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The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) continue to be the main cause of death in our country. Adequate control of lipid metabolism disorders is a key challenge in cardiovascular prevention that is far from being achieved in real clinical practice. There is a great heterogeneity in the reports of lipid metabolism from Spanish clinical laboratories, which may contribute to its poor control. For this reason, a working group of the main scientific societies involved in the care of patients at vascular risk, has prepared this document with a consensus proposal on the determination of the basic lipid profile in cardiovascular prevention, recommendations for its realization and unification of criteria to incorporate the lipid control goals appropriate to the vascular risk of the patients in the laboratory reports. [ABSTRACT FROM AUTHOR]

Ground contact time (GCT) is one of the most relevant factors when assessing running performance in sports practice. In recent years, inertial measurement units (IMUs) have been widely used to automatically evaluate GCT, since they can be used in field conditions and are friendly and easy to wear devices. In this paper we describe the results of a systematic search, using the Web of Science, to assess what reliable options are available to GCT estimation using inertial sensors. Our analysis reveals that estimation of GCT from the upper body (upper back and upper arm) has rarely been addressed. Proper estimation of GCT from these locations could permit an extension of the analysis of running performance to the public, where users, especially vocational runners, usually wear pockets that are ideal to hold sensing devices fitted with inertial sensors (or even using their own cell phones for that purpose). Therefore, in the second part of the paper, an experimental study is described. Six subjects, both amateur and semi-elite runners, were recruited for the experiments, and ran on a treadmill at different paces to estimate GCT from inertial sensors placed at the foot (for validation purposes), the upper arm, and upper back. Initial and final foot contact events were identified in these signals to estimate the GCT per step, and compared to times estimated from an optical MOCAP (Optitrack), used as the ground truth. We found an average error in GCT estimation of 0.01 s in absolute value using the foot and the upper back IMU, and of 0.05 s using the upper arm IMU. Limits of agreement (LoA, 1.96 times the standard deviation) were [−0.01 s, 0.04 s], [−0.04 s, 0.02 s], and [0.0 s, 0.1 s] using the sensors on the foot, the upper back, and the upper arm, respectively. [ABSTRACT FROM AUTHOR]

The self-assembly of cellulose nanocrystals can tether their interfacial interactions and the associated properties of their constructs. For instance, assemblies of highly aligned cellulose nanocrystals (CNCs) bear improved mechanical strength, barrier properties, and piezoelectric response. In this study, the self-assembly of CNC superstructure was assessed under various confinement geometries, enabling optimization of the long-range order within the microstructures formed. The confinement involved the planar capillary (with a rectangular cross-section)formed between two glass substrates with silicone boundaries. The impact of temperature, width and thickness of the capillary plane on self-assembly of the micro-scaled lamellar structures was evaluated. Thinner capillaries and lower temperatures were found to considerably improve long-range order and increase the frequency of the periodic microstructures formed. The drying process was monitored by rheological analysis, which showed an initial fast drying followed by slow drying due to the hindered diffusion through lamellae. The adhesive properties of the formed superstructures were evaluated. The shear strength was shown to depend on the orientation of the superstructures and therefore of the CNCs. About 4 MPa adhesion strength was obtained when the lamellar superstructures were perpendicularly aligned with respect to the in-plane force applied, while ca. 3 MPa adhesion was obtained for parallel alignment. The experimental framework described herein enabled the evaluation of the impact of the dimensions of a drying meniscus on self-assembly of anisometric colloids while also linking cellulosic assemblies with their interfacial supramolecular interactions. This simple framework brings forward the possibility to correlate the behavior of nanometric objects with micro- and macro-scaled observations, e.g., macro-scaled mechanics of adhesion. [ABSTRACT FROM AUTHOR]

Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It performs iterations where the (unlabeled) real-world training images are labeled by intermediate deep models trained with both the (labeled) synthetic images and the real-world ones labeled in previous iterations. More specifically, a self-training stage provides two domain-adapted models and a model collaboration loop allows the mutual improvement of these two models. The final semantic segmentation labels (pseudo-labels) for the real-world images are provided by these two models. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for onboard semantic segmentation. Our procedure shows improvements ranging from approximately 13 to 31 mIoU points over baselines. [ABSTRACT FROM AUTHOR]

This review focuses on the description of the main processes and materials used for the formulation of rigid polymer foams. Polyurethanes and their derivatives, as well as phenolic systems, are described, and their main components, foaming routes, end of life, and recycling are considered. Due to environmental concerns and the need to find bio-based alternatives for these products, special attention is given to a recent class of polymeric foams: tannin-based foams. In addition to their formulation and foaming procedures, their main structural, thermal, mechanical, and fire resistance properties are described in detail, with emphasis on their advanced applications and recycling routes. These systems have been shown to possess very interesting properties that allow them to be considered as potential substitutes for non-renewable rigid polymeric cellular foams. [ABSTRACT FROM AUTHOR]

The need to find suitable biomaterials and procedures from alternative products able to imitate or even enhance the performance of currently used products has become an important focus of research today due to the depletion of non-renewable resources and the increasing concern related to climate change, sustainability and environmental preservation. The aim of this Special Issue, and now this book, is to provide a clear picture of the latest frontiers reached in the biomaterials field and their latest applications such as 3D bioprinting inks, immobilization of enzymes (Figure 1E), lubricating greases, elastomers and adhesives (Figure 1F), in which bio-based materials show great potential. Thus, this book gathers different original articles and review manuscripts concerning the Special Issue B "Development of Bio-Based Materials: Synthesis, Characterization and Applications" b . [Extracted from the article]

The short-term prediction of a person's trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton's laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human–robot interaction applications. [ABSTRACT FROM AUTHOR]

Mathcad is a simple‐to‐use and intuitive mathematical software that helps students to minimize the mathematical difficulties involved in solving engineering problems. The design of nonisothermal plug flow reactors (PFR) is a fundamental issue within the field of chemical reaction engineering; however, its teaching–learning process is hindered by students' mathematical difficulties in solving ordinary differential equations. In this paper, the software Mathcad was conveniently integrated into an educational experience through the resolution of two real case studies. In the first one, a simple liquid‐phase reaction is considered in a PFR working at different operating conditions, whereas the second case evaluates a PFR taking place multiple reactions (parallel reactions) with a heat exchanger attached. The assessment of this experience, which was held into two 5‐h Mathcad workshops, revealed that Mathcad made the design of non‐isothermal PFR more appealing, facilitated the understanding of the design process, and brought another dimension to the way the students perform complex calculations. [ABSTRACT FROM AUTHOR]

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. [ABSTRACT FROM AUTHOR]

Pedometers are basically step counters usually used to estimate the distance walked by a pedestrian. Although their precision to compute the number of steps is quite accurate (about 1%), their feasibility to estimate the walked distance is very poor, as they do not consider the intrinsic variability of human gait. Reported results show values of 10% of precision in optimal conditions, increasing to 50% when conditions differ. Electronic accelerometer-based pedometers base their functioning on a basic processing of the vertical acceleration of the waist. Recently, different approaches have been proposed to relate such signals to the step length. This can lead to an improvement of the performance of this kind of device for estimating the walked distance. In this article, we analyze four gait models applied to the vertical accelerations of the body's center of gravity, three biomechanical and one empirical. We compare their precision and accuracy. Results support the superior performance of three of them over an ideal pedometer. We also analyze their feasibility to be implemented in pedometer-like devices. [ABSTRACT FROM AUTHOR]

Featured Application: Upper body kinematics (Head Yaw and Trunk Roll from evidence so far) can be used to anticipate walking turns in the short-term, and can therefore be used to improve the interaction between humans and robots in close proximity. Prediction of walking turns allows to improve human factors such as comfort and perceived safety in human-robot interaction. The current state-of-the-art suggests that upper body kinematics can be used for that purpose and contains evidence about the reliability and the quantitative anticipation that can be expected from different variables. However, the experimental methodology has not been consistent throughout the different works and the related data has not always been given in an explicit form, with different studies containing partial, complementary or even contradictory results. In this paper, with the purpose of providing a uniform view of the topic that can trigger new developments in the field, we performed a systematic review of the relevant literature addressing three main questions: (i) Which upper body kinematic variables permit to anticipate a walking turn? (ii) How long in advance can we anticipate the turn from them? (iii) What is the expected contribution of walking turn prediction systems from upper body kinematics for human-robot interaction? We have found that head yaw was the most reliable kinematical variable from the upper body to predict walking turns about 200ms. Trunk roll anticipates walking turns by a similar amount of time, but with less reliability. Both approaches may benefit human-robot interaction in close proximity, helping the robot to exhibit appropriate proxemic behavior interacting at intimate, personal or social distances. From the point of view of safety, they have to be considered with caution. Trunk yaw is not valid to anticipate turns. Gaze Yaw seems to be the earliest predictor, although existing evidence is still inconclusive. [ABSTRACT FROM AUTHOR]

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. [ABSTRACT FROM AUTHOR]

Copper-based chalcogenides that comprise abundant, low-cost, and environmental friendly elements are excellent materials for a number of energy conversion applications, including photovoltaics, photocatalysis, and thermoelectrics (TE). In such applications, the use of solution-processed nanocrystals (NCs) to produce thin films or bulk nanomaterials has associated several potential advantages, such as high material yield and throughput, and composition control with unmatched spatial resolution and cost. Here we report on the production of Cu3SbSe4 (CASe) NCs with tuned amounts of Sn and Bi dopants. After proper ligand removal, as monitored by nuclear magnetic resonance and infrared spectroscopy, these NCs were used to produce dense CASe bulk nanomaterials for solid state TE energy conversion. By adjusting the amount of extrinsic dopants, dimensionless TE figures of merit (ZT) up to 1.26 at 673 K were reached. Such high ZT values are related to an optimized carrier concentration by Sn doping, a minimized lattice thermal conductivity due to efficient phonon scattering at point defects and grain boundaries, and to an increase of the Seebeck coefficient obtained by a modification of the electronic band structure with Bi doping. Nanomaterials were further employed to fabricate ring-shaped TE generators to be coupled to hot pipes, which provided 20 mV and 1 mW per TE element when exposed to a 160 °C temperature gradient. The simple design and good thermal contact associated with the ring geometry and the potential low cost of the material solution processing may allow the fabrication of TE generators with short payback times. [ABSTRACT FROM AUTHOR]

Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images; (b) just infrared images; and (c) both of them. In order to obtain results for the last item, we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset that we have built for this purpose as well as on the publicly available KAIST multispectral dataset. [ABSTRACT FROM AUTHOR]

Usual human motion capture systems are designed to work in controlled laboratory conditions. For occupational health, instruments that can measure during normal daily life are essential, as the evaluation of the workers' movements is a key factor to reduce employee injury- and illness-related costs. In this paper, we present a method for joint angle measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors. This method estimates wrist flexion, wrist lateral deviation, elbow flexion, elbow pronation, shoulder flexion, shoulder abduction and shoulder internal rotation. The algorithms avoid numerical integration of the signals, which allows for long-time estimations without angle estimation drift. The system has been tested both under laboratory and field conditions. Controlled laboratory tests show mean estimation errors between 0.06° and of 1.05°, and standard deviation between 2.18° and 9.20°. Field tests seem to confirm these results when no ferromagnetic materials are close to the measurement system. [ABSTRACT FROM PUBLISHER]

The present review is devoted to the description of the state-of-the-art techniques and procedures concerning treatments and modifications of lignocellulosic materials in order to use them as precursors for biomaterials, biochemicals and biofuels, with particular focus on lignin and lignin-based products. Four different main pretreatment types are outlined, i.e., thermal, mechanical, chemical and biological, with special emphasis on the biological action of fungi and bacteria. Therefore, by selecting a determined type of fungi or bacteria, some of the fractions may remain unaltered, while others may be decomposed. In this sense, the possibilities to obtain different final products are massive, depending on the type of microorganism and the biomass selected. Biofuels, biochemicals and biomaterials derived from lignocellulose are extensively described, covering those obtained from the lignocellulose as a whole, but also from the main biopolymers that comprise its structure, i.e., cellulose, hemicellulose and lignin. In addition, special attention has been paid to the formulation of bio-polyurethanes from lignocellulosic materials, focusing more specifically on their applications in the lubricant, adhesive and cushioning material fields. High-performance alternatives to petroleum-derived products have been reported, such as adhesives that substantially exceed the adhesion performance of those commercially available in different surfaces, lubricating greases with tribological behaviour superior to those in lithium and calcium soap and elastomers with excellent static and dynamic performance. [ABSTRACT FROM AUTHOR]

In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time. [ABSTRACT FROM AUTHOR]

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time. Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods. [ABSTRACT FROM AUTHOR]

Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. [ABSTRACT FROM AUTHOR]

In order to identify new sustainable sources for producing cellulose nanofibers (CNFs), fast-growing poplar (Populus alba L.) wood was evaluated herein. For that purpose, bleached poplar kraft pulp was produced and submitted to TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl radical) mediated oxidation (TEMPO-ox) chemical pretreatment followed by microfluidization. The resulting CNFs were thoroughly characterized, including a rheological study at different pH values. Poplar CNFs showed properties comparable to eucalypt CNFs (reference material for CNFs production), showing high carboxylate content (1048 ± 128 µmol g−1), fibrillation yield (87.3% ± 8.1%), optical transmittance (83% at 700 nm) and thermal stability (up to more than 200 °C). Regarding the rheological study, whereas pH from 4 to 10 did not produce significant changes in rheological behavior, a reduction of pH down to 1 led to an order-of-magnitude increase on the viscoelastic functions. Therefore, poplar CNF shows potential in the pH-sensitive hydrogels application field. Finally, the possible ecotoxicity of poplar CNF was assessed. The decrease in cell viability was very low so that only concentrations causing a 10% cytotoxicity could be calculated for the assay detecting alterations in cell metabolism (10 µg mL−1) and plasma membrane integrity (60 µg mL−1). [ABSTRACT FROM AUTHOR]

In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot. [ABSTRACT FROM AUTHOR]

Dry mangosteen leaves are one of the raw materials used to produce marker ink. However, research using this free and abundant resource is rather limited. The less efficient one-factor-at-a-time (OFAT) approach was mostly used in past studies on plant-based marker ink. The use of statistical analysis and the regression coefficient model (mathematical model) was considered essential in predicting the best combination of factors in formulating mangosteen leaf-based marker ink. Ideally, ink should have maximum color lightness, minimum viscosity, and fast-drying speed. The objective of this study to study the effect of glycerol and carboxymethyl cellulose (CMC) on the color lightness and viscosity of mangosteen-leaves-based marker ink. The viscosity, color lightness, and drying properties of the ink were tested, the significant effect of glycerol and CMC (responses) on ink properties was identified and the prediction model on the optimum value of the responses was developed by using response surface methodology (RSM). The microstructure of mangosteen leaves was analyzed to study the surface morphology and cell structure during dye extraction. A low amount of glycerol used was found to increase the value of color lightness. A decrease in CMC amounts resulted in low viscosity of marker ink. The optimum formulation for the ink can be achieved when the weight percents of glycerol, benzalkonium chloride, ferrous sulphate, and CMC are set at 5, 5, 1, and 3, respectively. SEM micrographs showed the greatest amount of cell wall structure collapse on samples boiled with the lowest amount of glycerol. [ABSTRACT FROM AUTHOR]

Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. [ABSTRACT FROM AUTHOR]

Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any sacrifice to accuracy, we apply optimization to the original data of the point cloud, allowing the network to handle fewer data. In the experiment, our proposed method is conducted comprehensively through four evaluation indicators, proving the superiority of our method. [ABSTRACT FROM AUTHOR]

The development of biological strategies to obtain new high-added value biopolymers from lignocellulosic biomass is a current challenge for scientific community. This study evaluates the biodegradability and ecotoxicity of new formulated oleogels obtained from fermented agricultural residues with Streptomyces, previously reported to show improved rheological and tribological characteristics compared to commercial mineral lubricants. Both new oleogels exhibited higher biodegradation rates than the commercial grease. Classical ecotoxicological bioassays using eukaryotic organisms (Lactuca sativa, Caenorhabditis elegans) showed that the toxic impact of the produced bio-lubricants was almost negligible and comparable to the commercial grease for the target organisms. In addition, high throughput molecular techniques using emerging next-generation DNA-sequencing technologies (NGS) were applied to study the structural changes of lubricant-exposed microbial populations of a standard soil. Results obtained showed that disposal of biomass-based lubricants in the soil environment did not substantially modify the structure and phylogenetic composition of the microbiome. These findings point out the feasibility and sustainability, in terms of biodegradability and eco-safety, of the new bio-lubricants in comparison with commercial mineral greases. This technology entails a promising biological strategy to replace fossil and non-renewable raw materials as well as to obtain useful biopolymers from agricultural residues with potential for large-scale applications. [ABSTRACT FROM AUTHOR]