bluuebunny/crossref_metadata_2025 huggingface dataset:
Activation of Free Fatty Acid Receptor 4 Affects Intestinal Inflammation and Improves Colon Permeability in Mice
Authors: Salaga Maciej , Bartoszek Adrian , Binienda Agata , Krajewska Julia B. , Fabisiak Adam , Mosińska Paula , Dziedziczak Katarzyna , Niewinna Karolina , Talar Marcin , Tarasiuk Aleksandra , Kordek Radzisław , Fichna Jakub
Published: August 2021

Diet is considered an important trigger in inflammatory bowel diseases (IBD), as feeding habits can affect intestinal permeability and clearance of bacterial antigens, consequently influencing the immune system. Free fatty acid receptors (FFARs), expressed on the intestinal epithelial cells, belong to the family of luminal-facing receptors that are responsive to nutrients. The objective of this study was to characterize the anti-inflammatory activity and the effect on intestinal barrier function of synthetic FFAR agonists in mouse models of colitis. Therapeutic activity of GW9508 (FFAR1 agonist), 4-CMTB (FFAR2 agonist), AR420626 (FFAR3 agonist), and GSK137647 (FFAR4 agonist) was investigated in two models of semi-chronic colitis: induced by trinitrobenzenesulfonic acid (TNBS), mimicking Crohn’s disease, as well as induced by dextran sulfate sodium (DSS), which recapitulates ulcerative colitis in humans. Moreover, we assessed the influence of FFARs agonists on epithelial ion transport and measured the ion flow stimulated by forskolin and veratridine. Administration of FFAR4 agonist GSK137647 attenuated both TNBS-induced and DSS-induced colitis in mice, as indicated by macroscopic parameters and myeloperoxidase activity. The action of FFAR4 agonist GSK137647 was significantly blocked by pretreatment with selective FFAR4 antagonist AH7614. Moreover, FFAR1 and FFAR4 agonists reversed the increase in the colon permeability caused by inflammation. FFAR4 restored the tight junction genes expression in mouse colon. This is the first evaluation of the anti-inflammatory activity of selective FFAR agonists, showing that pharmacological intervention targeting FFAR4, which is a sensor of medium and long chain fatty acids, attenuates intestinal inflammation.
Ramadan Fasting Improves Body Composition without Exacerbating Depression in Males with Diagnosed Major Depressive Disorders
Authors: Jahrami Haitham , BaHammam Ahmed S. , Haji Eman Ahmed , Bragazzi Nicola L. , Rakha Ihab , Alsabbagh Amani , Nugraha Boya , Pasiakos Stefan M.
Published: August 2021

Background: Ramadan fasting (RF) is a form of intermittent fasting that generally improves body composition and related metabolic profiles. Whether RF exacerbates depressive symptomatology in individuals diagnosed with major depressive disorder (MDD) is undetermined. Methods: 100 men, who lived in Bahrain and were between the ages of 18 and 64 years with an established diagnosis of MDD, participated in this 4-week study. Based on preference, participants were assigned to a fasting group (FG, n = 50) and a non-fasting group (NFG, n = 50). The FG engaged in fasting from 03:40 to 18:10 (dawn and dusk timings). Changes in depressive symptoms, body mass, body composition, and components of metabolic syndrome were measured. Results: There were no significant changes in depressive symptoms within the FG vs. NFG after controlling for baseline covariates: mean difference 0.49 (SE = 0.63), p = 0.43. No adverse effects were reported in either group. The FG experienced significant reductions in body mass, 1.87 kg, p = 0.001; body mass index, 0.69 kg/m2, p = 0.001; body fat, 0.87%, p = 0.001; body surface area, 0.03 m2, p = 0.001; and lean mass, 0.77 kg, p = 0.001. Conclusions: RF did not negatively affect depressive symptoms and improved body composition, suggesting short-term intermittent fasting may be a safe dietary practice for adult males with MDD.
Does Responsiveness to Basic Tastes Influence Preadolescents’ Food Liking? Investigating Taste Responsiveness Segment on Bitter-Sour-Sweet and Salty-Umami Model Food Samples
Authors: Ervina Ervina , Almli Valérie L. , Berget Ingunn , Spinelli Sara , Sick Julia , Dinnella Caterina
Published: August 2021

The objective of this study was to investigate the relationships between taste responsiveness and food liking in preadolescents. Model food samples of grapefruit juice (GF) and vegetable broth (VB) modified with four additions of sucrose and sodium chloride, respectively, were employed. Intensity perception for sweetness, sourness, and bitterness were measured in GF while saltiness and umami were measured in VB. The children (N = 148) also completed food choice, familiarity, stated liking and neophobia questionnaires. The test was conducted at school, with instructions provided remotely via video call. Four segments were defined differing in basic taste responsiveness. Segments and sucrose concentrations significantly affected liking for GF, while no significant effect of segments and sodium chloride concentrations occurred on liking for VB. An increasing sucrose concentration was positively associated with liking for GF only in the segment with low responsiveness to bitter and sour tastes. No significant differences across segments were found for food choice, familiarity, stated liking, and neophobia. Conclusively, relationships between taste responsiveness and liking are product and basic taste-dependent in addition to being subject-dependent. Strategies to improve acceptance by using sucrose as a suppressor for warning sensations of bitterness and sourness can be more or less effective depending on individual responsiveness to the basic tastes.
Assessment of Sarcopenia in the Intensive Care Unit and 1-Year Mortality in Survivors of Critical Illness
Authors: Yanagi Naoya , Koike Tomotaka , Kamiya Kentaro , Hamazaki Nobuaki , Nozaki Kohei , Ichikawa Takafumi , Matsunaga Atsuhiko , Kuroiwa Masayuki , Arai Masayasu
Published: August 2021

Skeletal muscle wasting in the intensive care unit (ICU) has been associated with mortality, but it is unclear whether sarcopenia, defined by skeletal muscle mass and function, is useful for detailed risk stratification after ICU discharge. In this cohort study, 72 critically ill patients with an ICU stay of ≥48 h were identified. Skeletal muscle mass was assessed from the muscle thickness (MT) of the patients’ quadriceps using ultrasound images before ICU discharge. Skeletal muscle function was assessed from the patients’ muscle strength (MS) before ICU discharge according to the Medical Research Council sum score. A diagnosis of sarcopenia in the ICU was made in patients with low MT and low MS. The study endpoint was 1-year mortality. Sarcopenia in the ICU was diagnosed in 26/72 patients (36%). After adjusting for covariates in the Cox regression, sarcopenia in the ICU was significantly associated with 1-year mortality (hazard ratio 3.82; 95% confidence interval, 1.40–10.42). Sarcopenia in the ICU, defined by low skeletal muscle mass and function, was associated with 1-year mortality in survivors of critical illness. Skeletal muscle mass and function assessed at the bedside could be used to identify higher-risk patients in the ICU.
Neuropeptide S Receptor as an Innovative Therapeutic Target for Parkinson Disease
Authors: Holanda Victor A. D. , Didonet Julia J. , Costa Manara B. B. , do Nascimento Rangel Adriano H. , da Silva Edilson D. , Gavioli Elaine C.
Published: August 2021

Parkinson disease (PD) is a neurodegenerative disease mainly characterized by the loss of nigral dopaminergic neurons in the substantia nigra pars compacta. Patients suffering from PD develop severe motor dysfunctions and a myriad of non-motor symptoms. The treatment mainly consists of increasing central dopaminergic neurotransmission and alleviating motor symptoms, thus promoting severe side effects without modifying the disease’s progress. A growing body of evidence suggests a close relationship between neuropeptide S (NPS) and its receptor (NPSR) system in PD: (i) double immunofluorescence labeling studies showed that NPSR is expressed in the nigral tyrosine hydroxylase (TH)-positive neurons; (ii) central administration of NPS increases spontaneous locomotion in naïve rodents; (iii) central administration of NPS ameliorates motor and nonmotor dysfunctions in animal models of PD; (iv) microdialysis studies showed that NPS stimulates dopamine release in naïve and parkinsonian rodents; (v) central injection of NPS decreases oxidative damage to proteins and lipids in the rodent brain; and, (vi) 7 days of central administration of NPS protects from the progressive loss of nigral TH-positive cells in parkinsonian rats. Taken together, the NPS/NPSR system seems to be an emerging therapeutic strategy for alleviating motor and non-motor dysfunctions of PD and, possibly, for slowing disease progress.
Mechanistic Insight into the Effects of Curcumin on Neuroinflammation-Driven Chronic Pain
Authors: Hasriadi  , Dasuni Wasana Peththa Wadu , Vajragupta Opa , Rojsitthisak Pornchai , Towiwat Pasarapa
Published: August 2021

Chronic pain is a persistent and unremitting condition that has immense effects on patients’ quality of life. Studies have shown that neuroinflammation is associated with the induction and progression of chronic pain. The activation of microglia and astrocytes is the major hallmark of spinal neuroinflammation leading to neuronal excitability in the projection neurons. Excessive activation of microglia and astrocytes is one of the major contributing factors to the exacerbation of pain. However, the current chronic pain treatments, mainly by targeting the neuronal cells, remain ineffective and unable to meet the patients’ needs. Curcumin, a natural plant product found in the Curcuma genus, improves chronic pain by diminishing the release of inflammatory mediators from the spinal glia. This review details the role of curcumin in microglia and astrocytes both in vitro and in vivo and how it improves pain. We also describe the mechanism of curcumin by highlighting the major glia-mediated cascades in pain. Moreover, the role of curcumin on inflammasome and epigenetic regulation is discussed. Furthermore, we discuss the strategies used to improve the efficacy of curcumin. This review illustrates that curcumin modulating microglia and astrocytes could assure the treatment of chronic pain by suppressing spinal neuroinflammation.
Role of the Neuropeptide S System in Emotionality, Stress Responsiveness and Addiction-Like Behaviours in Rodents: Relevance to Stress-Related Disorders
Authors: Tobinski Ann-Marie , Rappeneau Virginie
Published: August 2021

The neuropeptide S (NPS) and its receptor (NPSR1) have been extensively studied over the last two decades for their roles in locomotion, arousal/wakefulness and anxiety-related and fear-related behaviours in rodents. However, the possible implications of the NPS/NPSR1 system, especially those of the single nucleotide polymorphism (SNP) rs324981, in stress-related disorders and substance abuse in humans remain unclear. This is possibly due to the fact that preclinical and clinical research studies have remained separated, and a comprehensive description of the role of the NPS/NPSR1 system in stress-relevant and reward-relevant endpoints in humans and rodents is lacking. In this review, we describe the role of the NPS/NPSR1 system in emotionality, stress responsiveness and addiction-like behaviour in rodents. We also summarize the alterations in the NPS/NPSR1 system in individuals with stress-related disorders, as well as the impact of the SNP rs324981 on emotion, stress responses and neural activation in healthy individuals. Moreover, we discuss the therapeutic potential and possible caveats of targeting the NPS/NPSR1 system for the treatment of stress-related disorders. The primary goal of this review is to highlight the importance of studying some rodent behavioural readouts modulated by the NPS/NPSR1 system and relevant to stress-related disorders.
Hybrid Quinolinyl Phosphonates as Heterocyclic Carboxylate Isosteres: Synthesis and Biological Evaluation against Topoisomerase 1B (TOP1B)
Authors: Selas Asier , Fuertes María , Melcón-Fernández Estela , Pérez-Pertejo Yolanda , Reguera Rosa M. , Balaña-Fouce Rafael , Knudsen Birgitta R. , Palacios Francisco , Alonso Concepcion
Published: August 2021

This work describes, for the first time, the synthesis of dialkyl (2-arylquinolin-8-yl)phosphonate derivatives. The preparation was carried out through a direct and simple process as a multicomponent Povarov reaction of aminophenylphosphonates, aldehydes, and styrenes and subsequent oxidation with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) or, alternatively, by a cycloaddition reaction between phosphonate aldimines and acetylenes. Based on phosphonate group structural characteristics, considered as phosphorous isosteres of carboxylic heterocycles, they may present interesting biological properties related to cell proliferation. In the current report, a new series of dialkyl (2-arylquinolin-8-yl)phosphonates have been synthesized and their antiproliferative effect evaluated on different human cancer and embryonic cells, as well as on Leishmania infantum parasites, a eukaryotic protist responsible for visceral leishmaniasis. Thereby, the antitumor effect was assessed in human lung adenocarcinoma cells (A549), human ovarian carcinoma cells (SKOV3), and human embryonic kidney cells (HEK293) versus the non-cancerous lung fibroblasts cell line (MRC5). On the other hand, the antileishmanial activity was tested against both stages of L. infantum cell cycle, namely free-living promastigotes and intramacrophage amastigotes, using a primary culture of Balb/c splenocytes to calculate the selectivity index. Besides the antiproliferative and antileishmanial capacities, their behavior as topoisomerase 1B inhibitors has been evaluated as a possible mechanism of action.
Rheological Characterization of the Influence of Pomegranate Peel Extract Addition and Concentration in Chitosan and Gelatin Coatings
Authors: Romanelli Vicente Bertolo Mirella , Leme Rafael , da Conceição Amaro Martins Virginia , de Guzzi Plepis Ana Maria , Bogusz Junior Stanislau
Published: August 2021

In this study, the effects of an agro-industrial residue with active properties, pomegranate peel extract (PPE), were evaluated on the rheological properties of potential coatings based on chitosan (C) and gelatin (G). For this, rheological properties of the polymeric solutions were investigated in relation to PPE concentration (2 or 4 mg PPE g−1 solution), and to its incorporation order into the system (in C or in CG mixture). All solutions were more viscous than elastic (G″ > G′), and the change in PPE concentration had a greater influence accentuating the viscous character of the samples in which PPE was added to the CG mixture (CGPPE2 and CGPPE4). PPE addition to the CG mixture increased the angular frequency at the moduli crossover, indicating the formation of a more resistant polymeric network. This tendency was also observed in flow results, in which PPE addition decreased the pseudoplastic behavior of the solutions, due to a greater cross-linking between the polymers and the phenolic compounds. In general, all the studied solutions showed viscosities suitable for the proposed application, and it was possible to state the importance of standardizing the addition order of the components during the preparation of a coating.
Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai
Authors: Yan Jinfeng , Xiao Ruiming , Su Fenzhen , Bai Jinbiao , Jia Feixue
Published: August 2021

Ports are an important type of land use in coastal cities, and the development of ports has a significant influence on the spatial pattern of land use in port cities. However, the research focusing on economic indicators hardly reflects the process of changes in the spatial distribution of land development in coastal port cities. This paper introduces a spatial association rule method to establish a coastline and land development intensity (CLDI) model and land use transfer (LUT) model in the vertical direction of coastal zones to mine the association rules between shoreline change and land development intensity along the sea–land gradient in the Qingdao and Yantai coastal zones and to explore the important land development sequence patterns. The results showed that, in the early stage of regional development, the land development intensity decreased from sea to land. In the later stage, as the industry transferred to nearby towns, the land units with extremely strong and strong levels started to move to the end or middle of the sequence. With the improvement of the urban construction level, the simple LUT pattern sequence that increased building land through the occupation of cultivated land and forestland was replaced gradually by complex sequences with multiple components. The relationship between land development and distance from the port showed that the areas with strong land development intensity gradually moved from coastal to inland areas over time. Port shipping has a profound influence on port city land use patterns. Industrial transfer drives the development of surrounding towns during the metaphase. This trend was used to build a second port to realize the division of transportation capacity, as the old port’s carrying capacity tended to become saturated. This paper revealed the general changes in the important land use patterns in port areas through a comparative study of the Qingdao and Yantai port areas and the differences among different geographical locations and development processes. This study provides a reference for the rational planning of coastal zone spatial layouts and provides a model basis for the analysis of the spatial structure of coastal zones. This information can be used to coordinate the relationship between ports and cities and promote the sustainable development of coastal zones.
Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses
Authors: Rim Beanbonyka , Lee Ahyoung , Hong Min
Published: August 2021

Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder–decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.
The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation
Authors: Piontek Dennis , Bugliaro Luca , Kar Jayanta , Schumann Ulrich , Marenco Franco , Plu Matthieu , Voigt Christiane
Published: August 2021

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.
Combining FY-3D MWTS-2 with AMSU-A Data for Inter-Decadal Diurnal Correction and Climate Trends of Atmospheric Temperature
Authors: Xia Xinlu , Zou Xiaolei
Published: August 2021

Microwave temperature sounding observations from polar-orbiting meteorological satellites have been widely used for research on climate trends of atmospheric temperature at different heights around the world. Taking the Amazon rainforest as the target area, this study combined the Microwave Temperature Sounder-2 (MWTS-2) data onboard the Chinese FengYun-3D (FY-3D) satellite with the Advanced Microwave Sounding unit-A (AMSU-A) data onboard the National Oceanic and Atmospheric Administration (NOAA) and the European Meteorological Operational (MetOp) polar-orbiting meteorological satellites (i.e., NOAA-15, −18, −19, MetOp-A, -B). The double difference method was used to estimate and thus eliminate the inter-sensor bias, and a decadal diurnal correction was used to reduce the impact of different local equator crossing times on climate trends. The “no-rain” conditions were determined for AMSU-A data by channels 1 and 15, and for MWTS-2 data by channels 1 and 7. Finally, the decadal linear trends of atmospheric temperature from 1998 to 2020 were obtained after applying the inter-sensor bias calibration and inter-decadal diurnal correction to AMSU-A and MWTS-2 data from NOAA-15, −18, −19; MetOp-A, -B; and FY-3D. A warming trend was found in the AMSU-A window and tropospheric channels (1–9 and 15) and a cooling trend in stratospheric channels (10–14). The warming (cooling) trends of channels 7–9 (10) were relatively small. The warming (cooling) trends of AMSU-A channels 1–6 (14–15) were significantly reduced after the inter-decadal diurnal correction.
Multiscale and Multitemporal Road Detection from High Resolution SAR Images Using Attention Mechanism
Authors: Wei Xiaochen , Fu Xikai , Yun Ye , Lv Xiaolei
Published: August 2021

Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, which may cause a decrease in image resolution due to the use of spatial filter methods to avoid coherent speckle noises. Some newly developed methods take into account the multi-temporal information in the preprocessing stage to filter the coherent speckle noise in the SAR imagery. They ignore the temporal characteristic of road objects such as the temporal consistency for the road objects in the multitemporal SAR images that cover the same area and are taken at adjacent times, causing the limitation in detection performance. In this paper, we propose a multiscale and multitemporal network (MSMTHRNet) for road detection from SAR imagery, which contains the temporal consistency enhancement module (TCEM) and multiscale fusion module (MSFM) that are based on attention mechanism. In particular, we propose the TCEM to make full use of multitemporal information, which contains temporal attention submodule that applies attention mechanism to capture temporal contextual information. We enforce temporal consistency constraint by the TCEM to obtain the enhanced feature representations of SAR imagery that help to distinguish the real roads. Since the width of roads are various, incorporating multiscale features is a promising way to improve the results of road detection. We propose the MSFM that applies learned weights to combine predictions of different scale features. Since there is no public dataset, we build a multitemporal road detection dataset to evaluate our methods. State-of-the-art semantic segmentation network HRNetV2 is used as a baseline method to compare with MSHRNet that only has MSFM and the MSMTHRNet. The MSHRNet(TAF) whose input is the SAR image after the temporal filter is adopted to compare with our proposed MSMTHRNet. On our test dataset, MSHRNet and MSMTHRNet improve over the HRNetV2 by 2.1% and 14.19%, respectively, in the IoU metric and by 3.25% and 17.08%, respectively, in the APLS metric. MSMTHRNet improves over the MSMTHRNet(TAF) by 8.23% and 8.81% in the IoU metric and APLS metric, respectively.
Research on Location Algorithm Based on Beacon Filtering Combining DV-Hop and Multidimensional Support Vector Regression
Authors: Zhang Dejing , Zhang Xiangcheng , Xie Fengfeng
Published: August 2021

The DV-Hop algorithm is widely used because of its simplicity and low cost, but it has the disadvantage of a large positioning error. In recent years, although some improvement measures have been proposed, such as hop correction, distance-weighted correction, and improved coordinate solution, there is room for improvement in location accuracy, and the accuracy is affected in anisotropic networks. A location algorithm based on beacon filtering combining DV-Hop and multidimensional support vector regression (MSVR) is proposed in this paper. In the process of estimating the coordinates of unknown nodes, received signal strength indication (RSSI), MSVR, and weighted least squares method are combined. In addition, the verification error of beacon nodes is proposed, which can select the beacon nodes with smaller errors to reduce the location error. Simulation results show that in different distributions, the location accuracy of the proposed algorithm is at least 34% higher than that of the classical DV-Hop algorithm and at least 28% higher than that of the localization based on multidimensional support vector regression (LMSVR) algorithm. The proposed algorithm has the potential of application in small-scale anisotropic networks.
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition
Authors: Sun Yan , Shen Yixin , Ma Liyan
Published: August 2021

Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequences. However, this method still has some imperfections: only short-range correlations are appreciated, due to the limited receptive field of graph convolution. However, long-range dependence is essential for recognizing human action. In this work, we propose the use of a spatial-temporal relative transformer (ST-RT) to overcome these defects. Through introducing relay nodes, ST-RT avoids the transformer architecture, breaking the inherent skeleton topology in spatial and the order of skeleton sequence in temporal dimensions. Furthermore, we mine the dynamic information contained in motion at different scales. Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance. Extensive experiments evaluate the proposed methods on three benchmarks for skeleton-based action recognition: NTU RGB+D, NTU RGB+D 120 and UAV-Human. The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
Using Virtual Scanning to Find Optimal Configuration of a 3D Scanner Turntable for Scanning of Mechanical Parts
Authors: Kot Tomáš , Bobovský Zdenko , Heczko Dominik , Vysocký Aleš , Virgala Ivan , Prada Erik
Published: August 2021

The article describes a method of simulated 3D scanning of triangle meshes based on ray casting which is used to find the optimal configuration of a real 3D scanner turntable. The configuration include the number of scanners, their elevation above the rotary table and the number of required rotation steps. The evaluation is based on the percentage of the part surface covered by the resulting point cloud, which determines the ability to capture all details of the shape. Principal component analysis is used as a secondary criterion to also evaluate the ability to capture the overall general proportions of the model.
Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems
Authors: Hatem Elias , Fortes Sergio , Colin Elizabeth , Abou-Chakra Sara , Laheurte Jean-Marc , El-Hassan Bachar
Published: August 2021

Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.
External Load and Muscle Activation Monitoring of NCAA Division I Basketball Team Using Smart Compression Shorts
Authors: Saucier David N. , Davarzani Samaneh , Burch V Reuben F. , Chander Harish , Strawderman Lesley , Freeman Charles , Ogden Logan , Petway Adam , Duvall Aaron , Crane Collin , Piroli Anthony
Published: August 2021

There is scarce research into the use of Strive Sense3 smart compression shorts to measure external load with accelerometry and muscle load (i.e., muscle activations) with surface electromyography in basketball. Sixteen external load and muscle load variables were measured from 15 National Collegiate Athletic Association Division I men’s basketball players with 1137 session records. The data were analyzed for player positions of Centers (n = 4), Forwards (n = 4), and Guards (n = 7). Nonparametric bootstrapping was used to find significant differences between training and game sessions. Significant differences were found in all variables except Number of Jumps and all muscle load variables for Guards, and all variables except Muscle Load for Forwards. For Centers, the Average Speed, Average Max Speed, and Total Hamstring, Glute, Left, and Right Muscle variables were significantly different (p < 0.05). Principal component analysis was conducted on the external load variables. Most of the variance was explained within two principal components (70.4% in the worst case). Variable loadings of principal components for each position were similar during training but differed during games, especially for the Forward position. Measuring muscle activation provides additional information in which the demands of each playing position can be differentiated during training and competition.
A Two-Stage Location Problem with Order Solved Using a Lagrangian Algorithm and Stochastic Programming for a Potential Use in COVID-19 Vaccination Based on Sensor-Related Data
Authors: Cabezas Xavier , García Sergio , Martin-Barreiro Carlos , Delgado Erwin , Leiva Víctor
Published: August 2021

Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient’s records in a data warehouse and generate 2S-SPLPO instances using sensors.
Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers
Authors: Jo Eunsan , Sunwoo Myoungho , Lee Minchul
Published: August 2021

Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. The subsequent movement of a vehicle is decided based on the multiple maneuvers of surrounding vehicles. Therefore, to predict the trajectories of surrounding vehicles, interactions among multiple maneuvers should be considered. Recent research has taken into account interactions that are difficult to express mathematically using data-driven deep learning methods. However, previous studies have only considered the interactions among observed trajectories due to subsequent maneuvers that are unobservable and numerous maneuver combinations. Thus, to consider the interaction among multiple maneuvers, this paper proposes a hierarchical graph neural network. The proposed hierarchical model approximately predicts the multiple maneuvers of vehicles and considers the interaction among the maneuvers by representing their relationships in a graph structure. The proposed method was evaluated using a publicly available dataset and a real driving dataset. Compared with previous methods, the results of the proposed method exhibited better prediction performance in highly interactive situations.
Methodology for Digital Transformation with Internet of Things and Cloud Computing: A Practical Guideline for Innovation in Small- and Medium-Sized Enterprises
Authors: Liu Yu , Ni Zhongjun , Karlsson Magnus , Gong Shaofang
Published: August 2021

Researches on the Internet of Things (IoT) and cloud computing have been pervasive in both the academic and industrial world. IoT and cloud computing are seen as cornerstones to digital transformation in the industry. However, restricted by limited resources and the lack of expertise in information and communication technologies, small- and medium-sized enterprises (SMEs) have difficulty in achieving digitalization of their business. In this paper, we propose a reference framework for SMEs to follow as a guideline in the journey of digital transformation. The framework features a three-stage procedure that covers business, technology, and innovation, which can be iterated to drive product and business development. A case study about digital transformation taking place in the vertical plant wall industry is detailed. Furthermore, some solution design principles that are concluded from real industrial practice are presented. This paper reviews the digital transformation practice in the vertical plant wall industry and aims to accelerate the pace of SMEs in the journey of digital transformation.
Vehicle Localization Using 3D Building Models and Point Cloud Matching
Authors: Ballardini Augusto Luis , Fontana Simone , Cattaneo Daniele , Matteucci Matteo , Sorrenti Domenico Giorgio
Published: August 2021

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.
A New Ground-Based Pseudolite System Deployment Algorithm Based on MOPSO
Authors: Tang Wenjie , Chen Junping , Yu Chao , Ding Junsheng , Wang Ruyuan
Published: August 2021

Pseudolite deployment is the premise of ground-based pseudolite system networking, which affects the coverage and positioning accuracy of ground-based pseudolite systems. Optimal deployment algorithms can help to achieve a higher signal coverage and lower mean horizontal precision factor (HDOP) with a limited number of pseudolites. In this paper, we proposed a multi-objective particle swarm optimization (MOPSO) algorithm for the deployment of a ground-based pseudolite system. The new algorithm combines Digital Elevation Model (DEM) data and uses the mean HDOP of the DEM grid to measure the geometry of the pseudolite system. The signal coverage of the pseudolite system was calculated based on the visual area analysis with respect to reference planes, which effectively avoids the repeated calculation of the intersection and improves the calculation efficiency. A selected area covering 10 km×10 km in the Jiuzhaigou area of China was used to verify the new algorithm. The results showed that both the coverage and HDOP achieved were optimal using the new algorithm, where the coverage area can be up to approximately 50% and 30% more than using the existing particle swarm optimization (PSO) and convex polyhedron volume optimization (CPVO) algorithms, respectively.
Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation
Authors: Hu Minzheng , Tao Shengyu , Fan Hongtao , Li Xinran , Sun Yaojie , Sun Jie
Published: August 2021

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.
Nonlinear Ultrasound Crack Detection with Multi-Frequency Excitation—A Comparison
Authors: Mevissen Frank , Meo Michele
Published: August 2021

Nonlinear ultrasound crack detection methods are used as modern, non-destructive testing tools for inspecting early damages in various materials. Nonlinear ultrasonic wave modulation, where typically two or more frequencies are excited, was demonstrated to be a robust method for failure indicators when using measured harmonics and modulated response frequencies. The aim of this study is to address the capability of multi-frequency wave excitation, where more than two excitation frequencies are used, for better damage identification when compared to single and double excitation frequencies without the calculation of dispersion curves. The excitation frequencies were chosen in such a way that harmonic and modulated response frequencies meet at a specific frequency to amplify signal energy. A new concept of nonlinearity parameter grouping with multi-frequency excitation was developed as an early failure parameter. An analytical solution of the one-dimensional wave equation was derived with four fundamental frequencies, and a total of 64 individual and 30 group nonlinearity parameters. Experimental validation of the approach was conducted on metal plates with different types of cracks and on turbine blades where cracks originated under service conditions. The results showed that the use of multi-frequency excitation offers advantages in detecting cracks.
Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction
Authors: Ye Ming , Chen Jing , Li Xu , Ma Kai , Liu Yonggang
Published: August 2021

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.
Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN
Authors: Tan Tan-Hsu , Badarch Luubaatar , Zeng Wei-Xiang , Gochoo Munkhjargal , Alnajjar Fady S. , Hsieh Jun-Wei
Published: August 2021

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.