distribution to not only keep the shape condition but also improve the robustness of the rolling process. First, the M-AGC with the Smith predictor control structure was studied. Second, based on the rolling force and thickness correction redistribution algorithm, shape balance automatic gauge control (SB-AGC), the phenomenon of rolling force imbalance was improved. Third, the transient response was improved by adding a command prefilter to the upstream stands, which is asynchronous shape balance automatic gauge control (ASB-AGC). The proposed algorithms can be applied to all steel grades and are easy to implement. Finally, cross-validation was conducted through numerical simulations and application results, confirming the alignment between the theoretical derivation and practical implementation. The simulation results show improvements in both the rolling force balance and the thickness response. The practical application results also confirm that better flatness conditions and thickness response can be achieved, significantly reducing the amount of head-end cutoff losses.

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for “non-lactating” adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (Cmax) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage.

The purpose of this study was to identify new metal-based anticancer drugs; to this end, we synthesized two new copper(II) complexes, namely [Cu(ncba)4(phen)] (1) and [Cu(ncba)4(bpy)] (2), comprised 4-chloro-3-nitrobenzoic acid as the main ligand. The single-crystal XRD approach was employed to determine the copper(II) complex structures. Binding between these complexes and calf thymus DNA (CT-DNA) and human serum albumin (HSA) was explored by electronic absorption, fluorescence spectroscopy, and viscometry. Both complexes intercalatively bound CT-DNA and statically and spontaneously quenched DNA/HSA fluorescence. A CCK-8 assay revealed that complex 1 and complex 2 had substantial antiproliferative influences against human cancer cell lines. Moreover, complex 1 had greater antitumor efficacy than the positive control cisplatin. Flow cytometry assessment of the cell cycle demonstrated that these complexes arrested the HepG2 cell cycle and caused the accumulation of G0/G1-phase cells. The mechanism of cell death was elucidated by flow cytometry-based apoptosis assays. Western blotting revealed that both copper(II) complexes induced apoptosis by regulating the expression of the Bcl-2(Bcl-2, B cell lymphoma 2) protein family.

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.