Replication Method Research Articles

Adapting the Planetary Health Diet Index for children and adolescents

BackgroundTools for measuring adherence to sustainable healthy diets among children and adolescents are lacking.ObjectiveTo advance methods for measuring adherence to sustainable healthy diets among children and adolescents by adapting an existing index, compare scores obtained using the original and adapted versions of the index in a sample of Chilean children, and describe the adapted index association with diet characteristics.MethodsThe Planetary Health Diet Index (PHDI) was adapted to better reflect children's and adolescents’ nutritional requirements. The adapted index (PHDI-C) comprises 16 components with a maximum score of 150 points. PHDI-C was piloted among a sample of 958 Chilean children (3–6 years) using dietary data collected in 2016 through single 24-h recalls. A decision tree and food disaggregation methodology were developed to guide the calculation of scores. Scores obtained using the original and adapted versions of the index were compared. Linear regression models adjusted by child’s gender and age were fitted to explore associations between total PHDI-C score, dietary recall characteristics and nutritional composition of children’s diets.ResultsPHDI accounted for 75.7% of children’s total caloric intake, whereas PHDI-C accounted for 99.6%. PHDI & PHCI-C scores were low among this sample of children; however, mean total score was lower when using PHDI compared to PHDI-C [40.7(12.1) vs 50.1(14.6)]. Children’s scores were very low for nuts & peanuts, legumes, dark green vegetables, whole cereals, tubers & potatoes, and added sugars components across both indices, but were higher for dairy products and eggs & white meats components when using the PHDI-C due to adjustments made to ensure nutritional adequacy. Mean total PHDI-C score was significantly lower on weekends and special occasions, and significantly higher when children reported having a special diet (e.g., vegetarian). Total PHDI-C score was negatively associated with total sugars, saturated fats, trans fats, and animal-based protein intake, and positively associated with total protein, plant-based protein, total carbohydrates, and total fibre intake.ConclusionsThis study provides a replicable method for measuring adherence to sustainable healthy diets among children and adolescents that can be used to monitor trends and measure the effectiveness of actions targeting improving children’s diets.

Manufacturing Process of Low‐Contamination Titanium Foam as Implant Material for Cranioplasty Based on Replica Technique

The article investigates the development of a manufacturing route for highly porous titanium foams suitable for craniofacial surgery applications, particularly in cranioplasties. The study focuses on the polyurethane replication method for foam production and emphasizes reducing residual gas content, as it significantly affects the mechanical properties and suitability for approval of the foams. Various factors such as starting materials, solvent debinding, heating schedules, and hydrogen atmosphere are analyzed for their impact on residual gas content. It is shown that significant reductions in residual gas content can only be achieved by reworking each step of the process. A combination of initial solvent debinding of the PU template with dimethyl sulphoxide, reduction of suspension additives, use of coarser Gd. 1 powders, and an integrated debinding and sintering process under partial hydrogen atmosphere achieves a significant reduction in residual gas content. This way, the potential for producing titanium foams that comply with relevant standards for craniofacial implants is demonstrated.

Liquidity Risk, Return Performance, and Tracking Error: Synthetic vs. Physical ETFs

As the global ETF market continues to grow, the significance of understanding replication strategies has become increasingly apparent. This study examines the compensation of synthetic exchange-traded funds (ETFs) due to their greater risk exposure than physical ETFs, using ETFs listed on developed European markets from 2009 to 2020. This is the first study to investigate liquidity risk related to the replication strategy of ETFs. We investigate the difference in systematic liquidity risk between synthetic and physical ETFs using a liquidity-adjusted capital asset pricing model (LCAPM). Our findings suggest that synthetic ETFs have higher liquidity risks than physical ETFs and offer additional returns within the low-liquidity group. We also analyze the tracking performance of ETFs based on their type and find that the synthetic replication method reduces tracking errors within the high-liquidity group. Specifically, the enhanced tracking performance of synthetic ETFs becomes more prominent during periods of liquidity shocks than in regular periods. These findings shed light on key considerations for resolving puzzles in tracking errors.

Relationship of business practices and characteristics to supervisors’ perceived effectiveness of disability recruitment

BACKGROUND: Employers are increasingly interested in the inclusion of employees with disabilities in the workplace. To be inclusive, employers need effective strategies for recruiting people with disabilities (PWD) that supervisors can endorse, as they are ultimately accountable for the success of all employees that they supervise, including people with disabilities. To date, little research has focused on effective practices for recruiting people with disabilities or the supervisors’ perspectives on successful disability recruitment practices. OBJECTIVE: This study presents results from the 2017 Kessler Foundation National Employment and Disability Survey-Supervisor Perspectives (KFNEDS-SP) that utilized a novel approach to investigating employer organizational characteristics and practices related to the recruitment of PWD. METHODS: The KFNEDS-SP is a web-based survey that used standard, replicable survey methods to collect data from a Qualtrics business-to-business panel of supervisors at U.S. organizations. Respondents included 6,530 supervisors ages 18 and older from private, nonprofit, and governmental organizations across industries. RESULTS: The results show that several organizational characteristics and practices are significantly associated with supervisor perceived effectiveness of disability recruitment processes. The degree of effort put forth to recruit people with disabilities, commitment from upper management in hiring and accommodating employees with disabilities, training hiring managers in accessible recruitment and interview practices for people with disabilities were all positively related to supervisors’ perceived effectiveness of their organizations’ disability recruitment processes. However, organizations that put a lot of effort into recruiting for diversity or had diversity hiring goals were less likely to be perceived by supervisors to have effective disability recruitment processes. CONCLUSION: Supervisors are at the center of an organization’s effort to include people with disabilities in the workplace and, as such, are good barometers of what works and does not work to support the recruitment, hiring and onboarding of people with disabilities. The results of this study point to several organizational characteristics and practices that individually and together are likely to support efforts to include people with disabilities in the workplace. Disability workforce intermediaries can use these findings to assist businesses successfully include people with disabilities in their workforce.

Amorphous and crystalline interface engineering of FeSeOx/Mo-NiSe2 nanoarrays for synergistically boosting electrocatalytic water oxidation

Exploring integrated regulation strategies to boosting electrocatalytic performance is essential to design more efficient and inexpensive OER catalysts. Herein, amorphous and crystalline (a-c) interface engineering and heteroatom doping are integrated to construct a novel electrocatalyst containing amorphous FeSeOx and crystalline Mo-doped NiSe2 (denoted as FeSeOx/Mo-NiSe2) via pseudocrystalline replication method and selenization treatment. The resultant FeSeOx/Mo-NiSe2 electrocatalyst with 3D hierarchical nanoarchitectures of nanorod arrays covered by interlocking nanoblocks exposes more active sites and accelerated reaction kinetics. Furthermore, a-c interface engineering and heteroatom doping can synergistically enhance the intrinsic activity and charge transfer capability of NiSe2 by decreasing the electron diffusion distance and flexibly modulating the electronic structure, which contributes to boost the oxygen evolution reaction (OER) performance. Consequently, the obtained FeSeOx/Mo-NiSe2 achieves a low overpotential of 205 mV at 10 mA cm−2, which outperforms many other transitions metal selenides electrocatalysts. Density functional theory (DFT) calculations demonstrate that the formation of an internal electric field between FeSeOx and Mo-NiSe2 accelerates charge transport and optimizes the adsorption/desorption of oxygen-containing intermediates, thereby accelerating the reaction kinetics and improving the OER performance. This work serves as a point of reference for designing innovative metal selenides based electrocatalysts combining a-c interface engineering and heteroatom doping.

Abstract 15711: Association Between State Tobacco Surcharge Policy and Health Insurance Enrollment, Health Care Utilization, and Smoking Cessation Behaviors

Introduction: The Patient Protection and Affordable Care Act allows insurers to charge up to 50% higher premiums to people who use tobacco. Several states have outlawed tobacco surcharges or capped the surcharges to lower levels. Evidence on the impact of state policies limiting tobacco surcharges is limited. This study aims to examine the association between state tobacco surcharge policies and individuals’ health insurance enrollment, health care utilization, and smoking cessation behaviors. Methods: This study used data from the Population Assessment of Tobacco and Health (PATH) Study Wave 6 (Mar-Nov 2021). The study sample was adults 18-64 years old who reported smoking cigarettes 20 or more days in the past 30 days (N=4,235). Exposure was living in states limiting tobacco surcharges (outlawed or capped). Outcome variables were currently covered by health insurance, seen a health professional in the past 12 months, intention to quit within 12 months, and any quit attempt in the past 12 months. Single-wave sample weights and balanced repeated replication method were applied to generate representative estimates and variances. Multivariate logistic regressions were used to estimate the associations between exposure and outcomes, controlling for socio-demographic variables, other tobacco use status, and state Medicaid expansion and tobacco control policies. Results: Among study sample, 19.8% (95% CI: 16.0%-24.3%) lived in states limiting tobacco surcharges. Bivariate associations showed that people living in states limiting surcharges were more likely to be covered by health insurance (84.5%, 95% CI: 81.2%-87.3% vs. 75.0%, 95% CI: 72.9%-77.1%, p < 0.001). After adjusting for covariates, the association between state policies and health insurance coverage became statistically insignificant (adjusted odds ratio, aOR=1.09, 95% CI: 0.80-1.47). Participants living in states limiting surcharges were less likely to have intention to quit within 12 months (aOR=0.78, 95% CI: 0.65-0.94). Other associations were not statistically significant. Conclusions: This study indicated that people living in states limiting tobacco surcharges had lower intention to quit smoking. More studies are needed to evaluate the impact of tobacco surcharge policies.

Associations of comorbidities with telehealth use in cancer survivors amidst the COVID-19 pandemic: Findings from the HINTS 6 (2022).

522 Background: Cancer survivors with comorbidities often need specialized care. Telehealth, using remote technologies such as phone calls or video conferences, addresses their specific needs, particularly during the COVID-19 pandemic, offering time and cost savings. However, understanding of telehealth use among cancer survivors with comorbidities is limited. This study examines the associations between comorbidities and telehealth use in cancer survivors during the pandemic. Methods: We analyzed the Health Information National Trends Survey (HINTS) 6, a nationally representative survey conducted in 2022. This study included 4,756 adults aged ≥18 (representing 195 million), including 730 cancer survivors (representing 20 million) in the U.S. The outcome variable was telehealth use in the past 12 months, including video or phone call with healthcare providers. Comorbidities included diabetes, hypertension, heart conditions, lung diseases, and depression. Design-based F-test was used for bivariate analyses. Logistic regression was used to determine associations of comorbidities and telehealth use, controlling for sociodemographic and cancer-related clinical factors. All data analyses accounted for the complex survey design and sample weights using jackknife replication method. Results: Cancer survivors’ telehealth use was slightly higher than adults without cancer (50.37% vs. 43.62%). Among cancer survivors, bivariate analysis revealed associations between telehealth use and younger age and higher number of comorbidities (P<.001). These associations remained significant in the logistic regression model (OR=0.25 and 0.12, 95% CI 0.11-0.57 and 0.05-0.29 for ages 61-75 and >75, respectively; OR=1.65, 95% CI 1.32-2.07 for number of comorbidities; all P<.001). When examining specific comorbidities, the logistic regression model indicated that cancer survivors with heart conditions (OR=6.03, 95% CI 2.34-15.54, P<.001), lung diseases (OR=5.53, 95% CI 1.89-16.17, P=.002), and depression (OR=4.46, 95% CI 2.35-8.46, P<.001) were more likely to utilize telehealth compared to those without comorbidities. Additionally, higher education levels were associated with increased telehealth use, even after adjusting for other factors (OR=2.41, 95% CI 1.04-5.59, P=.04). Conclusions: This national survey analysis detected the higher rate of telehealth use in cancer survivors with comorbidities during the pandemic. Further research is needed to explore the experiences of cancer survivors with heart conditions, lung diseases, and depression regarding telehealth. Additional support is necessary to encourage older individuals and those with lower education levels to embrace telehealth for the delivery of optimal and high-quality care.

Replicated biopolymer pattern on PLLA-Ag basis with an excellent antibacterial response

The design of functional micro or nanostructured surfaces is undergoing extensive research for their intriguing multifunctional properties and for large variety of potential applications in biomedical field (tissue engineering or cell adhesion), electronics, optics or microfluidics. Such nanosized topographies can be easily fabricated by various lithography techniques and can be also further reinforced by synergic effect by combining aforementioned structures along materials with already outstanding antibacterial properties. In this work we fabricated novel micro/nanostructured substrates using soft lithography replication method and subsequent thermal nanoimprint lithography method, creating nanostructured films based on poly (l-lactic acid) (PLLA) fortified by thin silver films deposited by PVD. Main nanoscale patterns were fabricated by replicating surface patterns of optical discs (CDs and DVDs), which proved to be easy, fast and inexpensive method for creating relatively large area patterned surfaces. Their antimicrobial activity was examined in vitro against the bacteria Escherichia coli and Staphylococcus epidermidis strains. The results demonstrated that nanopatterned films actually improved the conditions for bacterial growth compared to pristine PLLA films, the novelty is based on formation of Ag nanoparticles on the surface/and in bulk, while silver nanoparticle enhanced and nanopatterned films exhibited excellent antibacterial activity against both bacterial strains, with circa 80 % efficacy in 4 h and complete bactericidal effect in span of 24 h.

Bionic Micro-Texture Duplication and RE3+ Space-Selective Doping of Unclonable Silica Nanocomposites for Multilevel Encryption and Intelligent Authentication.

High-capacity optical data storage and information encryption by using glass substrates are fascinating due to merits of expanding the functionality and applicability of the optoelectronic field. However, the development of glass substrate-based multi-dimensional information encryption methods has remained a challenge because of the high hardness, brittleness and melting temperature of glasses. Herein, inspired by the unique natural structure of plant leaves, multicolor micro-texture-based physical unclonable functions (PUFs) fluorescence glass labels (MTPLs) are exploited by using ultraviolet photocurable silica nanocomposites and soft replication method. It is the first time that simultaneous rare-earth ion (RE3+ ) space-selective doping and bionic micro-texture replication onto transparent glass have been realized, in which the doping position and fluorescence color of RE3+ and height information of unclonable micro-texture can be selectively equipped for multilevel information encryption. The prepared micro-scale MTPLs are endowed with tunable multilevel authentication models, including macro-scale multicolor fluorescent 2D patterns, micro-scale pattern, color 3D information and intelligence authentication. A high-performance anti-counterfeiting platform with interactive authentication is also established based on the high robustness and security of MTPLs. Such unclonable bionic MTPLs based on RE3+ space-selective doping and micro-texture duplication provide an effective and potentially universal approach for multilevel encryption and intelligent authentication.

Towards efficient, ecological assessment of interaction: A scoping review of co-constructed communication.

The complexity of communication presents challenges for clinical assessment, outcome measurement and intervention for people with acquired brain injury. For the purposes of assessment or treatment, this complexity is usually managed by isolating specific linguistic functions or speech acts from the interactional context. Separating linguistic functions from their interactional context can lead to discourse being viewed as a static entity comprised of discrete features, rather than as a dynamic process of co-constructing meaning. The ecological validity of discourse assessments which rely on the deconstruction of linguistic functions is unclear. Previous studies have reported assessment tasks that preserve some of the dialogic features of communication, but as yet, these tasks have not been identified as a distinct genre of assessment. We suggest the term 'co-constructed communication' to describe tasks which are specifically designed to capture the dynamic, jointly produced nature of communication within a replicable assessment task. To identify and summarize how co-constructed communication has been assessed with individuals with non-progressive acquired communication disability regarding task design, measures and psychometric robustness. A scoping review methodology was used to identity relevant studies. Systematic database searches were conducted on studies published before July 2021. Studies in the yield were assessed against eligibility criteria, with 37 studies identified as eligible for inclusion. This is the first time that co-constructed communication has been defined as a genre of discourse assessment for stroke and traumatic brain injury populations. Co-constructed communication has been assessed for 144 individuals with aphasia and 111 with cognitive-communication disability. Five categories of co-constructed communication tasks were identified, ranging in complexity. Variability exists in how these assessment tasks are labelled and measured. Assessment measures require further psychometric profiling, specifically regarding test-retest reliability and validity. Co-constructed communication is a discourse genre which offers researchers and clinicians a replicable method to assess language and communication in an experimentally rigorous way, within an ecologically valid context, bridging the gap between experimental and ecological assessment approaches. What is already known on this subject Standardized assessments of language skills and monologue offer reliable, replicable ways to measure language. However, isolating language from an interactional context fundamentally changes the behaviour under study. This raises questions about the ecological validity of the measures we routinely use to determine diagnoses, guide treatment planning and measure the success of treatment. What this study adds to the existing knowledge This review highlights studies that conceptualize, and often quantify, interaction by combining experimental rigour and aspects of everyday dialogue. This is the first time this genre of discourse assessment has been identified. We propose the term 'co-constructed communication' to describe this genre and provide an operational definition for the term. What are the practical and clinical implications of this study? Co-constructed communication assessment tasks require refinement, particularly regarding aspects of psychometric robustness. In the future, these tasks offer pragmatic, meaningful ways to capture the effect and impact of aphasia and cognitive-communication disability within interaction.

Development of open-pore polymer and ceramic foams/Guefoams with tunable fluid-dynamic properties: Application in highly efficient water bacteria removal

The usual replication method for preparing open-pore metal foams using NaCl as a template cannot be successfully applied to the fabrication of foams using either water-based ceramic slurries in which NaCl can be dissolved during processing, or liquid polymers that wet NaCl and leave no interconnecting windows between pores. In this work, we propose to modify the replication process by including a second template, paraffin, which is deposited over the NaCl and serves a dual purpose: (i) it acts as a barrier to dissolution of the NaCl with the matrix precursor, and (ii) it creates size-controllable binding collars between the particles that ensure the formation of interconnecting windows in the foam. The usefulness of the process described here is illustrated by the fabrication of two types of materials: low porosity epoxy and cement matrix foams and Guefoams. As proof of concept, epoxy-based Guefoams were developed for the removal of Escherichia coli in water using iodine-impregnated activated carbon particles as the guest phase. The results indicate that the Guefoams outperform conventional bactericidal particle beds in several aspects, including their ease of handling and their tunable fluid-dynamic properties that enable high bacteria annihilation efficiency.

Using Design Thinking to Create Sustainable Communities

This innovative practice paper discusses an example of a participatory design-led sustainability project. Using a Design Thinking approach to problem solving, the project brought together university staff and students and a Council around a local sustainability challenge. Design Thinking was applied as an interdisciplinary methodology with the objective to equip students with sustainability skills and competences, resulting in a replicable method for responding to local sustainability challenges. By empowering diverse participants to contribute equally and freely, our application of Design Thinking created positive spillover effects on students and staff sense of community. The focus of the Challenge was on the discovery and co-creation of ideas to solve a problem related to local public transport. The driving principle of the Challenge was that participants, also public transport users, could bring their own lived experience to the Challenge and therefore could co-create and put forward potential solutions with the support of a local Council, and sustainability and Design Thinking experts on campus. In the process of doing so, participants framed and reframed the challenge from different angles and perspectives, opening a co-creation dialogue about community needs on and off campus, and their role in contributing to sustainability locally.

Agile DMAIC cycle: incorporating process mining and support decision

PurposeThe aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process.Design/methodology/approachThe approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators.FindingsIt was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators.Practical implicationsThrough the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible.Originality/valueThe authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

A Semi-Static Replication Method for Bermudan Swaptions under an Affine Multi-Factor Model

We present a semi-static replication algorithm for Bermudan swaptions under an affine, multi-factor term structure model. In contrast to dynamic replication, which needs to be continuously updated as the market moves, a semi-static replication needs to be rebalanced on just a finite number of instances. We show that the exotic derivative can be decomposed into a portfolio of vanilla discount bond options, which mirrors its value as the market moves and can be priced in closed form. This paves the way toward the efficient numerical simulation of xVA, market, and credit risk metrics for which forward valuation is the key ingredient. The static portfolio composition is obtained by regressing the target option’s value using an interpretable, artificial neural network. Leveraging the universal approximation power of neural networks, we prove that the replication error can be arbitrarily small for a sufficiently large portfolio. A direct, a lower bound, and an upper bound estimator for the Bermudan swaption price are inferred from the replication algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically study the accuracy and convergence of the method through several numerical experiments. The results indicate that the semi-static replication approaches the LSM benchmark with basis point accuracy and provides tight, efficient error bounds. For in-model simulations, the semi-static replication outperforms a traditional dynamic hedge.

Estimating the economic value of carbon losses from wildfires using publicly available data sources: Eagle Creek Fire, Oregon 2017

BackgroundWildfires are increasingly frequent in the Western US and impose a number of costs including from the instantaneous release of carbon when vegetation burns. Carbon released into the atmosphere aggravates climate change while carbon stored in vegetation helps to mitigate climate change. The need for climate change mitigation is becoming more and more urgent as achieving the Paris climate agreement target of limiting global warming to 1.5 °C seems ever more challenging. A clear understanding of the role of different carbon sources is required for understanding the degree of progress toward meeting mitigation objectives and assessing the cost and benefits of mitigation policies.ResultsWe present an easily replicable approach to calculate the economic cost from carbon released instantaneously from wildfires at state and county level (US). Our approach is straightforward and relies exclusively on publicly available data that can be easily obtained for locations throughout the USA. We also describe how to apply social cost of carbon estimates to the carbon loss estimates to find the economic value of carbon released from wildfires. We demonstrate our approach using a case study of the 2017 Eagle Creek Fire in Oregon. Our estimated value of carbon lost for this medium-sized (19,400 ha) fire is $187.2 million (2020 dollars), which highlights the significant role that wildfires can have in terms of carbon emissions and their associated cost. The emissions from this fire were equivalent to as much as 2.3% of non-fire emissions for the state of Oregon in 2020.ConclusionsOur results demonstrate an easily replicable method for estimating the economic cost of instantaneous carbon dioxide emissions for individual wildfires. Estimates of the potential economic costs associated with carbon dioxide emissions help to provide a more complete picture of the true economic costs of wildfires, thus facilitating a more complete picture of the potential benefits of wildfire management efforts.

Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.

Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks.

Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cope with small-sized training data. Nevertheless, BUS images are single-channel gray-level images, whereas pre-trained CNNs learned from color images with red, green, and blue (RGB) components. Thus, a gray-to-color conversion method is applied to fit the BUS image to the CNN's input layer size. This paper evaluates 13 gray-to-color conversion methods proposed in the literature that follow three strategies: replicating the gray-level image to all RGB channels, decomposing the image to enhance inherent information like the lesion's texture and morphology, and learning a matching layer. Besides, we introduce an image decomposition method based on the lesion's structural information to describe its inner and outer complexity. These gray-to-color conversion methods are evaluated under the same experimental framework using a pre-trained CNN architecture named ResNet-18 and a BUS dataset with more than 3000 images. In addition, the Matthews correlation coefficient (MCC), sensitivity (SEN), and specificity (SPE) measure the classification performance. The experimental results show that decomposition methods outperform replication and learning-based methods when using information from the lesion's binary mask (obtained from a segmentation method), reaching an MCC value greater than0.70 and specificity up to0.92, although the sensitivity is about0.80. On the other hand, regarding the proposed method, the trade-off between sensitivity and specificity is better balanced, obtaining about0.88 for both indices and an MCC of0.73. This study contributes to the objective assessment of different gray-to-color conversion approaches in classifying breast lesions, revealing that mask-based decomposition methods improve classification performance. Besides, the proposed method based on structural information improves the sensitivity, obtaining more reliable classification results on malignant cases and potentially benefiting clinical practice.

Production of replicated porous materials based on SiC: Microstructure, mechanical behavior & thermal shock resistance

High strength, thermal shock resistance, and suitable thermal conductivity of silicon carbide foams has led to their widespread applications. Doing that, in this research, SiC foams were produced using the slurry contains SiC as the main material with bentonite, alumina, zirconia, and silica sol. This slurry covered the PU foam one to five times during the replication process. Afterward, samples were sintered at three low sintering temperatures, i.e. 900, 1000, and 1100 °C. The microstructure, phases, compressive strength, and thermal shock resistance of these multi-layered porous structures were studied. It was found that the number of coating layers and sintering temperature influenced the type, extent, and size of defects. Applying multi-layer coating changed the morphology of the worst defect of the replication method, i.e. triangular hollow struts, to circular ones, which impressively enhanced the mechanical strength and thermal shock resistivity. Comparing nine types of samples indicated that the maximum strength was obtained in a five-layer coated sample sintered at 1000 °C, and not at the maximum sintering temperature. Notably, burning off bentonite and silica soles results in blister generation at 1100 °C, which reduced strength, and thermal shock resistance. The optimum strength of the porous sample after exposure to 11 sequences of thermal shock test was obtained in a four-layer-coated sample sintered at 1000 °C, which highlighted the critical strut thickness in porous structures.

The Taconnaz Rockfall (Mont-Blanc Massif, European Alps) of November 2018: A Complex and At-Risk Rockwall-Glacier-Torrent Morphodynamic Continuum

The glacial and torrential basin of Taconnaz (Mont-Blanc massif, France) dominates the Chamonix valley. It is one of the major paths for snow avalanches in the Alps, often triggered by serac falls from the Taconnaz glacier. On 24 November 2018, the basin’s multi-risk nature was further accentuated by a new type of hazard with a rockfall triggered at c. 2700 m a.s.l. It travelled down over a distance of 1.85 km and stopped 165 m away from the construction site of a micro-hydroelectric power station. We studied the triggering conditions at the permafrost lower limit, the effects of the supra-glacial path on the flow patterns, and the fate of the scar and the deposit on torrential activity. By comparing a pre-event Structure from Motion model with a post-event LiDAR model, we estimated the volume of the scar to be 42,900 m3 (±5%). A numerical model was employed to simulate the rapid runout. It revealed the complexity of the flow, attributed to the sequestration of a part of the deposit in crevasses, the incorporation of a significant volume of ice resulting in a transition from a dry granular flow to a mud-like flow, and the presence of numerous deposit zones. Subsequent monitoring of the area after the event allowed for the documentation of the scar’s evolution, including a landslide, as well as the progressive degradation and evacuation of the deposit by the torrent without producing debris flow. The study of the triggering factors indicated glacial retreat as the probable main cause, assisted by the melting of ice lenses left by the permafrost disappearance. Finally, we present replicable methods for managing risks at the site following the event. This event improves the understanding of cascading processes that increasingly impact Alpine areas in the context of climate change.

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures.