Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

From ambiguity to transparency: influence of environmental information disclosure on financial performance in the context of internationalization.

Environmental sustainability is an important strategy for firms to expand market reach and space in pursuit of profitable growth. In the context of internationalization, environment information disclosure (EID) is considered an environmental signal released by a firm to external stakeholders. Using a sample of Chinese construction firms listed in the global Engineering News-Record (ENR), two periods, from 2008 to 2014 and 2015 to 2019, were set up from the perspective of the EID guideline changes. The regression results illustrate a negative relationship between EID and financial performance in the first period. The moderating effect of internationalization on the relationship between EID and financial performance changes from negative to positive over time. Quantile regressions further reveal how EID and internationalization affect firms at different levels of financial performance. This study proposed a periodic contingency perspective to reconcile the changes in the influence of EID on financial performance within the context of internationalization.

Influencing factors of urban innovation and development: a grounded theory analysis.

Urban innovation and development are a core driver for promoting the industrial, economic, and social development of cities. However, the factors that affect the innovation and development of cities lack systematic analysis as well as interaction analysis. Based on a multidimensional perspective, this study suggests that natural, economic, and social factors are three major factors conditioning urban innovation and development. A grounded theoretical qualitative method is further adopted to code relevant research literatures, news reports and interview materials, resulting in an onion factors model. We find that natural factors-including environmental quality, geographic location, and city scale-are prerequisite for conditioning urban innovation and development. Economic factors are also key, including economic level, industrial structure, industrial agglomeration, and technological innovation. Social factors are guarantee factors, including administrative hierarchy, cultural environment, population structure, and government management and services, i.e., they are essential for cities to become adaptable in the current dynamic situation. The study provides theoretical support and practical directions for the formulation of policies for urban innovation development.

Mini-Mental State Examination and Montreal Cognitive Assessment as Tools for Following Cognitive Changes in Alzheimer's Disease Neuroimaging Initiative Participants.

BACKGROUND: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are two commonly used cognitive screening and diagnostic tools. OBJECTIVE: Our goal was to assess their efficacy for monitoring cognitive changes, as well as the correlation between the two tests. METHODS: At baseline, participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were divided into four groups based on their cognitive diagnoses: healthy control (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). MMSE or MoCA scores were compared among the four groups using an analysis of variance (ANOVA) model with repeated measures with post-hoc Bonferroni correction. For those participants who had both MMSE and MoCA assessments done, a Pearson correlation analysis was performed between the two assessments for each visit. RESULTS: The MMSE scores were significantly different among the four groups at baseline, which was true for each of the three annual follow-up visits. By contrast, the MoCA scores were not significantly different between HC and EMCI groups at either baseline or any of the follow-up visits. For participants with a diagnosis of LMCI, the cognitive performance deteriorated in a linear manner 12 months after the baseline, which was independent of MMSE or MoCA. At last, the MMSE scores were moderately related to MoCA scores, which got stronger along with the time of follow-up. CONCLUSION: MMSE and MoCA are comparable as cognitive assessment tools to monitor cognitive changes. In addition, the measurements of MMSE and MoCA are moderately correlated for the follow-up visits.

Examining the Impact of Regional Development Policy on Industrial Structure Upgrading: Quasi-Experimental Evidence from China.

"Guiding Opinions on Relying on the Golden Waterway to Promote the Development of the Yangtze River Economic Belt (YREB)", the "YREB Development Policy", is a national Chinese policy aiming to promote industrial structure upgrading and high-quality development in the YREB. To test the effects of this policy, we used 2009-2019 data from 283 cities to examine whether its implementation promoted regional industrial structure upgrading. The YREB Development Policy positively impacted industrial structure advancement but scarcely benefited industrial structure rationalization. Moreover, the impacts indicated a temporary, unsustainable influence on industrial structure advancement, presenting a clear U-shaped development trend. The YREB Development Policy can more significantly improve industrial structure upgrading in cities with large populations. The effects of this policy on industrial structure upgrading in the middle and lower reaches of the YREB are almost five times that in the upper reaches. In addition, the policy more greatly impacts the tertiary industry than the primary and secondary industries, especially in the lower reaches. These findings have policy-making implications, enrich the research regarding the YREB Development Policy impacts on industrial structure upgrading, and provide an empirical reference to improve subsequent policies.

Greenwashing behaviors in construction projects: there is an elephant in the room!

In the process of urbanization, a brisk building boom triggers a series of environmental problems. Construction contractors usually present environmentally fraudulent behaviors, i.e., greenwashing behaviors (GWBs), to legitimize their activities, ultimately hindering the sustainable development of the society. However, the formation mechanism of the contractors' GWBs is still unclear. Through the lens of fraud GONE theory (i.e., greed, opportunity, needs, and exposure), this study applies the multi-group structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the formation mechanisms of GWBs. The results of SEM show the relationships between four fraud factors and GWBs. Additionally, the projects are grouped into three categories: government investment projects, private-public-partnership (PPP) projects, and private investment projects. The results of multi-group SEM reveal that the effects of four fraud factors differ significantly across projects with different investment characteristics. The results of fsQCA suggest that there are three typical driving mechanisms for GWBs. Furthermore, this study develops a project information transparency framework and a "greenwashing tree" to form a systematic understanding of GWBs. Finally, on these bases, this study provides targeted suggestions and policy recommendations for governing contractors' GWBs.

Phase function estimation from a diffuse optical image via deep learning.

Objective.The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function.Approach.Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters.Main Results.Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey-Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%.Significance.We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.

Development of metaverse for intelligent healthcare.

The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of 'medical technology and AI' (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction.

How Effective Is the Green Development Policy of China's Yangtze River Economic Belt? A Quantitative Evaluation Based on the PMC-Index Model.

Although many countries around the world, especially China, highlight the strategy of green development, there has been little research evaluating the effectiveness of green development policies in local area. This study explores 16 policy texts with the theme of green development in the Yangtze River Economic Belt in China. Using the Policy Modeling Consistency Index (PMC-Index) model, the paper establishes a multi-input-output policy table and scientifically and systematically evaluates these policies. The results show that the average PMC index of the 16 policy texts is 6.83, indicating a high overall quality of policy texts. The index identifies two states of policy effectiveness as being good and excellent; 50% of the total texts fall into these categories and do not fall into the category of having a low level of policy effectiveness. Five indicators, including policy timeliness, social benefits, policy audience scope, and incentives and constraints, significantly impact the PMC-Index of the policy. Six representative policy samples were selected and analyzed. The advantages and disadvantages of the policy can be more fully understood by the degree of depression of the PMC's three-dimensional curved surface (PMC-Surface) model. Finally, the paper provides theoretical recommendations for the optimization of the green development policies.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation. METHODS: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross-validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population-average patient phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. RESULTS: The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population-averaged phantoms, the new patient-specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU-accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database. CONCLUSION: This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.

Quantitative analysis of a micro array anode structured target for hard x-ray grating interferometry.

Talbot-Lau interferometry (TLI) provides additional contrast modes for x-ray imaging that are complementary to conventional absorption radiography. TLI is particularly interesting because it is one of the few practical methods for realizing phase contrast with x-rays that is compatible with large-spot high power x-ray sources. A novel micro array anode structured target (MAAST) x-ray source offers several advantages for TLI over the conventional combination of an extended x-ray source coupled with an absorption grating including higher flux and larger field of view, and these advantages become more pronounced for x-ray energies in excess of 30 keV. A Monte Carlo simulation was performed to determine the optimal parameters for a MAAST source for use with TLI. It was found that the both spatial distribution of x-ray production and the number of x-ray produced in the MAAST have a strong dependence on the incidence angle of the electron beam.

An Investigation on the Comprehensive Property Assessment and Future Directions of Single Bamboo Fiber Reinforced Polypropylene Composites Fabricated by a Non-Woven Paving and Advanced Molding Process.

The demand for eco-friendly renewable natural fibers has grown in recent years. In this study, a series of polypropylene-based composites reinforced with single bamboo fibers (SBFs), prepared by non-woven paving and a hot-pressing process, were investigated. The influence of the content of SBF on impact strength, flexural strength, and water resistance was analyzed. The properties of the composites were greatly affected by the SBF content. Impact strength increased as SBF content increased. The modulus of rupture and modulus of elasticity show an optimum value, with SBF contents of 40% and 50%, respectively. The surface morphology of the fractured surfaces of the composites was characterized by scanning electron microscopy. The composites showed poor interfacial compatibility. The water resistance indicates that the composites with higher SBF contents have higher values of water absorption and thickness swelling, due to the hydrophilicity of the bamboo fibers. The thermal properties of the composites were characterized by thermal gravimetric analysis and by differential scanning calorimetry. The thermal stability of the composites was gradually reduced, due to the poor thermal stability of SBFs. In the composites, the maximum decomposition temperature corresponding to SBF shows an increasing trend. However, the maximum decomposition temperature of polypropylene was not influenced by SBF content. The melting point of the polypropylene in the composites was lower relative to pure polypropylene, although it was not affected by increasing SBF content.

Design optimization of a periodic microstructured array anode for hard x-ray grating interferometry.

Talbot-Lau grating interferometer (TLGI) has great advantages in x-ray imaging contrasts, especially for low-Z materials, over conventional absorption contrast. A microstructured array anode target (MAAT) source offers significantly higher imaging throughput than the combination of an extended x-ray source paired with an absorption grating (also known as source grating). The performance of the MAAT source can be optimized with respect to the areal density, dimensions, and choice of material for the microstructured metal inserts (MMI) and the substrate in which they are embedded. In this paper, we analyze the x-ray generation efficiency per incident electron, relative fraction of x-rays generated by MMI and substrate, x-ray spectrum, and angular distribution via Monte Carlo simulation. Based on the simulation results, the optimal parameters are obtained for a MAAT with incident electron energies from 30 keV to 120 keV. The corresponding temperature distribution within the MAAT is also simulated for the optimal set of the parameters via finite element analysis. As demonstrated by the thermal analysis data, the maximum allowable electron-beam power loading was derived that allows a stable operation of the transmission MAAT.

LEARN: Learned Experts' Assessment-Based Reconstruction Network for Sparse-Data CT.

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold the state-of-the-art "fields of experts"-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts' assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.

Economic analysis of a hypothetical bamboo-biochar plant in Zhejiang province, China.

Significant quantities of bamboo waste are generated in Zhejiang province, China. Many small businesses in this area convert this waste to biochar for use as a cooking fuel (in residential barbecues). This case study was conducted to evaluate the potential economic benefits of building and operating an industrial-sized plant in this province, yielding 500 tonnes per year. The researchers developed a conceptual design for a hypothetical biochar plant and then calculated net present value (NPV), investment payback period (PBP), internal rate of return (IRR), and sensitivity analysis. Results show that the static investment PBP would be 2.58 years, the IRR would be 38.8%, and the NPV would be US$ 486,700. The IRR would be higher than the forestry industry benchmark (11%), indicating that a production line of bamboo-biochar with the stated yield not only could generate higher profits, but also could achieve a better return on investment. Thus, this study indicates that there are good market prospects for the bamboo-biochar industry in this region. The influence of sales prices on the IRR was more than that of operational costs, indicating that a large-scale plant should be designed to produce a high-quality bamboo-biochar. Supply chain issues such as transportation distances between locations where bamboo wastes are generated and the biochar plant should be considered in advance when siting new bamboo-biochar plants. The results from this research provide guidance to those considering development of bamboo-biochar plants in other parts of China.

X-ray scatter correction for multi-source interior computed tomography.

PURPOSE: The schemes of multi-source interior computed tomography (CT) have shown promise for ultra-fast, organ-oriented, and low-dose dynamic imaging. Besides forward scattering, x-ray cross scattering from multiple x-ray sources activated simultaneously can further degrade image quality. Here, we investigate the overall x-ray scattering artifact in a recently proposed multi-source interior CT architecture, and present two methods for scatter correction. METHODS: Compared to single-source global CT, scattering in multi-source interior CT architecture is affected by two new factors: cross scattering from simultaneously activated multiple x-ray sources and region-of-interest (ROI) oriented interior CT mode. The scatter artifact in the multi-source interior CT architecture was evaluated through both numerical simulation and physical experimentation, and compared to that from conventional single-source global CT. Monte Carlo simulation was conducted with a modified numerical CATphan® 600 phantom. Physical experiments were performed in an in-house developed CT imaging platform with a custom-built phantom. The simulation and experiments were carried out on the single-source CT architecture and the multi-source CT architecture, respectively in the global CT mode and the interior CT mode for comparison. To correct the scattering artifact, two new methods were presented. The first is a beam-stopper-array (BSA)-based method, which enables an online correction of forward scattering and cross scattering simultaneously. The second is a source-trigger-sequence (STS)-based method dedicated to cross-scatter correction. It enables on-the-fly measurements of the cross scattering signals at a few pre-selected views. The CT image quality was quantitatively evaluated in terms of contrast-to-noise ratio (CNR) and CT number deviation before and after the scatter correction. RESULTS: X-ray cross scattering degraded image quality in both the simulation and experiments. Before the scatter correction, the multi-source interior CT mode yielded a reduction of CNR at the ROIs by up to 68.5% and 50.7% in the simulation and experiments, respectively. The stationary BSA-based method significantly improved CNR and CT number accuracy in the images from multi-source interior CT mode, by reducing the negative effects from both forward scattering and cross scattering. The STS-based method enabled multi-source interior CT mode to provide comparable image quality to that with the single-source interior CT mode, by correcting the artifact from cross scattering. The remaining forward scattering artifact can be corrected with the fast adaptive scatter kernel superposition (FASKS) technique. With the proposed scatter correction methods, the CT number error at the ROIs was reduced to less than 37 HU in both simulation and experiments, respectively. CONCLUSIONS: Cross scattering, in addition to forward scattering, can cause significant image quality degradation in the multi-source interior CT architecture. However, image quality can be significantly improved with the proposed scatter correction methods.

Using a Mixed Model to Evaluate Job Satisfaction in High-Tech Industries.

R&D professionals are the impetus behind technological innovation, and their competitiveness and capability drive the growth of a company. However, high-tech industries have a chronic shortage of such indispensable professionals. Accordingly, reducing R&D personnel turnover has become a major human resource management challenge facing innovative companies. This study combined importance-performance analysis (IPA) with the decision-making trial and evaluation laboratory (DEMATEL) method to propose an IPA-DEMATEL model. Establishing this model involved three steps. First, an IPA was conducted to measure the importance of and satisfaction gained from job satisfaction criteria. Second, the DEMATEL method was used to determine the causal relationships of and interactive influence among the criteria. Third, a criteria model was constructed to evaluate job satisfaction of high-tech R&D personnel. On the basis of the findings, managerial suggestions are proposed.

Dynamic Assessment of the Endothelialization of Tissue-Engineered Blood Vessels Using an Optical Coherence Tomography Catheter-Based Fluorescence Imaging System.

BACKGROUND: Lumen endothelialization of bioengineered vascular scaffolds is essential to maintain small-diameter graft patency and prevent thrombosis postimplantation. Unfortunately, nondestructive imaging methods to visualize this dynamic process are lacking, thus slowing development and clinical translation of these potential tissue-engineering approaches. To meet this need, a fluorescence imaging system utilizing a commercial optical coherence tomography (OCT) catheter was designed to visualize graft endothelialization. METHODS: C7 DragonFly™ intravascular OCT catheter was used as a channel for delivery and collection of excitation and emission spectra. Poly-dl-lactide (PDLLA) electrospun scaffolds were seeded with endothelial cells (ECs). Seeded cells were exposed to Calcein AM before imaging, causing the living cells to emit green fluorescence in response to blue laser. By positioning the catheter tip precisely over a specimen using high-fidelity electromechanical components, small regions of the specimen were excited selectively. The resulting fluorescence intensities were mapped on a two-dimensional digital grid to generate spatial distribution of fluorophores at single-cell-level resolution. Fluorescence imaging of endothelialization on glass and PDLLA scaffolds was performed using the OCT catheter-based imaging system as well as with a commercial fluorescence microscope. Cell coverage area was calculated for both image sets for quantitative comparison of imaging techniques. Tubular PDLLA scaffolds were maintained in a bioreactor on seeding with ECs, and endothelialization was monitored over 5 days using the OCT catheter-based imaging system. RESULTS: No significant difference was observed in images obtained using our imaging system to those acquired with the fluorescence microscope. Cell area coverage calculated using the images yielded similar values. Nondestructive imaging of endothelialization on tubular scaffolds showed cell proliferation with cell coverage area increasing from 15 ± 4% to 89 ± 6% over 5 days. CONCLUSION: In this study, we showed the capability of an OCT catheter-based imaging system to obtain single-cell resolution and to quantify endothelialization in tubular electrospun scaffolds. We also compared the resulting images with traditional microscopy, showing high fidelity in image capability. This imaging system, used in conjunction with OCT, could potentially be a powerful tool for in vitro optimization of scaffold cellularization, ensuring long-term graft patency postimplantation.

Determining scientific impact using a collaboration index.

Researchers collaborate on scientific projects that are often measured by both the quantity and the quality of the resultant peer-reviewed publications. However, not all collaborators contribute to these publications equally, making metrics such as the total number of publications and the H-index insufficient measurements of individual scientific impact. To remedy this, we use an axiomatic approach to assign relative credits to the coauthors of a given paper, referred to as the A-index for its axiomatic foundation. In this paper, we use the A-index to compute the weighted sums of peer-reviewed publications and journal impact factors, denoted as the C- and P-indexes for collaboration and productivity, respectively. We perform an in-depth analysis of bibliometric data for 186 biomedical engineering faculty members and from extensive simulation. It is found that these axiomatically weighted indexes better capture a researcher's scientific caliber than do the total number of publications and the H-index, allowing for fairer and sharper evaluation of researchers with diverse collaborative behaviors.

Molecular Optical Simulation Environment (MOSE): a platform for the simulation of light propagation in turbid media.

The study of light propagation in turbid media has attracted extensive attention in the field of biomedical optical molecular imaging. In this paper, we present a software platform for the simulation of light propagation in turbid media named the "Molecular Optical Simulation Environment (MOSE)". Based on the gold standard of the Monte Carlo method, MOSE simulates light propagation both in tissues with complicated structures and through free-space. In particular, MOSE synthesizes realistic data for bioluminescence tomography (BLT), fluorescence molecular tomography (FMT), and diffuse optical tomography (DOT). The user-friendly interface and powerful visualization tools facilitate data analysis and system evaluation. As a major measure for resource sharing and reproducible research, MOSE aims to provide freeware for research and educational institutions, which can be downloaded at http://www.mosetm.net.