Linseed Oil and Xanthan Gum: Promising Stabilisers for Earthen Building Materials

In the current context, the development of new bio-based and local building materials is becoming mandatory. Among them, earthen materials have a strong potential to be used as sustainable structural materials but their variability and their water sensitivity impact their mechanical properties that are difficult to guaranty. Recent developments have emphasised the ability of some bio-based additions to help to ensure these properties: linseed oil and xanthan gum are part of them. In this paper three different Breton earths, representative of a certain local variability, are studied. The impact of the selected bio-based additions on earths’ rheological behaviour is followed in order to adapt it to different forming processes. Then, the mechanical properties of different earth-addition combinations at the dry state, exposed to hygric variations and immersion are investigated for different forming processes. The findings highlight the fact that xanthan gum and linseed oil have a relevant ability to stabilise earthen blocks, that can be processed using different promising forming methods.

Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

Non-Destructive Assessment of the Elastic Properties of Low-Grade CLT Panels

The use of cross-laminated timber panels (CLT) made of low-grade structural timber has steadily increased in developing countries. These panels usually present several natural defects, which can cause a high local variation of their orthotropic elastic properties, generating future structural serviceability problems. Our work aims to estimate the local variability of the elastic properties in low-grade CLT panels by combining nondestructive transverse vibration testing, numerical simulations, and regional sensitivity analysis (RSA). Four three-layer Radiata pine CLT panels were subjected to transverse vibration tests with supports at four points. Besides, a series of numerical simulations of the panels, considering the local variability of the elastic properties of the panels in eight zones, were carried out using the finite element method. Then, RSA analysis was performed to study in which ranges of values the panels’ elastic properties generated lower differences between the measured versus simulated dynamic properties. Finally, a structural quality control indicator was proposed for the CLT panels based on keeping low the probability that the elastic properties in the central zones do not exceed minimum acceptable values. The results obtained suggest that the proposed methodology is suitable for segregating CLT panels with high concentrations of defects such as pith presence.

Introduction: The Promises and Challenges of Crime Ethnographies

This chapter outlines some of the scholarly and political appeals of crime ethnographies and identifies a series of factors that will pose challenges to this methodological approach over the longer term. It briefly charts the early evolution of crime ethnographies, noting how they have expanded to encompass the study of a larger range of criminal or deviant behaviors, while also focusing on the operation of criminal justice institutions. A more diverse group of scholars than was historically the case now conduct such research, individuals who typically embrace a more reflexive orientation to knowledge production than is characteristic of positivist science. Crime ethnographies provide invaluable grounded insights into the lives of participants and processes that are often otherwise hidden or hard to reach. Politically, ethnographies tend to humanize individuals and groups that are easily vilified, while reminding politicians and officials of the need to be conscious of local variability when adopting policy initiatives that originated in different contexts. Notwithstanding the many benefits of this approach, a series of developments now present challenges to crime ethnographies as they are currently practiced, including the changing technological profile of crime, as well as university-based developments, such as changes to the systems for overseeing and rewarding academic work and research ethics protocols that do not accord with the philosophical assumptions of ethnographers or the practical realties of ethnographic fieldwork.

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

Three-level Sleep Stage Classification Based on Wrist-worn Accelerometry Data Alone

Objective: The use of wearable sensor devices on daily basis to track real-time movements during wake and sleep has provided opportunities for automatic sleep quantification using such data. Existing algorithms for classifying sleep stages often require large training data and multiple input signals including heart rate and respiratory data. We aimed to examine the capability of classifying sleep stages using sensible features directly from accelerometers only with the aid of advanced recurrent neural networks. Materials and Methods: We analyzed a publicly available dataset with accelerometry data in 5s epoch length and polysomnography assessments. We developed long short-term memory (LSTM) models that take the 3-axis accelerations, angles, and temperatures from concurrent and historic observation windows to predict wake, REM and non-REM sleep. Leave-one-subject-out experiments were conducted to compare and evaluate the model performance with conventional nonsequential machine learning models using metrics such as multiclass training and testing accuracy, weighted precision, F1 score and area-under-the-curve (AUC). Results: Our sequential analysis framework outperforms traditional non-sequential models in all aspects of model evaluation metrics. We achieved an average of 65% and a maximum of 81% validation accuracy for classifying three sleep labels even with a relatively small training sample of clinical visitors. The presence of two additional derived variables, local variability and range, have shown to strongly improve the model performance. Discussion : Results indicate that it is crucial to account for deep temporal dependency and assess local variability of the features. The post-hoc analysis of individual model performances on subjects' demographic characteristics also suggest the need of including pathological samples in the training data in order to develop robust machine learning models that are capable of capturing normal and anomaly sleep patterns in the population.

Fluid invasion dynamics in porous media with complex wettability and connectivity

Fluid invasion into porous materials is very common in natural and industrial processes. The fluid invasion dynamics in simple pore networks are governed by a global balance of capillary, viscous and inertial forces. However, significant local variability in this balance may exist inside natural, heterogeneous porous materials. Here, we imaged slow fluid intrusion in two sister samples of a heterogeneous sandstone, one water-wet and one mixed-wet, using high-resolution 4D X-ray imaging. The pore-by-pore fluid invasion dynamics were quantified, revealing a new type of mixed-wet dynamics where 19% of the fluid invasions were orders of magnitude slower than in directly neighboring pores. While conventional understanding predicted strongly capillary-dominated conditions, our analysis suggests that viscous forces played a key role in these dynamics, facilitated by a complex interplay between the mixed-wettability and the pore structure. These previously unknown dynamics highlight the need for further studies on the fundamental controls on multiphase flow in complex natural porous materials, which are abundant in e.g. groundwater remediation and subsurface CO2 storage operations.